G C A T T A C G G C A T

Article Validation and Selection of New Reference Genes for RT-qPCR Analysis in Pediatric Glioma of Different Grades

Beatriz Hernández-Ochoa 1,2 , Fabiola Fernández-Rosario 3, Rosa Angelica Castillo-Rodríguez 4 , Alfonso Marhx-Bracho 5, Noemí Cárdenas-Rodríguez 6 ,Víctor Martínez-Rosas 1,3 , Laura Morales-Luna 3,7, Abigail González-Valdez 8, Ernesto Calderón-Jaimes 2, Verónica Pérez de la Cruz 9 , Sandra Rivera-Gutiérrez 10 , Sergio Meza-Toledo 11, Carlos Wong-Baeza 12 , Isabel Baeza-Ramírez 12 and Saúl Gómez-Manzo 3,*

1 Programa de Posgrado en Biomedicina y Biotecnología Molecular, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Ciudad de México 11340, Mexico; [email protected] (B.H.-O.); [email protected] (V.M.-R.) 2 Laboratorio de Inmunoquímica, Hospital Infantil de México Federico Gómez, Secretaría de Salud, Ciudad de México 06720, Mexico; [email protected] 3 Laboratorio de Bioquímica Genética, Instituto Nacional de Pediatría, Secretaría de Salud, Ciudad de México 04530, Mexico; [email protected] (F.F.-R.); [email protected] (L.M.-L.) 4 Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Pediatría, Secretaría de Salud, Ciudad de México 04530, Mexico; [email protected]  5  Departamento de Neurocirugía, Instituto Nacional de Pediatría, Secretaría de Salud, Ciudad de México 04530, Mexico; [email protected] 6 Citation: Hernández-Ochoa, B.; Laboratorio de Neurociencias, Instituto Nacional de Pediatría, Secretaría de Salud, Fernández-Rosario, F.; Ciudad de México 04530, Mexico; [email protected] 7 Castillo-Rodríguez, R.A.; Marhx-Bracho, Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico A.; Cárdenas-Rodríguez, N.; 8 Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Martínez-Rosas, V.; Morales-Luna, L.; Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico; González-Valdez, A.; Calderón-Jaimes, [email protected] E.; Pérez de la Cruz, V.; et al. Validation 9 Neurochemistry and Behavior Laboratory, National Institute of Neurology and Neurosurgery “Manuel and Selection of New Reference Velasco Suárez”, México City 14269, Mexico; [email protected] Genes for RT-qPCR Analysis in 10 Departamento de Microbiologia, Escuela Nacional de Ciencias Biológicas (ENCB), Instituto Politécnico Pediatric Glioma of Different Grades. Nacional (IPN), Prolongacion Carpio y Plan de Ayala s/n, Ciudad de México 11340, Mexico; Genes 2021, 12, 1335. https:// [email protected] 11 doi.org/10.3390/genes12091335 Laboratorio de Quimioterapia Experimental, Departamento de Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Ciudad de México 11340, Mexico; [email protected] 12 Laboratorio de Biomembranas, Departamento de Bioquímica, Escuela Nacional de Ciencias Biológicas, Academic Editor: Angela C. Hirbe Instituto Politécnico Nacional, Ciudad de México 11340, Mexico; [email protected] (C.W.-B.); [email protected] (I.B.-R.) Received: 14 July 2021 * Correspondence: [email protected]; Tel.: +52-55-1084-0900 (ext. 1442) Accepted: 26 August 2021 Published: 27 August 2021 Abstract: Gliomas are heterogeneous, solid, and intracranial tumors that originate from glial cells. Malignant cells from the tumor undergo metabolic alterations to obtain the energy required for Publisher’s Note: MDPI stays neutral proliferation and the invasion of the cerebral parenchyma. The alterations in the expression of the with regard to jurisdictional claims in genes related to the metabolic pathways can be detected in biopsies of gliomas of different CNS published maps and institutional affil- WHO grades. In this study, we evaluated the expression of 16 candidate reference genes in the iations. HMC3 microglia cell line. Then, statistical algorithms such as BestKeeper, the comparative ∆CT method, geNorm, NormFinder, and RefFinder were applied to obtain the genes most suitable to be considered as references for measuring the levels of expression in glioma samples. The results show that PKM and TPI1 are two novel genes suitable for genic expression studies on gliomas. Finally, Copyright: © 2021 by the authors. we analyzed the expression of genes involved in metabolic pathways in clinical samples of Licensee MDPI, Basel, Switzerland. gliomas of different CNS WHO grades. RT-qPCR analysis showed that in CNS WHO grade 3 and This article is an open access article 4 gliomas, the expression levels of HK1, PFKM, GAPDH, G6PD, PGD1, IDH1, FASN, ACACA, and distributed under the terms and conditions of the Creative Commons ELOVL2 were higher than those of CNS WHO grade 1 and 2 glioma biopsies. Hence, our results Attribution (CC BY) license (https:// suggest that reference genes from metabolic pathways have different expression profiles depending creativecommons.org/licenses/by/ on the stratification of gliomas and constitute a potential model for studying the development of this 4.0/). type of tumor and the search for molecular targets to treat gliomas.

Genes 2021, 12, 1335. https://doi.org/10.3390/genes12091335 https://www.mdpi.com/journal/genes Genes 2021, 12, 1335 2 of 18

Keywords: references genes; real-time quantitative PCR; stability; gliomas

1. Introduction Glioma is the most common and lethal tumors of the central (CNS); in the USA alone, an incidence rate of 3.22 per 100,000 population is reported [1]. The World Health Organization (WHO, Geneva, Switzerland) classified gliomas as either grade 1 tumors, which are curable and can be surgically removed; lower-grade gliomas correspond to grade 2; and malignant gliomas are classified as either grade 3 tumors such as anaplastic astrocytoma or grade 4 tumors such as glioblastoma multiforme (GBM) [2]. Gliomas account for approximately 55% of malignant primary brain tumors, and in approximately 70% of cases, the glioma is classified as an astrocytoma CNS WHO grade 4 (GBM) [3]—a very aggressive tumor. Patients have an expected survival of only a year after GBM diagnosis due to the proliferative, invasive, and infiltrative nature of GBM cells, and also the resistance of these cells to current cytotoxic treatment based on the use of temozolomide [4,5]. Therefore, it is necessary to identify and implement new predictive markers that will help us to perform early diagnosis and better treatment. As a matter of fact, the use of analysis combined with clinical data has allowed better diagnosis and a better prognosis for response to therapy [6,7]. However, despite intense medical efforts, there is a lack of definitive information on the etiology of malignant gliomas. Therefore, the identification and implementation of new markers that could help to diagnose, predict, and treat gliomas is an urgent priority. The real-time reverse transcription-quantitative PCR (RT-qPCR) technique has been used in several cancer studies to identify specific molecular targets in tumors [8,9]. Building on these successes, attempts are being made to identify specific molecular markers for brain glioma treatment and identify new specific therapeutic targets for tumor subtypes. The RT-qPCR technique has become the method of choice for the quantification of accurate gene expression [10]. The advantages of this procedure over other methods for measuring messenger RNA (mRNA) levels include the characteristics that it is easy to perform, sensitive, specific, and reproducible [11,12]. Besides these advantages, it is very sensitive and allows researchers to measure quite low expression levels of mRNA; thus, this technique has become an essential method in research as it allows the reliable quantification of transcripts [13]. Nevertheless, the validity of the obtained results depends on a careful and rigorous selection of reference genes that are characterized by high stability and low expression variability regardless of the tissue or cell type, stage of the disease, experimental development, or treatment [14]. Among the most common genes used as reference genes in RT-qPCR studies are glyceraldehyde-3-phosphate dehydrogenase (GAPDH), β actin (ACTB), β glucuronidase (GUSB), hypoxanthine guanine phosphoribosyl transferase (HPRT1), hydroxymethylbilane synthase (HMBS), TATA-box binding protein (TBP), and small ribosomal subunit (18S). However, some studies have reported that these common genes show variability in expres- sion levels in different samples and experimental conditions [15,16]. Based on the above, it is clear that the reference genes are specific for a group of samples and experimental models; thus, the validation and selection of suitable reference genes is essential for reliable analysis in RT-qPCR studies. Although some studies have validated reference genes for the accurate quantification of gene expression in glioma clinical samples, including TBP and HPRT1, 14-3-3 protein zeta/delta (YWHAZ), and 60S ribosomal protein L13a (RPL13A)[17–19], the reference genes found and reported in these studies are different. Therefore, the aim of this study was to evaluate the suitability of 16 candidate refer- ence genes: 1 (HK1), phosphofructokinase muscle (PFKM), triosephosphate isomerase 1 (TPI1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate ki- nase (PKM), A (LDHAL6A), -6-phosphate dehydrogenase (G6PD), phosphogluconate dehydrogenase 1 (PGD1), transketolase 1 (TKT1), succinate Genes 2021, 12, 1335 3 of 18

dehydrogenase B (SDHB), fatty acid synthase (FASN), acetyl CoA carboxylase (ACACA), fatty acid elongase 2 (ELOVL2), and TATA-binding protein (TBP) for expression analysis in RT-qPCR in brain glioma biopsies. The MHC3 microglia cell line was used to evaluate candidate reference genes, and stability was analyzed using statistical algorithms such as BestKeeper, the comparative ∆CT method, geNorm, NormFinder, and RefFinder. Further- more, a gene expression analysis was performed in brain glioma biopsies from pediatric patients, as knowing the alterations in the expression levels of genes that encode proteins that participate in metabolic pathways can provide potentially important clinical outcome markers or targets for therapeutics in gliomas. Therefore, the aim of this study was to evaluate the suitability of sixteen candidate reference genes: hexokinase 1 (HK1), phosphofructokinase muscle (PFKM), triosephos- phate isomerase 1 (TPI1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate (PKM), lactate dehydrogenase A (LDHAL6A), glucose-6-phosphate dehydrogenase (G6PD), phosphogluconate dehydrogenase 1 (PGD1), transketolase 1 (TKT1), succinate dehydrogenase B (SDHB), fatty acid synthase (FASN), acetyl CoA carboxylase (ACACA), fatty acid elongase 2 (ELOVL2), and TATA-binding protein (TBP) for expression analysis in RT-qPCR in brain glioma biopsies. The MHC3 microglia cell line was used to evaluate candidate reference genes, and stability was analyzed using statistical algorithms, such as BestKeeper, the comparative ∆CT method, geNorm, NormFinder, and RefFinder. Further- more, a gene expression analysis was performed in brain glioma biopsies from pediatric patients, since knowing the alterations in the expression levels of genes that encode proteins that participate in metabolic pathways can provide potentially important clinical outcome markers or targets for therapeutics in gliomas.

2. Materials and Methods 2.1. Reference Gene Selection Reference genes have been identified as those that have functions in essential biologi- cal processes including molecular transport, RNA , oxidative , proteolysis, protein translation, regulation of protein metabolism, and cell cycle control. Based above, sixteen candidate genes encoding involved in essential metabolic pathways for cell survival such as , the pentose phosphate pathway, the Krebs cy- cle, and fatty acid synthesis (Table1) were selected to evaluate their suitability as reference genes in gene expression studies by RT-qPCR in brain glioma samples. Six of the genes are involved in glycolysis (HK1, PFKM, TPI1, GAPDH, PKM, and LDHAL6A), three of these genes belong to the pentose phosphate pathway (PPP) (G6PD, PGD1, and TKT1), one gene participates in the Krebs cycle (SDHB), and finally, three genes are involved in the fatty acid synthesis pathway (FASN, ACACA, and ELOVL2). Besides, we included three suitable reference genes previously reported as GAPDHa, GAPDHb, and TBP [17–19]. A target screening of our sixteen candidate genes was done using database analysis in silico to reinforce our selection. We searched and selected the GSE16011 glioma database from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo, accessed on 8 July 2021). This database includes 276 glioma samples from grade 1 to grade 4 as well as 8 controls samples [20]. Then, we evaluated the expression of the selected genes using GEO2R online tool (http://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed on 8 July 2021) [21]. We used the Benjamini & Hochberg false discovery rate [22] method to the adjust p values and limit the false positives cases, with a cut of p < 0.05. The log2-fold change (logFC) for the analyzed genes is showed in Supplementary Table S1. When we compared the glioma samples versus controls, we observed that for all the candidate reference genes selected, the logFC was below 1 or over −1, thus we considered a non- differential expression among glioma versus controls. Genes 2021, 12, 1335 4 of 18

Table 1. Genes analyzed in this study.

Gen ID GenBank Gene Symbol Gene Full Name Function 3098 NM_000188.2 HK1 Hexokinase 1, variant 1 Transferase in glycolysis Phosphofructokinase, muscle, 5213 NM_000289.6 PFKM Transferase in glycolysis variant 4 Triose phosphate isomerase 1, 7167 NM_000365.5 TPI1 Isomerase in glycolysis variant 1 Glyceraldehyde-3-phosphate 2597 NM_001256799.2 GAPDH Oxidoreductase in glycolysis dehydrogenase, variant 2 5315 NM_002654.6 PKM M1/2, variant 1 Transferase in glycolysis Lactate dehydrogenase A like 6A, 160287 NM_001144071.1 LDHAL6A Oxidoreductase in glycolysis transcript variant 2 Glucose-6-phosphate Oxidoreductase in 2539 NM_001360016.2 G6PD dehydrogenase, variant 1 pentose phosphate phosphogluconate dehydrogenase, Oxidoreductase in 5226 NM_002631.4 PGD1 variant 1 pentose phosphate Transferase in 7086 NM_001064.4 TKT1 Transketolase, variant 1 pentose phosphate Succinate dehydrogenase complex 6390 NM_003000.2 SDHB Oxidoreductase in Krebs’s cycle iron sulfur subunit B Transferase in fatty 2194 NM_004104.5 FASN Fatty acid synthase acid synthesis Acetyl-CoA carboxylase α, 31 NM_198834.3 ACACA Ligase in fatty acid synthesis variant 1 Transferase in fatty 54898 NM_017770.4 ELOVL2 Fatty acid elongase 2 acid synthesis 6908 NM_003194 TBP TATA-binding protein General transcription factor

Then, the expression values were displayed in a heatmap (Supplementary Figure S1A). A boxplot of the mean expression of these genes is showed comparing glioma samples versus controls (Supplementary Figure S1B). We include GAPDH and TBP in this analysis and observed a similar tendency that our gene selection. These graphs were obtained using the online software Morpheus (https://software.broadinstitute.org/morpheus/, accessed on 12 July 2021).

2.2. Primer Design The sequences of the primer pairs for the sixteen candidate reference genes used in this study were designed based on the mRNA sequences of the selected genes (Table2) obtained from the GenBank database (https://www.ncbi.nlm.nih.gov/pmc/, accessed on 11 May 2019). The primers were designed with lengths of 18–22 base pairs (bp) and a mean alignment temperature (Tm) of 60 ◦C ± 2 ◦C, and the formation of secondary structures and dimers was verified with the online program OligoEvaluatorTM from Sigma Aldrich (http://www.oligoevaluator.com/LoginServlet, accessed on 25 May 2019). The primers were designed to amplify from 180 to 200 bp fragments corresponding to each gene (Table2) . The primer pairs were evaluated using endpoint PCR to analyze their specificity, using synthesized cDNA as a template and the Q5 High-Fidelity DNA polymerase (New England, BioLabs) with the following amplification conditions: 98 ◦C for 30 s, 30 cycles at 98 ◦C for 10 s, 60 ◦C for 30 s, and 72 ◦C for 5 min. The PCR products were separated by 2.0% (w/v) agarose gel electrophoresis, stained with Midori Green Advance (NIPPON Genetics Europe, Dueren, Germany), and finally analyzed in the MultiDoc-It (UVP) equipment. Genes 2021, 12, 1335 5 of 18

Table 2. Sequence of primers used in this study.

Gene Sequence 50 → 30 Amplicon Size (pb) Tm (◦C) Fw 50-AAAGCGAGGGGACTATGA-30 HK1 157 60 Rv 50-ATCAATGTGCCTCAGTTCC-30 Fw 50-AGAATCTGGTGGTTAAGCG-30 PFKM 176 60 Rv 50-GAGGCTCACTACACAGGCT-30 Fw 50-CGCAGATAACGTGAAGGAC-30 TPI1 190 60 Rv 50-CAGTCACAGAGCCTCCATAA-30 Fw 50-CTCTGATTTGGTCGTATTGG-30 GAPDH 184 60 Rv 50-GATGACAAGCTTCCCGTT-30 Fw 50-GGTTCGGAGGTTTGATGA-30 PKM 186 60 Rv 50-GGCTTCTTGATCATGCTCT-30 Fw 50-GCGACTCAAGTGTTCCTGT-30 LDHAL6A 179 60 Rv 50-GCTAATGCCCCAAGAAGTAT-30 Fw 50-ATATTTATGGCAGCCGAGG-30 G6PD 190 60 Rv 50-GTCAATGGTCCCGGTGT-30 Fw 50-CATTCGGAAGGCACTCTAC-30 PGD1 199 60 Rv 50-CTTGTCTTCAAGGCCCAA-30 Fw 50-GATCACGGGGGTAGAAGA-30 TKT1 200 60 Rv 50-TGTCCCCAACTTTGTAGCT-30 Fw 50-TTCTTATGCAGGCCTATCG-30 SDHB 185 60 Rv 50-GGTTGCCATCATTTTCTTG-30 Fw 50-CCGCTCTGGTTCATCTG-30 FASN 226 60 Rv 50-GGTCTATGAGGCCTATCTGG-30 Fw 50-AAGAGGCAATTTCAAACATG-30 ACACA 182 60 Rv 50-ATGGTGTCAGGTCGCTC-30 Fw 50-AAGCTGACATCCGGGTAG-30 ELOVL2 186 60 Rv 50-TGTCCACAAGGTATCCAGTT-30 Fw 50-AGATCCCTCCAAAATCAAGTGG-30 GAPDHa 129 60 Rv 50-GGCAGAGATGATGACCCTTTT-30 Fw 50-TGCACCACCAACTGCTTAGC-30 GAPDHb 87 60 Rv 50-GGCATGGACTGTGGTCATGAG-30 Fw 50-GAGCTGTGATGTGAAGTTTCC-30 TBP 117 60 Rv 50-TCTGGGTTTGATCATTCTGTAG-30 GAPDHa [17], GAPDHb [19], TBP [17,19].

2.3. Cell Culture A microglia-derived cell line was used in this study to validate the reference genes, thus the HMC3 cell line was purchased from the American Type Culture Collection (ATCC® CRL-3304™). The HMC3 cells were cultured in Eagle’s Minimum Essential Medium (EMEM) and supplemented with 10% fetal bovine serum (FBS; Gibco, Carlsbad, CA, USA) and antibiotics (100 U/mL penicillin and 100 µg/mL streptomycin), according to the ◦ manufacturer’s instructions. Cells were grown for 72 h and cultured at 37 C, 5% CO2, in a humidified atmosphere. The experiments were carried out with cultures in the log phase of growth (before the culture reached the monolayer).

2.4. Tumor Samples In this study, 7 samples of brain glioma biopsies were obtained with previous signed informed consent from pediatric patients at the Neurosurgery Department of the Instituto Nacional de Pediatria in Mexico City between November 2018 and August 2019. The protocol was approved by the Institutional Ethics Committee (INP protocol 039/2018) in Genes 2021, 12, 1335 6 of 18

accordance with the Declaration of Helsinki. All the glioma tumors had novo origin and did not receive chemotherapy before surgery. The tumor sample was resected, a portion was preserved for this study, and the other portion was submitted to the pathology service to identify the tumor grade. The characteristics of the pediatric glioma samples are given in Table3. The classification was performed according to the World Health Organization (CNS WHO grade 1, 2, 3 and 4).

Table 3. Diagnostic characteristics of the brain glioma biopsies of pediatric patients.

Code Gender Age Pathology Diagnoses CNS WHO Grade T1 Male 10 Pylocitic astrocytoma 1 T12 Male 3 Pylocitic astrocytoma 1 Gemystocitic T4 Female 3 2 Astrocytoma T18 Female 13 Diffuse astrocytoma 2 T7 Male 6 Stem Glioma 3 T10 Male 14 Anaplasic astrocytoma 3 T9 Female 9 Glioblastoma 4

2.5. Extraction of Total RNA and cDNA Synthesis The total RNA of HMC3 cell line was purified using the TRIzol method (Invitrogen, Carlsbad, CA, USA), following the manufacturer’s instructions. The quality of RNA was assessed at 260/280 nm and the integrity was evaluated on 2.0% (w/v) agarose gel electrophoresis. The samples were treated with 1 U of DNase I enzyme (Thermo Fisher Scientific, Waltham, MA, USA). The cDNA was synthesized using oligo (dT) 18 primers (Thermo Fisher Scientific), and RevertAid reverse transcriptase (Thermo Fisher Scientific). The brain glioma biopsies was disrupted using the TissueLyser system (Qiagen, Valencia, CA, USA) for 60 s at 25 Hz. Total RNA extraction and cDNA synthesis was performed as mentioned above. All the synthesized cDNAs were quantified and stored at −70 ◦C until use.

2.6. Quantitative RT-qPCR Analysis The amplification of the specific PCR products of the sixteen genes proposed in this study (Table1) was determined using the Fast SYBR ® Green Master Mix kit (Applied Biosystems, Foster City, CA, USA) in the StepOnePlusTM Real-Time PCR Systems platform (Life Technologies, Foster City, CA, USA), which was carried out according to the MIQE guidelines [14]. The RT-qPCR was performed in triplicate with the following conditions: 95 ◦C for 30 s, followed by 40 cycles of 95 ◦C for 30 s and 60 ◦C for 30 s. A reaction without the template was run in parallel for all plates to verify the purity of measurement within each experiment. The amplification efficiency of each primer pair was evaluated by the standard curve method using serial dilutions of cDNA (initial concentration, 100 ng) to obtain the correlation coefficient and slope values. Each run was completed with a melting curve analysis in the range of 60–95 ◦C.

2.7. Analysis of Gene Expression Stability To determine of expression stability of the sixteen candidate genes in this study, the cycle threshold (CT) values for each reference gene previously obtained from RT-qPCR were analyzed with four statistical algorithms for the evaluation and selection of reference genes: BestKeeper [23], the comparative ∆CT method [24], geNorm [25], and NormFinder [26]. The stability values (M) were calculated for each candidate gene using the NormFinder and geNorm software. Besides, we used the RefFinder algorithm [27], which integrates the results from BestKeeper, the comparative ∆CT method, geNorm, and NormFinder, and Genes 2021, 12, x FOR PEER REVIEW 7 of 18

genes: BestKeeper [23], the comparative ΔCT method [24], geNorm [25], and NormFinder [26]. The stability values (M) were calculated for each candidate gene using the NormFinder and geNorm software. Besides, we used the RefFinder algorithm [27], which integrates the results from BestKeeper, the comparative ΔCT method, geNorm, and

Genes 2021, 12, 1335 NormFinder, and calculates the geometric mean (geomean) for each reference gene7 to of give 18 the ranking index of stability [27].

2.8. Analysis of Relative Gene Expression in Human Glioma Samples calculates the geometric mean (geomean) for each reference gene to give the ranking index We evaluated the expression levels of the selected metabolic gene profile in HMC3 of stability [27]. cells and glioma samples using the best reference gene according to the stability value obtained2.8. Analysis previously. of Relative The Gene relative Expression change in Humanin the gene Glioma expression Samples of the target genes (HK1, PFKM,We TPI1 evaluated, GAPDH the, LDHAL6A expression, G6PD levels, ofPGD1 the selected, TKT1, SDHB metabolic, IDH1 gene, FASN profile, ACACA in HMC3, and ELOVL2cells and) was glioma analyzed samples using using the the 2−ΔΔCt best method, reference employing gene according the PKM to the reference stability gene value for normalizationobtained previously. [24]. The The relative relative change change in in expression the gene expression upon normalization of the target was genes evaluated (HK1, usingPFKM ANOVA, TPI1, GAPDH and the, LDHAL6ATukey–Kramer, G6PD test., PGD1 The, TKT1curve, SDHBwas fitted, IDH1 with, FASN GraphPad, ACACA Prism, and 9 softwareELOVL2 (GraphPad) was analyzed Software using Inc., the 2 −La∆∆ JoCtllmethod,a, CA, USA employing) and a thestatistiPKMcalreference analysis gene was forper- formed.normalization Five replicates [24]. The were relative included change for in expressioneach human upon glioma normalization sample, and was all evaluated reactions wereusing run ANOVA by triplicate. and the Tukey–Kramer test. The curve was fitted with GraphPad Prism 9 software (GraphPad Software Inc., La Jolla, CA, USA) and a statistical analysis was 3. performed.Results Five replicates were included for each human glioma sample, and all reactions were run by triplicate. 3.1. Determination of the Specificity and Efficiency of Primer Pairs 3. ResultsThe specificity analysis of the primers for all the proposed genes was evaluated by endpoint3.1. Determination PCR. The ofobtained the Specificity PCR andproducts Efficiency are of shown Primer in Pairs Figure S2, for the HK1, PFKM, TPI1, GAPDHThe specificity, GAPDH analysisa, GAPDH of theb, PKM primers, G6PD for all, TKT1 the proposed, SDHB, FASN genes, was ACACA evaluated, and byTBP genes,endpoint a single PCR. band The of obtained the expected PCR products size was are obtained, shown inindicating Figure S2, that for these the HK1 primers, PFKM did, notTPI1 form, GAPDH primer,-GAPDHdimers anda, GAPDH nonspecificb, PKM amplification, G6PD, TKT1 ,products.SDHB, FASN The, expectedACACA, andamplifica-TBP tiongenes, products a single were band not of obtained the expected regarding size was the obtained, genes indicatingLDHAL6A that, PGD1 these, and primers EL0VL2 did not(Fig- ureform S2, lines primer-dimers 7, 9, and 14, and respectively). nonspecific amplification products. The expected amplification productsThe transcriptional were not obtained levels regarding of the sixteen the genes candidateLDHAL6A reference, PGD1, andgenesEL0VL2 were (Figuredetermined S2, lines 7, 9, and 14, respectively). using RT-qPCR to compare the levels of mRNA of each gene. As seen in Figure 1, the CT The transcriptional levels of the sixteen candidate reference genes were determined values for the TPI1, PKM, TKT1, SDHB, FASN, GAPDHa, GAPDHb, and TBP genes have a using RT-qPCR to compare the levels of mRNA of each gene. As seen in Figure1, the C normal distribution according to the method of Kolmogorov and Smirnov. The G6PDT values for the TPI1, PKM, TKT1, SDHB, FASN, GAPDHa, GAPDHb, and TBP genes have gene showed the lowest CT value (14 cycles), followed by the PFKM, GAPDH, and ACACA a normal distribution according to the method of Kolmogorov and Smirnov. The G6PD genes, with values between 17 and 23 cycles. Finally, the least-expressed genes were HK1, gene showed the lowest CT value (14 cycles), followed by the PFKM, GAPDH, and ACACA a b TPI1genes,, PKM with, LDHAL6A values between, PGD1 17, TKT1 and 23, SDHB cycles., FASN Finally,, ELOV2 the least-expressed, GAPDH , GAPDH genes were, andHK1 TBP, withTPI1 C,TPKM values, LDHAL6A between, 26PGD1 and, TKT134 cycles., SDHB , FASN, ELOV2, GAPDHa, GAPDHb, and TBP with CT values between 26 and 34 cycles.

Figure 1. Expression levels of the sixteen candidate reference genes in the HMC3 microglial cell line (ATCC® CRL-3304™). The variation is shown as median values and 25th to 75th percentiles (boxes), with whisker plots indicating the lower and upper values of C of the candidate reference genes. The T values of three biological replicates are shown. The graph was plotted with the GraphPad Prism 9 software. Genes 2021, 12, 1335 8 of 18

To confirm the specificity of all the primers, melt curves analyses were performed for the sixteen genes. In Figure S3, the melting curves obtained for the analyzed genes are shown, and a single specific peak was obtained for the genes TPI1, GAPDH, PKM, G6PD, TKT1, SDHB, ACACA, GAPDHa, GAPDHb, and TBP. The presence of a single peak in the curves indicates the alignment specificity and thereby confirms that the primers are suitable to be used in amplification reactions to obtain the desired products under the predicted alignment temperature. However, for the genes HK1, PFKM, LDHAL6A, PGD1, FASN, and ELOVL2, we observed more than one peak, apparently due to the dimer primers, so these last genes were not suitable for the analysis of gene expression. The temperature of dissociation (Tm) value for the RT-qPCR products from the genes evaluated was between 75 ◦C(TBP) and 83 ◦C(TPI1). With respect to the control without nucleic acid (NTC), no signal was detected, which indicates the absence of contamination. Thus, we selected TPI1, PKM, GAPDH, G6PD, SDHB, TKT1, ACACA, GAPDHa, GAPDHb, and TBP as the final candidate reference genes. Next, we evaluate their ex- pression stability to select the appropriate reference gene to measure the gene expression levels in brain glioma biopsies.

3.2. Stability of Candidate Reference Genes To identify the best reference gene, we used as criteria a stable expression and mini- mum variability in HMC3 cells. Whereby, the expression stability of the ten final candidate reference genes was analyzed with the BestKeeper program. This algorithm calculates the standard deviation (SD) of the CT values and the coefficient variance (CV) for each gene. Genes with SD values > 1 are considered unstable and therefore are not suitable to be used as reference genes [23]. The results obtained with BestKeeper are shown in Table4 (Figure S4) and suggest that the PKM, TPI1, GAPDH, and GAPDHa genes are the best candidates, as they showed less variation according to the calculated deviation (lower b dispersion CT), while the TKT1, SDHB, G6PD, ACACA, GAPDH , and TBP genes had higher dispersion values.

Table 4. Expression stability of the ten candidate reference genes analyzed in the HMC3 microglial cell line.

Gene BestKeeeper Comparative ∆CT geNorm NormFinder RefFinder (Geomean) TPI1 0.024 4.29 0.017 0.009 1.41 PKM 0.016 4.29 0.017 0.009 1.73 GAPDH 0.029 4.29 0.035 0.020 2.06 GAPDHa 0.044 4.30 0.053 0.030 4.00 TKT1 1.40 4.83 0.768 1.689 5.23 SDHB 1.88 5.49 1.445 1.207 5.73 G6PD 3.52 6.21 2.373 5.329 7.00 ACACA 4.14 6.77 2.986 6.275 8.24 GAPDHb 4.88 7.84 3.477 7.396 9.49 TBP 12.18 16.76 6.646 16.71 11.00

We also used the comparative ∆CT method, which compares the relative expression by pairs of genes within each sample to identify the genes suitable for expression studies. The comparation will provide information on which genes show less variability and a more stable expression among the genes analyzed [24]. The results obtained are shown in Table4, and we observed too that the PKM, TPI1, GAPDH, and GAPDHa genes showed a relatively stable expression. Regarding the analysis with the geNorm program a stability value M for a particular gene; a low value of M indicates a high stability in the expression, the M values calculated for the eleven candidate reference genes are shown in Table4. The PKM, TPI1, GAPDH, GAPDHa, TKT1 and SDHB genes are the most stable reference genes, with M values of 0.017, 0.017, 0.035, 0.053, 0.768 and 1.446, respectively, while the G6PD, ACACA, GAPDHb, and TBP genes showed M values above 1.5, which is the limit suggested by the geNorm program [25]. Besides this, candidate reference genes were analyzed with the Genes 2021, 12, 1335 9 of 18

NormFinder software. This program uses a model-based algorithm to measure variation in expression between subgroups of samples [26]. The results obtained with NormFinder were similar to those of the geNorm method; both methods ranked PKM, TPI1, GAPDH, GAPDHa, TKT1 and SDHB as among the six most stable reference genes (Table4). Finally, the expression stability results obtained with the four major algorithms (Best- Keeper, the comparative ∆CT method, geNorm, and NormFinder) (Table5) were integrated using the RefFinder tool; this algorithm integrates the results based on the classification of each of the programs and calculates the geometric means (Geomean) for the final classifica- tion [27]. Table5 showed the results obtained with RefFinder tool; the overall results of this analysis indicate that the TPI1, PKM and GAPDH genes are the most stably expressed and are thus suitable to be used as reference genes in gene expression studies.

Table 5. Expression stability ranking of the selected candidate reference genes using RefFinder.

Rank BestKeeper Comparative ∆CT geNorm NormFinder RefFinder 1 PKM PKM PKM PKM TPI1 2 TPI1 TPI1 TPI1 TPI1 PKM 3 GAPDH GAPDH GAPDH GAPDH GAPDH 4 GAPDHa GAPDHa GAPDHa GAPDHa GAPDHa 5 TKT1 TKT1 TKT1 SDHB TKT1 6 SDHB SDHB SDHB TKT1 SDHB 7 G6PD G6PD G6PD G6PD G6PD 8 ACACA ACACA ACACA ACACA ACACA 9 GAPDHb GAPDHb GAPDHb GAPDHb GAPDHb 10 TBP TBP TBP TBP TBP

3.3. Evaluation of Gene Expression Levels in Brain Glioma Biopsies After validating and selecting the most appropriate reference genes to measure the gene expression levels, we found that the most suitable genes were PKM, TPI1, and GAPDH. Therefore, the PKM gene was used as a reference gene to measure the expression levels of genes of metabolic pathways in brain glioma biopsies of pediatric patients diagnosed with different CNS WHO grades of gliomas, because it showed the best stability ranking in the statistical analyses. To investigate the first limiting step in the glucose metabolism and subsequent steps, we analyzed the expression levels of genes that encode glycolytic enzymes such as hexoki- nase isoform 1 (HK1), phosphofructokinase muscle (PFKM), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), triosephosphate isomerase 1 (TPI1), and lactate dehydrogenase (LDHAL6A). We found that the HK1, PFKM, GAPDH, and LDHAL6A genes were overex- pressed in all the clinical samples analyzed compared with the HMC3 microglia cell line (Figure2). Moreover, the high expression of HK1, PFKM, and GAPDH genes correlated with the tumor degree of malignancy. Besides this, we observed that, in CNS WHO grade 1 and 2 samples, the expression levels of the HK1, PFKM, and GAPDH genes ranged from 2 to 65, 11 to 163, and 10 to 190-fold increases, respectively, vs. HMC3. Meanwhile, in CNS WHO grade 3 and 4 gliomas, the levels of expression ranged from 486 to 767, 2097 to 2713, and 1798 to 2628-fold increases, respectively (Figure2A–C). The LDHAL6A gene showed overexpression in all the clinical samples analyzed (Figure2E). Regarding the TPI1 gene, we found that it was not overexpressed in any clinical sample of pediatric glioma (Figure2D). Genes 2021, 12, x FOR PEER REVIEW 10 of 18

cell line (Figure 2). Moreover, the high expression of HK1, PFKM, and GAPDH genes cor- related with the tumor degree of malignancy. Besides this, we observed that, in CNS WHO grade 1 and 2 samples, the expression levels of the HK1, PFKM, and GAPDH genes ranged from 2 to 65, 11 to 163, and 10 to 190-fold increases, respectively, vs. HMC3. Meanwhile, in CNS WHO grade 3 and 4 gliomas, the levels of expression ranged from 486 to 767, 2097 to 2713, and 1798 to 2628-fold increases, respectively (Figure 2A–C). The LDHAL6A gene Genes 2021, 12, 1335 showed overexpression in all the clinical samples analyzed (Figure 2E). Regarding 10the of 18 TPI1 gene, we found that it was not overexpressed in any clinical sample of pediatric gli- oma (Figure 2D).

Genes 2021, 12, x FOR PEER REVIEW 11 of 18 Figure Figure2. Differential 2. Differential expression of expression genes involved of genesin essential involved metabolic in pathways essential of metabolic glioma cells. pathways (A–E) represent of glioma ex- cells. GenesGenes 2021 2021, ,12 12, ,x x FOR FOR PEER PEER REVIEW REVIEW 1111 ofof 1818 GenesGenesGenesGenes 2021 2021 2021 2021, ,12 ,12 12,, ,12x ,x FORx ,FOR xFOR FOR PEER PEER PEER GenesPEER REVIEW REVIEW REVIEW 2021REVIEWpression, 12 , x( A FORlevels–E) PEER representof the HK1REVIEW, PFKM expression , TPI1, GAPDH levels, and of the LDHAL6AHK1, PFKMgenes involved, TPI1, inGAPDH glycolysis., and (F–HLDHAL6A) represent thegenes expres-1111 involved11 11of of of 18of 1818 18 11 of 18

sion levelsin glycolysis. of the G6PD (F, –PGD1H) represent, and TKT1 the genes expression involved levels in pentose of the phosphate.G6PD, PGD1 Relative, and expressionTKT1 genes levels were involved in pentose phosphate. Relative expression levels were determined using thedeterminedPKM gene using as a reference. the PKM gene as a reference. Values are means ± SD of quadruplicate determinations. HMC3, determineddetermined using using the the PKM PKM gene gene as as a a reference. reference. Values Values are are means means ± ± SD SD of of quadruplicate quadruplicate determinations. determinations. HMC3,HMC3, CNS WHO 1 (T1), CNS WHO 1 (T12), CNS WHO 2 (T4), CNS WHO 2 (T18), CNS WHO 3 (T7), CNS determineddetermineddetermineddetermined using using using using the the the the determinedPKM PKM PKM PKM gene gene gene Valuesgene as usingas as aas a areareference. reference.a reference. thereference. means PKM Values Values geneValues SDValues of asare are quadruplicate are aare means reference.means means means ± ± ±SD SD ±ValuesSD determinations.SDof of of quadruplicate ofquadruplicate quadruplicate quadruplicateare means ± determinations. determinations.SD HMC3,determinations. determinations. of quadruplicateCNS WHO HMC3, HMC3, HMC3, determinations.HMC3, 1 (T1), CNS HMC3, CNSCNS WHOWHO 11 (T1),(T1),CNSCNSCNS CNS CNSCNS WHO WHO WHO WHO WHOWHO 1 1 1 (T1), (T1),1 1(T1), 1 (T1), (T12),(T12), CNS CNS CNS CNS CNSWHO CNSWHO WHO WHO WHO WHO1 1 1(T12), (T1),(T12),1 (T12), (T12), 22 (T4), (T4), CNS CNS CNSCNS CNS CNS WHO CNS CNSWHO WHO WHO WHOWHO WHO1WHO (T12),2 2 2(T4), 2(T4),2 (T4), 2(T4),2 (T18), (T18), CNS CNS CNS CNSCNS CNS WHO CNSWHO CNSWHO WHOWHO WHO WHO2 WHO2 2 (T4),22(T18), (T18),2 (T18), (T18), 33 (T7), (T7), CNS CNSCNS CNSCNS CNS WHO CNS CNSWHO WHOWHO WHO WHO WHO 2 (T18),3 3 3(T7), (T7),3 3(T7), (T7),(T10), CNSCNS CNS CNSCNS CNS CNSWHO WHO WHO 3 3 (T7), 4 (T9). CNS WHOWHO 3 3 (T10), (T10), CNSWHOCNSWHOWHOWHO WHO WHO 3 3 (T10),3 (T10),3 (T10), (T10),4 4 (T9). (T9). CNS CNSCNS CNSWHO WHO WHO WHO WHO 3 4 (T10),4 (T9).4 (T9).4 (T9). (T9). CNSCNS WHOWHO 44 (T9).(T9). We also evaluated the expression levels of the genes involved in PPP, such as G6PD, WeWe also also evaluated evaluatedWeWe Wethe Wethe also also alsoexpression expression also evaluated evaluated evaluated evaluated levels levels the theWe the the expression of expression alsoofexpression expression the the evaluated genes genes levels levels levelsinvolved levelsinvolved the of of of expressiontheof the the inthe ingenes genes PPP, genes PPP,genes genes involved such involvedsuch levelsinvolved involved involved as as of G6PD G6PD in thein in PPP,in inPPP, PPP,genes ,PPP, , PPP, such such such suchinvolved as as as asG6PD G6PD G6PD G6PD in, ,PPP, , ,, PGD1 such, andas G6PD TKT1, (Figure 2). The results obtained show that the gene encoding the G6PD PGD1PGD1, ,and and TKT1 TKT1PGD1 (FigurePGD1 (FigurePGD1PGD1, ,and ,and ,2)., and 2).and TKT1 TheTKT1The TKT1 TKT1 results results (Figure (Figure (Figure PGD1(Figure obtained obtained 2). ,2). and2). 2 2).The). The The The TheTKT1 show results showresults results resultsresults (Figure tha tha obtained obtained obtainedt obtainedt obtainedthe the 2). gene gene The show show show show show encoding resultsencoding tha tha tha thatthat tobtainedthe tthe t the the thethe gene gene gene geneG6PD geneG6PD encoding showencoding encoding encodingencoding tha tthe thethe the thethe G6PD G6PDgene G6PD G6PDG6PD encoding enzyme, the which G6PD is the first enzyme in the pathway and controls the flow of the PPP, enzyme,enzyme, which which isenzyme, isenzyme, enzyme, theenzyme, the first first which which which enzymewhich whichenzyme is is is is the theenzyme, inthe thein the thefirst thefirstfirst first first pathway pathway enzyme enzyme which enzyme enzyme isin andin andin in the in the the the controls controlsthepathway first pathway pathway pathway pathway enzyme the the and and and flow andflow controls and in controls controls the controls of ofcontrols the pathwaythe the PPP, the flowPPP, the the the flow flow of flow and flow the of of controls ofPPP, of the the the the showed PPP, PPP, PPP, PPP, the flowshowed of the overexpression PPP, in all clinical samples (Figure 2F). Moreover, this overexpression showedshowed overexpression overexpressionshowedshowedshowedshowedoverexpression in inoverexpression overexpression all overexpression alloverexpression clinical clinical inshowed samples allsamples clinicalin in in inall all overexpressionall all(Figure clinical (Figureclinical samplesclinical clinical 2F). samples 2F).samples samples (Figuresamples Moreover, Moreover, in (Figure all(Figure2 (Figure F).(Figure clinical Moreover, this this 2F). 2F). 2F). 2F).overexpression samplesoverexpression Moreover, Moreover, Moreover, Moreover, this (Figure overexpression this this this this overexpression 2F).overexpression overexpression overexpression Moreover, is related this is overexpression related to the grade of the tumor; the levels of expression of G6PD were in the range of isis related related to to the the grade gradeisisis related istorelated related related theof of the gradethe to to to tumor;thetotumor; the the ofthe grade grade thegrade gradethe theis tumor; related of levels of levelsof ofthe the the the the tumor; of tumor; tooftumor; tumor; levelsexp theexpression gradetheression the the of the levels expressionlevels levels oflevels of of the G6PD of G6PDof of tumor;ofexp exp exp ofexp were ressionwereressionG6PDressionression the in in levelswereof theofthe of ofG6PD G6PD range G6PD range inG6PD of the expwere were of wereof rangewereression in in in thein ofthe the 5theof range torange rangeG6PD range 245-fold of of ofwere of 5 toin 245the -rangefold in of CNS WHO grade 1 and 2 gliomas compared to CNS WHO grade 3 and 4 55 to to 245 245--foldfold in in CNS CNS55 5to to5in to WHO 245to WHO245 CNS245 245--foldfold- fold- WHOgrade foldgrade in in in inCNS CNS grade1 CNS 1 CNSand and5 WHO toWHO WHO 2 1WHO 2245 andgliomasgliomas grade -gradefold grade 2 grade gliomas in compared 1 compared1 1andCNS and1 and and compared2 2WHO 2gliomas gliomas2 gliomas to gliomasto CNS CNSgrade tocompared compared comparedWHO CNSWHOcompared 1 and WHO grade grade 2 to gliomasto to toCNS grade CNS 3 CNS 3 CNSand and WHO WHO compared 3WHO 4 WHO4 and grade grade 4grade grade gliomas, to 3 3 3andCNS and3 and and with 4 4WHO 4 4 gliomas, grade 3with and a 4 range of 2197 to 4946-fold. The same pattern was observed in the PGD1 gliomas,gliomas, with with a a range gliomas,rangegliomas,gliomas,gliomas,a range of of 2197 2197with with ofwith with 2197to toa a arange4946 range4946a range torangegliomas, 4946-fold.--fold. fold.of of of of2197 2197 The2197 The2197 with to Thetosame sameto to4946 4946 a4946 same 4946range pattern -pattern-fold.fold.-fold.- patternfold. of The The was 2197The wasThe same wassame sameobserved toobservedsame observed4946 pattern pattern pattern pattern-fold. in in was the inwas thewasThe was the PGD1observed PGD1 observed sameobservedPGD1 observed patterngene in in in inthe the (Figurethe thewas PGD1 PGD1 PGD1 PGD1 observed2G), gene in (Figure the PGD1 2G), which showed an expression level of 5 to 150-fold in the CNS WHO genegene (Figure(Figure 2G),2G),genegene genewhichwhichgenewhich (Figure(Figure (Figure (Figure showedshowed 2G),2G), 2G), 2G), an anan whichwhich genewhich expression whichexpression (Figure showedshowed showed showed level level 2G), anan an an ofexpression oexpression owhichfexpression f 5expression 55 to toto 150-fold 150showed150 - level-foldlevel foldlevel level in inan oino thef o f theo 5expressionfthe5 f 5 CNS to5to CNSto CNSto 150150 150 WHO150- - foldWHOfoldWHO-fold- foldlevel grade inin in in theothe the fthe 15 CNS CNS andtoCNS CNS 150 2WHOWHO gliomaWHO -WHOfold in grade the CNS 1 and WHO 2 glioma samples, compared to 4946-fold in the CNS WHO grade 3 and 4 gradegrade 11 andand 22 gliomagliomagradegradegradegradesamples, samples, samples,11 1 and1and and and compared 22 2 glioma compared2glioma comparedglioma gliomagrade samples,tosamples, samples, samples, 4946-fold to1to and 49464946 comparedcompared2 -compared -foldcomparedgliomafold in the inin CNS the thesamples, toto to to CNS4946CNS4946 WHO4946 4946- - foldWHOfoldWHO-comparedfold- gradefold inin in grade ingrade thethe 3the the and CNStoCNS CNS 3 3CNS 4946 4 andand glioma WHOWHO WHO -WHO 4fold4 grade samples.grade ingrade grade the 3 3 CNS3 and3and Finally,and and 4WHO4 4 4 glioma grade samples. 3 and 4 Finally, when we analyzed the TKT1 gene, we found that this gene was gliomaglioma samples. samples. gliomaFinally, gliomaFinally,gliomagliomawhen samples.when samples. wewhensamples. samples. analyzed we we Finally, Finally, analyzedFinally,analyzed Finally,glioma the when TKT1when when samples.whenthe the we gene,TKT1weTKT1 we we analyzed analyzed analyzedFinally, weanalyzedgene, gene, found we we the whenthe the foundthe that foundTKT1 TKT1 TKT1 TKT1we this that gene,that analyzedgene, genegene, gene, this this we we waswe gene we gene found thefound found not found was TKT1was overexpressed that that that that gene, this this this this gene genewe gene gene found inwas was was CNSwas that not this overexpressed gene was in CNS WHO grade 1 and 2 samples. However, in CNS WHO grade 3 notnot overexpressed overexpressednotnot notin notWHOin overexpressed overexpressedCNS CNSoverexpressed overexpressed grade WHO WHO 1 grade andgrade in innot in 2CNSin CNS 1 samples.CNS 1 overexpressed CNSand and WHO WHO WHO 2WHO2 samples. samples. However, grade grade grade grade in 1 However 1However 1andCNS and1 inand and CNS2 2WHO 2samples. samples.2 samples., ,samples. WHOin in CNS gradeCNS gradeHowever However WHO However WHOHowever1 and 3 samples,grade grade2, ,insamples. ,in ,in CNSin CNS 3 CNS3 CNS an WHO WHO However overexpressionWHO WHO grade grade grade grade, in 3 3 3CNS 3 samples, WHO gradean overexpression 3 of 2.3–2.5-fold was found, but in CNS WHO grade 4 samples, samples,samples, an an overexpression overexpressionsamples,samples,samples,samples,of 2.3–2.5-fold an an anof ofanoverexpression overexpression 2.3 overexpression 2.3overexpression––2.5 was2.5samples,--foldfold found, was was of of anof but found, 2.3of found,2.3 2.3overexpression 2.3– in–2.5–2.5–2.5 CNS 2.5-but -foldbutfold-fold-fold in WHOin was was CNS wasCNS was found, found,of gradefound,WHO found,WHO 2.3– but2.5 4but grade butgrade samples,but- foldin in in CNSin CNS4 CNS4was CNSsamples, samples, WHO it WHO found,WHO wasWHO grade notgrade gradebut grade overexpressed in 4 4 4samples, CNS samples,4 samples, samples, WHO gradeit was 4not samples, overexpressed (Figure 2H). However, this last result is important for corrobo- (Figure2H). However, this last result is important for corroborating the analysis of more itit was was not not overexpressed overexpresseditit itwas itwas was was not not not (Figure not(Figure overexpressed overexpressed overexpressed overexpressed 2H). 2H).it However, wasHowever, (Figure (Figurenot (Figure (Figure overexpressed this this2H). 2H). 2H). 2H).last last However, However, However, result However,result (Figure is is this important thisimportant this this last2H). last last last result resultHowever, result for resultfor corrobo- is corrobo-is is important isimportant important importantthis last for resultfor for for corrobo- corrobo- corrobo- corrobo- is importantrating for the corrobo- analysis of more clinical samples of brain glioma biopsies (CNS WHO grade 4). clinical samples of brain glioma biopsies (CNS WHO grade 4). ratingrating the the analysis analysisratingrating ratingof ratingof more more the the the the clinicalanalysis clinicalanalysis analysis analysis samples samplesof of ratingof moreof more more more theof clinicalof clinical clinicalbrain clinicalbrainanalysis samplesglioma samplesglioma samples samples of more biopsies biopsiesof of of brainof brain clinicalbrain brain (CNS (CNSglioma glioma glioma gliomasamples WHO WHO biopsies biopsies biopsies biopsies ofgrade grade brain (CNS (CNS (CNS4). 4).(CNS glioma WHO WHO WHO WHO biopsies grade grade grade grade 4). 4). (CNS4). 4). WHOThen, grade we 4). evaluated the genes that code for two enzymes of the Krebs cycle (IDH1 Then,Then, wewe evaluatedevaluatedThen,Then,Then,Then, thethe we we geneswe geneswe evaluatedevaluated evaluated evaluated thatthat codecodeThen, thethe the the for genesforgenes geneswe genes twotwo evaluated thatthat enzymesthat enzymesthat codecode code code the forfor offorof for genes twothetwothe two two Krebs enzymesKrebsenzymes thatenzymes enzymes cyclecodecycle ofof of for(of the(IDH1theIDH1 the thetwo KrebsKrebs Krebs Krebs enzymes cyclecycle cycle cycle ((IDH1 ofIDH1( IDH1( IDH1the Krebs and SDHBcycle ().IDH1 As seen in Figure 3A, we found that the IDH1 gene was overexpressed in all andand SDHB SDHB).). As As seen seenandandandand in SDHB inSDHB SDHB Figure SDHBFigure).). ).As ).As 3A, As3A, As seen seen seenwe seenweand in found in foundin inFigure FigureSDHB Figure Figure that that ).3A, 3A, 3A,theAs 3A,the we we seenIDH1we IDH1we found found found foundin gene geneFigure that that that thatwas was the the 3A,the theoverexpressed overexpressedIDH1 IDH1 weIDH1 IDH1 found gene gene gene gene was thatwas was wasin in overexpressed theoverexpressedall alloverexpressed overexpressed IDH1 gene in wasin in allin all all overexpressed all clinical samples, in all and the expression level correlates with the grade of the tumor. In CNS clinicalclinical samples, samples, clinicaland clinicalandclinicalclinical the the samples, expressionsamples, expressionsamples, samples, and and and andlevel clinicallevel the the the the correlates expressioncorrelatesexpression expression samples,expression with with level andlevel level level the the correlates correlates correlates grade correlatesexpressiongrade of of with with the withthe with level tumor. the tumor.the the the grade correlatesgrade grade grade In In CNS of CNSof of ofthe the the with the tumor. tumor. tumor. tumor. the gradeIn In In InCNS CNS CNS CNS of the WHO tumor. grade In CNS 3 and 4 tumors, we found a greater overexpression 5 to 155-fold in CNS WHO WHOWHO grade grade 3 3 and andWHOWHO 4WHO 4WHO tumors, tumors, grade grade grade grade we we 3 3 and3 foundand 3 foundand and 4 4 WHOtumors,4 tumors, 4 a tumors,a tumors,greater greater grade we we we overexpression weoverexpression found found3 found andfound a4 a greateratumors, greatera greater greater 5 5 overexpression to overexpressionweto overexpression overexpression155 155 found--foldfold a in ingreater CNS CNS 5 5 to5 to5 toWHO 155WHOtooverexpression 155 155 155--foldfold- fold -fold in in in CNSin CNS CNS CNS 5WHO WHO toWHO WHO 155 - fold grade in CNS 1 and WHO 2 gliomas and 2305 to 5167-fold in CNS WHO grade 3 and 4 gliomas. With gradegrade 1 1 and and 2 2 gliomas gliomasgradegradegradegrade and 1and1 1and and1 and and2305 2305 2 2 2gliomas gliomas2 to gliomas togliomas 5167 5167grade and -and-fold foldand and1 2305 and2305 in2305in 2305 CNS CNS 2to to gliomasto to5167 5167 WHO 5167 WHO5167--foldfold- fold -andgrade foldgrade in in in 2305inCNS CNS 3 CNS 3CNS and andto WHO WHO WHO 51674 WHO4 gliomas. gliomas. -grade foldgrade grade grade in 3 With 3 With CNS3and and3 and and 4 WHO4 4gliomas. gliomas.4 gliomas. gliomas. grade With With With With3 and respect4 gliomas. to the With SDHB gene (Figure 3B), we found that in CNS WHO grade 1 glioma it was respectrespect to to the the SDHB SDHBrespectrespectrespectrespect gene gene to to (Figureto (Figuretheto the the the SDHB SDHB SDHB SDHB3B), 3B),respect gene wegene wegene gene found found(Figure (Figure to(Figure (Figure the that that SDHB3B), 3B), 3B), in 3B),in we CNS we CNSwe genewe found found found WHO foundWHO (Figure that that thatgrade thatgrade in in3B), in inCNS CNS 1 CNS1 CNSweglioma glioma WHO WHOfound WHO WHO it it grade wasgrade thatwasgrade grade in 1 1 1gliomaCNS glioma1 glioma glioma WHO it it it wasitwas was gradewas not 1 glioma overexpressed. it was In CNS WHO grade 2–4 samples an increase in the expression of this notnot overexpressed. overexpressed.notnotnot notIn Inoverexpressed. overexpressed. CNS overexpressed. CNSoverexpressed. WHO WHO grade grade In notIn In InCNS CNS 2 overexpressed.CNS 2–CNS–44 WHOsamples WHOsamples WHO WHO grade grade grade an gradean increaseIn increase2 2 –2CNS– 42–4 –4samples samples4 samples samplesWHO in in the the an angrade expression an expression anincrease increase increase increase 2–4 samples in inof ofin inthe thethis thisthe the expression expression expressionan expression increase of of of inofthis this this thethis expression gene was observed,of this ranging from 1.7 to 5.8-fold for samples of grade 2, 2.6 to 7.4-fold for genegene was was observed, observed,genegenegenegene ranging ranging was was was was observed, observed, observed,from observed,from 1.7 1.7 gene ranging to ranging toranging ranging5.8 5.8 was--foldfold from from observed,from fromfor for 1.7 1.7samples samples1.7 1.7 to to to to5.8ranging 5.8 5.8 5.8- -offold offold-fold- foldgrade grade for fromfor for for samples samples2, 2, samples 1.7 samples2.6 2.6 to to to 5.8 of 7.4 of 7.4of -ofgrade fold-grade -foldgrade foldgrade for for 2, for2, 2, samples2.6 2,2.6 2.6 2.6 to to to to7.4 7.4 7.4 7.4of--foldfold- foldgrade-fold for for for for 2, 2.6grade to 7.4 3 samp-fold les,for and 1.8-fold for grade 4 samples. gradegrade 3 3 samp samples,les, gradeand gradeandgradegrade 1.8 1.8 3 3 -3samp -foldsamp3 foldsamp samp for les,forles,les, les,grade gradeand and and andgrade 1.8 1.8 4 1.84 1.8samples.- samples.-fold fold-3fold- foldsamp for for for for gradeles, grade grade grade and 4 4 4samples. 1.8samples.4 samples. samples.-fold for grade 4 samples. Finally, the FASN, ACACA, and ELOVL2 genes, which participate in the fatty acid Finally,Finally, thethe FASNFASNFinally,Finally,,Finally, , Finally,ACACAACACA thethe the ,the , and FASNandFASN FASN FASN ELOVLELOVL, , ACACA,Finally,ACACA ,ACACA ACACA22 genes,genes,, ,the and,and ,and and FASN whichwhich ELOVLELOVL ELOVL ELOVL, ACACA participateparticipate22 2 genes,2genes, genes, genes,, and whichwhich whichin inwhich ELOVL thethe participate participate fattyparticipate fattyparticipate2 genes, acidacid in inwhichin in thethe the the fattyfatty fattyparticipate fatty acidacid acid acid synthesisin the fatty pathway, acid were evaluated. As shown in Figure 3C, it was observed that the FASN synthesissynthesis pathway, pathway,synthesissynthesissynthesis synthesiswere were evaluated. evaluated. pathway, pathway, pathway, pathway, As wereAs synthesiswere were shownwere shown evaluated. evaluated. evaluated. evaluated. in pathway,in Figure Figure As As As As 3C, shown 3C,wereshown shown shown it it was evaluated.was in in in Figureobservedin Figureobserved Figure Figure 3C,As 3C, 3C, 3C,that shownthatit it itwas itwas thewas thewas observed inFASNobserved FASNobserved observedFigure that3C, that that that itthe the wasthe the FASN FASN FASN observedFASN gene that was the not FASN overexpressed in the CNS WHO grade 1 and 2 samples, while in the CNS genegene was was not not overexpressed overexpressedgenegenegenegene was was was was not not not in notin overexpressed overexpressedthe overexpressedthe overexpressed CNS CNSgene WHO WHOwas in innot grade in gradeinthe the the overexpressedthe CNS CNS 1 CNS 1CNS and and WHO WHO WHO 2 WHO2 samples, samples, grade grade ingrade grade the 1 while 1 whileCNS1and 1and and and 2WHOin 2 in2samples, samples,2 the samples, thesamples, CNS gradeCNS while while while 1while and in in in 2inthe the samples,the the CNS CNS CNS CNS while WHO in grade the CNS 3 and 4 samples an overexpression of the gene was observed from 72 to 180 WHOWHO grade grade 3 3 and andWHOWHOWHO 4 WHO4 samples samples grade grade grade grade an 3an3 3and and3 overexpression andoverexpression and 4 4WHO 4samples samples4 samples samples grade an an an of anofoverexpression 3overexpression theoverexpression and theoverexpression gene gene4 samples was was observedof observed of anof ofthe the overexpressionthe the gene gene gene genefrom from was was was was72 72 observed observed to observed toobserved of180 180 the from genefrom from from 72was 72 72 72to to toobserved to180 180 180 180 -fold. from Regarding 72 to 180 the ACACA and ELOVL2 genes, we observed that these two genes were --fold.fold. Regarding Regarding -the -fold.thefold.-fold.-fold. ACACA ACACA Regarding Regarding Regarding Regarding and and ELOVL2the ELOVL2the the the- fold.ACACA ACACA ACACA ACACA genes,Regarding genes, and and and and we weELOVL2 ELOVL2 ELOVL2 observedELOVL2 observedthe ACACA genes, genes, genes, genes, that that andwe we thesewethese we observedELOVL2observed observed observed two two genes genes genes,that that that that were these werethese these wethese observedtwo two two two genes genes genes genes that were were were were these overexpressed two genes were in all glioma samples, and there was a relationship between the degree of overexpressedoverexpressed in inoverexpressed overexpressedall alloverexpressedoverexpressed glioma glioma samples, samples, in in in inall all all overexpressed allandglioma andglioma glioma glioma there there samples, samples, samples, samples,was was ina a rel alland reland and ationship andationshipglioma there there there there was samples,was was between wasbetween a a arel rela rel relationship ationshipand ationshipthe ationshipthe there degree degree between betweenwas between between of of a rel the theationship the the degree degree degree degree betweenof of of of tumor the anddegree expression. of For the ACACA gene, an overexpression of 6 to 153-fold was found tumortumor and and expression. expression.tumortumortumortumor For Forand and and and the the expression. expression. expression. ACACAexpression. ACACAtumor gene, gene,For For For For andthe thean thean the ACACA overexpression expression.ACACAoverexpression ACACA ACACA gene, gene, gene, gene, For an an anof the ofanoverexpression overexpression 6 overexpression6 overexpressionACACAto to 153 153--foldfold gene, was was of of anof 6of found 6found 6tooverexpression to6 to 153to 153 153 153 --foldfold-fold-fold was was was was foundof found found found6 to 153 in-fold CNS was WHO found grade 1 and 2 gliomas, while the expression levels of CNS WHO grade 3 inin CNSCNS WHOWHO gradegradeininin inCNS CNS CNS1 1CNS and and WHO WHO WHO WHO 22 gliomas,gliomas, grade grade grade gradein 1 while 1 whileCNS1and and1 and and 2 WHO the2 the2 gliomas,2gliomas, gliomas, expressiongliomas,expression grade while while while 1while and levelslevels the the the 2the expression gliomas,expression ofexpressionof expression CNSCNS WHOwhileWHO levels levels levels levels gradethegrade of of of expressionofCNS CNS CNS3 3CNS WHO WHO WHO WHO levels grade grade grade grade of 3 3 CNS3 3 and WHO 4 gliomas grade showed3 1534 to 5133-fold increases with respect to HMC3 (Figure 3D). In andand 4 4 gliomas gliomas showed showedandandandand 4 4 4gliomas1534 gliomas41534 gliomas gliomas to to 5133showed 5133showed showed showed--foldandfold 1534 1534increases 41534increases 1534gliomas to to to to5133 5133 5133 5133with showedwith--foldfold-fold- foldre re increases spectincreasesspect increases 1534increases to toto HMC3 HMC3with 5133with with with -re fold re (Figurere(Figure spectrespectspect spectincreases to to 3D). to3D). toHMC3 HMC3 HMC3 HMC3 In Inwith (Figure (Figure (Figure re(Figurespect 3D). 3D). 3D). to3D). HMC3In In In In the (Figure case of 3D). the InELOVL2 gene, the level of overexpression was 1.4 to 9.5-fold in CNS WHO thethe case case of of the the ELOVL2 ELOVL2thethethethe case case case casegene, gene, of of of theof the the thethe ELOVL2 ELOVL2 levelELOVL2 levelELOVL2the of of gene,case overexpression gene, overexpressiongene, gene, of the the the the level level ELOVL2level level of of was of wasoverexpressionof overexpression overexpression gene,overexpression 1.4 1.4 to to the 9.5 9.5 level--foldfold was was ofwasin inwas overexpressionCNS 1.4 CNS1.4 1.4 1.4 to to WHOto WHO9.5to 9.5 9.5 9.5--foldfold- fold- fold in in wasin CNSin CNS CNS CNS 1.4 WHO WHO toWHO WHO 9.5 - fold grade in CNS 1 and WHO 2 samples and 337 to 353-fold in CNS WHO grade 3 and 4 samples (Figure gradegrade 1 1 and and 2 2 samples samplesgradegradegradegrade 1 and1and 1and and1 and and337 337 2 2 2 samplesto samples2 tosamples samples353 353grade--foldfold and and and and 1in in337 and337 CNS 337 CNS337 to2to to samplesto353WHO 353WHO 353 353--foldfold-fold -grade foldgrade andin in in inCNS CNS 3 337CNS 3CNS and and WHO toWHO WHO 4 WHO3534 samples samples -grade foldgrade grade grade in 3 (Figure3 (Figure CNS3and and3 and and 4 WHO4 4 samplessamples4 samples samples grade (Figure (Figure (Figure (Figure3 and 4 3E).samples (Figure 3E).3E). 3E).3E).3E).3E). 3E).

Genes 2021, 12, 1335 11 of 18

Then, we evaluated the genes that code for two enzymes of the Krebs cycle (IDH1 and SDHB). As seen in Figure3A, we found that the IDH1 gene was overexpressed in all clinical samples, and the expression level correlates with the grade of the tumor. In CNS WHO grade 3 and 4 tumors, we found a greater overexpression 5 to 155-foldGenes 2021 in CNS, 12, x WHOFOR PEER REVIEW 12 of 18

Genes 2021, 12, x FORGenesGenes PEERGenesGenes 2021 2021 REVIEW2021 2021, 12, 12,, 12,x, 12xFOR, xFOR, xFOR FOR PEER GenesPEERGenes PEER PEER REVIEW 2021 gradeREVIEW2021 REVIEW REVIEW, ,12 12 , 1,x x andFOR FOR PEER 2PEER gliomas REVIEW REVIEW and 2305 to 5167-fold in CNS WHO grade 3 and12 4 gliomas.of 18 With1212 12of 12of 18of of18 18 18 1212 ofof 1818

respect to the SDHB gene (Figure3B), we found that in CNS WHO grade 1 glioma it was Genes 2021, 12, notx FOR overexpressed. PEER REVIEW In CNS WHO grade 2–4 samples an increase in the expression of this 12 of 18 gene was observed, ranging from 1.7 to 5.8-fold for samples of grade 2, 2.6 to 7.4-fold for grade 3 samples, and 1.8-fold for grade 4 samples.

Figure 3. Expression levels of genes that encode enzymes that participate in the essential metabolic Figure 3. Expression levels of genes that encode enzymes that participate in the essential metabolic Figure pathways 3. Expression of glioma levels of genes that encode enzymes that participate in the essential metabolic pathways of glioma cells. (Apathways,B) represent of the glioma expression cells. (levelsA,B) representof IDH1 A the and expression SDHB genes levels involved of IDH1 in the A Krebs and SDHB cycle. genes(C–E) represent involved the Figure 3. ExpressionFigureFigureFigure Figurelevels 3. 3. Expression 3. ofExpression3. Expression genesExpression thatFigure Figurelevels levels levelsencode levels of 3.3. of ExpressiongenesofExpression ofgenesenzymes genes genes that that that that encodethat levels levelsencode encode encode participate of ofenzymes enzymes genes genesenzymes enzymes thatinthat that thatthe that encodeencode thatparticipate essential participate participate participate enzymesenzymes metabolic in in thein inthe that thatthe essentialthe essential pathways participateessentialparticipate essential metabolic metabolic metabolic ofmetabolic inin glioma thethe pathways essentialpathwaysessential pathways cells. pathways ( A metabolicofmetabolic ,Bof gliomaof) ofgliomarepresent glioma glioma pathwayspathways the expression ofof gliomaglioma levels of IDH1 A and SDHB genes involved in the Krebs cycle. (C–E) represent the expressionin thelevels Krebs of the cycle. FASN (C, ACACA–E) represent, and ELOVL2 the expression genes involved levels in of the the fatty FASN, acid synthesis ACACA, genes. and ELOVL2Relative expression genes cells. (A,B) representcells.cells.cells. cells.the ( A( A,expression(B A,(B)A, B)represent, B)represent )represent represent levels cells.the the the expressionthe of (expressionA expression IDH1,expressionB) represent A levelsand levels levels levels SDHB theof of IDH1of expressionofIDH1 genesIDH1 IDH1 A A andAinvolved andA and levels andSDHB SDHB SDHB SDHB ofin genes IDH1genesthe genes genes Krebs involved Ainvolved involved andinvolved cycle. SDHB in in (theinC inthe – genestheE Krebsthe) Krebsrepresent Krebs Krebs involved cycle. cycle. cycle. cycle. the ( C (in C– expression( EC –(theC)E– )Erepresent– E )representKrebs )represent represent levels cycle. the the the ofthe (C the– E )FASN represent, ACACA the , and ELOVL2 genes involved in the fatty acid synthesis genes. Relative expression levels werecells. determined (A,B) represent using the the PKM expression gene as levelsa reference. of IDH1 Values A andare means SDHB ± genesSD of quadruplicateinvolved in thedeterminations. Krebs cycle. (C –E) represent the expression levels expressionofexpression expressiontheexpression FASN levels ,levels ACACAlevels levels of of theof involvedexpression,ofthe andthe FASNthe FASN FASNELOVL2 FASN, inACACA, ACACAlevels, the ACACA, ACACA genes fatty ,of and, theand acid, involvedand, andELOVL2 FASN ELOVL2 synthesis ELOVL2 ELOVL2, inACACA genes thegenes genes.genes genes fatty involved, involvedand involved Relative acidinvolved ELOVL2 synthesis in in expression thein inthe thegenes fattythe fatty genes.fatty fatty acidinvolved acid levels acid acidRelative synthesis synthesis synthesis were synthesis in theexpression determined genes. genes.fatty genes. genes. acidRelative Relative levelsRelative usingRelative synthesis expressionwere theexpression expression expression PKM genes.determined Relative using expression the PKM gene as a reference. Values are means ± SD of quadruplicate determinations. HMC3,expression CNS WHO levels 1 (T1), of the CNSFASN WHO, ACACA 1 (T12),, and ELOVL2CNS WHO genes 2 (T4), involved CNS in WHO the fatty2 (T18), acid synthesis CNS WHO genes. 3 (T7), Relative expression levels were determinedlevelslevels usingwere were thedetermined determined PKMgene gene using asusing as a areference. the reference.the PKM PKM gene Values geneValues as as area areareference. reference. means means± ±Values SD ValuesSD ofof quadruplicatearequadruplicate are means means ± ±SD SD determinations.determinations. of of quadruplicate quadruplicate determinations. HMC3,HMC3,determinations. CNSCNS WHO 1 (T1), CNS WHO 1 (T12), CNS WHO 2 (T4), CNS WHO 2 (T18), CNS WHO 3 (T7), levelslevels were were determined determined CNSlevelslevels WHO using werewere3 using (T10), the determineddetermined PKMthe CNSPKM gene WHO gene usingusingas a4 asreference.(T9). the thea reference. PKM PKM Values genegene Values asas are aa reference. reference.meansare means ± SD ValuesValues ± ofSD quadruplicate of areare quadruplicate meansmeans ±± SDdeterminations.SD ofofdeterminations. quadruplicatequadruplicate determinations.determinations. HMC3, CNS WHOHMC3,HMC3,HMC3,HMC3, 1 (T1), CNS CNS CNS CNSCNS WHO WHO WHO HMC3, WHOHMC3,WHO 1 1(T1), 1(T1), 1 1 (T1), (T1),(T12), CNSCNSCNS CNS CNS CNS CNS WHO WHOWHO CNSWHO WHO WHOWHO 1 1WHO1 (T1), 1(T1),(T12), 1(T12), 1 1(T12), (T12),(T12), 2 (T4), CNS CNS CNS CNS CNSWHOCNSWHO CNS CNSWHO WHO WHOWHO 1WHO1 (T12),WHO(T12), 2 2(T4), 2 2(T4), (T4), 2 (T4), 2(T4), (T18), CNS CNS CNS CNSCNS CNS WHOCNSWHO WHO WHOCNSWHO WHO WHO 22 (T4),WHO2(T4), 2 2(T18), (T18), 2(T18), 2 (T18), (T18), 3 (T7), CNS CNS CNS CNS CNS WHOCNSWHO CNSCNS WHOWHO WHO WHO 2 WHO2WHO (T18), 3(T18),3 (T7), 3(T7), 3(T7), 33 (T7), (T7),(T10), CNS CNS WHOWHO CNS 33WHO (T7),(T7), 4 (T9). CNS WHO 3 (T10), CNS WHO 4 (T9). CNS CNS CNS CNS WHO WHO WHO WHO 3 3(T10), 3(T10), 3(T10), (T10), CNS CNS CNS CNS WHO CNS WHOCNS WHO WHO WHO 3 WHO3 (T10), (T10), 44. 4(T9). Discussion4(T9). 4(T9). (T9). CNS CNS CNS WHO WHOWHO 44 4 (T9).(T9). (T9). 4. Discussion The RT-qPCR technique has become the method of choice for gene expression due to 4. Discussion Finally,4. Discussion the FASN , ACACA, and ELOVL2 genes, which participate in the fatty acid 4.4. Discussion Discussion4. Discussionits wide range4.4. DiscussionDiscussion of quantification in biological samples, and it also shows a high sensitivity The RT-qPCR technique has become the method of choice for gene expression due to synthesis pathway, were evaluated. As shown in Figure3C, it was observed that the The RT-qPCR techniqueTheTheTheThe RT andRT RT- RTqPCR- hasqPCRprecision.-qPCR-qPCR become technique technique technique technique TheHowever,The the RT RT hasmethod -has-qPCR qPCRhas duehasbecome become become tobecome techniqueoftechnique the choice the extremethe the themethod method for methodhas hasmethod sensitivitygene become become of of expressionchoiceof ofchoice choice choicethe ofthe thefor methodformethod for RTgenefor duegene -geneqPCR gene expression toof ofexpression expression choice technique,expressionchoice for for due due an geneduegene due toad- to toexpression expressionto its wide due duerange to to of quantification in biological samples, and it also shows a high sensitivity FASN gene was not overexpressed in the CNS WHO grade 1 and 2 samples, while in the its wide range ofits itsquantification itswideits wide wide wide range rangeequate range range of of in normalizationofquantification of itsquantificationbiologicalits quantification quantification widewide rangerange samples, of inthe in ofof inbiological inbiological resultsquantification quantificationbiological biologicaland for it samples,also thesamples, samples, samples, expressionshows inin biological biologicaland and aand and highit levelsit alsoit alsoit alsosensitivity samples, samples,also ofshows shows theshows shows genes a and anda high ahigh a highis ithighit required.sensitivity also alsosensitivity sensitivity sensitivity showsshows For aa high highand sensitivity sensitivityprecision. However, due to the extreme sensitivity of the RT-qPCR technique, an ad- CNS WHO grade 3 and 4 samples an overexpression of the gene was observed from 72 to and precision. However,andandandand precision. precision. precision. precision. duethis reason, toHowever, However, theHowever, However,andand extremethe precision.precision. use due due ofdue duesensitivity toan to totheinternal toHowever,theHowever, the theextreme extreme extreme extreme ofcontrol the due duesensitivity sensitivityRT calledsensitivity sensitivity toto-qPCR thethe a reference extreme extremeof technique,of ofthe ofthe the theRT geneRT sensitivitysensitivity RT- RTqPCR- qPCR -anisqPCR- qPCRrequired, ad- technique, technique, oftechnique,of technique, thethe which RTRT an- -qPCRanideallyqPCR an ad-an ad- ad- ad- technique,technique,equate normalization anan ad-ad- of the results for the expression levels of the genes is required. For 180 -fold. Regardingmust show the ACACA stable expressionand ELOVL2 levels,genes, and it wemust observed not vary thatsignificantly these two between genes samples equate normalizationequateequateequateequate of normalization normalization thenormalization normalization resultsequateequate for of ofthe ofthe of normalizationthenormalization theexpression theresults results results results for for for levelsthefor theofof the theexpression the theexpression ofexpression expression results resultsthe genes forlevelsfor levels levels thelevelsisthe required.of expressionofexpression ofthe ofthe the thegenes genes genesFor genes levelslevelsis is required.is required.is required. ofrequired.of thethe For genesgenes For For For isis this required.required. reason, ForFor the use of an internal control called a reference gene is required, which ideally were overexpressedand inexperimental all glioma conditions samples, and[28,29]. there Although was a relationship there are reports between of reference the degree genes being this reason, the thisusethisthis this ofreason, reason, anreason, reason, internal the the the theuse use controlusethis thisuseof of anof reason,ofanreason, an internal calledan internal internal internal the the a controlreference usecontroluse control control of of an calledan called genecalledinternal internalcalled a is areference a reference required,a referencecontrol controlreference gene called genecalledwhich gene gene is is a required,a is ideally required,referenceis reference required, required, which whichgene gene which which ideallyis is ideally required, ideallyrequired, ideally mustwhich which show ideally ideally stable expression levels, and it must not vary significantly between samples of tumor and expression.validated for For expression the ACACA studiesgene, in human an overexpression gliomas, it is not ofpossi 6 toble 153-fold to establish was a consen- must show stablemustmust mustexpressionmust show show show show stable stable levels,stable stable expression mustexpression expression andexpression show it must levels, stablelevels, levels, levels,not and expressionvaryand and and it it significantly mustit mustit must must levels,not not not notvary vary varyand betweenvary significantly significantly itsignificantly significantlymust samples not between varybetween between between significantly samples samples samples samples between and experimental samples conditions [28,29]. Although there are reports of reference genes being found in CNS WHOsus on grade thismust issue. 1 and show For 2 gliomas, example, stable expression while Valente the et expression al. levels, [17] reported and levels it mustthat of CNSthe not TBP WHOvary and significantly gradeHPRT1 genes between samples and experimentalandand andconditionsand experimental experimental experimental experimental [28,29]. conditions andconditions conditions Althoughconditions experimental [28,29]. [28,29]. [28,29].there [28,29]. Althoughconditionsare Although Although Although reports there [28,29]. thereof there there reference are are are Althougharereports reports reports reports genes of ofthere ofreference being ofreference reference reference are reports genes genes genes genes beingof being beingreference being validated genes being for expression studies in human gliomas, it is not possible to establish a consen- 3 and 4 gliomasare showed suitableand 1534 as experimental references to 5133-fold for increasesgeneconditions expression with [28,29]. respectstudies Although in to clinical HMC3 there samples (Figure are reports3ofD). glioblastoma. In of reference genes being validated for expressionvalidatedvalidatedvalidatedvalidated studies for for for expressionfor expression expression inexpression validatedhuman studies studies gliomas,studies studies for inexpression in humanin inithuman human is human not gliomas, studiesgliomas,possi gliomas, gliomas,ble init toit is humanit is establishit notis notis not notpossi possi gliomas,possi possi able bleconsen-ble toble to establishitto toestablish is establish establishnot possi a aconsen- aconsen- a bleconsen- consen- to establishsus on a consen-this issue. For example, Valente et al. [17] reported that the TBP and HPRT1 genes the case of the ELOVL2Another gene,studyvalidated thefound level for that expression of the overexpression YWHAZ studies gene in was[18] human 1.4is a to suitable gliomas, 9.5-fold reference init is CNS not gene; WHOpossi later,ble to the establish a consen- sus on this issue.sussus Forsus suson on example,on thison this this this issue. issue. issue. issue. Valente For Forsus For Forexample, example,on etexample, example, thisal. [17] issue. Valente Valente reportedValente Valente For et example,et al.et thatetal. al.[17] al. [17] [17]the [17] reported Valentereported TBPreported reported and thatet that HPRT1 al.that that the [17]the the theTBP TBPgenesreported TBP TBP and and and and HPRT1 HPRT1that HPRT1 HPRT1 the genes genes TBPgenes genes and are HPRT1 suitable genes as references for gene expression studies in clinical samples of glioblastoma. grade 1 and 2 samplesRPL13A and andsus 337 TBPon tothis genes 353-fold issue. were inFor also CNS example, proposed WHO Valente gradeby Aithal 3 andet et al. al. 4 [17] samples[19] reportedas reference (Figure that genes.3E). the TBPIn the and HPRT1 genes are suitable as referencesareareare aresuitable suitable suitable suitable presentfor as asgene asreferences asreferences referencesstudy, are referencesareexpression suitablesuitable we for forproposed for genefor studiesgene as asgene gene references referencesexpression expressionto expression expressioninevaluate clinical forfor studies astudies gene genestudiessamplesprofile studies expressionexpressionin inof inclinical inofclinicalmetabolic clinical clinicalglioblastoma. samples studies studiessamples samples genessamples in inofas of clinical ofclinicalglioblastoma.reference ofglioblastoma. glioblastoma. glioblastoma. samplessamples genes, ofof Another glioblastoma.glioblastoma. study found that the YWHAZ gene [18] is a suitable reference gene; later, the Another study AnotherfoundAnotherAnotherAnother that study includingstudy studythe study foundYWHAZ found found Another AnotherHK1found that , thatPFKM thatgene that the study studythe ,the TPI1[18]theYWHAZ YWHAZ YWHAZ found foundYWHAZ,is GAPDH a suitable gene that thatgene gene, PKMgene the[18]the [18] reference,[18] LDHAL6A[18]YWHAZ YWHAZis is ais isa suitable a suitablea suitable gene;suitable ,gene geneG6PD reference later,[18]reference,[18] PGD1reference reference is isthe , a aTKT1 suitablegene;suitable gene; gene; ,gene; SDHB later, later, referencelater, referencelater,, FASN the the the the, gene; gene;RPL13A later,later, and thethe TBP genes were also proposed by Aithal et al. [19] as reference genes. In the RPL13A and TBPRPL13ARPL13A genesRPL13ARPL13A wereand andACACA and and TBP alsoTBP TBP TBP ,genes and RPL13AproposedRPL13Agenes genes genes ELOVL2, were were were andwereand by also alsobecause TBPAithalTBPalso also proposed proposed genesproposedgenes proposed theseet al. werewere genes[19]by by by Aithal by alsoasalsoAithal areAithal Aithalreference proposedrequiredproposed et et al.et etal. al.[19] al. [19]genes.for [19] by[19] by asthe as AithalasAithalreference asmaintenanceInreference reference referencethe etet al.al. genes. genes. [19] [19]genes. ofgenes. basic as asIn In reference referenceInthe Incel-the the the present genes.genes. Instudy,In thethe we proposed to evaluate a profile of metabolic genes as reference genes, lular functions that are important for the existence of any cell type, as they express pro- present study, wepresentpresentpresent proposedpresent study, study, study, study, to we evaluatewe we presentweproposedpresent proposed proposed proposed a study,study,profile to to toevaluate to evaluate we weevaluate ofevaluate proposedmetabolicproposed a a profile a profilea profile profile togenesto ofevaluate evaluateof ofmetabolic ofmetabolicas metabolic metabolicreference aa profileprofile genes genes genes genesgenes, of ofas as metabolic metabolicasreference asreference reference reference genesgenesgenes, genes, genes, genes, as as reference reference including genes,genes, HK1 , PFKM, TPI1, GAPDH, PKM, LDHAL6A, G6PD, PGD1, TKT1, SDHB, FASN, teins that are essential for the viability of the cells. In this context, we considered it including HK1, includingPFKMincludingincludingincluding, TPI1 HK1 HK1, HK1GAPDH HK1, PFKM, PFKM, PFKM,including includingPFKM, ,PKM TPI1, TPI1, TPI1, TPI1,, LDHAL6AGAPDHHK1 ,HK1 GAPDH, GAPDH, GAPDH,, PFKM PFKM, PKM, ,PKM, G6PD PKM, ,PKMTPI1 TPI1, LDHAL6A, LDHAL6A,, , LDHAL6A,, PGD1 GAPDH LDHAL6AGAPDH, TKT1, G6PD,,, PKM G6PD,PKM G6PD, G6PD, SDHB,, , PGD1LDHAL6A ,LDHAL6A PGD1, PGD1, PGD1, FASN, TKT1, TKT1, TKT1, TKT1,,, G6PD G6PD, SDHB, SDHB, SDHB, SDHB,, PGD1 PGD1, FASN, FASN, FASN, FASN,, TKT1 TKT1, , , , ,, ACACASDHB SDHB,, FASN ,FASN and ,ELOVL2,, because these genes are required for the maintenance of basic cel- ACACA, and ELOVL2,ACACAACACAACACAACACA because, and, and, ,and and ELOVL2, ELOVL2, theseELOVL2, ELOVL2,ACACAACACA genes because because because ,because, andandare theserequired ELOVL2,ELOVL2,these these these genes genes genes genes for becausebecause are theare are arerequired maintenancerequired required these theserequired genes genesfor for for thefor the of aretheare the maintenancebasic maintenance requiredmaintenancerequired maintenance cel- forfor of ofthethe ofbasic ofbasic basicmaintenance maintenancebasic cel- cel- cel- cel- lular ofof basicbasicfunctions cel-cel- that are important for the existence of any cell type, as they express pro- lular functions thatlularlularlularlular are functions functions functions importantfunctions that that that lularthatlularfor are are arethe are importantfunctions functionsimportant importantexistence important thatthatfor forof for thefor are anyarethe the theexistence important importantexistencecell existence existence type, of offoras for ofany ofanythey theanythe any cell cell existence expressexistencecell celltype, type, type, type, aspro- as of ofasthey as they any anythey they express cellexpresscell express express type,type, pro- pro- aspro- aspro- theytheyteins expressexpress that pro- pro- are essential for the viability of the cells. In this context, we considered it teins that are essentialteinsteinsteinsteins that that that for that are are the are are essential essential viability essential essentialteins for that for of for for the the theare the the viability cells. viabilityessential viability viability In of thisof for of the of the context,the the cells. cells. cells.viability cells. In weIn In this In this considered thisof this context, thecontext, context, context, cells. we it we Inwe we considered consideredthis considered considered context, it it it we it considered it teins that are essential for the viability of the cells. In this context, we considered it

Genes 2021, 12, 1335 12 of 18

4. Discussion The RT-qPCR technique has become the method of choice for gene expression due to its wide range of quantification in biological samples, and it also shows a high sensitivity and precision. However, due to the extreme sensitivity of the RT-qPCR technique, an adequate normalization of the results for the expression levels of the genes is required. For this reason, the use of an internal control called a reference gene is required, which ideally must show stable expression levels, and it must not vary significantly between samples and experimental conditions [28,29]. Although there are reports of reference genes being validated for expression studies in human gliomas, it is not possible to establish a consensus on this issue. For example, Valente et al. [17] reported that the TBP and HPRT1 genes are suitable as references for gene expression studies in clinical samples of glioblastoma. Another study found that the YWHAZ gene [18] is a suitable reference gene; later, the RPL13A and TBP genes were also proposed by Aithal et al. [19] as reference genes. In the present study, we proposed to evaluate a profile of metabolic genes as reference genes, including HK1, PFKM, TPI1, GAPDH, PKM, LDHAL6A, G6PD, PGD1, TKT1, SDHB, FASN, ACACA, and ELOVL2, because these genes are required for the maintenance of basic cellular functions that are important for the existence of any cell type, as they express proteins that are essential for the viability of the cells. In this context, we considered it important to perform an analysis to validate and select reference genes for studies of expression levels in brain glioma biopsies. Surprisingly, our results showed that GAPDH, which is commonly used as a reference gene in gene expression studies, is not the most stable housekeeping gene in HMC3 cells. It is interesting to note that our results agreed with other reports, where it was also found that the GAPDH gene is not the most suitable for use as an internal control gene in cancer studies [30]. Moreover, the analysis that we realized using Bestkeeper, the comparative ∆CT method, geNorm, NormFinder and RefFinder to calculate a final classification of candidate genes for validation as reference genes allowed us to propose the following genes (from most to least stable): PKM > TPI1 > GAPDH > GAPDHa > TKT1 > SDHB > G6PD > ACACA > PGD1 > GAPDHb > TBP (Tables4 and5). Thus, we propose PKM and TPI1 as suitable reference genes in gene expression studies in brain glioma biopsies. The results show that it is not easy to identify a universal reference gene that possesses all the ideal characteristics. In the initial endpoint PCR analysis of the sixteen genes, three genes were not amplified (LDHAL6A, PGD1, and ACACA2). Later, in the validation of the RT-qPCR assays, some of them were found to be unspecific and/or inefficient (HK1, PFKM, PGD1, FASN, ACACA, and ELOVL2). This highlights the importance of selecting appropriate reference genes for mRNA quantification by RT-qPCR, because the use of non-validated reference genes can lead to questionable results and errors in data interpretation [31]. Metabolic reprogramming is a hallmark of cancer [32], and the Warburg effect is a characteristic phenotype of this reprogramming, allowing a rapid generation of useful intermediates in the biosynthesis of macromolecules. It has been reported that glioma cells overexpress glucose transporters such as GLUT1 and GLUT3, allowing them to increase the flux of glucose towards glycolysis [33]. Due to this, we decided to analyze the expression levels of some glycolytic genes in pediatric glioma samples of different grades, and found that the HK1, PFKM, GAPDH, and LDHAL6A genes are overexpressed in human brain glioma biopsies. Furthermore, our study also demonstrated that an increase in HK1, PFKM, and GAPDH expression could be associated with the CNS WHO grading of gliomas. For this reason, the HK1, PFKM, and GAPDH genes could be essential for the progression of glioma to malignant grades, and in this way, they could be useful biomarkers and even targets for the therapy of gliomas. These results also suggest that gliomas have a high glycolytic flux (Figure4). One of the enzymes that determines the pyruvate’s final fate is LDH, an enzyme that catalyzes the conversion of pyruvate to lactate. In the analyzed samples, we found an overexpression of the LDHAL6A gene in all samples, regardless of the CNS WHO grading, which is an agreement with the Warburg effect in glioma Genes 2021, 12, 1335 13 of 18

cells. Besides this, our results agree with other studies where glioma cells have shown high glycolytic levels compared to healthy brain tissue [34,35]. These results imply that glioma cells catabolize glucose in the glycolysis pathway, generating lactate as the final product (Figure4). Regarding the TPI1 gene, we found that it was not overexpressed in any clinical sample of pediatric glioma. The TPI enzyme catalyzes the isomerization of dihydroxyacetone phosphate to glyceraldehyde-3-phosphate (G3P) to continue the glycolytic pathway. However, the glioma cells could obtain G3P from the PPP, and thus, the TPI enzyme expression did not increase (Figure4). Furthermore, TPI is a highly efficient Genes 2021, 12, x FOR PEER REVIEW 17 of 21 enzyme, thus the rate with which it catalyzes the isomerization of G3P is only limited by the diffusion of the substrate [36].

* *

* *

* * * *

* *

FigureFigure 4. Metabolic4. Metabolic routes routes relevant relevant to thisto this study. study. The The genes genes analyzed analyzed in thisin this study study are are shown shown in ain red a red box, box red, red arrows arrows indicateindicate genes genes that that showed showed overexpression, overexpression, and and an an asterisk asterisk represents represents those those genes genes that that showed showed an an increase increase in in the the levellevel of of expressionexpression regardingregarding thethe CNSCNS WHOWHO gradegrade ofof thethe brainbrain gliomaglioma biopsies.biopsies. TheThe gliomasgliomas undergoundergo metabolicmetabolic remodelingremodeling to to meetmeet the needs of the the highly highly proliferative proliferative cells, cells, which which can can be be summarized summarized as asan an increase increase in inthe the expression expression of some of some genes involved in glycolysis, such as HK1, PFKM, GAPDH, and LDHAL6A. Besides this, the overexpression of some genes genes involved in glycolysis, such as HK1, PFKM, GAPDH, and LDHAL6A. Besides this, the overexpression of some genes involved in PPP was also demonstrated (G6PD, PGD1, and TKT1), which will probably allow the glioma cells to have a involved in PPP was also demonstrated (G6PD, PGD1, and TKT1), which will probably allow the glioma cells to have more significant enzymatic activity in this pathway, and thus the cells obtain precursors for nucleotide synthesis and a morereducing significant power. enzymatic Finally, the activity Krebs incycle this and pathway, fatty acid and synthesis thus the cellspathways obtain play precursors central roles for nucleotide in cellular synthesis metabolism; and we reducingalso found power. that Finally, the theIDH1 Krebs, SDHB cycle, andFASN fatty, ACACA acid synthesis, and ELOVL2 pathways genes play centralwere overexpressed, roles in cellular resulting metabolism; in wesignificant also founddisturbances that the IDH1 in the, SDHB cellular, FASN metabolism, ACACA ,of and gliomas.ELOVL2 genes were overexpressed, resulting in significant disturbances in the cellular metabolism of gliomas. Besides, when we analyzed relevant genes in the fatty acid synthesis pathway, we found that the FASN gene was overexpressed in CNS WHO grade 3 and 4 glioma samples in the range of 72 to 180-fold. These results agree with a study performed by Tao et al. [49], where they found that the expression levels of FASN were higher in CNS WHO grade 3 and 4 gliomas (62-fold) than in CNS WHO grade 1 and 2 gliomas. The overexpression of FASN has also been reported in different human tumors, such as adenocarcinoma of the prostate, ovarian neoplasm, and thyroid [50–53]. It is important to mention that the present study is the first time that the levels of expression of the ACACA and ELOVL2 genes have been analyzed in clinical samples of pediatric gliomas, and a relationship was also found between the level of overexpression and the CNS WHO grade of the glioma,

Genes 2021, 12, 1335 14 of 18

The expression level of the G6PD gene, which encodes the enzyme that controls the pentose phosphate pathway’s flow, was also analyzed. A higher level of overexpression was found in CNS WHO grade 3 and 4 gliomas compared to CNS WHO grade 1 and 2 gliomas. We found the same pattern in the expression level of the PGD1 gene, which suggests that PPP is also an important pathway for pediatric glioma cells, since PPP provides the pentoses phosphate necessary for nucleic acid synthesis in the cancer cells (Figure4), allowing rapid growth and proliferation, characteristics of CNS WHO grade 3 and 4 gliomas [37]. Furthermore, PPP supplies NADPH to the cell, which is an important molecule in the synthesis of lipids and the protection of cells under conditions of [38]. These results suggest the importance of PPP in the brain glioma biopsies analyzed. Another important finding in the present work is that the TKT1 gene is not overex- pressed in CNS WHO grade 1 and 2 gliomas, while in CNS WHO grade 3 and 4 gliomas, it shows an increase in expression, which correlates well with the CNS WHO disease grade (Figure3). It is important to mention that these results agree with previous studies that have reported the overexpression of enzymes of the PPP in human cancer tissues and could be useful as a marker of malignancy in gliomas [39–41]. Besides this, genes that code for Krebs cycle proteins such as IDH1 and SDHB were also analyzed. The IDH1 and SDHB genes are both involved in the fundamental processes of the production of reducing equivalents in the form of reduced adenine dinucleotide phosphate (NADPH) and reduced flavin adenine dinucleotide (FADH2), which are oxidized in the electron transport chain (ETC) to produce adenosine triphosphate (ATP) [42]. Regarding the IDH1 gene, we found that it is overexpressed in all clinical samples and correlates with the grade of the tumor. In the process of cancer development, the metabolic activity of cancer cells is increased; consequently, an increase in intracellular ROS is observed, related to a wide spectrum of activities [43]. To prevent toxic levels of ROS occurring, cancer cells increase flux through the metabolic pathways that produce NADPH to meet the demands of this molecule; therefore, glioma cells probably show a greater overexpression of the IDH1 gene in CNS WHO grade 3 and 4 gliomas. The SDH enzyme has been classically considered to be a mitochondrial enzyme with the unique characteristic of participating in both the Krebs cycle and ECT. Furthermore, several studies have highlighted the role of succinate in biological processes other than metabolism, with tumorigenesis being the most prominent [44]. For this reason, succinate has been defined as an oncometabolite, as well as fumarate, and the SDHB gene has been identified as a tumor suppressor, with even alterations in SDH activity leading to succinate accumulation. Various reports have shown that the expression levels of SDH and succinate and fumarate production can be altered in cancer cells [45,46]. Our results show that in CNS WHO grade 1 glioma samples there is no overexpression of SDHB, meanwhile the samples of CNS WHO grade 2 showed an expression level of 1.7 and 5.7-fold, and CNS WHO grade 3 and 4 samples showed overexpression of 2.6 to 7.47-fold. Besides this, the level of overexpression of the SDHB gene is lower compared with that of the IDH1 gene. The lowest expression of SDHB probably causes a decrease in the amount of SDHB enzyme translated, inducing an altered metabolic phenotype by accumulating succinate and leading to a bioenergetic shift from mitochondrial respiration to cytosolic glycolysis [47,48]. It has been reported that the silencing of SDHB in cell lines leads to an alteration in energy metabolism and an almost complete loss of mitochondrial membrane potential, as well as a decrease in the expression of Complex III and IV of oxidative phosphorylation, causing an increase in the acidity of the medium, an increase in glucose uptake, and an overexpression of hexokinase 1 (HK1)[48], which is in agreement with the HK1 overexpression found in the present work. Besides, when we analyzed relevant genes in the fatty acid synthesis pathway, we found that the FASN gene was overexpressed in CNS WHO grade 3 and 4 glioma samples in the range of 72 to 180-fold. These results agree with a study performed by Tao et al. [49], where they found that the expression levels of FASN were higher in CNS WHO grade 3 and 4 gliomas (62-fold) than in CNS WHO grade 1 and 2 gliomas. The overexpression of Genes 2021, 12, 1335 15 of 18

FASN has also been reported in different human tumors, such as adenocarcinoma of the prostate, ovarian neoplasm, and thyroid [50–53]. It is important to mention that the present study is the first time that the levels of expression of the ACACA and ELOVL2 genes have been analyzed in clinical samples of pediatric gliomas, and a relationship was also found between the level of overexpression and the CNS WHO grade of the glioma, indicating that these genes could also be useful biomarkers in the diagnosis and progression of gliomas. Finally, although the research objective was to determine the differential expression levels of genes of metabolic pathways, our results should be interpreted with care. They cannot be entirely representative of the different grades of gliomas. Due to the scope of this initial research project, the variation represented in the results is limited according to the number of samples analyzed. This preliminary study involved only seven glioma tumor biopsies, and an increase in sample size being necessary to confirm the relationship between the expression levels of the metabolic genes analyzed and the CNS WHO grade glioma. Furthermore, another limitation of the study is that despite having managed to find and propose two new reference genes for expression studies in Gliomas, we performed the analysis variation of the candidate reference genes studied only in a single cell line.

5. Conclusions The results obtained in this study suggest that the PKM and TPI1 genes could be used as reference genes to normalize and quantify the expression of target genes in pediatric gliomas samples by RT-qPCR. This is the first report on the stability of the expression of the different candidate reference genes that participate in metabolic pathways in gliomas. The analysis of the expression levels of the genes involved in important metabolic pathways for glioma cells revealed that, in order to meet the needs of the highly proliferative cells of gliomas, they undergo metabolic remodeling, which can be summarized as an increase in glucose consumption for a higher production of glycolytic ATP and lactate as the final product, where the overexpression of genes involved in glycolysis such as HK1, PFKM, GAPDH, and LDHAL6A was observed. Besides this, the overexpression of some genes involved in the PPP was also demonstrated (G6PD, PGD1, and TKT1), which will probably allow the glioma cells a more significant enzymatic activity in this pathway, thus the cells obtain precursors for nucleotide synthesis and maintaining redox homeostasis. Finally, the Krebs cycle and fatty acid synthesis pathways play central roles in cellular metabolism; we also found that the IDH1, SDHB, FASN, ACACA, and ELOV2 genes were overexpressed, resulting in significant disturbances in the cellular metabolism of gliomas.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/genes12091335/s1, Figure S1. Heat map and boxplot of the expression of the candidate reference genes based on GSE16011 database. Figure S2. Analysis of the specificity of the amplification of the candidate reference genes by endpoint PCR. Figure S3. The specificity of RT-qPCR amplification for sixteen candidate reference genes. Figure S4. Expression stability of the eleven candidate reference genes analyzed in the HMC3 microglial cell line. Table S1. Expression values of candidate reference genes. Author Contributions: Conceptualization, B.H.-O., I.B.-R. and S.G.-M.; methodology, software, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, B.H.-O., F.F.-R., N.C.-R., R.A.C.-R., A.M.-B., V.M.-R., L.M.-L., A.G.-V., E.C.-J., V.P.d.l.C., S.R.- G., S.M.-T., C.W.-B., I.B.-R. and S.G.-M.; supervision, B.H.-O., N.C.-R., R.A.C.-R., I.B.-R. and S.G.-M.; project administration, S.G.-M.; funding acquisition, S.G.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the E022 Program, National Institute of Pediatrics, Mexico City, Mexico (Recursos Fiscales para la Investigación). S.G.-M. was supported by INP 031/2018. N.C.-R. was supported by INP 041/2018. E.C.-J was supported by Fondos Federales, México, HIM/2019/036 SSA 1595. B.H.-O., V.M.-R., and L.M.-L. thank the financial support from the CONACYT fellowship. B.H.-O. thanks CONACyT for its doctorate scholarship (no. 549950). Genes 2021, 12, 1335 16 of 18

Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Instituto Nacional de Pediatria (INP protocol 031/2018). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper. Data Availability Statement: Not applicable. Acknowledgments: The technical assistance of Maria Jose Gomez-Gonzalez and Ximena Gomez- Gonzalez are greatly appreciated. Finally, we would like to thank Javier Gallegos Infante (Instituto de Fisiología Celular, UNAM) for assistance with the bibliographic materials. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Ostrom, Q.T.; Cioffi, G.; Gittleman, H.; Patil, N.; Waite, K.; Kruchko, C.; Barnholtz-Sloan, J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012–2016. Neuro Oncol. 2019, 21 (Suppl. 5), v1–v100. [CrossRef][PubMed] 2. Louis, D.N.; Perry, A.; Wesseling, P.; Brat, D.J.; Cree, I.A.; Figarella-Branger, D.; Hawkins, C.; Ng, H.K.; Pfister, S.M.; Reifenberger, G.; et al. The 2021 WHO Classification of Tumors of the Central Nervous System: A summary. Neuro Oncol. 2021, 23, 1231–1251. [CrossRef] 3. McNeill, K.A. Epidemiology of brain tumors. Neurol. Clin. 2016, 34, 981–998. [CrossRef] 4. Wen, P.Y.; Reardon, D.A. Neuro-oncology in 2015: Progress in glioma diagnosis, classification and treatment. Nat. Rev. Neurol. 2016, 2, 69–70. [CrossRef] 5. Lee, S.Y. Temozolomide resistance in glioblastoma multiforme. Genes Dis. 2016, 3, 198–210. [CrossRef][PubMed] 6. Qian, Z.; Liu, H.; Li, M.; Shi, J.; Li, N.; Zhang, Y.; Zhang, X.; Lv, J.; Xie, X.; Bai, Y.; et al. Potential diagnostic power of blood circular RNA expression in active pulmonary tuberculosis. EBioMedicine 2018, 27, 18–26. [CrossRef] 7. Zhao, G.; Jiang, T.; Liu, Y.; Huai, G.; Lan, C.; Li, G.; Jia, G.; Wang, K.; Yang, M. Droplet digital PCR-based circulating microRNA detection serve as a promising diagnostic method for gastric cancer. BMC Cancer 2018, 18, 676. [CrossRef][PubMed] 8. Villegas-Ruiz, V.; Juarez-Mendez, S. Data mining for identification of molecular targets in ovarian cancer. Asian Pac. J. Cancer Prev. 2016, 17, 1691–1699. [CrossRef] 9. Chen, L.; Lu, D.; Sun, K.; Xu, Y.; Hu, P.; Li, X.; Xu, F. Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis. Gene 2019, 692, 119–125. [CrossRef] 10. Guenin, S.; Mauriat, M.; Pelloux, J.; Van Wuytswinkel, O.; Bellini, C.; Gutierrez, L. Normalization of qRT-PCR data: The necessity of adopting a systematic, experimental conditions-specific, validation of references. J. Exp. Bot. 2009, 60, 487–493. [CrossRef] 11. Dheda, K.; Huggett, J.F.; Bustin, S.A.; Johnson, M.A.; Rook, G.; Zumla, A. Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques 2004, 37, 112–114. [CrossRef] 12. Hendriks-Balk, M.C.; Michel, M.C.; Alewijnse, A.E. Pitfalls in the normalization of real-time polymerase chain reaction data. Basic Res. Cardiol. 2007, 102, 195–197. [CrossRef] 13. Nolan, T.; Hands, R.E.; Bustin, S.A. Quantification of mRNA using real-time RT-PCR. Nat. Protoc. 2006, 3, 1559–1582. [CrossRef] [PubMed] 14. Bustin, S.A. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): Trends and problems. J. Mol. Endocrinol. 2002, 29, 23–39. [CrossRef][PubMed] 15. Barber, R.D.; Harmer, D.W.; Coleman, R.A.; Clark, B.J. GAPDH as a housekeeping gene: Analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol. Genom. 2005, 21, 389–395. [CrossRef] 16. Bas, A.; Forsberg, G.; Hammarstrom, S.; Hammarstrom, M.L. Utility of the housekeeping genes 18S rRNA, β-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes. Scand. J. Immunol. 2004, 59, 566–573. [CrossRef] 17. Valente, V.; Teixeira, S.A.; Neder, L.; Okamoto, O.K.; Oba-Shinjo, S.M.; Marie, S.K.; Scrideli, C.A.; Paçó-Larson, M.L.; Carlotti, C.G. Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR. BMC Mol. Biol. 2009, 10, 17. [CrossRef] 18. Kreth, S.; Heyn, J.; Grau, S.; Kretzschmar, H.A.; Egensperger, R.; Kreth, F.W. Identification of valid endogenous control genes for determining gene expression in human glioma. Neuro-Oncology 2010, 6, 570–579. [CrossRef] 19. Aithal, M.G.; Rajeswari, N. Validation of housekeeping genes for gene expression analysis in glioblastoma using quantitative real-time polymerase chain reaction. Brain Tumor Res. Treat. 2015, 1, 24–29. [CrossRef] 20. Gravendeel, L.A.; Kouwenhoven, M.C.; Gevaert, O.; de Rooi, J.J.; Stubbs, A.P.; Duijm, J.E.; Daemen, A.; Bleeker, F.E.; Bralten, L.B.; Kloosterhof, N.K.; et al. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 2009, 23, 9065–9072. [CrossRef] 21. Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; et al. NCBI GEO: Archive for functional genomics data sets—Update. Nucleic Acids Res. 2013, D991–D995. [CrossRef] Genes 2021, 12, 1335 17 of 18

22. Benjamini, Y.; Drai, D.; Elmer, G.; Kafkafi, N.; Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 2001, 125, 279–284. [CrossRef] 23. Pfaffl, M.W.; Tichopad, A.; Prgomet, C.; Neuvians, T.P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotech. Lett. 2004, 26, 509–515. [CrossRef] 24. Silver, N.; Best, S.; Jiang, J.; Thein, S.L. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol. Biol. 2009, 7, 33. 25. Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3, RESEARCH0034. 26. Andersen, C.L.; Jensen, J.L.; Orntoft, T.F. Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64, 5245–5250. [CrossRef] 27. Xie, F.; Xiao, P.; Chen, D.; Xu, L.; Zhang, B. miRDeepFinder: A miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol. Biol. 2012, 80, 75–84. [CrossRef][PubMed] 28. Thellin, O.; Zorzi, W.; Lakaye, B.; De Borman, B.; Coumans, B.; Hennen, G.; Grisar, T.; Igout, A.; Heinen, E. Housekeeping genes as internal standards: Use and limits. J. Biotechnol. 1999, 75, 291–295. [CrossRef] 29. Kozera, B.; Rapacz, M. Reference genes in real-time PCR. J. Appl. Genet. 2013, 54, 391–406. [CrossRef][PubMed] 30. Dang, W.; Zhang, X.; Ma, Q.; Chen, L.; Cao, M.; Miao, J.; Cui, Y.; Zhang, X. Selection of reference genes suitable for normalization of RT-qPCR data in glioma stem cells. Biotechniques 2020, 3, 130–137. [CrossRef][PubMed] 31. Derveaux, S.; Vandesompele, J.; Hellemans, J. How to do successful gene expression analysis using real-time PCR. Methods 2010, 50, 227–230. [CrossRef] 32. Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [CrossRef] 33. Khurshed, M.; Molenaar, R.J.; Lenting, K.; Leenders, W.P.; van Noorden, C.J.F. In silico gene expression analysis reveals glycolysis and acetate anaplerosis in IDH1 wild-type glioma and lactate and glutamate anaplerosis in IDH1-mutated glioma. Oncotarget 2017, 30, 49165–49177. [CrossRef] 34. Oudard, S.; Arvelo, F.; Miccoli, L.; Apiou, F.; Dutrillaux, A.M.; Poisson, M.; Dutrillaux, B.; Poupon, M.F. High glycolysis in gliomas despite low hexokinase transcription and activity correlated to 10 loss. Br. J. Cancer 1996, 74, 839–845. [CrossRef] 35. Fack, F.; Espedal, H.; Keunen, O.; Golebiewska, A.; Obad, N.; Harter, P.N.; Mittelbronn, M.; Bahr, O.; Weyerbrock, A.; Stuhr, L.; et al. Bevacizumab treatment induces metabolic adaptation toward anaerobic metabolism in glioblastomas. Acta Neuropathol. 2015, 129, 115–131. [CrossRef] 36. Blacklow, S.C.; Raines, R.T.; Lim, W.A.; Zamore, P.D.; Knowles, J.R. Triosephosphate isomerase catalysis is diffusion controlled. Appendix: Analysis of triose phosphate equilibria in aqueous solution by 31P NMR. Biochemistry 1988, 27, 158–167. [CrossRef] 37. Davis, M.E. Epidemiology and Overview of Gliomas. Semin. Oncol. Nurs. 2018, 34, 420–429. [CrossRef] 38. Patra, K.C.; Hay, N. The pentose phosphate pathway and cancer. Trends. Biochem. Sci. 2014, 8, 347–354. [CrossRef] 39. Riganti, C.; Gazzano, E.; Polimeni, M.; Aldieri, E.; Ghigo, D. The pentose phosphate pathway: An antioxidant defense and a crossroad in tumor cell fate. Free Radic. Biol. Med. 2012, 53, 421–436. [CrossRef] 40. Zhang, C.; Zhang, Z.; Zhu, Y.; Qin, S. Glucose-6-phosphate dehydrogenase: A biomarker and potential therapeutic target for cancer. Anticancer Agents Med. Chem. 2014, 14, 280–289. [CrossRef] 41. Lucarelli, G.; Galleggiante, V.; Rutigliano, M.; Sanguedolce, F.; Cagiano, S.; Bufo, P.; Lastilla, G.; Maiorano, E.; Ribatti, D.; Giglio, A.; et al. Metabolomic profile of glycolysis and the pentose phosphate pathway identifies the central role of glucose-6-phosphate dehydrogenase in clear cell-renal cell carcinoma. Oncotarget 2015, 30, 13371–13386. [CrossRef] 42. Laurenti, G.; Tennant, D.A. Isocitrate dehydrogenase (IDH), succinate dehydrogenase (SDH), fumarate hydratase (FH): Three players for one phenotype in cancer? Biochem. Soc. Trans. 2016, 44, 1111–1116. [CrossRef] 43. Pelicano, H.; Carney, D.; Huang, P. ROS stress in cancer cells and therapeutic implications. Drug Resist. Updates 2004, 7, 97–110. [CrossRef] 44. Dalla, P.E.; Dando, I.; Pacchiana, R.; Liboi, E.; Scupoli, M.T.; Donadelli, M.; Palmieri, M. Regulation of succinate dehydrogenase and role of succinate in cancer. Semin. Cell Dev. Biol. 2020, 98, 4–14. [CrossRef] 45. Pollard, P.J.; Brière, J.J.; Alam, N.A.; Barwell, J.; Barclay, E.; Wortham, N.C.; Hunt, T.; Mitchell, M.; Olpin, S.; Moat, S.J.; et al. Accumulation of Krebs cycle intermediates and over-expression of HIF1alpha in tumours which result from germline FH and SDH . Hum. Mol. Genet. 2005, 14, 2231–2239. [CrossRef] 46. Sulkowski, P.L.; Sundaram, R.K.; Oeck, S.; Corso, C.D.; Liu, Y.; Noorbakhsh, S.; Niger, M.; Boeke, M.; Ueno, D.; Kalathil, A.N.; et al. Krebs-cycle-deficient hereditary cancer syndromes are defined by defects in homologous-recombination DNA repair. Nat. Genet. 2018, 50, 1086–1092. [CrossRef] 47. Lee, M.R.; Mantel, C.; Lee, S.A.; Moon, S.H.; Broxmeyer, H.E. MiR-31/SDHA Axis Regulates Reprogramming Efficiency through Mitochondrial Metabolism. Rep. 2016, 12, 1–10. [CrossRef] 48. Tseng, P.L.; Wu, W.H.; Hu, T.H.; Chen, C.W.; Cheng, H.C.; Li, C.F.; Tsai, W.H.; Tsai, H.J.; Hsieh, M.C.; Chuang, J.H.; et al. Decreased succinate dehydrogenase B in human hepatocellular carcinoma accelerates tumor malignancy by inducing the Warburg effect. Sci. Rep. 2018, 8, 3081. [CrossRef] Genes 2021, 12, 1335 18 of 18

49. Tao, B.B.; He, H.; Shi, X.H.; Wang, C.L.; Li, W.Q.; Li, B.; Dong, Y.; Hu, G.H.; Hou, L.J.; Luo, C.; et al. Up-regulation of USP2a and FASN in gliomas correlates strongly with glioma grade. J. Clin. Neurosci. 2013, 20, 717–720. [CrossRef] 50. Epstein, J.I.; Carmichael, M.; Partin, A.W. OA-519 (fatty acid synthase) as an independent predictor of pathologic state in adenocarcinoma of the prostate. Urology 1995, 45, 81–86. [CrossRef] 51. Gansler, T.S.; Hardman, W.; Hunt, D.A. Increased expression of fatty acid synthase (OA-519) inovarian neoplasms predicts shorter survival. Hum. Pathol. 1997, 28, 686–692. [CrossRef] 52. Vlad, L.D.; Axiotis, C.; Merino, M.J. Fatty acid synthase is highly expressed in aggressive thyroid tumors. Mod. Pathol. 1999, 12, 70A. 53. Swinnen, J.V.; Roskams, T.; Joniau, S.; Van Poppel, H.; Oyen, R.; Baert, L.; Heyns, W.; Verhoeven, G. Overexpression of fatty acid synthase is an early and common event in the development of prostate cancer. Int. J. Cancer 2002, 98, 19–22. [CrossRef]