Six Glycolysis-Related Genes As Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

Hindawi BioMed Research International Volume 2021, Article ID 8824195, 13 pages https://doi.org/10.1155/2021/8824195

Research Article Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

LangXiong Chen ,1,2 XiaoSong He,1,2 ShiJiang Yi,1,2 GuanCheng Liu,1,2 Yi Liu,1,2 and YueFu Ling 1,2

1Otolaryngology & Head and Neck Surgery, The Aﬃliated Hospital of Guilin Medical University, Guilin, Guangxi Zhuang Autonomous Region, China 2The Guilin Medical University, Guilin, Guangxi Zhuang Autonomous Region, China

Correspondence should be addressed to YueFu Ling; [email protected]

Received 19 September 2020; Revised 10 January 2021; Accepted 15 January 2021; Published 10 February 2021

Academic Editor: R. K. Tripathy

Copyright © 2021 LangXiong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. Head and neck squamous cell carcinoma (HNSCC) is one of the worst-prognosis malignant tumors. This study used bioinformatic analysis of the transcriptome sequencing data of HNSCC and the patients’ survival and clinical data to construct a prediction signature of glycolysis-related genes as the prognostic risk markers. Methods. Gene expression profile data about HNSCC tissues (n = 498) and normal tissues in the head and neck (n =44) were got from The Cancer Genome Atlas (TCGA), as well as patients’ survival and clinical data. Then, we obtained core genes; their expression in head and neck squamous cell carcinoma tissues is significantly different from that in normal head and neck tissues. The predicted glycolysis-related genes are screened through univariate Cox regression analysis, and then, the prognostic risk markers were constructed through further correction of multivariate Cox regression analysis. The Kaplan-Meier curve and receiver operating characteristic curve are used to analyze the potential value of these risk markers in diagnosis and prognosis. We also evaluated that the glycolysis-related prognostic risk markers composed of 6 oncogenes are correlated with clinical features, such as age, gender, grade, and clinical stage of the tumor, by univariate and multivariate Cox regression analyses. Results.Differentially expressed glycolytic genes in HNSCC tissues and normal head and neck tissues were screened from TCGA databases using the bioinformatic method. We confirmed a set of six glycolytic genes that were significantly associated with OS in the test series. According to our analysis, the prognostic risk markers composed of HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 may be an independent risk factor for the prognosis of HNSCC. Conclusions. Through this analysis, we constructed new prognostic risk markers related to glycolysis as a prognostic risk marker for patients with HNSCC and provided new ideas and molecular targets for the research and individualized treatment of HNSCC.

1. Introduction HNSCC patients can be diagnosed and treated early [4]. In order to study the mechanism of HNSCC, countries from Head and neck cancer is one of the main causes of global all around the world have invested a lot in scientiﬁc research morbidity and mortality, of which HNSCC accounts for and made great progress in diagnosis and treatment of 90% [1]. Current research indicated that HNSCC is closely HNSCC [5]. However, the incidence and mortality of related to numerous factors, including smoking, drinking, HNSCC remain high [6]. In addition, due to the heteroge- and human papilloma virus [2, 3]. In recent years, ﬁber rhi- neity of molecular mechanisms and tumor behaviors nopharyngoscope and other related examinations have been related to HNSCC, the widely recognized clinical factors widely applied to detect early lesions of HNSCC; hence, including lymph node metastasis and histological grade 2 BioMed Research International

Enrichment plot: CHEN_LUNG_CANCER_SURVIVAL Enrichment plot: GO_LACTATE_TRANSPORT 0.6 0.5 0.5 NES = 1.7451 0.4 NES = 1.0236 0.4 FDR = 0.0084 0.3 FDR = 0.4334 0.3 0.2 0.2 0.1 0.0 0.1 –0.1 0.0 Enrichment score (ES) score Enrichment

Enrichment score (ES) score Enrichment –0.2 –0.1 –0.3

‘T’ (positively correlated) ‘T’ (positively correlated) 1.0 1.0 0.5 0.5 0.0 Zero cross at 41430 0.0 Zero cross at 41430 –0.5 –0.5 –0.1 –0.1 ‘N’ (negatively correlated) ‘N’ (negatively correlated) 0 10,000 20,000 30,000 40,000 50,000 0 10,000 20,000 30,000 40,000 50,000 Ranked list metricRanked (signal2noise) Ranked list metricRanked (signal2noise) Rank in ordered dataset Rank in ordered dataset

Enrichment plot: HALLMARK_GLYCOLYSIS Enrichment plot: RAECTOME_GLYCOLYSIS

0.5 0.6 NES = 2.0215 NES = 1.9905 0.4 FDR < 0.001 0.5 FDR < 0.001 0.3 0.4 0.2 0.3 0.2 0.1 0.1 0.0 Enrichment score (ES) score Enrichment Enrichment score (ES) score Enrichment 0.0 –0.1

Enrichment profle Hits Ranking metric scores

Figure 1: GSEA of glycolysis-related gene sets. Enrichment plots of four glycolysis-related gene sets between HNSCC and paired normal tissues identified by GSEA. are not sufficient to predict the prognosis of a patient. applied, and studies that select and analyze a specific charac- Therefore, identifying clinically relevant biomarkers for teristic or a single signal pathway are rare. This may be a new HNSCC can facilitate correct treatment decisions and key breakthrough for our research on the occurrence and improve prognosis [7]. However, studies have not identi- development of tumors. This study intends to set up a predic- fied an effective clinical biomarker for the prognosis of tion signature of glycolysis-related genes as the prognostic HNSCC so far. risk markers by mining the transcriptome sequencing data Aerobic glycolysis is one of the most important features of HNSCC. of tumor metabolism [8]. In recent years, research has found that this feature can be a new breakthrough for studying 2. Materials and Methods tumor occurrence and development and antitumor treatment. An increasing number of studies have found that 2.1. Obtained HNSCC Gene Expression Profile and Clinical glycolysis-related genes may make for prognostic risk Related Data from TGCA. The transcriptome gene expres- markers [9], but most of the markers have not been clinically sion profile of HNSCC was obtained from TCGA (https:// BioMed Research International 3

Table 1: The core gene were screened from 3 glycolysis-related gene sets.

The ID of core gene (n = 207) DLD, NUP35, NUP188, HPRT1, IL13RA1, B4GALT7, PEBP1, NUP54, NUP205, GPI, STC2, NUP214, SL16A8, MDH2, TGFBI, NDC1, NUP58, CD44, PGP, PPIA, AGL, SERPINA1, TFF3, HSPA5, RBCK1, PPP2R5D, SLC16A3, B4GALT4, ARPP19, PKM, EXT1, B4GALT1, UGP2, XYLT2, KRT7, PFKFB1, AGRN, PGM2L1, NUP160, POLR3K, SLC16A7, SDC3, PFKP, STC1, ABCB6, NUP155, NDUFV3, B3GAT3, BPNT1, NUP88, P4HA1, TXN, CENPA, NASP, PMM2, BPGM, NUP37, PLCB3, KIF2A, NUP133, HMMR, ISG20, EGLN3, CXCR4, GYS2, GCKR, NUP107, CDK1, GOT1, HK3, SDC1, ATP5F1D, MED24, CASP6, PGK1, SLC16A1, CLN6, GCK, HS2ST1, PFKL, VCAN, HK1, GNPDA1, LDHA, ELF3, B4GALT2, MXI1, CACNA1H, EXT2, GMPPA, CLDN3, ANXA1, ARTN, SLC5A8, PAXIP1, CHPF, GPR87, NANP, PDK3, GAPDHS, NUP62, COG2, SOD1, SDC2, NT5E, PLOD2, NUP50, PFKFB4, PRPS1, TPST1, COPB2, GCLC, GLCE, SAP30, PC, SPAG4, GALE, NUP85, ECD, POM121, ANG, NUP98, IGFBP3, CHST4, CHST12, GNE, PYGL, PSMC4, P4HA2, SLC25A13, PFKFB3, ALDH7A1, PLOD1, GRK4, KIF20A, TPBG, LCT, HK2, MERTK, CTH, PPP2CB, NUP153, AKR1A1, EPHX1, AK3, PYGB, ACTB, DH1, G6PD, RAE1, ALG1, MIOX, GPC1, TPR, GSTP1, STMN1, ME1, CYB5A, FBP2, EGFR, NUP210, ENO1, ENO3, SLC5A12, KDELR3, GALK2, B3GALT6, COL5A1, FUT8, PGM2, AAAS, CAPN5, CHST2, PGAM1, ADPGK, LHPP, ADORA2B, FKBP4, VLDLR, RRAGD, GAPDH, SEH1L, NUP93, RPE, CHPF2, DEPDC1, DDIT4, ENO2, ME2, CITED2, VEGFA, TGFA, NUP43, AURKA, CHST1, ALDOC, LHX9, BIK, MET, GUSB, CALU, ANKZF1, ALDH9A1, POM121C, TPI1, GMPPB, MIF. portal.gdc.cancer.gov/repository), and the corresponding patient’s OS, their diagnostic efficacy is further verified clinical follow-up information was also obtained from through receiver operating characteristic (ROC) curve analy- TCGA. Then, we obtained a total of 498 cases of HNSCC sis. The relation between the risk score and clinical character- patients and 44 cases of normal tissue gene expression pro- istics was analyzed by univariate and multivariate Cox files and corresponding clinical information. regression analyses, including age, lymph node metastasis, stage, gender, and classification. 2.2. Identified Prognostic Glycolysis-Related Genes and p value < 0.05 was considered statistically significant. Constructed New Predicted Risk Markers. Gene Set Enrich- ment Analysis (GSEA) is used to detect 4 gene sets about 3. Results glycolysis (GO_LACTATE_TRANSPORT, CHEN_LUNG_ CANCER_SURVIVAL, HALLMARK_GLYCOLYSIS, and 3.1. Glycolysis-Related Gene Sets. This study analyzed REACTOME_GLYCOLYSIS) from GSEA (https://www HNSCC such as oral squamous cell carcinoma and throat .gsea-msigdb.org/gsea/login.jsp); the gene files of gmp for- squamous cell carcinoma. The transcriptome expression pro- mat were got from the Molecular Signatures Database file of HNSCC can be obtained from TCGA, and the corre- (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Then, sponding clinical follow-up data can also be obtained from we analyzed the expression differences of the above- TCGA. We observed and screened the glycolysis-related gene mentioned glycolysis-related gene set in HNSCC tissues and sets on the GSEA website. We, respectively, explored 4 differ- normal tissues in the head and neck. The expression different gene sets (CHEN_LUNG_CANCER_SURVIVAL, GO_ ences of glycolysis-related genes were characterized by associ- LACTATE_TRANSPORT, HALLMARK_GLYCOLYSIS, ated p values and logFC (fold change), and the core genes was and REACTOME_GLYCOLYSIS) as to whether the expres- constructed. The risk markers related to prognosis were sion of tumor samples in these 4 glycolysis-related gene sets screened by univariate and multivariate Cox regression analy- is different from that of normal head and neck samples. We ses based on the core genes, and the prognostic risk markers found that the CHEN_LUNG_CANCER_SURVIVAL, were constructed. REACTOME_GLYCOLYSIS, and HALLMARK_GLYCO- LYSI gene sets in HNSCC are significantly different between 2.3. Statistics and Survival Analysis. The screening of differ- the normal head and neck samples and the tumor samples ential genes and the analysis of the Cox proportional hazards (FDR are 0.008, <0.001, and <0.001, respectively). For regression model in this research are mainly achieved by R HNSCC, the GO_LACTATE_TRANSPORT gene set is not language (R x64 3.6.3). We loaded the limma (Linear Models significantly different between the tumor sample and the nor- for Microarray Data) package to analyze the differential mal head and neck sample (FDR = 0:433) (Figure 1). Next, expression of the preprocessed data. the core genes were screened from 3 glycolysis-related gene Sorting out the gene expression information through lin- sets (CHEN_LUNG_CANCER_SURVIVAL, HALLMARK_ ear models, a comparison model was constructed to compare GLYCOLYSI, and REACTOME_GLYCOLYSIS), which are the gene expression data of patients in the high-risk and low- the differentially expressed genes about glycolysis in the risk groups. These patients’ unique risk score was obtained tumor tissue and normal tissue, as shown in Table 1. based on the prognostic risk score formula and the expression We further verified the correlation between core genes level. Risk score = expression of gene 1 × β1 + expression of and glycolysis; GO analysis is used to evaluate their biological gene 2 × β2+⋯+expression of gene n × βn. A unique risk processes (BP), molecular function (MF), and cellular com- score for each patient with HNSCC was divided into the high- ponent (CC). Next, KEGG pathway enrichment analysis is and low-risk groups by the median of risk score values. used to verify their biological pathways. The results show that A Kaplan-Meier curve is used to analyze the constructed the biological processes (BP) of these core genes have the prognostic risk markers to predict the patients’ OS with highest enrichment among several metabolic processes, HNSCC. When using prognostic risk markers to predict a molecular function (MF) is related to the enzyme activity 4 eoe KG)ptwy b eesigni were (b) pathways (KEGG) Genomes Figure :Tefntoa nihetaayi ftecr ee.Gn nooy(O em a n yt nylpdao ee and Genes of Encyclopedia Kyoto and (a) terms (GO) Ontology Gene genes. core the of analysis enrichment functional The 2: Glycosaminoglycan biosynthesis -chondroitin sulfate /dermatan sulfate Intramolecular transferase activity, phosphotransferases Pyridine-containing compound process metabolic Transferase activity, transferring glycosyl groups Transferase activity, transferring hexosyl groups Oxidoreduction process metabolic coenzyme Glycosaminoglycan biosynthesis -heparan sulfate /heparin Nicotinamide nucleotide metabolic process Pyridine nucleotidePyridine metabolic process Structural constituent of nuclear pore Glycosaminoglycan biosynthesis -keratan sulfate Carbohydrate catabolic process Amino sugar and nucleotide sugar metabolism Pyruvate biosynthetic process Ficolin-1-rich granule lumen Carbohydrate activity kinase fi Pyruvate metabolic processPyruvate metabolic ATP generation from ADP atyerce ytecr genes. core the by enriched cantly Glycine, serine andGlycine, threonine serine metabolism Pentose and glucuronate interconversions Glyoxylate and dicarboxylate metabolism Monosaccharide binding Nuclear pore outer ring ADP process metabolic Carbohydrate digestion and absorption Carbohydrate binding Cysteine and methionine metabolism Central carbonCentral metabolism incancer Other organismOther part Other organismOther cell Coenzyrne binding Coenzyrne Nuclear membrane Fructose andFructose metabolism mannose Glycolytic process Isomerase activity Nuclear envelope Arginine and proline metabolism Glucose binding Other organismOther Starch and sucrose metabolism Nuclear pore Nuclear Amyotrophic lateral sclerosis Glycolysis /Gluconeogenesis Glucagon signaling pathway Biosynthesis of amino acids Pentose phosphate pathway AMPK signaling pathway Citrate (TCA cycle cycle) Host cell HIF-1 signaling pathway Type IIdiabetes mellitus Galactose metabolism Galactose Pyruvate metabolism Host Carbon metabolism Carbon Count RNA transport (b) (a) 60 40 20 0.1 Count 30 20 10 Gene ratioGene .50.10 0.05 0.2 Gene ratioGene 0.15 0.3 iMdRsac International Research BioMed

0.20 MF CC BP p.adjust p.adjust 3e–06 2e–06 1e–06 0.04 0.03 0.02 0.01 BioMed Research International 5

Table 2: The information of six glycolysis-related genes associated risk markers composed of 6 oncogenes are correlated with with overall survival in patients with HNSCC. clinical features such as age, gender, grade, and clinical stage of the tumor, we used clinical parameters as covariates in the β Gene Cox ( )HRentire data set with univariate and multivariate Cox regres- HPRT1 0.019464 1.019654 sion analyses. The result data shows that in the univariate STC2 0.0261 1.026443 Cox regression analysis, age, tumor clinical stage, and risk PLCB3 0.012804 1.012886 score are both significantly correlated with overall survival fi GPR87 0.005647 1.005663 time, while gender and tumor grade are not signi cantly correlated. After multivariate adjustment, the risk score is still PYGL 0.005313 1.005327 significantly correlated with the overall survival time SLC5A12 0.384181 1.468411 (p <0:001, 95% CI 1.304-1.702, HR = 1:884). The result of univariate and multivariate Cox regression analyses and and glucose binding of multiple metabolic pathways, and the strati fied analysis shows that the glycolysis-related prognos- KEGG pathway enrichment analysis involves carbon metab- tic risk markers composed of the six screened oncogenes pre- olism and glycolysis/gluconeogenesis. It was further verified dict patient prognosis and can be independent of clinical that the core genes screened out were related to glycolysis characteristics. In a word, the prognostic risk markers could (Figure 2). be applied to predict OS in patients with HNSCC (Figure 4). We further evaluate the risk score correlation with clini- 3.2. Construction of Prognostic Risk Markers Based on Core cal characteristics, excluding patients with unknown clinical Genes. The overall survival data of patients with HNSCC data (including age, gender, grade, T, N, and tumor clinical were sorted out, and these core genes were further analyzed. stage). We performed a stratified analysis of each clinical The glycolysis-related genes related to the patient’s OS were characteristic, respectively. The prognostic risk markers screened through univariate Cox regression analysis. The stratified patients based on age; one is the ≤65-year-old group correlation of glycolytic gene expression profile and patient’s with high risk (n = 159) and low risk (n = 165), and the other OS was further verified though multivariate Cox regression is the >65-year-old group with high risk (n =90) and low risk analysis. After analysis, the screened result is the prognostic (n =84). It is concluded that the overall survival rates risk markers composed of OS-related glycolysis genes, that between the two subgroups in both groups have significant is, HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12. differences (p <0:001 and 0.003, respectively). The patients The prognostic risk markers based on glycolysis-related were stratified by gender and are divided into males and oncogenes are constructed (Table 2). females. Among them, women have 66 high-risk cases and The regression coefficient (β) of each prognostic risk 67 low-risk cases; men have 183 high-risk cases and 182 marker was calculated through multivariate Cox analysis. low-risk cases. The analysis shows that the overall survival These patients’ unique risk score was obtained based on rates between two subgroups in both groups are significantly the regression coefficient and the expression level. Risk score different (p =0:007 and <0.001, respectively). Then, all = expression of HPRT1 × 0:019464 + expression of STC2 × patients were stratified based on the tumor grade, divided 0:0261 + expression of PLCB3 × 0:012804 + expression of into two subgroups, named grades I and II (n = 359) and GPR87 × 0:005647 + expression of PYGL × 0:005313 + grades III and IV (n = 120), respectively. Based on the rick expression of SLC5A12 × 0:384181. A unique risk score for score, grades I and II have 183 high-risk cases and 176 low- each patient with HNSCC was divided into the high- and risk cases. We found that overall survival of the two sub- low-risk groups by the median of risk score values and then groups is significantly different (p <0:001). The same results the high-risk (n = 249) and low-risk (n = 249) groups, are also observed in two subgroups of grade III and IV respectively. The Kaplan-Meier (KM) survival curve was patients (p =0:003). Based on the T stage of tumors, patients used to evaluate the prognosis difference between both were divided into the T1 and T2 groups (n = 176) and the T3 groups. Results show that the survival rate of the high-risk and T4 groups (n = 266). Among them, the survival rate of group is significantly lower than the low-risk group (p value high-risk patients in the T1 and T2 groups is not significantly < 0.001) (Figure 3(a)). In order to test the impact of different from that of the low-risk subgroup (p =0:338), glycolysis-related prognostic risk markers composed of six while the survival rate of high-risk patients in the T3 and oncogenes in predicting the 3-, 5-, and 10-year OS rate of T4 groups is significantly different from the low-risk sub- HNSCC patients, their diagnostic efficacy was further veri- group (p <0:001). According to the presence or absence of fied by ROC curve analysis. The results found that the area lymph node metastasis, these patients were divided into under the curve (AUC) is 0.710, 0.675, and 0.740, respec- two groups. We found that survival rates between high-risk tively (Figure 3(b)), indicating that the prognostic risk and low-risk patients in both groups are significantly differ- markers have a good performance in predicting the OS rate ent (p =0:023; p <0:001). Lastly, we divided patients based of HNSCC patients. Based on the risk scores, the risk curve on the clinical stage of the tumor: one is the stage I and II was constructed (Figure 3(c)); we further found that the group (n =94) with high risk (n =45) and low risk (n =49) higher the risk score, the less the patients’ survival time. and the other is the stage III and IV group (n = 336) with high risk (n = 174) and low risk (n = 162). In the stage III 3.3. The Prognostic Risk Markers and Clinical Characteristics. and IV group, the survival rate of high-risk patients is signif- In order to assess whether the glycolysis-related prognostic icantly different from low-risk patients (p <0:001). In 6 BioMed Research International

ROC curve (risk score) 1.0 1.00

0.8 0.75

0.6

0.50 0.4 True positive rate positive True Survival probability Survival 0.25 p < 0.0010.001 0.2

0.00 0.0

0123456789 1011 12 13 14 15 16 17 18 19 20 0.0 0.2 0.4 0.6 0.8 1.0

Time (years) False positive rate Risk 3years (AUC = 0.710) + High (n = 249) 5years (AUC = 0.675) + Low (n = 249) 10years (AUC = 0.740) (a) (b)

4 Risk score Risk

0 100 200 300 400 500

Patients (increasing risk score) High risk Low risk

5 Survival time (years)

0 100 200 300 400 500

Patients (increasing risk score) Dead Alive (c)

Figure 3: Risk score based on the 6 glycolysis-related gene prognostic risk markers in patients with HNSCC. (a) Kaplan-Meier curve of OS in the high- and low-risk groups. (b) Time-dependent ROC curves of the 6 glycolysis-related gene signatures for prediction of 3-, 5-, and 10-year OS. (c) The distribution of the 6 glycolysis-related gene risk scores and survival status for each patient. BioMed Research International 7

Univariate Cox regression analysis of each clinical parameter

pvalue Hazard ratio

Age <0.001 1.024 (1.010-1.039)

Gender 0.103 0.761 (0.547-1.057)

Grade 0.345 1.122 (0.883-1.426)

Stage <0.001 1.403 (1.160-1.698)

Riskscore <0.001 1.521 (1.333-1.734)

0.0 0.5 1.0 1.5

Hazard ratio

Multivariate Cox regression analysis of each clinical parameter pvalue Hazard ratio

Age 0.002 1.025 (1.009-1.042)

Gender 0.149 0.773 (0.545-1.097)

Grade 0.771 1.038 (0.809-1.331)

Stage <0.001 1.462 (1.200-1.782)

Riskscore <0.001 1.490 (1.304-1.702)

0.0 0.5 1.0 1.5

Hazard ratio

Figure 4: Univariable and multivariable analyses for the risk score and each clinical feature. another group, p =0:700 indicates that there is no significant two subgroups based on the median expression value, high- difference in the survival rate of patients in the two subgroups and low-expression groups, respectively. The Kaplan-Meier (Figures 5(a)–5(f)). curve analyzes whether the high or low expression of each gene is correlated with the OS of HNSCC patients. It is found 3.4. Expression of Six Glycolysis-Related Genes in HNSCC. In that HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 the cBioPortal online database, we analyzed the mutations of may be correlated with the poor prognosis of HNSCC 6 glycolysis-related genes in HNSCC through clinical sam- (p =0:004, <0.001, 0.027, 0.035, 0.030, and 0.009, respec- ples. The result shows that in overall 498 cases, 93 patients tively) (Figure 7(a)). When the HPRT1, STC2, PLCB3, (18.6%) had gene mutations. Among them, HPRT1 gene GPR87, PYGL, and SLC5A12 were used as independent bio- mutations include 4 cases of amplification, 2 cases of mis- markers, their prognostic efficacy needs further verification. sense mutations, and 1 case of deep deletion; STC2 gene We performed ROC curve analysis further which verified mutations account for 0.6%, PLCB3 gene 4%, GPR87 gene their prognostic efficacy (Figure 7(b)), but the AUC values 9%, PYGL gene 2%, and SLC5A12 gene 1.4% (Figure 6(a)). of the six glycolysis-related genes predicting OS of HNSCC We further analyzed the differential expression of patients were all less than 0.710, 0.675, and 0.740, respec- HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 in tively, and their predictive performance was all worse than HNSCC and normal head and neck tissue and found that prognostic risk markers. the expression of 6 glycolysis-related genes in HNSCC tissues is significantly upregulated compared with normal tissues 4. Discussion (among which p <0:001 for HPRT1, STC2, PLCB3, GPR87, and SLC5A12; p <0:01 for PYGL) (Figure 6(b)). We sorted The active proliferation, differentiation, and other life activi- the expressions of each gene in the tumor samples from ties of cancer cells are significantly not like the normal ones HNSCC patients, and then, these patients were split into [10]. In order to provide the large quantity of protein, lipid, 8 BioMed Research International

1.00 1.00

0.75 0.75

0.50 0.50

Survival probability 0.25 Survival probability 0.25 p < 0.001 p = 0.003

0.00 0.00 01234567891011121314151617181920 01234567891011 12 13 14 15

Time (years) Time (years) Age ≤ 65 Age > 65 + High risk (n = 159) + High risk (n = 90) + Low risk (n = 165) + Low risk (n = 84) (a)

1.00 1.00

0.75 0.75

0.50 0.50

Survival probability 0.25 Survival probability 0.25 p = 0.007 p < 0.001

0.00 0.00 01234567891011 12 13 14 15 01234567891011121314151617181920

Time (years) Time (years) FEMALE MALE + High risk (n = 66) + High risk (n = 183) + Low risk (n = 67) + Low risk (n = 182) (b)

1.00 1.00

0.75 0.75

0.50 0.50

Survival probability 0.25 Survival probability 0.25 p < 0.001 p = 0.003

0.00 0.00 01234567891011 12 13 14 15 01234567891011 12 13

Time (years) Time (years) G1-2 G3-4 + High risk (n = 183) + High risk (n = 57) + Low risk (n = 176) + Low risk (n = 63) (c)

Figure 5: Continued. BioMed Research International 9

1.00 1.00

0.75 0.75

0.50 0.50

Survival probability 0.25 Survival probability 0.25 p = 0.338 p < 0.001

0.00 0.00 01234567891011121314151617181920 01234567891011 12

Time (years) Time (years) T1-2 T3-4 + High risk (n = 75) + High risk (n = 146) + Low risk (n = 101) + Low risk (n = 120) (d)

1.00 1.00

0.75 0.75

0.50 0.50

Survival probability 0.25 Survival probability 0.25 p = 0.023 p < 0.001

0.00 0.00 01234567891011 12 13 01234567891011121314151617181920

Time (years) Time (years) N0 N1-3 + High risk (n = 81) + High risk (n = 120) + Low risk (n = 88) + Low risk (n = 116) (e)

1.00 1.00

0.75 0.75

0.50 0.50 Survival probability Survival 0.25 probability Survival 0.25 p < 0.700 p < 0.001

0.00 0.00 01234567891011 12 13 14 15 01234567891011121314151617181920

Time (years) Time (years) Stage I-II Stage III-IV + High risk (n = 45) + High risk (n = 174) + Low risk (n = 49) + Low risk (n = 162) (f)

Figure 5: Stratification analysis of various clinicopathological factors by Kaplan-Meier curves for the patients with HNSCC in the TCGA dataset. Kaplan-Meier curves of OS in different subgroups stratified by (a) age, (b) gender, (c) grade, (d) T stage, (e) N stage, and (f) AJCC stage. 10 BioMed Research International

HPRT1 1.4% STC2 0.6% PLCB3 4% GPR87 9% PYGL 2% SLC5A12 1.4%

Genetic alteration

Missense mutation (unknown signifcance) Deep deletion Truncating mutation (unknown signifcance) No alterations Amplifcation (a) ⁎⁎⁎ ⁎⁎⁎

90 60 n

60 40 C2 expressio T S STC2 expression STC2

HPRT1 expression HPRT1 20 30

0 0 NNormalormal TTumorumor NNormalormal TTumorumor ⁎⁎ ⁎⁎⁎ 150 60 n 100 40 87 expressio 5050 PLBC3 expression GPR 20 GPR87 expression

0 0 NorNormalmal Tum Tumoror NorNormalmal TumTumoror ⁎⁎⁎ ⁎⁎⁎ 3 150 n 2 100 12 expressio A 5

C 1 PYGL expression PYGL

50 L SLC5A12 expression S SLC5A12

0 0 Normal Tumor Normal Tumor

Normal

Tumor (b)

Figure 6: Analysis of the expression of 6 genes in the sample (a) to screen the mutations of genes in sample of M cancer patients (b); the diﬀerential expression analysis of the 6 screened genes (∗means < 0:05, ∗∗means < 0:01, and ∗∗∗ means < 0:001). BioMed Research International 11

Cancer: HNSC ROC curve (HPRT1) 1.00 1.0 nucleic acid, and other molecular materials that tumor cells

0.75 0.8 need, the proliferation and metastasis of malignant cells 0.50

0.6 require metabolic reprogramming to accelerate anabolism 0.25 Overall survival p = 0.004

0.00 0.4 to support cell growth [11]. In order to meet its biosynthetic 024681012 14 16 18 20 True positive rate positive True Time (years) 0.2 needs, tumor cells increase glucose uptake through glycolysis High 2498231167410000 Low 249 11043149764100 and oxidative phosphorylation. A series of changes make 024681012 14 16 18 20 0.0 HPRT1 levels HPRT1 Time (years) 0.0 0.2 0.4 0.6 0.8 1.0 tumor cells reregulate the production of ATP or increase glu- HPRT1 levels False positive rate + High 3 years (AUC = 0.640) 5 years (AUC = 0.647) cose uptake to facilitate energy production. Studies have + Low 10 years (AUC = 0.648) shown that with adequate oxygen, tumor cells can promote Cancer: HNSC ROC curve (PYGL) 1.00 1.0 rapid energy supply of glycolysis due to the change of micro- 0.75 0.8 environment, which is known as the “Warburg Eﬀect” [12]. 0.50 0.6 “ ﬀ ” 0.25 Overall survival p = 0.005 In the Warburg E ect, a tumor cell promotes glycolysis to

0.00 0.4 ﬃ 024681012 14 16 18 20 produce su cient ATP to maintain its proliferation and dif- True positive rate positive True Time (years) 0.2 ferentiation [13]. At the same time, lactic acid, the end prod- High 24983361710742100 Low 249 10939136432000 024681012 14 16 18 20 0.0 uct of glycolysis, is released into both internal and external of PYGL levels PYGL Time (years) 0.0 0.2 0.4 0.6 0.8 1.0 PYGL levels False positive rate the cell, acidifying its microenvironment and facilitating the + High 3 years (AUC = 0.609) 5 years (AUC = 0.543) formation of microcapillaries. This process helps tumor cells + Low 10 years (AUC = 0.509)

Cancer: HNSC ROC curve (STC2) to meet their nutritional requirements and strengthen their 1.00 1.0 resistance to oxidative damage, which may also be an impor- 0.75 0.8 0.50 tant immune escape mechanism in tumors [14]. 0.6 0.25 Overall survival p < 0.001 According to reports, when clinicians predict the progno- 0.00 0.4 024681012 14 16 18 20 True positive rate positive True sis of cancer patients, traditional clinical parameters and Time (years) 0.2 High 2498928104311000 pathological characteristics are not enough to accurately pre- Low 249 103462012863100 024681012 14 16 18 20 0.0 STC2 levels STC2 dict. The comprehensive genomics research based on high- Time (years) 0.0 0.2 0.4 0.6 0.8 1.0 STC2 levels False positive rate throughput ribonucleic acid sequence and microarray map + High 3 years (AUC = 0.637) 5 years (AUC = 0.580) + ﬁ Low 10 years (AUC = 0.682) has made signi cant research progress, and some research

Cancer: HNSC ROC curve (PLCB3) results have been developed and applied to clinical practice 1.00 1.0

0.75 [15]. Some biomarkers used to predict the prognosis of can- 0.8 0.50 cer patients have been confirmed, such as glycolysis-related 0.6 0.25 Overall survival p = 0.027 genes. Although the specific mechanism between glycolysis 0.00 0.4 024681012 14 16 18 20 True positive rate positive True and metastasis in the process of cancer progression is still Time (years) 0.2 High 249 85 32 13 4 3 2 2 0 0 0 Low 249 107 42 17 12 8 5 2 1 0 0 unknown, more and more glycolysis-related genes were ver- 024681012 14 16 18 20 0.0 PLCB3 levels fi ff Time (years) 0.0 0.2 0.4 0.6 0.8 1.0 i ed that are related to the development, di erentiation, PLCB3 levels False positive rate + High 3 years (AUC = 0.567) invasion, and metastasis of cancer cells in recent years. A 5 years (AUC = 0.550) + Low 10 years (AUC = 0.592) variety of glycolysis-related genes are related to regulating

Cancer: HNSC ROC curve (GPR87) 1.00 1.0 cell metabolism to maintain tumor proliferation, metastasis,

0.75 0.8 and invasion. Yin et al. [16] found that 4-mRNA signature 0.50

0.6 related to glycolysis may be prognosis marks of patients with 0.25 Overall survival p = 0.035

0.00 0.4 bladder cancer and may correlate with the occurrence and 024681012 14 16 18 20 True positive rate positive True

Time (years) development of tumor cells. In addition, Chen et al. [17] 0.2 High 249 87 32 11 4 2 2 2 1 0 0 Low 249 105 42 19 12 9 5 2 0 0 0 found that glycolysis-based seven genes were identiﬁed, 024681012 14 16 18 20 0.0 GPR87 levels GPR87 Time (years) 0.0 0.2 0.4 0.6 0.8 1.0 which can be used as a prognostic marker for patients with GPR87 levels False positive rate + High 3 years (AUC = 0.567) colon adenocarcinoma. Meanwhile, the progress achieved 5 years (AUC = 0.593) + Low 10 years (AUC = 0.629) in tumor glycolysis and metabolism has promoted rapid Cancer: HNSC ROC curve (SLC5A12) 1.00 1.0 development of personalized cancer treatment and manage- 0.75 0.8 ment [18]. The identiﬁcation of key biomarkers may help 0.50

0.6 0.25 Overall survival to formulate personalized treatment and prognosis of p < 0.026 0.00 0.4 HNSCC. Although surgical resection is still an important 024681012 14 16 18 20 True positive rate positive True Time (years) 0.2 treatment for HNSCC patients, increasing adjuvant treat- High 249 83 36 16 8 4 1 0 0 0 0 Low 249 109 38 14 8 7 6 4 1 0 0 024681012 14 16 18 20 0.0 ments (such as chemotherapy, radiotherapy, and immuno- SLC5A12 levels SLC5A12 Time (years) 0.0 0.2 0.4 0.6 0.8 1.0 SLC5A12 levels False positive rate therapy) have achieved rapid development in recent years, + High 3 years (AUC = 0.555) 5 years (AUC = 0.549) especially for patients with advanced HNSCC. Due to the + Low 10 years (AUC = 0.634) heterogeneity of HNSCC and its resistance to chemotherapy, Figure 7: The expression based on each glycolysis-related gene it is still a challenge for clinicians and pathologists to predict predicts OS in patients with HNSCC and Kaplan-Meier curve of high-risk HNSCC and on how to perform precise treatment. OS in the high- and low-expression groups. (b) Time-dependent Therefore, reliable prognostic markers are urgently needed to ROC curves of each glycolysis-related gene for prediction of 3-, 5-, guide the treatment options for patients with HNSCC. In this and 10-year OS. study, by analysis of overall survival of patients with HNSCC by the Kaplan-Meier curve, we noticed that the glycolysis- 12 BioMed Research International related prognostic risk markers composed of HPRT1, Authors’ Contributions STC2, PLCB3, GPR87, PYGL, and SLC5A12 can be applied as a predictive biomarker for the prognosis of HNSCC LangXiong Chen, ShiJiang Yi, XiaoSong He, and YueFu Ling patients. Subsequent univariate and multivariate Cox pro- contributed to the conception or design of the work. Guan- portional hazards regression analyses indicated that the risk Cheng Liu, LangXiong Chen, and Yi Liu are responsible for score is significantly correlated with patients’ OS, suggest- the data collection. LangXiong Chen and ShiJiang Yi ana- ing that the prognostic risk markers of glycolysis-related lyzed and interpreted the data. LangXiong Chen drafted the genes predict patient prognosis and can be independent of article. ShiJiang Yi, XiaoSong He, and YueFu Ling critically fi clinical characteristics. And then, we analyzed that HPRT1, revised the article. All authors gave the nal approval of the STC2, PLCB3, GPR87, PYGL, and SLC5A12 are highly version to be published. expressed in HNSCC tissues; besides, HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 may be related to the poor prognosis of HNSCC. This study constructs prog- Acknowledgments nostic risk markers related to glycolysis as a prognostic Thanks are due to all the authors for their joint efforts. marker for patients with HNSCC, providing new ideas and molecular targets for the research and individualized treatment of HNSCC. References Our result indicates that the prediction signature composed of glycolysis-related genes has shown potential in pre- [1] J. D. McDermott and D. W. 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