cancers

Communication LncRNA-mRNA Co-Expression Analysis Identifies AL133346.1/CCN2 as Biomarkers in Pediatric B-Cell Acute Lymphoblastic Leukemia

1,2,3, 1,2, 2,3,4, 2,4 Marta Cuadros †, Daniel J. García † , Alvaro Andrades † , Alberto M. Arenas , Isabel F. Coira 2,4 , Carlos Baliñas-Gavira 2,3,4, Paola Peinado 2,3,4, María I. Rodríguez 1,2,3, Juan Carlos Álvarez-Pérez 2,3,4 , Francisco Ruiz-Cabello 1,5 , Mireia Camós 6,7,8, Antonio Jiménez-Velasco 9 and Pedro P. Medina 2,3,4,*

1 Department of Biochemistry and Molecular Biology III and Immunology, University of Granada, Av. de la Investigación 11, 18016 Granada, Spain; [email protected] (M.C.); [email protected] (D.J.G.); [email protected] (M.I.R.); [email protected] (F.R.-C.) 2 GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, Av. de la Ilustración 114, 18016 Granada, Spain; [email protected] (A.A.); [email protected] (A.M.A.); [email protected] (I.F.C.); [email protected] (C.B.-G.); [email protected] (P.P.); [email protected] (J.C.Á.-P.) 3 Instituto de Investigación Biosanitaria (ibs. Granada), Av. Fuerzas Armadas 2, 18014 Granada, Spain 4 Department of Biochemistry and Molecular Biology I, University of Granada, Av. de Fuente Nueva S/N, 18071 Granada, Spain 5 Department of Clinical Analysis and Immunology, UGC Laboratorio Clínico, University Hospital Virgen de las Nieves, 18014 Granada, Spain 6 Hematology Laboratory, Institut de Recerca Hospital Sant Joan de Déu, 08950 Barcelona, Spain; [email protected] 7 Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 28029 Madrid, Spain 8 Leukemia and Other Pediatric Hemopathies, Developmental Tumors Biology Group, Institut de Recerca Hospital Sant Joan de Déu, 08950 Barcelona, Spain 9 Hematology Laboratory, Universitary Regional Hospital, Av. de Carlos Haya, 29010 Málaga, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-958-243-252 These authors contributed equally to this work. †


Received: 2 November 2020; Accepted: 15 December 2020; Published: 17 December 2020


Simple Summary: Dysregulation of noncoding RNAs has been described in numerous types of cancers and it has been associated with oncogenic or tumor suppressor activities. However, the signature of clinically relevant noncoding RNAs in pediatric B-cell acute lymphoblastic leukemia is still poorly understood. In a search for long non-coding RNAs that characterize pediatric B-cell acute lymphoblastic leukemia, we found that the long non-coding RNA AL133346.1 and a neighbouring protein-coding mRNA (CCN2) were significantly over-expressed in leukemia samples compared to healthy bone marrow. Survival analysis showed that patients with high CCN2 expression had a significantly better prognosis. These data suggest that AL133346.1/CCN2 could be useful for discriminating subtypes of leukemia and that CCN2 expression could predict the prognosis of pediatric patients with B-cell acute lymphoblastic leukemia.

Abstract: Pediatric acute B-cell lymphoblastic leukemia (B-ALL) constitutes a heterogeneous and aggressive neoplasia in which new targeted therapies are required. Long non-coding RNAs have recently emerged as promising disease-specific biomarkers for the clinic. Here, we identified pediatric B-ALL-specific lncRNAs and associated mRNAs by comparing the transcriptomic signatures of tumoral and non-tumoral samples. We identified 48 lncRNAs that were differentially expressed between pediatric B-ALL and healthy bone marrow samples. The most relevant lncRNA/mRNA pair

Cancers 2020, 12, 3803; doi:10.3390/cancers12123803 www.mdpi.com/journal/cancers Cancers 2020, 12, 3803 2 of 15

was AL133346.1/CCN2 (previously known as RP11-69I8.3/CTGF), whose expression was positively correlated and increased in B-ALL samples. Their differential expression pattern and their strong correlation were validated in external B-ALL datasets (Therapeutically Applicable Research to Generate Effective Treatments, Cancer Cell Line Encyclopedia). Survival curve analysis demonstrated that patients with “high” expression levels of CCN2 had higher overall survival than those with “low” levels (p = 0.042), and this gene might be an independent prognostic biomarker in pediatric B-ALL. These findings provide one of the first detailed descriptions of lncRNA expression profiles in pediatric B-ALL and indicate that these potential biomarkers could help in the classification of leukemia subtypes and that CCN2 expression could predict the survival outcome of pediatric B-cell acute lymphoblastic leukemia patients.

Keywords: CTGF; CCN2; AL133346.1; lncRNA expression; biomarker; pediatric B-ALL

1. Introduction Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy. Pediatric B-cell ALL (B-ALL) constitutes a heterogeneous and aggressive neoplasia in which new targeted cancer therapies are required [1–6]. In recent years, novel genomic and transcriptomic techniques have accelerated the discovery and identification of various types of non-coding RNAs (ncRNAs) that may play an important role in cellular biology [7–10]. Long ncRNAs (lncRNAs) are transcripts longer than 200 bp that lack protein coding capacity. LncRNAs are crucial regulators of gene expression due to their involvement in post-transcriptional modifications of mRNAs, including splicing, editing, trafficking, translation, and degradation [11,12]. Dysregulation of lncRNAs has been described in numerous human diseases, including cancer [13–17], leading to oncogenic or tumor-suppressive activities, as we recently reviewed [18]. However, few dysregulated lncRNAs have been directly related to leukemia development. In acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and multiple myeloma (MM), HOTAIR was reported to be dysregulated, and its high expression was associated with reduced survival times in AML patients [19]. In t(8;21) positive AML, which is usually associated with a good prognosis, over-expression of CCAT1 and PVT1 was correlated with poor prognosis [20]. Even fewer studies have identified dysregulated expression of lncRNAs in pediatric B-ALL patients. TCL6 upregulation was recently associated with ETV6-RUNX1-positive pediatric B-ALL, and patients expressing low levels of TCL6 had lower disease-free survival than patients expressing high levels of TCL6 [21]. In another microarray-based study, the expression levels of BALR-2 were related to: (i) cytogenetic abnormalities such as t(12;21)[ETV6/RUNX1], t(1;19)[TCF3/PBX1] and MLL-rearranged; (ii) disease subtypes; and (iii) survival of B-ALL patients [22]. Interestingly, these studies and others have shown that lncRNA expression profiles can distinguish molecular leukemia subtypes [23–28]. Despite all the reported expression profiles, a consensus of a clinically relevant lncRNA signature for pediatric B-ALL is yet to be identified. Identifying a signature of differentially expressed lncRNAs and associated protein coding genes in pediatric B-ALL could lead to a better diagnosis, to a better understanding of the disease and, ultimately, to a better prognosis for the patients. Previously,we developed a comparative study of the lncRNA profiles of pediatric B-ALL patients with and without the ETV6-RUNX1 gene fusion (Gene Expression Omnibus Accession GSE128254). We reported that high expression of the lncRNA TCL6 was associated with ETV6-RUNX1-positive pediatric B-ALL and with better disease-free survival, even within the ETV6-RUNX1-positive subtype [21]. Here, we performed a comparative study of the lncRNA profiles of pediatric B-ALL patients by leveraging data from our previous study as well as from multiple independent external cohorts. Cancers 2020, 12, x FOR PEER REVIEW 3 of 17 Cancers 2020, 12, 3803 3 of 15 2. Results and Discussion

2.1. 2.Aberrantly Results and Expressed Discussion lncRNAs in Pediatric B‐ALL 2.1.To Aberrantlymeasure Expressedthe lncRNA lncRNAs expression in Pediatric profiles B-ALL in pediatric B‐ALL, we compared lncRNA expression levels in 42 pediatric B‐ALL patient samples against 4 healthy bone marrow samples To measure the lncRNA expression profiles in pediatric B-ALL, we compared lncRNA expression (Figurelevels 1). in Despite 42 pediatric the B-ALLmolecular patient heterogeneity samples against of pediatric 4 healthy B bone‐ALL, marrow we identified samples a (Figure common1). differentialDespite theexpression molecular profile heterogeneity that could of pediatric distinguish B-ALL, the we pediatric identified B‐ aALL common samples differential from the expression healthy boneprofile marrow that samples. could distinguish Setting a the threshold pediatric of B-ALL fold sampleschange > from 1.5 theand healthy FDR < bone0.05, marrowwe identified samples. 48 lncRNAsSetting that a threshold were differentially of fold change expressed> 1.5 and FDR between< 0.05, all we B identified‐ALL and 48 healthy lncRNAs tissues, that were out di offferentially which 20 wereexpressed upregulated between and 28 all were B-ALL downregulated and healthy tissues, (Table out S1). of The which top upregulated 20 were upregulated lncRNAs and in pediatric 28 were B‐ALLdownregulated were XLOC_007191 (Table S1). (~7‐ Thefold), top PCNA upregulated‐AS1 (~7 lncRNAs‐fold) and in AL133346.1 pediatric B-ALL (~6‐fold), were whereas XLOC_007191 the top downregulated(~7-fold), PCNA-AS1 lncRNAs (~7-fold) were RP11 and‐807H22.6 AL133346.1 (~14 (~6-fold),‐fold), CXCR2P1 whereas (~6 the‐ topfold), downregulated and BC127858 lncRNAs (~4‐fold) (Figurewere 2a). RP11-807H22.6 CCN2 was the (~14-fold), 4th top upregulated CXCR2P1 (~6-fold), protein and‐coding BC127858 gene if (~4-fold) we ranked (Figure by 2folda). CCN2change was and statisticalthe 4th significance top upregulated (logFC protein-coding = 2.56, FDR = gene 3.0 × if 10 we−6) ranked (Figure by 2b). fold change and statistical significance (logFC = 2.56, FDR = 3.0 10 6) (Figure2b). × −

Figure 1. Heatmap of differentially expressed lncRNAs in B-ALL vs. healthy bone marrows. On top of Figure 1. Heatmap of differentially expressed lncRNAs in B‐ALL vs. healthy bone marrows. On top the heatmap, ETV6-RUNX1-negative B-ALL samples are labeled in blue, ETV6-RUNX1-positive B-ALL of thesamples heatmap, are labeled ETV6‐RUNX1 in red, and‐negative healthy B bone‐ALL marrows samples are are labeled labeled in in green. blue, ClusteringETV6‐RUNX1 was‐ performedpositive B‐ ALLbased samples on the are Spearman labeled correlationin red, and coe healthyfficient. bone marrows are labeled in green. Clustering was performed based on the Spearman correlation coefficient. We further investigated the putative biological functions of the differentially expressed lncRNAs between pediatric B-ALL and healthy bone marrow samples. Due to the lack of functional annotation of lncRNAs, we performed a Gene Ontology analysis on the protein-coding genes that were predicted

Cancers 2020, 12, 3803 4 of 15 to be functionally associated with the differentially expressed lncRNAs according to the microarray manufacturer. The top upregulated Gene Ontology terms included terms related to the regulation of angiogenesis (p = 0.019) (related genes: ANGPTL4, NF1, and TNFAIP3), DNA repair (p = 0.025) (PCNA and UBE2V1) and the RAS protein signal transduction pathway (p = 0.025) (NF1, RAB4A, and RAB11B) (Figure A1a). The top downregulated Gene Ontology terms included terms related to the negative regulation of interleukin-1 secretion (p = 7.36 10 5)(CARD17 and CARD18), the regulation × − of response to DNA damage stimulus (p = 6.20 10 4)(HMGA2 and FEM1B) and the regulation × − of apoptotic process (p = 1.04 10 3)(ACTN1, EPHA1, HMGA2, JUN, and NOTCH2) (Figure A1b). × − Taken together, these results suggest an overall oncogenic signature associated with lncRNA/mRNA expressionCancers 2020, 12 profiles, x FOR PEER in pediatric REVIEW B-ALL compared to healthy bone marrow samples. 4 of 17

Figure 2. Volcano plot of the differentially expressed lncRNAs (a) and protein-coding genes (b) in pediatricFigure 2. B-ALLVolcano vs. plot healthy of the differentially bone marrows. expressed The horizontal lncRNAs ( dasheda) and protein line represents‐coding genes a threshold (b) in ofpediatricFDR = B0.05‐ALL. Thevs. healthy vertical bone dashed marrows. lines representThe horizontal the thresholds dashed line of represents fold change a threshold= 1.5 and of FDR fold − change= 0.05. The= 1.5. vertical Red dots dashed represent lines represent the statistically the thresholds significant of fold differentially change = − expressed1.5 and fold lncRNAs. change = 1.5. Red dots represent the statistically significant differentially expressed lncRNAs. 2.2. mRNA Levels of the AL133346.1/CCN2 Pair Are Increased in Pediatric B-ALL We further investigated the putative biological functions of the differentially expressed lncRNAs Among the lncRNA/mRNA pairs that were differentially expressed between pediatric B-ALL between pediatric B‐ALL and healthy bone marrow samples. Due to the lack of functional annotation and healthy bone marrows, AL133346.1/CCN2 (previously known as RP11-69I8.3/CTGF) were also of lncRNAs, we performed a Gene Ontology analysis on the protein‐coding genes that were predicted upregulated in ETV6-RUNX1-positive vs. ETV6-RUNX1-negative pediatric B-ALL (fold change = 1.79, to be functionally associated with the differentially expressed lncRNAs according to the microarray adjusted p = 0.036) [21]. In addition, both AL133346.1 and CCN2 were, individually, among the top manufacturer. The top upregulated Gene Ontology terms included terms related to the regulation of differentially expressed lncRNAs and protein coding genes, respectively (Figure2). In our previous angiogenesis (p = 0.019) (related genes: ANGPTL4, NF1, and TNFAIP3), DNA repair (p = 0.025) (PCNA analysis of multiple independent datasets, the expression levels of AL133346.1 and CCN2 were the most and UBE2V1) and the RAS protein signal transduction pathway (p = 0.025) (NF1, RAB4A, and strongly correlated among all lncRNA/mRNA pairs [21]. Their differential expression patterns and RAB11B) (Figure A1a). The top downregulated Gene Ontology terms included terms related to the their strong correlation led us to further investigate AL133346.1/CCN2 in additional external datasets. negative regulation of interleukin‐1 secretion (p = 7.36 × 10−5) (CARD17 and CARD18), the regulation We extended our analyses of AL133346.1/CCN2 to Therapeutically Applicable Research To of response to DNA damage stimulus (p = 6.20 × 10−4) (HMGA2 and FEM1B) and the regulation of Generate Effective Treatments (TARGET-ALL-P1 and TARGET-ALL-P2), which is, to our knowledge, apoptotic process (p = 1.04 × 10−3) (ACTN1, EPHA1, HMGA2, JUN, and NOTCH2) (Figure A1b). Taken the largest available collection of pediatric ALL gene expression data. After excluding patients who together, these results suggest an overall oncogenic signature associated with lncRNA/mRNA were older than 14.99 years and those without AL133346.1 and CCN2 expression data, the TARGET expression profiles in pediatric B‐ALL compared to healthy bone marrow samples. population consisted of 320 patients (120 B-ALL and 200 T-ALL). The B-ALL TARGET dataset mostly consisted2.2. mRNA of Levels pre-B of samples the AL133346.1/CCN2 (112/120, 93.33%), Pair whereas Are Increased our cohort in Pediatric was more B‐ALL representative of B common phenotype (38/42, 90.48%). Because there were no better controls, we assessed the specificity of the expressionAmong of the AL133346.1 lncRNA/mRNA and CCN2 pairs by that comparing were differentially their expression expressed in B-ALL between and in T-ALL.pediatric We B‐ foundALL higherand healthy expression bone levelsmarrows, of AL133346.1 AL133346.1/CCN2 and CCN2 (previously in B-ALL compared known as to RP11 T-ALL‐69I8.3/CTGF) (Figure3a). AL133346.1 were also andupregulated CCN2 were in ETV6 expressed‐RUNX1 in‐positive all analyzed vs. ETV6 subtypes‐RUNX1 of pediatric‐negative B-ALL,pediatric but B‐ theyALL had(fold the change highest = expression1.79, adjusted levels p = in 0.036) ETV6 [21]./RUNX1-positive In addition, both B-ALL AL133346.1 compared and to other CCN2 subtypes were, individually, of leukemia among (Figure theA2). top differentially expressed lncRNAs and protein coding genes, respectively (Figure 2). In our previous analysis of multiple independent datasets, the expression levels of AL133346.1 and CCN2 were the most strongly correlated among all lncRNA/mRNA pairs [21]. Their differential expression patterns and their strong correlation led us to further investigate AL133346.1/CCN2 in additional external datasets. We extended our analyses of AL133346.1/CCN2 to Therapeutically Applicable Research To Generate Effective Treatments (TARGET‐ALL‐P1 and TARGET‐ALL‐P2), which is, to our knowledge, the largest available collection of pediatric ALL gene expression data. After excluding patients who were older than 14.99 years and those without AL133346.1 and CCN2 expression data, the TARGET population consisted of 320 patients (120 B‐ALL and 200 T‐ALL). The B‐ALL TARGET dataset mostly consisted of pre‐B samples (112/120, 93.33%), whereas our cohort was more representative of B common phenotype (38/42, 90.48%). Because there were no better controls, we

Cancers 2020, 12, x FOR PEER REVIEW 5 of 17

assessed the specificity of the expression of AL133346.1 and CCN2 by comparing their expression in B‐ALL and in T‐ALL. We found higher expression levels of AL133346.1 and CCN2 in B‐ALL Cancerscompared2020, 12 to, 3803 T‐ALL (Figure 3a). AL133346.1 and CCN2 were expressed in all analyzed subtypes5 of of 15 pediatric B‐ALL, but they had the highest expression levels in ETV6/RUNX1‐positive B‐ALL compared to other subtypes of leukemia (Figure A2). We further confirmed our findings using ALL Wecell further line data confirmed from the our Cancer findings Cell using Line ALL Encyclopedia cell line data (CCLE) from (Figure the Cancer 3b). Cell Overall, Line Encyclopediathese results (CCLE)confirm (Figure that AL133346.13b). Overall, and these CCN2 results are confirmexpressed that in AL133346.1pediatric B‐ALL and CCN2 and that are their expressed expression in pediatric is low B-ALLor absent and in that T‐ALL. their expression is low or absent in T-ALL.

FigureFigure 3. 3.Comparison Comparison of of AL133346.1AL133346.1/CCN2/CCN2 expression between pediatric B B-ALL‐ALL and and T T-ALL‐ALL samples samples fromfrom TARGET TARGET ( a()a) and and CCLE CCLE ( b(b).). The The scatterplots scatterplots areare coloredcolored according to the ALL subtype. Sample Sample sizessizes and and gene gene expression expression correlation: correlation: TARGETTARGET 120120 B-ALLB‐ALL (Spearman correlation coefficient coefficient == 0.717,0.717, pp< <0.0001) 0.0001) vs. vs. 200200 T-ALLT‐ALL (Spearman(Spearman correlationcorrelation coecoefficientfficient == 0.099, p = 0.163). CCLE: CCLE: 14 14 B B-ALL‐ALL (Spearman(Spearman correlation correlation coe coefficientfficient = = 0.799,0.799, pp == 0.001) vs. 16 16 T T-ALL‐ALL (Not (Not available). available).

2.3. AL133346.1/CCN2 Expression is Correlated in Pediatric B-ALL Samples 2.3. AL133346.1/CCN2 Expression is Correlated in Pediatric B‐ALL Samples LncRNAsLncRNAs can can regulate regulate the the expression expression ofof mRNAsmRNAs by various mechanisms, which which may may result result in in a a significantsignificant correlation correlation pattern pattern between between thethe expressionexpression of the lncRNA and and the the associated associated mRNA mRNA [29]. [29]. WeWe previously previously reported reported a strong a strong positive positive correlation correlation between between the RNAthe RNA levels levels of AL133346.1 of AL133346.1 and CCN2 and inCCN2 our cohort, in our ascohort, well as inwell external as in external data from data three from di threefferent different microarray microarray studies studies in pediatric in pediatric B-ALL patientsB‐ALL patients [21]. To [21]. confirm To confirm our findings, our findings, we performed we performed Spearman Spearman correlation correlation analyses analyses between between the expressionthe expression levels levels of AL133346.1 of AL133346.1 and and CCN2 CCN2 measured measured by by RNA RNA sequencing sequencing in in B-ALLB‐ALL samples from from TARGETTARGET and and CCLE. CCLE. We We found found a a statistically statistically significant significant correlationcorrelation between AL133346.1 and and CCN2 CCN2 expressionexpression in in TARGET TARGET pediatricpediatric B-ALLB‐ALL patients ( (NN == 120,120, Spearman Spearman correlation correlation coefficientcoefficient = 0.717,= 0.717 p , p<< 0.0001).0.0001). In In contrast, contrast, the the expression expression levels levels of of AL133346.1 AL133346.1 and and CCN2 CCN2 were were not not correlated correlated in in pediatric pediatric T-ALLT‐ALL ( N(N= = 200,200, SpearmanSpearman correlation coefficientcoefficient == 0.099,0.099 ,pp = =0.163),0.163), suggesting suggesting that that the the correlation correlation is isspecific specific to to B‐ B-ALLALL or or that that AL133346.1 AL133346.1 is not is not expressed expressed above above the thebackground background in T‐ inALL. T-ALL. Similarly, Similarly, we weobtained obtained a strong a strong correlation correlation in B‐ALL in B-ALL cell lines cell from lines CCLE from (N CCLE = 14, Spearman (N = 14, Spearmancorrelation correlationcoefficient coe= 0.799,fficient p == 0.799,0.001),p whereas= 0.001), this whereas correlation this correlation was not found was not in T found‐ALL incell T-ALL lines cell(N = lines 16) because (N = 16) becauseAL133346.1 AL133346.1 was not was expressed not expressed in any T‐ inALL any cell T-ALL line. cellIn addition, line. In addition,mRNA and mRNA protein and levels protein of CCN2 levels ofare CCN2 moderately are moderately correlated correlated (Spearman (Spearman correlation correlation coefficient coe = 0.518,fficient p = 0.001)0.518, accordingp = 0.001) to according data from to dataYang from et al. Yang [30]. et al. [30]. ToTo assess assess whether whether AL133346.1 AL133346.1 couldcould bebe involvedinvolved in the regulation of other genes, genes, we we studied studied thethe pairwise pairwise correlations of of AL133346.1 AL133346.1 expression expression and and expression expression of all of genes all genes (protein (protein-coding‐coding and andnon non-coding)‐coding) in our in microarray our microarray and in and pediatric in pediatric TARGET TARGET B‐ALL B-ALL data. In data. our own In our microarray, own microarray, which whichincluded included 1906 protein 1906 protein-coding‐coding genes, genes, AL133346.1 AL133346.1 expression expression was only was significantly only significantly correlated correlated with withCCN2 CCN2 expression. expression. In TARGET In TARGET B‐ALL B-ALL data, data, 9080 9080 genes genes showed showed a astatistically statistically significant significant correlation correlation withwith AL133346.1 AL133346.1 expression expression in in pediatric pediatric B-ALLB‐ALL data,data, andand CCN2 was among the the top top 100. 100. In In any any case, case, we note that correlation between these two genes, AL133346.1 and CCN2, does not necessarily mean that one of them modulates the other. Cancers 2020, 12, x FOR PEER REVIEW 7 of 17 Cancers 2020, 12, 3803 6 of 15

2.4. Analysis of Regulation Mechanisms of AL133346.1/CCN2 2.4. AnalysisEven if ofthe Regulation expression Mechanisms of a lncRNA of AL133346.1 and its neighboring/CCN2 protein‐coding gene are correlated, the lncRNAEven may if the not expression regulate its of neighboring a lncRNA andprotein its‐ neighboringcoding gene. protein-codingLandmark studies gene have are shown correlated, that thethe lncRNAregulatory may function not regulate of certain its neighboringlncRNA genes protein-coding may not reside gene. in the Landmark RNA product studies but in have regulatory shown thatelements the regulatory contained functionwithin the of lncRNA certain gene lncRNA, and genes that a may common not reside transcription in the RNAfactor product may modulate but in regulatorythe expression elements of both contained the lncRNA within and the the lncRNA neighboring gene, and protein that‐ acoding common gene transcription resulting in factor correlated may modulatetranscription the expression[31,32]. To of assess both thewhether lncRNA this and phenomenon the neighboring could protein-coding explain the genecoexpression resulting inof correlatedAL133346.1 transcription and CCN2, [ 31we,32 studied]. To assess the genomic whether location this phenomenon of the AL133346.1 could explain and CCN2 the coexpression genes and we of AL133346.1searched for and nearby CCN2, promoters we studied and the proximal genomic regulatory location ofregions. the AL133346.1 AL133346.1and andCCN2 CCN2genes are andlocated we searchedin the chromosome for nearby 6 promoters (q23.2) but and in different proximal strands. regulatory The two regions. genesAL133346.1 are orientedand headCCN2‐to‐head,are located partly inoverlapping, the chromosome and their 6 (q23.2) transcription but in di startfferent sites strands. (TSS) Theare twoseparated genes by are 2770 oriented pb. head-to-head,According to partlyGeneHancer overlapping, [33], which and theirintegrates transcription promoter start and sites enhancer (TSS) data are separated from various by 2770 databases, pb. According AL133346.1 to GeneHancerand CCN2 [share33], which two integrates of their promoterpredicted and high enhancer‐score data promoters/enhancers: from various databases, GH06J131946AL133346.1 andand CCN2GH06J131976share two (Figure of their 4). predicted These two high-score enhancers promoters are classified/enhancers: as high GH06J131946‐likelihood enhancers and GH06J131976 (noted as (Figure“elite”)4 ).and These they two display enhancers a strong are classifiedenhancer–gene as high-likelihood association. enhancersThe positive (noted gene as correlation “elite”) and results they displayobserved a strongabove in enhancer–gene B‐ALL point to association. the possibility The that positive the protein gene correlation‐coding gene results CCN2 observed and its antisense above in B-ALLlncRNA point AL133346.1 to the possibility are expressed that the in protein-coding a tissue‐specific gene mannerCCN2 whenand its their antisense locus lncRNA is transcriptionallyAL133346.1 areactive. expressed Based on in aour tissue-specific results, we propose manner whenfor our their model locus that is transcriptionallyeither: (i) AL133346.1 active. regulates Based on CCN2 our results,expression we proposein cis; or for (ii) our AL133346.1 model that and either: CCN2 (i) AL133346.1are specifically regulates modulated CCN2 expressionin B‐ALL by in cis;the orsame (ii) AL133346.1regulatory elements.and CCN2 are specifically modulated in B-ALL by the same regulatory elements.

Figure 4. Genomic location of AL133346.1/CCN2 in chromosome 6. The box with dashed stripes Figure 4. Genomic location of AL133346.1/CCN2 in chromosome 6. The box with dashed stripes represents a zoom-in of the locus. All CCN2 exons are shown while only the first AL133346.1 exon is represents a zoom‐in of the locus. All CCN2 exons are shown while only the first AL133346.1 exon is included. GH06J131946 and GH06J131976 regulatory regions are represented by red boxes. included. GH06J131946 and GH06J131976 regulatory regions are represented by red boxes. 2.5. High Expression of CCN2 is Associated with Better Prognosis 2.5. High Expression of CCN2 is Associated with Better Prognosis To determine the prognostic value of AL133346.1 and CCN2 expression, we performed a survival analysisTo usingdetermine pediatric the B-ALLprognostic clinical value data of from AL133346.1 TARGET. and The clinicalCCN2 expression, information we included performed gender, a agesurvival at diagnosis, analysis central using nervouspediatric system B‐ALL involvement, clinical data t(12;21)[ETV6 from TARGET./RUNX1], The clinical t(1;19)[TCF3 information/PBX1], t(9;22)[BCRincluded gender,/ABL1], age MLL at rearrangement, diagnosis, central trisomy nervous of chromosomes system involvement, 4 and 10 and numbert(12;21)[ETV6/RUNX1], of chromosomes (Tablet(1;19)[TCF3/PBX1], S2). We found t(9;22)[BCR/ABL1], a statistically significant MLL rearrangement, difference between trisomy “high” of chromosomes and “low” 4 AL133346.1 and 10 and andnumber CCN2 of chromosomes expression subgroups (Table S2). and We clinical found covariates a statistically (Figure significant A3, Figure difference A4, Table between S3). Two“high” of theand most “low” reliable AL133346.1 clinical and parameters CCN2 expression to determine subgroups aggressiveness, and clinical thecovariates t(12;21)[ETV6 (Figure/ RUNX1]A3, A4, Table and theS3). t(1;19)[TCF3Two of /PBX1]the most translocations reliable [clinical34], were parameters not distributed to homogeneouslydetermine aggressiveness, in the analysed the subgroups.t(12;21)[ETV6/RUNX1] We observed and that thethe “high” t(1;19)[TCF3/PBX1] AL133346.1 and translocations CCN2 expression [34], subgroups were not included distributed 90% andhomogeneously 100% of the t(12;21)[ETV6-RUNX1]-positive in the analysed subgroups. We B-ALL observed patients, that whereas the “high” they AL133346.1 had 29% and and 0% CCN2 of the t(1;19)[TCF3-PBX1]-positiveexpression subgroups included cases, 90% respectively and 100% of (Figure the t(12;21)[ETV6 A3, Figure A4‐RUNX1], Table S3).‐positive B‐ALL patients, whereasThe univariatethey had 29% Cox and analyses 0% of forthe variablest(1;19)[TCF3 traditionally‐PBX1]‐positive related cases, with respectively survival in pediatric (Figure A3, B-ALL A4, revealedTable S3). that male gender (p = 0.030), TCF3-PBX1 gene fusion (p < 0.001) and ploidy (Hipodiploidy:

Cancers 2020, 12, 3803 7 of 15 p = 0.005; Partial Hiperdiploidy: p = 0.033; High Hiperdiploidy: p = 0.019) were significantly associated with a shorter survival, while age at diagnosis (p = 0.186), CNS involvement (p = 0.943), ETV6-RUNX1 gene fusion (p = 0.490), BCR-ABL1 gene fusion (p = 0.689) and trisomy of chromosomes 4 and 10 (p = 0.418) were not statistically significant in this cohort. Splitting the patients by their median AL133346.1 or CCN2 expression, we found that “high” CCN2 patients were associated with longer overall survival (OS) times (p = 0.042; HR: 0.566, 95% CI: 0.328–0.980) (Figure5) while there was no association between OS and AL133346.1 expression (p = 0.77) (Figure A5). Although AL133346.1 expression and CCN2 expression were significantly correlated, and CCN2 was the top protein coding gene correlated with AL133346.1 in our microarray, only CCN2 was significantly associated with OS. This fact can be explained because the correlation between AL133346.1 and CCN2 is not perfect. This suggests that AL133346.1 may participate in CCN2-independent biological processes, which would interfere with the association of AL133346.1 with OS. In addition, all clinical covariates with p < 0.2 in a Cox univariate analysis, together with CCN2 expression, were used for a Cox multivariate analysis. The TCF3-PBX1 gene fusion was the most important variable influencing OS in pediatric B-ALL (p = 0.003). These results revealed that “high” CCN2 expression could be a prognostic factor associated to longer OS (p = 0.045) (HR: 0.448, 95%CI: 0.204–0.984) (Table S4). Although the “high” CCN2 expression subgroup was significantly enriched in t(12;21)[ETV6-RUNX1]-positive (p = 0.0001) and t(1;19)[TCF3-PBX1]-negative (p = 0.002) B-ALL patients (Table S3), we found no association between t(12;21)[ETV6/RUNX1] and OS, and we accounted for t(1;19)[TCF3-PBX1] in the Cox multivariate model. Thus, the expression of CCN2 could distinguish between two clinically different subgroups Cancers 2020, 12, x FOR PEER REVIEW 9 of 17 independently of the translocations in the sample.

Figure 5. Kaplan-Meier overall survival curves of pediatric B-ALL patients divided in two groups Figure 5. Kaplan‐Meier overall survival curves of pediatric B‐ALL patients divided in two groups according to whether CCN2 expression was above or beyond the median: “CCN2 high” and “CCN2 according to whether CCN2 expression was above or beyond the median: “CCN2 high” and “CCN2 low”. The p-value of Cox univariate analyses is shown. low”. The p‐value of Cox univariate analyses is shown. Finally, to assess whether CCN2 could be a specific prognostic biomarker of pediatric B-ALL, 3.we Materials performed and a Methods survival analysis using T-ALL clinical data (Table S5). “High” CCN2 expression was notUnless associated specified (p = otherwise,0.29) with aall survival analyses benefit were for patientsperformed with using T-ALL R (Figure (version A6a), 4.0.1) indicating and Bioconductora specific role (version of CCN2 3.11). in pediatric B-ALL. Moreover, there was no association between OS and AL133346.1 expression (p = 0.39) in pediatric T-ALL patients (Figure A6b). 3.1. StatisticalCCN2 belongs Analyses to the Cellular Communication Network (CCN) family of proteins. This family of extracellular matrix-associated proteins is characterized by having four conserved cysteine-rich Normality of continuous data was assessed using quantile‐quantile plots and the Shapiro‐Wilk domains and is involved in intercellular signaling [35]. In hematopoietic malignancies the CCN family test. For normal data, mean and standard deviation were reported and two‐tailed Student’s t‐tests plays a significant role in the differentiation of hematopoietic stem cells (HSC) and mesenchymal were applied after checking for equality of variances. For non‐normal data, the median and quartiles were reported and two‐sided Wilcoxon rank‐sum tests were applied. Whenever applicable, multiple comparisons adjustments were performed using the Benjamini‐Hochberg method.

3.2. Microarray Data Analyses Raw and normalized gene expression values for each sample in the analysis from our previous microarray study [21] are publicly available at the Gene Expression Omnibus (GSE128254). The methods for sample processing, microarray hybridization and data analysis are fully detailed in the section “Data pre‐processing and differential expression analyses” Supplementary Methods of the original publication [21]. The differentially expressed lncRNAs/mRNAs between tumor and normal samples were obtained by false discovery rate (FDR) < 0.05 and fold change (absolute value) > 1.5. Then, we performed a hierarchical clustering based on the Spearman correlation coefficient to identify lncRNA and mRNA expression patterns among tumoral and healthy bone marrow samples.

3.3. Gene Ontology Analyses We performed Gene Ontology analyses to identify significantly enriched biological pathways, molecular functions or cellular components related to the differentially expressed lncRNAs identified in the microarray analyses between tumor and normal samples (FDR < 0.05, fold change (absolute value) > 1.5). The protein‐coding genes associated to the differentially expressed lncRNA/mRNA pairs were predicted by the microarray manufacturer based on their genomic proximity, predicted miRNA binding, and the scientific literature as described in “Microarray design and predicted regulation between lncRNAs and protein‐coding genes” section of the Supplementary Methods of

Cancers 2020, 12, 3803 8 of 15 stem cells [36]. The prognosis value of the CCN family of proteins varies in the different hematologic malignancies [36]. In agreement with our results, several studies significantly correlated the expression of CCN2 with greater overall survival in lymphoma treated with chemotherapy [37,38]. However, further specific studies are needed in pediatric B-ALL to determine if CCN2 expression levels could influence patient treatment outcome.

3. Materials and Methods Unless specified otherwise, all analyses were performed using R (version 4.0.1) and Bioconductor (version 3.11).

3.1. Statistical Analyses Normality of continuous data was assessed using quantile-quantile plots and the Shapiro-Wilk test. For normal data, mean and standard deviation were reported and two-tailed Student’s t-tests were applied after checking for equality of variances. For non-normal data, the median and quartiles were reported and two-sided Wilcoxon rank-sum tests were applied. Whenever applicable, multiple comparisons adjustments were performed using the Benjamini-Hochberg method.

3.2. Microarray Data Analyses Raw and normalized gene expression values for each sample in the analysis from our previous microarray study [21] are publicly available at the Gene Expression Omnibus (GSE128254). The methods for sample processing, microarray hybridization and data analysis are fully detailed in the section “Data pre-processing and differential expression analyses” Supplementary Methods of the original publication [21]. The differentially expressed lncRNAs/mRNAs between tumor and normal samples were obtained by false discovery rate (FDR) < 0.05 and fold change (absolute value) > 1.5. Then, we performed a hierarchical clustering based on the Spearman correlation coefficient to identify lncRNA and mRNA expression patterns among tumoral and healthy bone marrow samples.

3.3. Gene Ontology Analyses We performed Gene Ontology analyses to identify significantly enriched biological pathways, molecular functions or cellular components related to the differentially expressed lncRNAs identified in the microarray analyses between tumor and normal samples (FDR < 0.05, fold change (absolute value) > 1.5). The protein-coding genes associated to the differentially expressed lncRNA/mRNA pairs were predicted by the microarray manufacturer based on their genomic proximity, predicted miRNA binding, and the scientific literature as described in “Microarray design and predicted regulation between lncRNAs and protein-coding genes” section of the Supplementary Methods of our previous manuscript [21]. Using these predictions, we performed the Gene Ontology analysis as follows: i. Find the significant upregulated or downregulated lncRNAs in our analysis of pediatric B-ALL vs. healthy bone marrow. We analyzed up- and downregulated lncRNAs separately. ii. Using the lncRNA/mRNA predictions, find the mRNAs that were predicted to be associated with the selected lncRNAs. iii. Enter the mRNA list as input to Enrichr [39,40] to find significantly enriched Gene Ontology terms. iv. Simplify the Gene Ontology terms using ReviGO [41] with default parameters and providing the FDR values for each GO term.

3.4. Analysis of External Datasets We downloaded gene expression of ALL patients from Genomic Data Commons (TARGET Project, last updated 6 June 2020; N = 506). Data were downloaded using the R package TCGABiolinks (v2.16.0). In the case of samples from the same patient, those with the smallest library size were removed Cancers 2020, 12, 3803 9 of 15

(N = 477). For the expression data, raw counts per gene were normalized using the TMM method and then log2(counts per million) were calculated using the R package edgeR (v3.30.3). For the survival analysis, patients who were more than 14.99 years old were filtered out (N = 321). Then, B-subtype (N = 120) and T-subtype (N = 200) samples were used to perform the following analysis. In this case, the B-subtype dataset consisted of pre-B (112/120, 93.33%) and B-ALL (8/120, 6,67%) samples, while all T-subtype samples were T-ALL. In addition, we downloaded external gene expression of ALL cell lines from Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/ccle/data). TPM-normalized gene expression data were directly downloaded from the CCLE data repository (version 2019-09-29, N = 1019). Using cell line sample information from DepMap portal, we selected B-ALL (N = 14) and T-ALL (N = 16) cell lines. Normalized gene expression data were transformed to log2 scale for the following analyses. Then these transformed data were filtered to obtain AL133346.1 and CCN2 expression. The downloaded data from both databases were used for comparison of gene expression between both subtypes and to study the lncRNA/mRNA correlation using the Spearman correlation coefficient.

3.5. Survival Analyses The clinical data associated with TARGET ALL patients were downloaded from NCI TARGET (Therapeutically Applicable Research To Generate Effective Treatments) repository (https://target-data. nci.nih.gov/Public/ALL/clinical/). We used AL133346.1 and CCN2 expression levels from TARGET data to analyze their correlation with overall survival of ALL patients, considering deaths as events. We split the patients in two groups for both genes according to their expression levels. These gene expression variables were dichotomized into “low” and “high” subgroups using the median expression value for CCN2 and AL133346.1 as a threshold for B-ALL. These variables were dichotomized into the same levels if the expression values were null or non-null for T-subtype. The association between clinical characteristics and gene expression groups was determined using Fisher’s exact tests. We plotted Kaplan-Meier curves for “High” and “Low” patients groups for both genes using the R packages “survival” (v3.1-11) and “survminer” (v0.4.6), and significance was tested using the univariate log-rank test. In addition, we studied the effect of different clinical covariates on the overall survival patients performing univariate Cox regression. Those clinical covariates that showed p < 0.2 in univariate analysis were used for a multivariate Cox regression.

4. Conclusions In conclusion, we observed increased AL133346.1 and CCN2 expression in pediatric B-ALL. Importantly, the stratification based on CCN2 expression could be of clinical interest, since our results revealed that “high” CCN2 expression was associated with better OS of pediatric B-ALL patients. We suggest that these potential biomarkers would further help in the classification of leukemia subtypes and the determination of the clinical outcome of pediatric B-ALL patients.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-6694/12/12/3803/s1, Table S1: 48 lncRNAs, represented by 50 probes, that were differentially expressed between pediatric B-ALL vs. healthy bone marrow samples; Table S2: Demographic and clinical characteristics of the pediatric B-ALL patients of the TARGET dataset; Table S3: Comparison of clinical characteristics according to AL133346.1 and CCN2 expression in pediatric B-ALL patients from TARGET dataset; Table S4: Cox Univariate and multivariate overall analysis of clinical covariates in pediatric B-ALL patients from TARGET dataset; Table S5: Demographic and clinical characteristics of the pediatric T-ALL patients of the TARGET dataset. Author Contributions: Conceptualization, P.P.M.; Data curation, D.J.G. and A.A.; Formal analysis, M.C. (Marta Cuadros), D.J.G. and A.A.; Funding acquisition, P.P.M.; Investigation, M.C. (Marta Cuadros), D.J.G., A.A., A.M.A., I.F.C., C.B.-G., P.P., M.I.R., J.C.Á.-P. and M.C. (Mireia Camós); Methodology, M.C. (Marta Cuadros), D.J.G. and A.A.; Project Administration, P.P.M.; Resources, M.I.R.; Software, D.J.G. and A.A.; Supervision, M.C. (Marta Cuadros), J.C.Á.-P., and P.P.M.; Validation, M.C. (Marta Cuadros), D.J.G., A.A. and I.F.C.; Writing—original draft preparation, M.C. (Marta Cuadros), D.J.G., A.A. and P.P.M.; Writing—review and editing, P.P.M., A.M.A., I.F.C., C.B.-G., P.P., M.I.R., J.C.Á.-P., F.R.-C., M.C. (Mireia Camós) and A.J.-V. All authors have read and agreed to the published version of the manuscript. Cancers 2020, 12, 3803 10 of 15

Funding: P.P.M.’s laboratory is funded by Aula de Investigación sobre la Leucemia infantil: Héroes contra la Leucemia the Ministry of Economy of Spain (SAF2015-67919-R), Junta de Andalucía (PIGE-0440-2019, Pl-0245-2017, PI-0135-2020), University of Granada (PPJIA2019-06, B-CTS-126-UGR18), and Spanish Association for Cancer Research (LAB-AECC). D.J.G. was supported by a “Fundación Benéfica Anticáncer Santa Cándida y San Francisco Javier” predoctoral fellowship. A.A. and A.M.A. were supported by a Spanish Ministry of Education, Culture and Sports FPU fellowship (FPU17/00067, and FPU17/01258, respectively). P.P. was supported by a PhD “La Caixa Foundation” fellowship (LCF/BQ/DE15/10360019). J.C.A.-P. was supported by a Marie Sklodowska Curie Actions postdoctoral fellowship (H2020-MSCA-IF-2018). Acknowledgments: We would like to thank the “Biobanc de l’Hospital Infantil Sant Joan de Déu per a la Investigació”, integrated in the Spanish Biobank Network of ISCIII, as well as Asociación Malagueña para la Investigación en Leucemias (AMPILE), for the sample and data procurement, and to the Héroes hasta la Médula Association. The authors thank the Ph.D. program of Biochemistry and Molecular Biology of the University of Granada. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the Cancers 2020,study; 12, x FOR in the PEER collection, REVIEW analyses, or interpretation of data; in the writing of the manuscript, or in the decision to 12 of 17 publish the results.

AppendixAppendix A A

Figure A1. Gene Ontology (GO) analysis of the mRNAs associated with the differentially expressed lncRNAs between pediatric B-ALL and healthy samples. The top 10 significantly enriched upregulated Figure A1. Gene Ontology (GO) analysis of the mRNAs associated with the differentially expressed (a) and downregulated (b) GO terms are shown. Only non-redundant GO terms are included as lncRNAs describedbetween in Methods.pediatric B‐ALL and healthy samples. The top 10 significantly enriched upregulated (a) and downregulated (b) GO terms are shown. Only non‐redundant GO terms are included as described in Methods.

Figure A2. Comparison of AL133346.1 and CCN2 expression between different karyotypes of pediatric B‐ALL patients from our own microarray (a) and different subtypes of pediatric B‐ALL patients from TARGET (b). The boxplots are colored according to the ALL subtype. The BCR‐ABL and MLL subtypes had 3 and 0 positive samples respectively, thus they were excluded from the boxplot. Sample sizes: [TCF3‐PBX1]: 7; Trisomy 4–10: 10; [ETV6‐RUNX1]: 10.

Cancers 2020, 12, x FOR PEER REVIEW 12 of 17

Appendix A

Figure A1. Gene Ontology (GO) analysis of the mRNAs associated with the differentially expressed lncRNAs between pediatric B‐ALL and healthy samples. The top 10 significantly enriched Cancersupregulated2020, 12, 3803 (a) and downregulated (b) GO terms are shown. Only non‐redundant GO terms are11 of 15 included as described in Methods.

Figure A2.A2. ComparisonComparison of of AL133346.1 AL133346.1 and and CCN2 CCN2 expression expression between between different different karyotypes karyotypes of pediatric of pediatricB-ALL patients B‐ALL from patients our own from microarray our own microarray (a) and diff erent(a) and subtypes different of pediatricsubtypes B-ALL of pediatric patients B‐ fromALL patientsTARGET from (b). The TARGET boxplots (b) are. The colored boxplots according are colored to the ALLaccording subtype. to the The ALL BCR-ABL subtype. and The MLL BCR subtypes‐ABL Cancersandhad 2020 3MLL and, 12 subtypes 0, x positive FOR PEER had samples REVIEW 3 and respectively, 0 positive samples thus they respectively, were excluded thus from they the were boxplot. excluded Sample from sizes: the13 of 17 boxplot.[TCF3-PBX1]: Sample 7; Trisomysizes: [TCF3 4–10:‐PBX1]: 10; [ETV6-RUNX1]: 7; Trisomy 4–10: 10. 10; [ETV6‐RUNX1]: 10.

Figure A3. Comparison of clinical characteristics according to AL133346.1 expression in pediatric B-ALL patientsFigure from A3. Comparison TARGET dataset. of clinical FDR-adjusted characteristics p-values according of Fisher’s to AL133346.1 exact tests expression are shown. in Abbreviations: pediatric B‐ Chr:ALL Chromosome; patients from CNS: TARGET central dataset. nervous FDR system;‐adjusted CNS p‐ 1values means of no Fisher’s CNS involvement, exact tests are CNS shown. 2 is a veryAbbreviations: low level, and Chr: CNS Chromosome; 3 is definite CNS: CNS central involvement; nervous NA: system; not CNS available; 1 means MLL: no MLLCNS involvement,gene; t(12;21): translocation(12;21);CNS 2 is a very low t(1;19): level, and translocation(1;19); CNS 3 is definite t(9;22): CNS involvement; translocation(9;22); NA: not (+ available;): Positive; MLL: ( ): Negative.MLL gene; t(12;21): translocation(12;21); t(1;19): translocation(1;19); t(9;22): translocation(9;22); (+):− Positive; (−): Negative.

Cancers 2020, 12, 3803 12 of 15 Cancers 2020, 12, x FOR PEER REVIEW 14 of 17

FigureFigure A4. Comparison A4. Comparison of clinical of clinical characteristics characteristics according according toto CCN2 expression expression in in pediatric pediatric B‐ALL B-ALL patientspatients from TARGET from TARGET dataset. dataset. FDR-adjusted FDR‐adjusted p-values p of‐values Fisher’s of exactFisher’s tests exact are shown. tests are Abbreviations: shown. Chr: Chromosome;Abbreviations: Chr: CNS: Chromosome; central nervous CNS: central system; nervous CNS 1system; means CNS no 1 CNS means involvement, no CNS involvement, CNS 2 is a Cancersvery low 2020CNS level,, 122 is, x aFOR and very PEER CNS low REVIEWlevel, 3 is definiteand CNS CNS 3 is definite involvement; CNS involvement; NA: not available;NA: not available; MLL: MLL MLL:gene; MLL gene; t(12;21):15 of 17 translocation(12;21);t(12;21): translocation(12;21); t(1;19): translocation(1;19); t(1;19): translocation(1;19); t(9;22): translocation(9;22); t(9;22): translocation(9;22); (+): Positive; (+): Positive; ( ): Negative. (−): − Negative.

Figure A5. Kaplan-Meier overall survival curves of pediatric B-ALL patients divided in two groups Figure A5. Kaplan‐Meier overall survival curves of pediatric B‐ALL patients divided in two groups accordingaccording to whether to whether AL133346.1 AL133346.1 expression expression was was above above or or beyond beyond the the median: median: “AL133346.1 high” high” and “AL133346.1and “AL133346.1 low”. The low”. p-value The p‐ ofvalue Cox of univariate Cox univariate analyses analyses are are shown. shown.

Figure A6. Kaplan‐Meier overall survival curves of pediatric T‐ALL patients divided in two groups according to whether AL133346.1 (a) and CCN2 (b) expression was non‐null or null: “AL133346.1 high”, “AL133346.1 low”, “CCN2 high” and “CCN2 low”. The p‐values of Cox univariate analysis are shown.

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Figure A5. Kaplan‐Meier overall survival curves of pediatric B‐ALL patients divided in two groups according to whether AL133346.1 expression was above or beyond the median: “AL133346.1 high” Cancersand2020 “AL133346.1, 12, 3803 low”. The p‐value of Cox univariate analyses are shown. 13 of 15

Figure A6. Kaplan-Meier overall survival curves of pediatric T-ALL patients divided in two groups Figure A6. Kaplan‐Meier overall survival curves of pediatric T‐ALL patients divided in two groups according to whether AL133346.1 (a) and CCN2 (b) expression was non-null or null: “AL133346.1 according to whether AL133346.1 (a) and CCN2 (b) expression was non‐null or null: “AL133346.1 high”, “AL133346.1 low”, “CCN2 high” and “CCN2 low”. The p-values of Cox univariate analysis high”, “AL133346.1 low”, “CCN2 high” and “CCN2 low”. The p‐values of Cox univariate analysis are shown. are shown. References References 1. Karol, S.E.; Pui, C.-H. Personalized therapy in pediatric high-risk B-cell acute lymphoblastic leukemia. 1. Ther.Karol, Adv. S.E.; Hematol. Pui, C.‐H.2020 Personalized, 11, 2040620720927575. therapy in pediatric [CrossRef high][‐PubMedrisk B‐cell] acute lymphoblastic leukemia. Ther. 2. Malouf,Adv. Hematol. C.; Ottersbach, 2020, 11, 2040620720927575, K. Molecular processes doi:10.1177/2040620720927575. involved in B cell acute lymphoblastic leukaemia. Cell. Mol. 2. LifeMalouf, Sci. 2017C.; Ottersbach,, 75, 417–446. K. [MolecularCrossRef][ processesPubMed ]involved in B cell acute lymphoblastic leukaemia. Cell. Mol. 3. Hein,Life Sci. D.; 2017 Borkhardt,, 75, 417–446, A.; Fischer,doi:10.1007/s00018 U. Insights‐017 into‐2620 the‐z. prenatal origin of childhood acute lymphoblastic 3. leukemia.Hein, D.; Borkhardt,Cancer Metastasis A.; Fischer, Rev. 2020 U. ,Insights39, 161–171. into [theCrossRef prenatal][PubMed origin ]of childhood acute lymphoblastic 4. Mohseni,leukemia. M.; Cancer Uludag, Metastasis H.; Brandwein, Rev. 2020, J.M.39, 161–171, Advances doi:10.1007/s10555 in biology of acute‐019 lymphoblastic‐09841‐1. leukemia (ALL) and 4. therapeuticMohseni, M.; implications. Uludag, H.; Brandwein,Am. J. blood Res.J.M. Advances2018, 8, 29–56. in biology of acute lymphoblastic leukemia (ALL) and 5. Terwilliger,therapeutic implications. T.; Abdul-Hay, Am. M. J. Acute blood Res. lymphoblastic 2018, 8, 29–56. leukemia: A comprehensive review and 2017 update. 5. BloodTerwilliger, Cancer T.; J. 2017 Abdul, 7,‐ e577.Hay, M. [CrossRef Acute lymphoblastic] leukemia: A comprehensive review and 2017 update. 6. Coccaro,Blood Cancer N.; J. Anelli,2017, 7, L.; e577, Zagaria, doi:10.1038/bcj.2017.53. A.; Specchia, G.; Albano, F. Next-Generation Sequencing in Acute 6. LymphoblasticCoccaro, N.; Anelli, Leukemia. L.; Zagaria,Int. J. Mol. A.; Sci. Specchia,2019, 20 ,G.; 2929. Albano, [CrossRef F. Next] ‐Generation Sequencing in Acute 7. Medina,Lymphoblastic P.P.; Slack, Leukemia. F.J. MicroRNAs Int. J. Mol. and Sci. cancer: 2019, 20 An, 2929, overview. doi:10.3390/ijms20122929.Cell Cycle 2008, 7, 2485–2492. [CrossRef] 8. Trang, P.; Medina, P.P.; Wiggins, J.F.; Ruffino, L.; Kelnar, K.; Omotola, M.; Homer, R.; Brown, D.; Bader, A.G.; Weidhaas, J.B.; et al. Regression of murine lung tumors by the let-7 microRNA. Oncogene 2010, 29, 1580–1587. [CrossRef] 9. Medina, P.P.; Nolde, M.J.; Slack, F.J. OncomiR addiction in an in vivo model of microRNA-21-induced pre-B-cell lymphoma. Nat. Cell Biol. 2010, 467, 86–90. [CrossRef] 10. Zhang, G.; Pian, C.; Chen, Z.; Zhang, J.; Xu, M.; Zhang, L.; Chen, Y.Identification of cancer-related miRNA-lncRNA biomarkers using a basic miRNA-lncRNA network. PLoS ONE 2018, 13, e0196681. [CrossRef] 11. Ponting, C.P.; Oliver, P.L.; Reik, W. Evolution and Functions of Long Noncoding RNAs. Cell 2009, 136, 629–641. [CrossRef][PubMed] 12. Marchese, F.P.; Raimondi, I.; Huarte, M. The multidimensional mechanisms of long noncoding RNA function. Genome Biol. 2017, 18, 1–13. [CrossRef][PubMed] 13. Fu, S.; Wang, Y.; Li, H.; Chen, L.; Liu, Q. Regulatory Networks of LncRNA MALAT-1 in Cancer. Cancer Manag. Res. 2020, 12, 10181–10198. [CrossRef] 14. Carlevaro-Fita, J.; PCAWG Drivers and Functional Interpretation Group; Lanzós, A.; Feuerbach, L.; Hong, C.; Mas-Ponte, D.; Pedersen, J.S.; Johnson, R.; PCAWG Consortium. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis. Commun. Biol. 2020, 3, 1–16. [CrossRef][PubMed] 15. Huarte, M. The emerging role of lncRNAs in cancer. Nat. Med. 2015, 21, 1253–1261. [CrossRef] 16. Gandhi, M.; Groß, M.; Holler, J.M.; Coggins, S.A.; Patil, N.; Leupold, J.H.; Munschauer, M.; Schenone, M.; Hartigan, C.R.; Allgayer, H.; et al. The lncRNA lincNMR regulates nucleotide metabolism via a YBX1-RRM2 axis in cancer. Nat. Commun. 2020, 11.[CrossRef] Cancers 2020, 12, 3803 14 of 15

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