DNA methylation patterns suggest the involvement of DNMT3B and TET1 in osteosarcoma development

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We investigated DNA methylation and related genetic variants in 28 primary osteosarcomas aiming to identify deregulated driver pathways. Methylation and genomic data was obtained using the Illumina HM450K beadchips and the TruSight One sequencing panel, respectively. Aberrant DNA methylation was spread throughout the osteosarcomas genomes. We identified 3,146 differentially methylated CpGs comparing osteosarcomas and bone tissue samples, with high methylation heterogeneity, global hypomethylation and focal hypermethylation at CpG islands. Differentially methylated regions (DMR) were detected in 585 loci (319 hypomethylated and 266 hypermethylated), mapped to the promoter regions of 350 genes. These DMR-genes were enriched for biological processes related to skeletal system morphogenesis, proliferation, inflammatory response and signal transduction. Six tumor suppressor genes harbored deletions or promoter hypermethylation ( DLEC1, GJB2, HIC1, MIR149, PAX6, WNT5A ), and four oncogenes presented gains or hypomethylation ( ASPSCR1 , NOTCH4, PRDM16 , RUNX3 ). Our analysis also revealed hypomethylation at 6p22, a region that contains several histone genes. DNMT3B gain was found to be a recurrent copy number change in osteosarcomas, providing a possible explanation for the observed phenotype of CpG island hypermethylation. While the detected open-sea hypomethylation likely contributes to the well-known osteosarcoma genomic instability, enriched CpG island hypermethylation suggests an underlying mechanism possibly driven by overexpression of DNMT3B likely resulting in silencing of tumor suppressors and DNA repair genes. osteosarcoma DNA methylation mutations copy number alterations DNMT TET. Figures Figure 1 Figure 2 Figure 3 Introduction Primary bone tumors represent up to 6% of all tumors of childhood and adolescents (0 to 19 years), with osteosarcoma (OS) being the most prevalent [ 1 ]. In agreement with the fact that OS arises more commonly during puberty, several genes and signaling pathways involved with bone growth and remodeling have been associated with the disease. Recurrent somatic point mutations include ATRX, BRCA1/2, PTEN , RB1 and TP53 , although most osteosarcomas are characterized by a high genomic instability that is likely to play a significant role in their pathogenesis [ 2 , 3 ]. In addition, these tumors present a highly heterogeneous pattern of somatic copy number alterations (CNA), including 1p, 1q, 5p, 6p, 8q, 17p and 18p gains, MYC, CCNE1, COPS3, CDK4, MDM2, AKT and RECQL4 amplifications, and 3, 5q, 6q, 8p, 9, 10, 13, 16, 17, 18q and 19 losses, including TP53, RB1, DLG2, PTEN, ATRX and CDKN2A deletions [ 3 , 4 ]. Nevertheless, some tumors exhibit complex chromosomal phenomena such as kataegis and chromothripsis [ 3 , 5 ]. Anomalous DNA methylation is also a cancer hallmark affecting metastasis and resistance to chemotherapy in patients with osteosarcomas. Promoters of CDKN2A [ 6 , 7 ], RASSF1 , MGMT , DAPK1 , TIMP3 [ 8 ], HIC1 and GADD45 [ 9 ] are hypermethylated in a proportion of cases in comparison to normal bone tissues as well as hypomethylation of IRX1 , SEMA4D , RAF1 and PAK1 [ 9 ]. A combined analysis of DNA methylation from osteosarcoma samples and gene expression from osteosarcoma cell lines disclosed an association between PRAME and SEMA3A hypomethylation and increased expression levels as well as CALD1 and DNALI1 hypermethylation with gene repression in osteosarcomas [ 10 ]. In xenografts, increased IL-6 expression due to DNA demethylation mediated by TET2 within the tumor microenvironment increased the metastatic capability of osteosarcoma cells [ 11 ]. The potential to form metastasis was also associated with a nonrandom pattern of the enhancer histone marks H3K4me1 and H3K27ac in metastatic and nonmetastatic osteosarcomas [ 12 ]. Thus, we explored methylomes of primary tissues of osteosarcomas and the possible link with somatic genetic variants, aiming to provide insights into the mechanisms underlying its tumorigenesis. We highlighted genes, biological processes and signaling pathways, including metabolic changes in the blood, which might be involved with osteosarcoma development. Methods Characteristics of the patients Our cohort consisted of patients diagnosed with osteosarcoma between 2008 and 2014, with fresh frozen samples stored at the biobank from the Barretos Cancer Hospital (SP, Brazil). Samples from 28 osteosarcomas (14 females and 14 males) were obtained before any systemic treatment (chemotherapy or radiotherapy), as well as nine non-paired normal bone tissues from surgeries of non-cancer patients. Pediatric patients were aged between 10-20 years (n = 23) and five patients were adults (29-61 years) ( Table 1 ). The treatment of patients followed the clinical protocol of the Brazilian Osteosarcoma Treatment Group [13]. The conduction of this study followed national and institutional ethical policies with approval from the Barretos Cancer Hospital Ethical Committee (CEP-HCB 898.403). Informed consent was obtained from the patients and/or their legal guardians. Table 1. Characteristics of the 28 cases of osteosarcomas. ID Age (years) Sex Histology Metastasis Huvos grade Tumor size (cm) OS-1 14 M Mixed - 1 15 OS-2 13 M Chondroblastic + 1 13 OS-3 18 M Chondroblastic + 1 NA OS-4 37 F Osteoblastic NA NA NA OS-5 17 M Osteoblastic + 1 18.8 OS-6 17 M Telangiectatic NA NA 10.7 OS-7 20 M Osteoblastic - 2 NA OS-8 16 F Telangiectatic + 1 9.4 OS-9 18 F Osteoblastic + 2 9.9 OS-10 19 F Osteoblastic + 2 5 OS-11 12 M Osteoblastic - 1 18.3 OS-12 29 F Osteoblastic - 1 28 OS-13 13 M NA + NA 18.3 OS-14 19 F Parosteal - NA 15.4 OS-15 11 F Osteoblastic + 1 10.5 OS-16 43 M Osteoblastic - 2 NA OS-17 18 F Osteoblastic - 2 22 OS-18 12 F Osteoblastic + 2 12 OS-19 17 F Fibroblastic - 1 NA OS-20 17 M Osteoblastic + 1 14.2 OS-21 35 M Osteoblastic + 1 NA OS-22 18 F Osteoblastic - 2 33 OS-23 13 F Osteoblastic - 1 10 OS-24 15 F Osteoblastic - 1 17.5 OS-25 61 M Pleomorphic - NA NA OS-26 17 M Osteoblastic - 2 9.1 OS-27 10 F Osteoblastic - 1 18 OS-28 20 M Osteoblastic - 1 16 Abbreviations: F: female; M: male; NA: not available; (+): present; (-): absent. Samples preparation and quality control Tumor (OS) and normal (BT) samples were microdissected and reviewed by a pathologist, who selected only areas containing at least 70% of cells of interest for the study. DNA was isolated using the QIAsymphony equipment with the DSP DNA Mini Kit (Qiagen). Quantity and quality were assessed by NanoDrop (Thermo Fisher Scientific) and agarose gel 0.8%, respectively. DNA was bisulfite converted using the EZ DNA Methylation kit (Zymo Research Corp), according to manufacturer recommendations, and hybridized in the Human Methylation 450 BeadChip microarrays (HM450K, Illumina), following the Illumina Infinium HD methylation protocol. Raw data was extracted using the iScan SQ scanner (Illumina) by GenomeStudio software (v.2011.1), with the methylation module v.1.9.0 (Illumina), into IDAT files, which were used for further analyses. Preprocessing and identification of differentially methylated genes The R package ChAMP [14, 15] was applied to the dataset and all 37 samples passed QC parameters. We used the standard pipeline: filtered out probes included those with a bead count <3 in at least 5% of samples (n =786 probes), located in non-CpG sites (n = 3,047 probes) or in SNPs (n = 49 977 probes), as well as probes that aligned to multiple locations (n =7 143) or located in the XY chromosomes (n=10 046). A total of 410 617 probes were retained for further analyses. Signal intensities from probes type I and II were normalized using the updated version of BMIQ method [16]. The singular value decomposition method (SVD) assessed unwanted components of variation, which was corrected using ComBat [17] ( Supplemental Figure S1 ). To avoid the effects of cell type composition, the R package RefFreeEWAS [18] was applied to β-values, as implemented in ChAMP, resulting in the identification of differentially methylated CpG positions (DMPs) between OS and BT samples. DMRcate [19] was applied to identify differentially methylated regions (DMRs), defined as at least seven consecutive CpG sites presenting methylation changes in the same direction (hypo- or hypermethylation) in a DNA sequence of 300 nucleotides. DMRs with FDR20% were considered significant. To evaluate the global methylation heterogeneity within the tumor samples, we calculated the coefficient of variation (CV), through the ratio between standard deviation/mean of DNA methylation levels across the genome [20]. Enrichment analysis using the sets of differentially methylated genes Functional enrichment analyses were applied to genes mapped within the DMRs as input data and the whole-genome as background using Gene Set Enrichment Analysis (GSEA [21]). We decided to evaluate the set of genes mapped to DMRs as the possible impact has been more widely explored in the literature. Genes were clustered based on biological processes and molecular functions (Gene Ontology), pathways (Reactome), transcription factor targets and chemical and genetic perturbations. Features with adjP≤0.05 (Benjamini-Hochberg adjustment) that comprised at least 10 genes were considered statistically significant. Clinical exome sequencing and variant analysis Exome sequencing was performed for the 28 OS samples using the TruSight One Sequencing panel (Illumina), covering approximately 4 800 clinically relevant genes. Sequencing was performed using the Illumina Hi-seq2000 platform, with median depth coverage of 178x. Reads were mapped to the Human Genome Reference GRC37/hg19 using Burrows-Wheeler Aligner software (BWA) package version 0.6.1 [22], and local realignment was carried out with the Genome Analysis Tool Kit (GATK) version 1.6 [23]. Single nucleotide (SNV) and indel variant annotation and filtering were run using VarSeq software (Golden Helix, Bozeman, USA) with the hg19 reference genome (RefSeq genes 105, NCBI). Germline variants deposited in the following public databases were excluded: 1000 Genomes (http://www.1000genomes.org/), dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/), gnomAD (http://gnomad.broadinstitute.org/), UK10K (https://www.uk10k.org/), and AbraOM (http://abraom.ib.usp.br/; exome data from 609 healthy Brazilians [24]). The clinical and cancer databases OMIM (https://omim.org/), ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), CIViC (https://civicdb.org/home), COSMIC (https://cancer.sanger.ac.uk/cosmic) and ICGC (https://icgc.org/) were also used for variant annotation. Pathogenicity predictions were obtained from dbNSFP Functional Predictions and Scores [25]. For the analysis of the somatic mutational burden, we first applied the following exclusion criteria to all variants: presence in an in-house database of control non-cancer samples or in the population databases above mentioned, indels in repetitive sequences, and hypervariable genes [26]. Further selection was based on Phred scores for variant confidence ≥17, read depth ≥20 reads with at least 10% of allelic frequency, and coding SNVs/indels resulting in missense or loss-of-function variants (essential splice site, frameshift or gain/loss of stop-codons). The set of filtered variants was also verified by visual inspection of bam files. Analysis of copy number alterations (CNA) was performed using the Nexus Copy Number 9.0 software (BioDiscovery, California, USA), with healthy control samples as references. Identification of CNAs was based on a sample/reference ratio threshold log 2 ≥0.25 for gains, ≤-0.25 for losses, ≥1.2 for high copy gains and ≤-1.2 for homozygous copy losses. Common germline CNAs based on DGV data (http://dgv.tcag.ca/dgv/app/home) were disregarded. All the variants called by the software algorithm were visually inspected for validation. Results Osteosarcomas present high inter-sample heterogeneity of DNA methylation and a global hypomethylation pattern Differences between OS and BT samples were characterized using the methylation levels of 410 617 CpG sites. Multidimensional scaling (MDS) using the top 1% most variable CpG sites showed that BT samples grouped tightly together, while the OS group had high inter-sample variability ( Figure 1A; Supplemental Figure S2 ). OS samples presented a high level of heterogeneity, with a median CV of 0.77, compared to a median CV of 0.73 obtained in BT controls (t-test; pvalue=0.015). This heterogeneity is unlikely to be related to morphologic subtypes, tumor content or purity, as data were corrected for such variables. Methylation levels of OS and BT were compared in a linear Bayesian framework model, revealing 3 146 differentially methylated positions (DMPs; qvalue10%; Supplemental Table S1 ). Unsupervised hierarchical clustering based on these 3 146 DMPs (Pearson correlation with complete linkage) discriminated BT controls from 27 out of 28 OS samples ( Supplemental Figure S3 ). The clustering did not show significant association with clinical parameters such as histology, Huvos grade or metastasis at diagnosis ( Figure 1B ). Considering the genomic location, OS displayed a global hypomethylation profile ( Figure 1C ) and a non-random DMPs distribution, with most hypermethylated CpG sites located at CpG islands whereas hypomethylated CpG sites were mapped in open sea regions, with no changes in shores and shelves (p<0.0001, chi-square distribution test, Figure 1D; Supplemental Table S1 ). The methylation status of CpG islands was not related to clinical features, suggesting a mechanism inherent to all OS samples rather than specific to OS characteristics or subtypes. To validate our data, we interrogated the 3 146 DMPs in an available independent set (GSE125645) of OS and bone tissue samples. The unsupervised hierarchical clusterization fully discriminated tumor from normal samples, validating our findings ( Supplemental Figure S4 ). DMR-genes are involved with bone development and cancer hallmarks From the 3 146 DMPs, 2 018 CpG sites were located in or next to 1 489 genes. Since DNA methylation changes can affect multiple neighboring CpG sites, we searched for differentially methylated regions (DMRs) and identified 319 hypomethylated and 266 hypermethylated DMRs (FDR 20%) mapped to the promoter regions of 172 and 178 genes, respectively ( Supplemental Table S2 ). The DMR-genes were enriched (adjP<0.05) for biological processes related to cell differentiation, morphogenesis and organ development, including skeleton system development, metabolism and RNA-related processes ( Table 2 ). The enrichment analysis in Reactome database also pointed to signaling pathways related to development and suggested the involvement of transcriptional factors usually associated with the early stages of development, such as RUNX2 , POU5F1 , SOX2 and NANOG ( Table 3 ), although they were not among the enriched transcriptional factors in the TRANSFAC database ( Supplemental Table S3 ). Because these genes seem to be related to developmental processes, the 350 DMR-genes were also searched in the Chemical and Genetic Perturbations (CGP) database. Again, several categories pointed to the involvement of mechanisms usually associated with development, including 55 genes that harbor the trimethylated H3K27 (H3K27me3) mark in their promoters in human embryonic stem cells, brains and liver or have been identified by ChIP on chip as targets of the Polycomb proteins EED and SUZ12. Moreover, eight genes have been described to be de novo methylated in cancer ( ALX3, DUSP6, EGR3, EPHX3, GJB2, HIC1, PAX6 and TBX3 ) ( Supplemental Table S4 ). Table 2. Top 20 enriched GO biological processes of genes mapped to the DMRs. Category # Genes FDR Animal organ morphogenesis 54 8.13 e -24 Positive regulation of RNA biosynthetic process 60 9.71 e -21 Positive regulation of transcription by RNA polymerase II 52 1.6 e -20 Embryonic morphogenesis 38 6.68 e -20 Embryo development 48 1.36 e -19 Positive regulation of nucleobase containing compound metabolic process 62 2.2 e -19 Embryonic organ development 33 3.46 e -19 Positive regulation of cellular biosynthetic process 63 5.42 e -19 Sensory organ development 35 6.59 e -18 Embryonic organ morphogenesis 27 9.73 e -18 Cell fate commitment 26 1.49 e -17 Regulation of cell differentiation 60 2.55 e -17 Tube development 46 4.61 e -17 Skeletal system development 28 6.45 e -13 Pattern specification process 28 2.39 e -14 Negative regulation of transcription by RNA polymerase II 38 3.05 e -14 Head development 35 1.01 e -13 Negative regulation of RNA biosynthetic process 45 1.28 e -13 Embryonic skeletal system development 17 1.28 e -13 Cell motility 52 1.61 e -13 Genes from skeletal system development: AHSG, ALX1, ALX3, COL1A1, DLX1, EN1, FOXC1, GLI3, GRHL2, GSC, HOXA13, HOXD10, HOXD12, HOXD3, HOXD4, MEIS1, MMP13, OSR2, PITX2, PRRX1, RFLNA, RUNX3, TBX15, TBX3, TBX4, TGFB3, TWIST1, WNT5A Abbreviations: GO: Gene Ontology; DMRs: differentially methylated regions; #: number; FDR: false discovery rate. Table 3. Most significantly enriched Reactome pathways of genes mapped to the DMRs . Category # Genes FDR Transcriptional regulation by RUNX2 9 6.96 e -4 Developmental biology 26 7.96 e -4 Activation of anterior HOX genes in hindbrain development during early embryogenesis 8 2.79 e -3 Meiotic synapsis 6 1.19 e -2 Pre-NOTCH expression and processing 7 1.19 e -2 POU5F1 (OCT4), SOX2, NANOG repress genes related to differentiation 3 1.19 e -2 Nuclear receptor transcription pathway 5 1.19 e -2 Signaling by GPCR 23 1.19 e -2 Transcriptional regulation of granulopoiesis 6 1.3 e -2 Assembly of collagen fibrils and other multimeric structures 5 1.77 e -2 Abbreviations: DMRs: differentially methylated regions; #: number; FDR: false discovery rate. Since the DMR-genes pointed to the enrichment of metabolism-related biological processes, we took a closer look at the levels of metabolic compounds present in the peripheral blood of the same patients of this study (analysis reported in [27]). The investigation revealed high levels of histidine and glutamine, which are 2-OG precursors, then valine as a succinyl-CoA precursor, and phenylalanine and tyrosine ( Figure 2 ) that can be metabolized to fumarate. Cross-checking the list of DMRs-genes with databases of tumor suppressor genes (https://bioinfo.uth.edu/TSGene/) and oncogenes (http://ongene.bioinfo-minzhao.org/), 17 tumor suppressor genes and 10 oncogenes were mapped within the hypermethylated and hypomethylated DMRs, respectively ( Table 4 ). Table 4. Tumor suppressor genes and oncogenes contained where hypermethylated and hypomethylated DMRs are located, respectively. Classification* Genes Tumor suppressors (Hypermethylated) AIF1, DIDO1, DLEC1, DUSP6, FOXC1, GJB2, HIC1, LXN, MIR142, MIR149, PAX6, PROX1, SPI1, STAT5A, TNFAIP8L2, WNT5A, ZIC1 Oncogenes (Hypomethylated) AGAP2, ASPSCR1, MCF2L, MIR380, NANOG, NOTCH4, PAX4, PRDM16, RUNX3, TCL1B *: Reference lists of human tumor suppressors and oncogenes were obtained from the Tumor Suppressor Genes Database (https://bioinfo.uth.edu/TSGene/) and the Oncogene Database (http://ongene.bioinfo-minzhao.org/). Furthermore, CNAs present in at least 25% of the OS samples affected 161 DMR-genes. Six tumor suppressor genes were identified as predominantly hypermethylated and deleted ( DLEC1, GJB2, HIC1, MIR149, PAX6, and WNT5A ), while four oncogenes were identified with both hypomethylation and copy number gains ( ASPSCR1, NOTCH4, PRDM16 and RUNX3 ) ( Supplemental Table S5 ). In addition, the DMR-genes were enriched at 6p22, which, among others, contains genes encoding for histones ( H2BC1, H4C9, H2BC6, H2BC9 ). All these 6p22 DMRs were hypomethylated in OS, and this region presented a high frequency of gains and losses in OS (≥ 20% of the samples) ( Supplemental Table S5 ). Overrepresentation of CNAs at DNA methylation enzymes Analyzing the genetic alterations in enzymes that mediate DNA methylation, only one sample harbored a likely benign missense mutation in DNMT1; however, there were several CNAs in both families of DNA methylases and demethylases ( Figure 3 ), mostly gain events affecting DNMTs and losses in TET genes. DNMT3B stood out as the gene with the highest number of gains (46% of the samples), and TET1 with the highest number of losses (39%). Other epigenetic modifiers were also affected by alterations, mostly losses: the chromatin remodeler ARID1B (two missense variants, three gains, ten losses); the histone methyltransferase SETD2 (one LoF variant, one missense, one gain, 12 losses), and the methyl-CpG-binding domain protein MBD1 (one LoF variant, three gains, six losses) ( Supplemental Table S5; Supplemental Table S6 ). Discussion Osteosarcomas are highly complex and heterogeneous tumors. A large number of genes can be mutated though few are recurrent. Rather, these tumors have aneuploidy, chromothripsis , and uncontrolled cell cycle, suggesting defects in DNA repair mechanisms and/or epigenetic instability [ 9 ]. Nevertheless, the high diversity of genomic alterations may share similar functional modules related to events such as DNA damage, response to stress, epigenetic processes, mitosis and cellular motility [ 28 ]. The reversibility of epimutations turns them into interesting targets for the development of therapies. In osteosarcomas cell lines, the demethylating agent decitabine (5-aza-dC, a DNMT1-inhibitor) can promote the recovery of gene expression silenced by hypermethylation, resulting in reduced cell growth [ 29 ] or increased sensibility to chemotherapeutic agents at concentrations within therapeutic levels [ 30 ]. Therefore, DNA methylation profiles may directly point to robust disrupted mechanisms in osteosarcomas. The genome-wide methylation characterization of 28 osteosarcomas showed a significant epigenetic heterogeneity, as shown by a coefficient of variation (CV = 0.7), much higher than other solid tumors (~ 0.08 in Ewing sarcoma; ~0.03 in glioblastoma) [ 20 , 31 ]. It is an expected finding as these tumors usually present a high mutational and chromosomal alteration burden [ 2 ], particularly compared to tumors with few genetic alterations such as Ewing sarcoma [ 20 ]. It could indicate that the high DNA methylation heterogeneity is an intrinsic event of osteosarcoma tumorigenesis or that is related to the high number of cell types found in these tissues. This second hypothesis is supported by the high CV in control bone samples (0.73), what raises the possibility that DNA methylation is intrinsic of the bone/cell composition and not related to the tumorigenesis itself. We had one exception among our samples that grouped with the bone samples in the unsupervised clusterization; this sample also had few SNVs and no detectable CNAs. At a closer look at the clinical and pathological parameters, this case was not different from the others, suggesting that this biological behavior may be explained by the intratumor heterogeneity, with the sample used for the experiments being epi- and genetically not representative of the tumor as a whole. Osteosarcomas displayed a global hypomethylation with a pattern of CpG islands hypermethylation, similar to other cancers [ 32 , 33 ]. Global hypomethylation is usually related to the activation of oncogenes and increase of genomic instability, while hypermethylation of CpG islands usually results in the inactivation of tumor suppressors and other regulatory elements [ 34 ]. Consistently, among the genes contained within hypermethylated DMRs, we identified 17 tumor suppressors, from which six were also affected by alternative inactivation mechanisms such as deletions ( DLEC1, GJB2, HIC1, MIR149, PAX6 , and WNT5A ). Silencing by aberrant promoter methylation of cell proliferation regulators and genomic stability maintainers such as DLEC1 and HIC1 is a recurrent event in a variety of cancers [ 35 – 38 ]. We hypothesize that the preferential hypermethylation pattern at CpG islands has a connection with DNMT3B gains in osteosarcomas and genetic perturbations related to H3K27me3 and PRC2. DNMT1 and DNMT3A also had extra copies in some samples. DNMT1 overexpression is a recurrent event across osteosarcomas, with a negative impact on the expression of several tumor suppressor genes [ 30 ]. DNMT3A and DNMT3B seem to have both redundant and exclusive targets; while DNMT3A remains active in adults [ 39 ], expression of DNMT3B catalytic isoforms decreases during cell differentiation [ 40 ]. There is evidence that DNMT3B induces DNA methylation of tumor suppressor genes such as RASSF1, CDH1, CDKN1A and CDKN2A in colorectal cancer [ 41 ], gliomas [ 42 ], bladder cancer [ 43 ], prostate cancer [ 44 ], acute myeloid leukemia [ 45 ], esophageal squamous cell carcinomas [ 46 ], breast adenocarcinoma and lung carcinoma [ 47 ]. Ectopic DNMT3B expression results in widespread CpG island hypermethylation in several cancer types available in TCGA [ 48 ]. Although we did not analyze gene expression, it is likely that there is a correlation with this variable DNA methylome. DNMT3B-induced methylation coincides with H3K27me3 and results in methylation of the surrounding CpG island DNA, affecting PRC2 recruitment, a mechanism involved in the tumorigenesis of gliomas [ 49 ]. The involvement of H3K27me3 in osteosarcomas is strengthened by the fact that 87% of the tumors from young patients present imprinting defects at the 14q32-locus, an enriched region for both the active marker H3K4me3 and the silence marker H3K27me3 [ 50 ]. An enrichment of hypomethylated DMRs was reported at 6p22, where several histone-coding genes are located. Samples exhibited both gains and losses events encompassing 6p22, reinforcing the genomic instability. Interestingly, only gains of 6p/6p22 were reported as a recurrent event in a variety of cancers, including osteosarcomas [ 51 , 52 ], with impact in tumor progression, aggressiveness and metastatic potential [ 53 ]. In agreement with the hypomethylation found in 6p22, a cluster of over-expressed genes in 6p has been reported [ 54 ]. DMRs often overlapped with CNAs in our analysis. It is possible that at least part of the DMRs' hypomethylation was induced by the unbalance between copy number gains and the reestablishment of original DNA methylation patterns. Both mutational processes are associated with high genomic instability [ 3 , 9 , 34 ]. Furthermore, several genes related to DNA damage response and repair, such as BAP1, BRCA1, BRCA2, PALB2, PARP1 , PTEN, RB1 , SETD2 , and TP53 , presented copy number losses or loss-of-function mutations (but no differential methylation), although these pathways were not enriched in our analyses. TP53 is not a recurrent target for epigenetic regulation, but changes in the pathways or genes modulated by its activity have already been reported [ 9 ]. Thus, further studies are necessary to disclose the joint role of DNA methylation and chromosomal alterations in the tumorigenesis of osteosarcomas, mainly targeting the gene expression patterns associated with these changes. Methylation data from 19 OS cell lines revealed 328 genes with at least one differentially expressed CpG site located at the promoter region [ 54 ]. From these, 62 genes overlapped with our list of DMR-genes. However, considering the technical differences between the approaches (Illumina 27K and 450K Beadchip arrays), we decided to compare biological processes instead of individual genes. The enriched biological processes within the DMR-genes suggested the involvement of mechanisms associated with all steps of cellular differentiation, similar to other studies [ 29 , 54 ] and consistent with the period of onset of the disease [ 1 , 55 ]. Osteogenic lineages generated by the induction of differentiation from adipose-derived stem cells present a DNA methylation pattern similar to that of osteocytes, with the majority of CpG sites that change DNA methylation located at promoter-associated CpG islands [ 56 ]. Therefore, dysregulation of DMR-genes, as we described, might be associated with the interruption of the differentiation process. Accordingly, among the biological processes, skeletal development, represented by 28 genes (Table 2 ), was enriched within the DMRs. We reported alterations of TCA anaplerotic substrates and a very high glycolysis rate in the peripheral blood of these same patients [ 27 ]. Alterations in intermediates of the TCA cycle (histidine and glutamine, which are 2-OG precursors; valine as a succinyl-CoA precursor; and phenylalanine and tyrosine, that can be metabolized to fumarate) can interfere with TET1 activity [ 57 ], what ultimately could result in the global DNA hypomethylation found in osteosarcomas. We emphasize that only frozen samples with more than 70% of the cells of interest were selected for this study because the ratio of contamination of normal cells affects the data of Infinium HumanMethylation 450. This criterion resulted in the main limitation of this study, which was the lack of paired normal and tumor samples. This prevented us from safely determining the spectrum of somatic alterations, methylation, or mutation. To overcome these, we used normal bone tissues in the methylation analysis and applied restrictive data filtering criteria, excluding any variant present in population databases. Conclusion Overall, we reinforce that aberrant DNA methylation is spread throughout the genome of osteosarcomas and is involved in the dysregulation of biological processes related to skeletal development and cell differentiation. While DNA hypomethylation contributes to global genomic instability, CpG islands hypermethylation enrichment suggests the involvement of a controlled mechanism likely related to the silencing of tumor suppressor genes. Our investigation reinforces the relevance of exploring the role of epigenetic modulation in tumor initiation, maintenance and progression. Abbreviations CGP Chemical and Genetic Perturbations CNA Copy number alteration CV Coefficient of variation DMP Differentially methylated position DMR Differentially methylated region FDR False discovery rate GO Gene Ontology GSEA Gene Set Enrichment Analysis MDS Multidimensional scaling OS Osteosarcoma SVD Singular value decomposition Declarations Acknowledgements The authors would like to thank the patients and their families for the collaboration. This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and Fundação de Amparo à Pesquisa do Estado de São Paulo. Funding Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – 88887.508357/2020-00); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP – 14/10250-7, 15/06281-7, 18/21047-9, 18/06510-4, and 18/24069-3). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflict of interest statement None declared. Ethics approval and consent to participate This study was conducted following national and institutional ethical policies with approval from the Barretos Cancer Hospital Ethical Committee (CEP-HCB 898.403). Informed consents were obtained from the patients or their legal guardians. Authors’ contributions SFP: Investigation; Formal analysis; Writing – original draft; JSB: Investigation; Formal analysis; SSC: Methodology; MOS: Methodology; Data processing; AHL: Data curation; Writing – review; editing; EB: Data curation; Investigation; SRMS: Methodology; Investigation; LT: Methodology; Data processing; Writing – editing; DOV: Investigation; Data curation; Writing – review; editing; ACVK: Conceptualization; Formal analysis; Funding acquisition; Project administration; Supervision; Writing – review & editing; MM: Conceptualization; Data curation; Formal analysis; Funding acquisition; Project administration; Supervision; Writing – original draft, review; editing. All authors have read and approved the final manuscript. References Misaghi A, Goldin A, Awad M, Kulidjian AA (2018) Osteosarcoma: A comprehensive review. SICOT-J 4:1–8. https://doi.org/10.1051/sicotj/2017028 Rickel K, Fang F, Tao J (2017) Molecular genetics of osteosarcoma. 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Covariates are indicated in the x axis, and the components in the y axis, ranked based on the percentage of explained variance. Statistical p-values are indicated, ranging from p > 0.05 (white) to p < 1 x 10-10 (dark red). SupplementalFigureS2.tiff Supplemental Figure S2 (.tiff file). Box plot of methylation levels detected for individual samples. Control (blue) and tumor (pink) samples are displayed as columns, with their corresponding β-values. In the y axis, β-values range from 0 (unmethylated) to 1 (fully methylated). The plot contains the median (line across the box), interquartile range (box edges), minimum and maximum values (whiskers), and outliers (dots). SupplementalFigureS3.jpg Supplemental Figure S3 (.jpg file). Unsupervised hierarchical clustering based on 3 146 differentially methylated positions (DMPs), using Pearson's correlation distance. DMPs are disclosed in the right, with linkage distances varying from -5 (blue) to 5 (yellow). Individual samples are shown in the top, with IDs ending in N for normal bone tissues, and in T, for tumors. SupplementalFigureS4.tif Supplemental Figure S4 (.tiff file). Unsupervised hierarchical clustering of OS and bone tissue samples available in GSE125645, based on the 3 146 DMPs disclosed in the present study. DMPs are represented in the right (partially shown), with linkage distances varying from minor (blue) to major (yellow). Individual samples are shown in the top, with IDs ending in N for normal bone tissues, and in O, for tumors. SupplementalFigureS5.png Supplemental Figure S5 (.tiff file). Copy number alterations (CNA) pattern of 6p.22 alterations for each osteosarcoma sample. Each row indicates one sample. At the top of the image, chromosome 6 (6p22.3-6p22.1) and the frequencies of alterations within the cohort (from 0-100% of the samples) are indicated. Gains and losses are shown in blue and red, respectively. SupplementalTableS1.xlsx Supplemental Table S1 (.xlsx file). Description of differentially methylated positions (DMPs), with methylation differences (DB) >10% between tumors and normal samples. SupplementalTableS2.xlsx Supplemental Table S2 (.xlsx file). Description of differentially methylated regions (DMRs), with methylation differences (DB) >20% between tumors and normal samples. SupplementalTableS4.docx Supplemental Table S4 (.docx file). Most significantly enriched chemical or genetic perturbations of the genes mapped to the DMRs. SupplementalTableS5.xlsx Supplemental Table S5 (.xlsx file). Description of copy number alterations identified in genes contained within the DMRs. SupplementalTableS6.xlsx Supplemental Table S6 (.xlsx file). Description of somatic coding mutations identified in epigenetic modifiers and genes contained within the DMRs. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1999076","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132333951,"identity":"1a59a69f-c1e8-4e4d-a4d4-fc822e193092","order_by":0,"name":"Sara Ferreira Pires","email":"","orcid":"","institution":"University of São Paulo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Ferreira","lastName":"Pires","suffix":""},{"id":132333952,"identity":"fc6a5bf3-6046-4c2f-8887-46078b0c64ac","order_by":1,"name":"Juliana Sobral de Barros","email":"","orcid":"","institution":"University of São 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18:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1999076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1999076/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00438-023-02010-8","type":"published","date":"2023-04-05T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25896266,"identity":"9764e506-b54a-4170-b6a4-97da5512b6f0","added_by":"auto","created_at":"2022-08-31 16:14:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":980166,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of the methylation levels of CpG sites. \u003cstrong\u003eA.\u003c/strong\u003e Multidimensional scaling (MDS) plot of the 1% most variable CpG sites within all samples. Osteosarcomas (OS, pink), normal bone samples (BT, blue). \u003cstrong\u003eB.\u003c/strong\u003e Hierarchical cluster based on the methylation levels from the 3 146 differentially methylated positions. Squares indicate (A) type, (B) histology, (C) Huvos grade, and (D) presence of metastasis at diagnosis. \u003cstrong\u003eC\u003c/strong\u003e. Boxplot of global methylation level detected for normal bone samples and osteosarcomas. Average β-values are indicated in the y axis (0 indicates completely unmethylated and 1, fully methylated). The median (horizontal line across the boxes), 95% confidence interval (notches), interquartile range (box edges), and minimum/maximum values (extreme horizontal lines) are indicated. \u003cstrong\u003eD.\u003c/strong\u003e Boxplot of average DMPs methylation levels distributed according to the CpG site genomic locations in bone samples (left) and osteosarcomas (right). The mean β-values (y axis; 0 indicates completely unmethylated and 1, fully methylated) of DMPs across CpG islands, shore-regions (up to 2 kb from CpG islands), shelf-regions (2-4 kb from CpG islands) and open-sea regions (other regions of the genome). The median values are indicated by horizontal lines across the box, interquartile range in box edges, and minimum/maximum values are extreme horizontal lines; one outlier is depicted as a black dot.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/3180fe6a26bf6bbf449415a6.png"},{"id":25896274,"identity":"d5978f25-c2e7-4894-9eb9-9be1c8e47bf5","added_by":"auto","created_at":"2022-08-31 16:14:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128189,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots representing the variations of concentrations of six main metabolites indicated as discriminant by \u003csup\u003e1\u003c/sup\u003eH-NMR metabolomics PLS-DA (VIP scores \u0026gt; 2) between osteosarcoma patients (OS, pink) and control bone from healthy individuals (HB, blue). Underlined presented a statistically significant difference between OS and HB. The gray dots represent the concentrations from all samples and the yellow diamond is the mean.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/a9846081abf3ee6c632e3cd4.png"},{"id":25898491,"identity":"a1c9fd36-1e55-4051-a927-128990e1fbe4","added_by":"auto","created_at":"2022-08-31 16:29:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1350860,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of point mutations and copy number alterations across DNMT and TET gene families. The alterations are color-coded by type. Each row indicates a gene; each column indicates a tumor sample.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/37c077ecc84ac5d7e7cbc6d4.png"},{"id":35539355,"identity":"4b24f07a-a784-4ee3-91cc-9829d9ade97a","added_by":"auto","created_at":"2023-04-10 17:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":893427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/1213f41b-1eb3-43c3-9984-d10b4c49547f.pdf"},{"id":25897113,"identity":"5a8aee05-e4c7-4eb9-8595-ea67eb0f810d","added_by":"auto","created_at":"2022-08-31 16:19:07","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":840278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S1 (.jpg file). \u003c/strong\u003eSingular value decomposition (SVD) plot, before (A) and after (B) correction. Covariates are indicated in the x axis, and the components in the y axis, ranked based on the percentage of explained variance. Statistical p-values are indicated, ranging from p \u0026gt; 0.05 (white) to p \u0026lt; 1 x 10-10 (dark red).\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/15991976da4646c8ea836f03.jpg"},{"id":25897117,"identity":"7a97ff62-75f1-45c7-92be-706a6e069d70","added_by":"auto","created_at":"2022-08-31 16:19:07","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4335122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S2 (.tiff file). \u003c/strong\u003eBox plot of methylation levels detected for individual samples. Control (blue) and tumor (pink) samples are displayed as columns, with their corresponding β-values. In the y axis, β-values range from 0 (unmethylated) to 1 (fully methylated). The plot contains the median (line across the box), interquartile range (box edges), minimum and maximum values (whiskers), and outliers (dots).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigureS2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/acf13bfaeade61ec66ac443b.tiff"},{"id":25896278,"identity":"fb6629b4-0b6d-4889-8b41-f8c3d3c6ff52","added_by":"auto","created_at":"2022-08-31 16:14:08","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7659386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S3 (.jpg file). \u003c/strong\u003eUnsupervised hierarchical clustering based on 3 146 differentially methylated positions (DMPs), using Pearson's correlation distance. DMPs are disclosed in the right, with linkage distances varying from -5 (blue) to 5 (yellow). Individual samples are shown in the top, with IDs ending in N for normal bone tissues, and in T, for tumors.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/c58a585d25cfbd2f56a1c279.jpg"},{"id":25897857,"identity":"cd6e6dcb-abbb-4984-8412-bc35f9649fd4","added_by":"auto","created_at":"2022-08-31 16:24:07","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1497956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S4 (.tiff file). \u003c/strong\u003eUnsupervised hierarchical clustering of OS and bone tissue samples available in GSE125645, based on the 3 146 DMPs disclosed in the present study. DMPs are represented in the right (partially shown), with linkage distances varying from minor (blue) to major (yellow). Individual samples are shown in the top, with IDs ending in N for normal bone tissues, and in O, for tumors.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/0b14aae773b1c20832872175.tif"},{"id":25896267,"identity":"6097be6f-a784-4044-8405-298a87324fc8","added_by":"auto","created_at":"2022-08-31 16:14:07","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":751318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S5 (.tiff file). \u003c/strong\u003eCopy number alterations (CNA) pattern of 6p.22 alterations for each osteosarcoma sample. Each row indicates one sample. At the top of the image, chromosome 6 (6p22.3-6p22.1) and the frequencies of alterations within the cohort (from 0-100% of the samples) are indicated. Gains and losses are shown in blue and red, respectively.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/f5f53399ece3603ca18967ba.png"},{"id":25897116,"identity":"98135414-ba4c-452f-bce0-af4800c29e22","added_by":"auto","created_at":"2022-08-31 16:19:07","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":992696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table S1 (.xlsx file).\u003c/strong\u003e Description of differentially methylated positions (DMPs), with methylation differences (DB) \u0026gt;10% between tumors and normal samples.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/a6e8d7caf01fdff6dff31312.xlsx"},{"id":25898492,"identity":"be8a55ef-68f1-4aae-830b-b38b6316dde8","added_by":"auto","created_at":"2022-08-31 16:29:07","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":200618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table S2 (.xlsx file).\u003c/strong\u003e Description of differentially methylated regions (DMRs), with methylation differences (DB) \u0026gt;20% between tumors and normal samples.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/eefafdce0a82247f4b6b57a5.xlsx"},{"id":25896271,"identity":"6b1fe7d9-cfbd-42d6-b91f-0f3be592de83","added_by":"auto","created_at":"2022-08-31 16:14:07","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table S4 (.docx file).\u003c/strong\u003e Most significantly enriched chemical or genetic perturbations of the genes mapped to the DMRs.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/82de86d3aa3a8e8c240aa46c.docx"},{"id":25896277,"identity":"e288abfe-479d-46c9-add4-06507e18641f","added_by":"auto","created_at":"2022-08-31 16:14:07","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":473804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table S5 (.xlsx file).\u003c/strong\u003e Description of copy number alterations identified in genes contained within the DMRs.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalTableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/79ccdc78734fcf233943252a.xlsx"},{"id":25897859,"identity":"7af01998-1209-4946-b43c-43f64658dae0","added_by":"auto","created_at":"2022-08-31 16:24:07","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":87384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table S6 (.xlsx file). \u003c/strong\u003eDescription of somatic coding mutations identified in epigenetic modifiers and genes contained within the DMRs.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalTableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1999076/v1/182405d1184d532e3b4bddf4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"DNA methylation patterns suggest the involvement of DNMT3B and TET1 in osteosarcoma development","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary bone tumors represent up to 6% of all tumors of childhood and adolescents (0 to 19 years), with osteosarcoma (OS) being the most prevalent [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In agreement with the fact that OS arises more commonly during puberty, several genes and signaling pathways involved with bone growth and remodeling have been associated with the disease. Recurrent somatic point mutations include \u003cem\u003eATRX, BRCA1/2, PTEN\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, although most osteosarcomas are characterized by a high genomic instability that is likely to play a significant role in their pathogenesis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, these tumors present a highly heterogeneous pattern of somatic copy number alterations (CNA), including 1p, 1q, 5p, 6p, 8q, 17p and 18p gains, \u003cem\u003eMYC, CCNE1, COPS3, CDK4, MDM2, AKT\u003c/em\u003e and \u003cem\u003eRECQL4\u003c/em\u003e amplifications, and 3, 5q, 6q, 8p, 9, 10, 13, 16, 17, 18q and 19 losses, including \u003cem\u003eTP53, RB1, DLG2, PTEN, ATRX\u003c/em\u003e and \u003cem\u003eCDKN2A\u003c/em\u003e deletions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, some tumors exhibit complex chromosomal phenomena such as \u003cem\u003ekataegis\u003c/em\u003e and \u003cem\u003echromothripsis\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnomalous DNA methylation is also a cancer hallmark affecting metastasis and resistance to chemotherapy in patients with osteosarcomas. Promoters of \u003cem\u003eCDKN2A\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], \u003cem\u003eRASSF1\u003c/em\u003e, \u003cem\u003eMGMT\u003c/em\u003e, \u003cem\u003eDAPK1\u003c/em\u003e, \u003cem\u003eTIMP3\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], \u003cem\u003eHIC1\u003c/em\u003e and \u003cem\u003eGADD45\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] are hypermethylated in a proportion of cases in comparison to normal bone tissues as well as hypomethylation of \u003cem\u003eIRX1\u003c/em\u003e, \u003cem\u003eSEMA4D\u003c/em\u003e, \u003cem\u003eRAF1\u003c/em\u003e and \u003cem\u003ePAK1\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A combined analysis of DNA methylation from osteosarcoma samples and gene expression from osteosarcoma cell lines disclosed an association between \u003cem\u003ePRAME\u003c/em\u003e and \u003cem\u003eSEMA3A\u003c/em\u003e hypomethylation and increased expression levels as well as \u003cem\u003eCALD1\u003c/em\u003e and \u003cem\u003eDNALI1\u003c/em\u003e hypermethylation with gene repression in osteosarcomas [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In xenografts, increased \u003cem\u003eIL-6\u003c/em\u003e expression due to DNA demethylation mediated by \u003cem\u003eTET2\u003c/em\u003e within the tumor microenvironment increased the metastatic capability of osteosarcoma cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The potential to form metastasis was also associated with a nonrandom pattern of the enhancer histone marks H3K4me1 and H3K27ac in metastatic and nonmetastatic osteosarcomas [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, we explored methylomes of primary tissues of osteosarcomas and the possible link with somatic genetic variants, aiming to provide insights into the mechanisms underlying its tumorigenesis. We highlighted genes, biological processes and signaling pathways, including metabolic changes in the blood, which might be involved with osteosarcoma development.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of the patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur cohort consisted of patients diagnosed with osteosarcoma between 2008 and 2014, with fresh frozen samples stored at the biobank from the Barretos Cancer Hospital (SP, Brazil). Samples from 28 osteosarcomas (14 females and 14 males) were obtained before any systemic treatment (chemotherapy or radiotherapy), as well as nine non-paired normal bone tissues from surgeries of non-cancer patients. Pediatric patients were aged between 10-20 years (n = 23) and five patients were adults (29-61 years) (\u003cstrong\u003eTable 1\u003c/strong\u003e). The treatment of patients followed the clinical protocol of the Brazilian Osteosarcoma Treatment Group \u0026nbsp;[13]. The conduction of this study followed national and institutional ethical policies with approval from the Barretos Cancer Hospital Ethical Committee (CEP-HCB 898.403). Informed consent was obtained from the patients and/or their legal guardians.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eCharacteristics of the 28 cases of osteosarcomas.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMetastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHuvos grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChondroblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChondroblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTelangiectatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTelangiectatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParosteal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFibroblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePleomorphic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoblastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e F: female; M: male; NA: not available; (+): present; (-): absent.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSamples preparation and quality control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor (OS) and normal (BT) samples were microdissected and reviewed by a pathologist, who selected only areas containing at least 70% of cells of interest for the study. DNA was isolated using the QIAsymphony equipment with the DSP DNA Mini Kit (Qiagen). Quantity and quality were assessed by NanoDrop (Thermo Fisher Scientific) and agarose gel 0.8%, respectively. DNA was bisulfite converted using the EZ DNA Methylation kit (Zymo Research Corp), according to manufacturer recommendations, and hybridized in the Human Methylation 450 BeadChip microarrays (HM450K, Illumina), following the Illumina Infinium HD methylation protocol. Raw data was extracted using the iScan SQ scanner (Illumina) by GenomeStudio software (v.2011.1), with the methylation module v.1.9.0 (Illumina), into IDAT files, which were used for further analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing and identification of differentially methylated genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R package ChAMP\u0026nbsp;[14, 15] was applied to the dataset and all 37 samples passed QC parameters. We used the standard pipeline: filtered out probes included those with a bead count \u0026lt;3 in at least 5% of samples (n =786 probes), located in non-CpG sites (n = 3,047 probes) or in SNPs (n = 49 977 probes), as well as probes that aligned to multiple locations (n =7 143) or located in the XY chromosomes (n=10 046). A total of 410 617 probes were retained for further analyses. Signal intensities from probes type I and II were normalized using the updated version of BMIQ method\u0026nbsp;[16].\u003c/p\u003e\n\u003cp\u003eThe singular value decomposition method (SVD) assessed unwanted components of variation, which was corrected using ComBat\u0026nbsp;[17] (\u003cstrong\u003eSupplemental Figure S1\u003c/strong\u003e). To avoid the effects of cell type composition, the R package RefFreeEWAS\u0026nbsp;[18] was applied to \u0026beta;-values, as implemented in ChAMP, resulting in the identification of differentially methylated CpG positions (DMPs) between OS and BT samples. DMRcate\u0026nbsp;[19] was applied to identify differentially methylated regions (DMRs), defined as at least seven consecutive CpG sites presenting methylation changes in the same direction (hypo- or hypermethylation) in a DNA sequence of 300 nucleotides. DMRs with FDR\u0026lt;0.05 and methylation differences \u0026gt;20% were considered significant.\u003c/p\u003e\n\u003cp\u003eTo evaluate the global methylation heterogeneity within the tumor samples, we calculated the coefficient of variation (CV), through the ratio between standard deviation/mean of DNA methylation levels across the genome [20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment analysis using the sets of differentially methylated genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analyses were applied to genes mapped within the DMRs as input data and the whole-genome as background using Gene Set Enrichment Analysis (GSEA [21]). We decided to evaluate the set of genes mapped to DMRs as the possible impact has been more widely explored in the literature. Genes were clustered based on biological processes and molecular functions (Gene Ontology), pathways (Reactome), transcription factor targets and chemical and genetic perturbations. Features with adjP\u0026le;0.05 (Benjamini-Hochberg adjustment) that comprised at least 10 genes were considered statistically significant. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical exome sequencing and variant analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExome sequencing was performed for the 28 OS samples using the TruSight One Sequencing panel (Illumina), covering approximately 4 800 clinically relevant genes. Sequencing was performed using the Illumina Hi-seq2000 platform, with median depth coverage of 178x. Reads were mapped to the Human Genome Reference GRC37/hg19 using Burrows-Wheeler Aligner software (BWA) package version 0.6.1\u0026nbsp;[22], and local realignment was carried out with the Genome Analysis Tool Kit (GATK) version 1.6\u0026nbsp;[23].\u003c/p\u003e\n\u003cp\u003eSingle nucleotide (SNV) and indel variant annotation and filtering were run using VarSeq software (Golden Helix, Bozeman, USA) with the hg19 reference genome (RefSeq genes 105, NCBI). Germline variants deposited in the following public databases were excluded: 1000 Genomes (http://www.1000genomes.org/), dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/), gnomAD (http://gnomad.broadinstitute.org/), UK10K (https://www.uk10k.org/), and AbraOM (http://abraom.ib.usp.br/; exome data from 609 healthy Brazilians \u0026nbsp;[24]). The clinical and cancer databases OMIM (https://omim.org/), ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), CIViC (https://civicdb.org/home), COSMIC (https://cancer.sanger.ac.uk/cosmic) and ICGC (https://icgc.org/) were also used for variant annotation. Pathogenicity predictions were obtained from dbNSFP Functional Predictions and Scores [25]. For the analysis of the somatic mutational burden, we first applied the following exclusion criteria to all variants: presence in an in-house database of control non-cancer samples or in the population databases above mentioned, indels in repetitive sequences, and hypervariable genes [26]. Further selection was based on Phred scores for variant confidence \u0026ge;17, read depth \u0026ge;20 reads with at least 10% of allelic frequency, and coding SNVs/indels resulting in missense or loss-of-function variants (essential splice site, frameshift or gain/loss of stop-codons). The set of filtered variants was also verified by visual inspection of bam files.\u003c/p\u003e\n\u003cp\u003eAnalysis of copy number alterations (CNA) was performed using the Nexus Copy Number 9.0 software (BioDiscovery, California, USA), with healthy control samples as references. Identification of CNAs was based on a sample/reference ratio threshold log\u003csub\u003e2\u003c/sub\u003e \u0026ge;0.25 for gains, \u0026le;-0.25 for losses, \u0026ge;1.2 for high copy gains and \u0026le;-1.2 for homozygous copy losses. Common germline CNAs based on DGV data (http://dgv.tcag.ca/dgv/app/home) were disregarded. All the variants called by the software algorithm were visually inspected for validation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eOsteosarcomas present high inter-sample heterogeneity of DNA methylation and a global hypomethylation pattern\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferences between OS and BT samples were characterized using the methylation levels of 410 617 CpG sites. Multidimensional scaling (MDS) using the top 1% most variable CpG sites showed that BT samples grouped tightly together, while the OS group had high inter-sample variability (\u003cstrong\u003eFigure 1A; Supplemental Figure S2\u003c/strong\u003e). OS samples presented a high level of heterogeneity, with a median CV of 0.77, compared to a median CV of 0.73 obtained in BT controls (t-test; pvalue=0.015). This heterogeneity is unlikely to be related to morphologic subtypes, tumor content or purity, as data were corrected for such variables. Methylation levels of OS and BT were compared in a linear Bayesian framework model, revealing 3 146 differentially methylated positions (DMPs; qvalue\u0026lt;0.01 with methylation differences (DB) \u0026gt;10%; \u003cstrong\u003eSupplemental Table S1\u003c/strong\u003e). Unsupervised hierarchical clustering based on these 3 146 DMPs (Pearson correlation with complete linkage) discriminated BT controls from 27 out of 28 OS samples (\u003cstrong\u003eSupplemental Figure S3\u003c/strong\u003e). The clustering did not show significant association with clinical parameters such as histology, Huvos grade or metastasis at diagnosis (\u003cstrong\u003eFigure 1B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eConsidering the genomic location, OS displayed a global hypomethylation profile (\u003cstrong\u003eFigure 1C\u003c/strong\u003e) and a non-random DMPs distribution, with most hypermethylated CpG sites located at CpG islands whereas hypomethylated CpG sites were mapped in open sea regions, with no changes in shores and shelves (p\u0026lt;0.0001, chi-square distribution test, \u003cstrong\u003eFigure 1D; Supplemental Table S1\u003c/strong\u003e). The methylation status of CpG islands was not related to clinical features, suggesting a mechanism inherent to all OS samples rather than specific to OS characteristics or subtypes.\u003c/p\u003e\n\u003cp\u003eTo validate our data, we interrogated the 3 146 DMPs in an available independent set (GSE125645) of OS and bone tissue samples. The unsupervised hierarchical clusterization fully discriminated tumor from normal samples, validating our \u0026nbsp;findings (\u003cstrong\u003eSupplemental Figure S4\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDMR-genes are involved with bone development and cancer hallmarks\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the 3 146 DMPs, 2 018 CpG sites were located in or next to 1 489 genes. Since DNA methylation changes can affect multiple neighboring CpG sites, we searched for differentially methylated regions (DMRs) and identified 319 hypomethylated and 266 hypermethylated DMRs (FDR \u0026lt;0.0001, mean methylation differences \u0026gt;20%) mapped to the promoter regions of 172 and 178 genes, respectively (\u003cstrong\u003eSupplemental Table S2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DMR-genes were enriched (adjP\u0026lt;0.05) for biological processes related to cell differentiation, morphogenesis and organ development, including skeleton system development, metabolism and RNA-related processes (\u003cstrong\u003eTable 2\u003c/strong\u003e). The enrichment analysis in Reactome database also pointed to signaling pathways related to development and suggested the involvement of transcriptional factors usually associated with the early stages of development, such as \u003cem\u003eRUNX2\u003c/em\u003e, \u003cem\u003ePOU5F1\u003c/em\u003e, \u003cem\u003eSOX2\u0026nbsp;\u003c/em\u003eand \u003cem\u003eNANOG\u003c/em\u003e (\u003cstrong\u003eTable 3\u003c/strong\u003e), although they were not among the enriched transcriptional factors in the TRANSFAC database (\u003cstrong\u003eSupplemental Table S3\u003c/strong\u003e). Because these genes seem to be related to developmental processes, the 350 DMR-genes were also searched in the Chemical and Genetic Perturbations (CGP) database. Again, several categories pointed to the involvement of mechanisms usually associated with development, including 55 genes that harbor the trimethylated H3K27 (H3K27me3) mark in their promoters in human embryonic stem cells, brains and liver or have been identified by ChIP on chip as targets of the Polycomb proteins EED and SUZ12. Moreover, eight genes have been described to be \u003cem\u003ede novo\u003c/em\u003e methylated in cancer (\u003cem\u003eALX3, DUSP6, EGR3, EPHX3, GJB2, HIC1, PAX6\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;TBX3\u003c/em\u003e) (\u003cstrong\u003eSupplemental Table S4\u003c/strong\u003e).\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eTop 20 enriched GO biological processes of genes mapped to the DMRs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e# Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnimal organ morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.13 e\u003csup\u003e-24\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive regulation of RNA biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.71 e\u003csup\u003e-21\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive regulation of transcription by RNA polymerase II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 e\u003csup\u003e-20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEmbryonic morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.68 e\u003csup\u003e-20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEmbryo development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.36 e\u003csup\u003e-19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive regulation of nucleobase containing compound metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2 e\u003csup\u003e-19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEmbryonic organ development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.46 e\u003csup\u003e-19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive regulation of cellular biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.42 e\u003csup\u003e-19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSensory organ development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.59 e\u003csup\u003e-18\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEmbryonic organ morphogenesis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.73 e\u003csup\u003e-18\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCell fate commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49 e\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegulation of cell differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.55 e\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTube development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.61 e\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSkeletal system development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.45 e\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePattern specification process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.39 e\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative regulation of transcription by RNA polymerase II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.05 e\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHead development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01 e\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative regulation of RNA biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28 e\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEmbryonic skeletal system development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28 e\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCell motility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.61 e\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes from skeletal system development:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAHSG, ALX1, ALX3, COL1A1, DLX1, EN1, FOXC1, GLI3, GRHL2, GSC, HOXA13, HOXD10, HOXD12, HOXD3, HOXD4, MEIS1, MMP13, OSR2, PITX2, PRRX1, RFLNA, RUNX3, TBX15, TBX3, TBX4, TGFB3, TWIST1, WNT5A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e GO: Gene Ontology; DMRs: differentially methylated regions; #: number; FDR: false discovery rate.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMost significantly enriched Reactome pathways of genes mapped to the DMRs\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e\u003cstrong\u003e# Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eTranscriptional regulation by RUNX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e6.96 e\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eDevelopmental biology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e7.96 e\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eActivation of anterior HOX genes in hindbrain development during early embryogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e2.79 e\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eMeiotic synapsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.19 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003ePre-NOTCH expression and processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.19 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003ePOU5F1 (OCT4), SOX2, NANOG repress genes related to differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.19 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eNuclear receptor transcription pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.19 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eSignaling by GPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.19 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eTranscriptional regulation of granulopoiesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.3 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.7679180887372%\"\u003e\n \u003cp\u003eAssembly of collagen fibrils and other multimeric structures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.774744027303754%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457337883959044%\"\u003e\n \u003cp\u003e1.77 e\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e DMRs: differentially methylated regions; #: number; FDR: false discovery rate.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSince the DMR-genes pointed to the enrichment of metabolism-related biological processes, we took a closer look at the levels of metabolic compounds present in the peripheral blood of the same patients of this study (analysis reported in \u0026nbsp;[27]). The investigation revealed high levels of histidine and glutamine, which are 2-OG precursors, then valine as a succinyl-CoA precursor, and phenylalanine and tyrosine (\u003cstrong\u003eFigure 2\u003c/strong\u003e) that can be metabolized to fumarate.\u003c/p\u003e\n\u003cp\u003eCross-checking the list of DMRs-genes with databases of tumor suppressor genes (https://bioinfo.uth.edu/TSGene/) and oncogenes (http://ongene.bioinfo-minzhao.org/), 17 tumor suppressor genes and 10 oncogenes were mapped within the hypermethylated and hypomethylated DMRs, respectively (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eTumor suppressor genes and oncogenes contained where hypermethylated and hypomethylated DMRs are located, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClassification*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTumor suppressors\u003c/p\u003e\n \u003cp\u003e(Hypermethylated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eAIF1, DIDO1, DLEC1, DUSP6, FOXC1, GJB2, HIC1, LXN, MIR142, MIR149, PAX6, PROX1, SPI1, STAT5A, TNFAIP8L2, WNT5A, ZIC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOncogenes\u003c/p\u003e\n \u003cp\u003e(Hypomethylated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eAGAP2, ASPSCR1, MCF2L, MIR380, NANOG, NOTCH4, PAX4, PRDM16, RUNX3, TCL1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e*: Reference lists of human tumor suppressors and oncogenes were obtained from the Tumor Suppressor Genes Database (https://bioinfo.uth.edu/TSGene/) and the Oncogene Database (http://ongene.bioinfo-minzhao.org/).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFurthermore, CNAs present in at least 25% of the OS samples affected 161 DMR-genes. Six tumor suppressor genes were identified as predominantly hypermethylated and deleted (\u003cem\u003eDLEC1, GJB2, HIC1, MIR149, PAX6,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;WNT5A\u003c/em\u003e), while four oncogenes were identified with both hypomethylation and copy number gains (\u003cem\u003eASPSCR1, NOTCH4, PRDM16\u003c/em\u003e and \u003cem\u003eRUNX3\u003c/em\u003e) (\u003cstrong\u003eSupplemental Table S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition, the DMR-genes were enriched at 6p22, which, among others, contains genes encoding for histones (\u003cem\u003eH2BC1, H4C9, H2BC6, H2BC9\u003c/em\u003e). All these 6p22 DMRs were hypomethylated in OS, and this region presented a high frequency of gains and losses in OS (\u0026ge; 20% of the samples) (\u003cstrong\u003eSupplemental Table S5\u003c/strong\u003e). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverrepresentation of CNAs at DNA methylation enzymes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyzing the genetic alterations in enzymes that mediate DNA methylation, only one sample harbored a likely benign missense mutation in \u003cem\u003eDNMT1;\u003c/em\u003e however, there were several CNAs in both families of DNA methylases and demethylases (\u003cstrong\u003eFigure 3\u003c/strong\u003e), mostly gain events affecting \u003cem\u003eDNMTs\u003c/em\u003e and losses in \u003cem\u003eTET\u003c/em\u003e genes. \u003cem\u003eDNMT3B\u0026nbsp;\u003c/em\u003estood out as the gene with the highest number of gains (46% of the samples), and \u003cem\u003eTET1\u0026nbsp;\u003c/em\u003ewith the highest number of losses (39%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther epigenetic modifiers were also affected by alterations, mostly losses: the chromatin remodeler \u003cem\u003eARID1B\u003c/em\u003e (two missense variants, three gains, ten losses); the histone methyltransferase \u003cem\u003eSETD2\u003c/em\u003e (one LoF variant, one missense, one gain, 12 losses), and the methyl-CpG-binding domain protein \u003cem\u003eMBD1\u003c/em\u003e (one LoF variant, three gains, six losses) (\u003cstrong\u003eSupplemental Table S5; Supplemental Table S6\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOsteosarcomas are highly complex and heterogeneous tumors. A large number of genes can be mutated though few are recurrent. Rather, these tumors have aneuploidy, \u003cem\u003echromothripsis\u003c/em\u003e, and uncontrolled cell cycle, suggesting defects in DNA repair mechanisms and/or epigenetic instability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nevertheless, the high diversity of genomic alterations may share similar functional modules related to events such as DNA damage, response to stress, epigenetic processes, mitosis and cellular motility [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe reversibility of epimutations turns them into interesting targets for the development of therapies. In osteosarcomas cell lines, the demethylating agent decitabine (5-aza-dC, a DNMT1-inhibitor) can promote the recovery of gene expression silenced by hypermethylation, resulting in reduced cell growth [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] or increased sensibility to chemotherapeutic agents at concentrations within therapeutic levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, DNA methylation profiles may directly point to robust disrupted mechanisms in osteosarcomas.\u003c/p\u003e \u003cp\u003eThe genome-wide methylation characterization of 28 osteosarcomas showed a significant epigenetic heterogeneity, as shown by a coefficient of variation (CV\u0026thinsp;=\u0026thinsp;0.7), much higher than other solid tumors (~\u0026thinsp;0.08 in Ewing sarcoma; ~0.03 in glioblastoma) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is an expected finding as these tumors usually present a high mutational and chromosomal alteration burden [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], particularly compared to tumors with few genetic alterations such as Ewing sarcoma [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It could indicate that the high DNA methylation heterogeneity is an intrinsic event of osteosarcoma tumorigenesis or that is related to the high number of cell types found in these tissues. This second hypothesis is supported by the high CV in control bone samples (0.73), what raises the possibility that DNA methylation is intrinsic of the bone/cell composition and not related to the tumorigenesis itself. We had one exception among our samples that grouped with the bone samples in the unsupervised clusterization; this sample also had few SNVs and no detectable CNAs. At a closer look at the clinical and pathological parameters, this case was not different from the others, suggesting that this biological behavior may be explained by the intratumor heterogeneity, with the sample used for the experiments being epi- and genetically not representative of the tumor as a whole.\u003c/p\u003e \u003cp\u003eOsteosarcomas displayed a global hypomethylation with a pattern of CpG islands hypermethylation, similar to other cancers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Global hypomethylation is usually related to the activation of oncogenes and increase of genomic instability, while hypermethylation of CpG islands usually results in the inactivation of tumor suppressors and other regulatory elements [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consistently, among the genes contained within hypermethylated DMRs, we identified 17 tumor suppressors, from which six were also affected by alternative inactivation mechanisms such as deletions (\u003cem\u003eDLEC1, GJB2, HIC1, MIR149, PAX6\u003c/em\u003e, and \u003cem\u003eWNT5A\u003c/em\u003e). Silencing by aberrant promoter methylation of cell proliferation regulators and genomic stability maintainers such as \u003cem\u003eDLEC1\u003c/em\u003e and \u003cem\u003eHIC1\u003c/em\u003e is a recurrent event in a variety of cancers [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe hypothesize that the preferential hypermethylation pattern at CpG islands has a connection with \u003cem\u003eDNMT3B\u003c/em\u003e gains in osteosarcomas and genetic perturbations related to H3K27me3 and PRC2. \u003cem\u003eDNMT1\u003c/em\u003e and \u003cem\u003eDNMT3A\u003c/em\u003e also had extra copies in some samples. \u003cem\u003eDNMT1\u003c/em\u003e overexpression is a recurrent event across osteosarcomas, with a negative impact on the expression of several tumor suppressor genes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eDNMT3A\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e seem to have both redundant and exclusive targets; while \u003cem\u003eDNMT3A\u003c/em\u003e remains active in adults [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], expression of \u003cem\u003eDNMT3B\u003c/em\u003e catalytic isoforms decreases during cell differentiation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. There is evidence that \u003cem\u003eDNMT3B\u003c/em\u003e induces DNA methylation of tumor suppressor genes such as \u003cem\u003eRASSF1, CDH1, CDKN1A\u003c/em\u003e and \u003cem\u003eCDKN2A\u003c/em\u003e in colorectal cancer [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], gliomas [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], bladder cancer [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], prostate cancer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], acute myeloid leukemia [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], esophageal squamous cell carcinomas [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], breast adenocarcinoma and lung carcinoma [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Ectopic \u003cem\u003eDNMT3B\u003c/em\u003e expression results in widespread CpG island hypermethylation in several cancer types available in TCGA [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Although we did not analyze gene expression, it is likely that there is a correlation with this variable DNA methylome.\u003c/p\u003e \u003cp\u003eDNMT3B-induced methylation coincides with H3K27me3 and results in methylation of the surrounding CpG island DNA, affecting PRC2 recruitment, a mechanism involved in the tumorigenesis of gliomas [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The involvement of H3K27me3 in osteosarcomas is strengthened by the fact that 87% of the tumors from young patients present imprinting defects at the 14q32-locus, an enriched region for both the active marker H3K4me3 and the silence marker H3K27me3 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn enrichment of hypomethylated DMRs was reported at 6p22, where several histone-coding genes are located. Samples exhibited both gains and losses events encompassing 6p22, reinforcing the genomic instability. Interestingly, only gains of 6p/6p22 were reported as a recurrent event in a variety of cancers, including osteosarcomas [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], with impact in tumor progression, aggressiveness and metastatic potential [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In agreement with the hypomethylation found in 6p22, a cluster of over-expressed genes in 6p has been reported [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDMRs often overlapped with CNAs in our analysis. It is possible that at least part of the DMRs' hypomethylation was induced by the unbalance between copy number gains and the reestablishment of original DNA methylation patterns. Both mutational processes are associated with high genomic instability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, several genes related to DNA damage response and repair, such as \u003cem\u003eBAP1, BRCA1, BRCA2, PALB2, PARP1\u003c/em\u003e, \u003cem\u003ePTEN, RB1\u003c/em\u003e, \u003cem\u003eSETD2\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e, presented copy number losses or loss-of-function mutations (but no differential methylation), although these pathways were not enriched in our analyses. \u003cem\u003eTP53\u003c/em\u003e is not a recurrent target for epigenetic regulation, but changes in the pathways or genes modulated by its activity have already been reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, further studies are necessary to disclose the joint role of DNA methylation and chromosomal alterations in the tumorigenesis of osteosarcomas, mainly targeting the gene expression patterns associated with these changes.\u003c/p\u003e \u003cp\u003eMethylation data from 19 OS cell lines revealed 328 genes with at least one differentially expressed CpG site located at the promoter region [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. From these, 62 genes overlapped with our list of DMR-genes. However, considering the technical differences between the approaches (Illumina 27K and 450K Beadchip arrays), we decided to compare biological processes instead of individual genes. The enriched biological processes within the DMR-genes suggested the involvement of mechanisms associated with all steps of cellular differentiation, similar to other studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and consistent with the period of onset of the disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Osteogenic lineages generated by the induction of differentiation from adipose-derived stem cells present a DNA methylation pattern similar to that of osteocytes, with the majority of CpG sites that change DNA methylation located at promoter-associated CpG islands [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, dysregulation of DMR-genes, as we described, might be associated with the interruption of the differentiation process. Accordingly, among the biological processes, skeletal development, represented by 28 genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), was enriched within the DMRs.\u003c/p\u003e \u003cp\u003eWe reported alterations of TCA anaplerotic substrates and a very high glycolysis rate in the peripheral blood of these same patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Alterations in intermediates of the TCA cycle (histidine and glutamine, which are 2-OG precursors; valine as a succinyl-CoA precursor; and phenylalanine and tyrosine, that can be metabolized to fumarate) can interfere with \u003cem\u003eTET1\u003c/em\u003e activity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], what ultimately could result in the global DNA hypomethylation found in osteosarcomas.\u003c/p\u003e \u003cp\u003eWe emphasize that only frozen samples with more than 70% of the cells of interest were selected for this study because the ratio of contamination of normal cells affects the data of Infinium HumanMethylation 450. This criterion resulted in the main limitation of this study, which was the lack of paired normal and tumor samples. This prevented us from safely determining the spectrum of somatic alterations, methylation, or mutation. To overcome these, we used normal bone tissues in the methylation analysis and applied restrictive data filtering criteria, excluding any variant present in population databases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, we reinforce that aberrant DNA methylation is spread throughout the genome of osteosarcomas and is involved in the dysregulation of biological processes related to skeletal development and cell differentiation. While DNA hypomethylation contributes to global genomic instability, CpG islands hypermethylation enrichment suggests the involvement of a controlled mechanism likely related to the silencing of tumor suppressor genes. Our investigation reinforces the relevance of exploring the role of epigenetic modulation in tumor initiation, maintenance and progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eCGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eChemical and Genetic Perturbations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eCNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eCopy number alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eCoefficient of variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eDMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eDifferentially methylated position\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eDMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eDifferentially methylated region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eFalse discovery rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eMDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eMultidimensional scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eOsteosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003eSVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.14925373134328%\"\u003e\n \u003cp\u003eSingular value decomposition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the patients and their families for the collaboration. This work was supported by Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior and Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCoordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES \u0026ndash; 88887.508357/2020-00); Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo (FAPESP \u0026ndash; 14/10250-7, 15/06281-7, 18/21047-9, 18/06510-4, and 18/24069-3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted following national and institutional ethical policies with approval from the Barretos Cancer Hospital Ethical Committee (CEP-HCB 898.403). Informed consents were obtained from the patients or their legal guardians.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSFP:\u003c/strong\u003e Investigation; Formal analysis; Writing \u0026ndash; original draft; \u003cstrong\u003eJSB:\u003c/strong\u003e Investigation; Formal analysis;\u003cstrong\u003e SSC:\u003c/strong\u003e Methodology;\u003cstrong\u003e MOS:\u003c/strong\u003e Methodology; Data processing; \u003cstrong\u003eAHL: \u003c/strong\u003eData curation; Writing \u0026ndash; review; editing; \u003cstrong\u003eEB:\u003c/strong\u003e Data curation; Investigation; \u003cstrong\u003eSRMS:\u003c/strong\u003e Methodology; Investigation;\u003cstrong\u003e LT: \u003c/strong\u003eMethodology; Data processing; Writing \u0026ndash; editing; \u003cstrong\u003eDOV:\u003c/strong\u003e Investigation; Data curation; Writing \u0026ndash; review; editing; \u003cstrong\u003eACVK:\u003c/strong\u003e Conceptualization; Formal analysis; Funding acquisition; Project administration; Supervision; Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eMM:\u003c/strong\u003e Conceptualization; Data curation; Formal analysis; Funding acquisition; Project administration; Supervision; Writing \u0026ndash; original draft, review; editing. \u003cstrong\u003eAll authors have read and approved the final manuscript.\u003c/strong\u003e\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMisaghi A, Goldin A, Awad M, Kulidjian AA (2018) Osteosarcoma: A comprehensive review. 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Oncotarget 7:21298\u0026ndash;21314. https://doi.org/10.18632/oncotarget.6965\u003c/li\u003e\n\u003cli\u003eLim G, Karaskova J, Vukovic B, et al (2004) Combined spectral karyotyping, multicolor banding, and microarray comparative genomic hybridization analysis provides a detailed characterization of complex structural chromosomal rearrangements associated with gene amplification in the osteosarcoma cell line MG-63. Cancer Genetics and Cytogenetics 153:158\u0026ndash;164. https://doi.org/10.1016/j.cancergencyto.2004.01.016\u003c/li\u003e\n\u003cli\u003eMan TK, Lu XY, Jaeweon K, et al (2004) Genome-wide array comparative genomic hybridization analysis reveals distinct amplifications in osteosarcoma. BMC Cancer. https://doi.org/10.1186/1471-2407-4-45\u003c/li\u003e\n\u003cli\u003eSantos GC, Zielenska M, Prasad M, Squire JA (2007) Chromosome 6p amplification and cancer progression. Journal of Clinical Pathology 60:1\u0026ndash;7\u003c/li\u003e\n\u003cli\u003eKresse SH, Rydbeck H, Sk\u0026aring;rn M, et al (2012) Integrative Analysis Reveals Relationships of Genetic and Epigenetic Alterations in Osteosarcoma. PLoS ONE 7:e48262. https://doi.org/10.1371/journal.pone.0048262\u003c/li\u003e\n\u003cli\u003eMorrow JJ, Khanna C (2015) Osteosarcoma genetics and epigenetics: Emerging biology and candidate therapies. Critical Reviews in Oncogenesis 20:173\u0026ndash;197. https://doi.org/10.1615/CritRevOncog.2015013713\u003c/li\u003e\n\u003cli\u003eBerdasco M, Melguizo C, Prados J, et al (2012) DNA methylation plasticity of human adipose-derived stem cells in lineage commitment. American Journal of Pathology 181:2079\u0026ndash;2093. https://doi.org/10.1016/j.ajpath.2012.08.016\u003c/li\u003e\n\u003cli\u003eFletcher SC, Coleman ML (2020) Human 2-oxoglutarate-dependent oxygenases: nutrient sensors, stress responders, and disease mediators. Biochemical Society Transactions 48:1843\u0026ndash;1858. https://doi.org/10.1042/BST20190333\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplemental Table S3","content":"\u003cp\u003eSupplemental Table S3 is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"osteosarcoma, DNA methylation, mutations, copy number alterations, DNMT, TET.","lastPublishedDoi":"10.21203/rs.3.rs-1999076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1999076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOsteosarcomas commonly arise during the bone growth and remodeling in puberty, making it plausible to infer the involvement of epigenetic alterations in their development. We investigated DNA methylation and related genetic variants in 28 primary osteosarcomas aiming to identify deregulated driver pathways. Methylation and genomic data was obtained using the Illumina HM450K beadchips and the TruSight One sequencing panel, respectively. Aberrant DNA methylation was spread throughout the osteosarcomas genomes. We identified 3,146 differentially methylated CpGs comparing osteosarcomas and bone tissue samples, with high methylation heterogeneity, global hypomethylation and focal hypermethylation at CpG islands. Differentially methylated regions (DMR) were detected in 585 \u003cem\u003eloci\u003c/em\u003e (319 hypomethylated and 266 hypermethylated), mapped to the promoter regions of 350 genes. These DMR-genes were enriched for biological processes related to skeletal system morphogenesis, proliferation, inflammatory response and signal transduction. Six tumor suppressor genes harbored deletions or promoter hypermethylation (\u003cem\u003eDLEC1, GJB2, HIC1, MIR149, PAX6, WNT5A\u003c/em\u003e), and four oncogenes presented gains or hypomethylation (\u003cem\u003eASPSCR1\u003c/em\u003e, \u003cem\u003eNOTCH4, PRDM16\u003c/em\u003e, \u003cem\u003eRUNX3\u003c/em\u003e). Our analysis also revealed hypomethylation at 6p22, a region that contains several histone genes. \u003cem\u003eDNMT3B\u003c/em\u003e gain was found to be a recurrent copy number change in osteosarcomas, providing a possible explanation for the observed phenotype of CpG island hypermethylation. While the detected open-sea hypomethylation likely contributes to the well-known osteosarcoma genomic instability, enriched CpG island hypermethylation suggests an underlying mechanism possibly driven by overexpression of \u003cem\u003eDNMT3B\u003c/em\u003e likely resulting in silencing of tumor suppressors and DNA repair genes.\u003c/p\u003e","manuscriptTitle":"DNA methylation patterns suggest the involvement of DNMT3B and TET1 in osteosarcoma development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-31 16:14:05","doi":"10.21203/rs.3.rs-1999076/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4ffdff2-4a11-41a4-8150-72f8f12029b6","owner":[],"postedDate":"August 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-04-10T17:09:38+00:00","versionOfRecord":{"articleIdentity":"rs-1999076","link":"https://doi.org/10.1007/s00438-023-02010-8","journal":{"identity":"molecular-genetics-and-genomics","isVorOnly":false,"title":"Molecular Genetics and Genomics"},"publishedOn":"2023-04-05 00:00:00","publishedOnDateReadable":"April 5th, 2023"},"versionCreatedAt":"2022-08-31 16:14:05","video":"","vorDoi":"10.1007/s00438-023-02010-8","vorDoiUrl":"https://doi.org/10.1007/s00438-023-02010-8","workflowStages":[]},"version":"v1","identity":"rs-1999076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1999076","identity":"rs-1999076","version":["v1"]},"buildId":"369fNeqWncA4NS6XSWjrt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}