Identification of Aberrantly Methylated Differentially Expressed Genes Targeted by Differentially Expressed Mirna in Osteosarcoma

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Original Article Page 1 of 16

Identification of aberrantly methylated differentially expressed genes targeted by differentially expressed miRNA in osteosarcoma

Ting-Xuan Wang1#, Wen-Le Tan2#, Jin-Cheng Huang3#, Zhi-Fei Cui1, Ri-Dong Liang4, Qing-Chu Li5, Hai Lu1

1Department of Orthopedics, The Fifth Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Zhuhai 519000, China; 2Department of Orthopedics, Luoding People’s Hospital, Luoding 527200, China; 3Department of Orthopedics, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou 450003, China; 4Department of Orthopedics, Southern Medical University Affiliated Nanhai Hospital, Southern Medical University, Foshan 523800, China; 5Department of Orthopedics, The Third Affiliated Hospital of Southern Medical University, Academy of Orthopedics, Southern Medical University, Guangzhou 510000, China Contributions: (I) Conception and design: H Lu; (II) Administrative support: H Lu; (III) Provision of study materials or patients: H Lu, QC Li; (IV) Collection and assembly of data: TX Wang, WL Tan, JC Huang; (V) Data analysis and interpretation: TX Wang; (VII) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Hai Lu, PhD. Department of Orthopedics, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 East Meihua Road, Xiangzhou District, Zhuhai 519000, China. Email: [email protected]; Qing-chu Li, PhD. Department of Orthopedics, The Third Affiliated Hospital of Southern Medical University, No.183 West Zhongshan Avenue, Tianhe District, Guangzhou 510000, China. Email: [email protected].

Background: Osteosarcoma (OS) is the most common primary bone tumors diagnosed in children and adolescents. Recent studies have shown a prognostic role of DNA methylation in various cancers, including OS. The aim of this study was to identify the aberrantly methylated genes that are prognostically relevant in OS. Methods: The differentially expressed mRNAs, miRNAs and methylated genes (DEGs, DEMs and DMGs respectively) were screened from various GEO databases, and the potential target genes of the DEMs were predicted by the RNA22 program. The protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software. The functional enrichment and survival analyses of the screened genes was performed using the R software. Results: Forty-seven downregulated hypermethylated genes and three upregulated hypomethylated genes were identified that were enriched in cell activation, migration and proliferation functions, and were involved in cancer-related pathways like JAK-STAT and PI3K-AKT. Eight downregulated hypermethylated tumor suppressor genes (TSGs) were identified among the screened genes based on the TSGene database. These hub genes are likely involved in OS genesis, progression and metastasis, and are potential prognostic biomarkers and therapeutic targets. Conclusions: TSGs including PYCARD, STAT5A, CXCL12 and CXCL14 were aberrantly methylated in OS, and are potential prognostic biomarkers and therapeutic targets. Our findings provide new insights into the role of methylation in OS progression.

Keywords: Osteosarcoma (OS); methylation; miRNA; tumor suppressor gene (TSG); bioinformatics

Submitted Nov 27, 2019. Accepted for publication Jan 18, 2020. doi: 10.21037/atm.2020.02.74 View this article at: http://dx.doi.org/10.21037/atm.2020.02.74

Introduction from mesenchymal cells and is characterized by the appearance of spindle cells and aberrant osteoid formation. Osteosarcoma (OS) is the most common primary malignant OS can occur in any bone, although the distal femur, bone tumors in children and young adults. It originates proximal tibia and the proximal humerus are the most

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 2 of 16 Wang et al. Identification of aberrantly methylated genes in OS common sites, accounting for respectively 43%, 23% and GSE36002 were downloaded from the GEO database 10% of the cases. Within the bone, OS typically affects the (https://www.ncbi.nlm.nih.gov/geo/). The GSE28423 metaphysis close to the growth plate (1). OS also has a high was registered on the GPL8227 microarray platform metastasis rate of 20%, and commonly invades the lungs (Agilent-019118 Human miRNA Microarray 2.0 G4470B) and other bones (2). and contained data from 19 OS and 4 normal bone samples. Studies in recent years have shown that epigenetic GSE65071 was registered on the microarray platform of changes play an important role in tumorigenesis and GPL19631 (Exiqon human V3 microRNA PCR panel I+II) progression. DNA methylation involves addition of methyl and included data of 20 OS and 15 normal bone samples. groups (-CH3) to cytosines in the CpG dinucleotides, and GSE36001 and GSE36002 were from the GPL6102 both hypermethylation or hypomethylation can result in microarray platform (Illumina human-6 v2.0 expression long-term silencing of imprinted genes, transposons and beadchip), and included data of 19 OS cell lines and 6 the inactive X chromosome (3). Aberrant methylation normal osteoblasts and bones samples. is frequently observed in tumors, and silencing of the GEO2R was used to screen for the DEGs, DEMs and promoter regions of tumor suppressor genes (TSGs) by DMGs from the respective datasets using |logFC| >1 or hypermethylation of CpG islands (4) affects the cell cycle log|β| >0.2 (for DMGs) and P value <0.05 as the thresholds. checkpoint, apoptosis, signal transduction, cell adhesion The putative target genes of the overlapping DEMs of and angiogenesis genes (5). MicroRNAs (miRNAs) are GSE28423 and GSE65071 datasets were predicted using the short non-coding RNAs that regulate gene expression at RNA22 tool (https://cm.jefferson.edu/rna22/Interactive/). the post-translational level by binding to the 3'-UTR of The expression matrix of the DEGs was analyzed by the target mRNA, and are also aberrantly expressed in various GSEA tool according to P values <0.05 and q values <0.05, cancers (6). Abnormal DNA methylation patterns have been and the putative TSGs were identified from the TSGene detected in OS that interfere with the TSGs, and promote database (https://bioinfo.uth.edu/TSGene/download. tumor initiation and progression (7). Although the DNA cgi). Finally, the Venn diagram tool (http://bioinformatics. methylation patterns in OS have gained attention as vital psb.ugent.be/webtools/Venn/) was used to determine (I) biomarkers and therapeutic targets, the regulatory network overlapping hypomethylated mRNAs, up-regulated genes between DNA methylation, miRNAs and target genes and targets to identify the up-regulated hypomethylated remains elusive. mRNAs; (II) overlapping hypermethylated mRNAs, Previous studies have identified several differentially down-regulated mRNAs and targets for downregulated methylated genes (DMGs) and differentially expressed hypermethylated mRNAs; (III) overlapping down-regulated genes (DEGs) in OS through bioinformatics analysis (8). mRNAs, hypermethylated mRNAs, targets and TSGs to However, the regulatory networks of OS-related miRNAs, identify the down-regulated hypermethylated TSGs. mRNAs and methylation patterns in OS are largely unknown. To this end, we screened the mRNA, miRNA Functional analyses of the relevant genes and gene methylation profiles from OS and normal bone microarray datasets, in order to identify the DEGs, DMGs, Biological process (BP) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis TSGs and differentially expressed miRNAs (DEMs) related were performed using the ClusterProfiler package of R to OS. The aim of our study was to identify and validate software, with P<0.05 as the threshold. Gene set enrichment the aberrantly methylated TSGs in OS. Our findings analysis (GSEA) was conducted using the GSEA desktop provide new insights into the potential tumorigenic role tool. The Search Tool for the Retrieval of Interacting of abnormal DNA methylation, and identify potential Genes (STRING, https://string-db.org/) database was used biomarkers and therapeutic targets for OS. to construct the PPI networks, which were then visualized using CYTOSCAPE. Methods

Identification of DEGs, DEMs and DMGs Validation of TSGs

The miRNA datasets GSE28423 and GSE65071, the The prognostic value of the 8 TSGs was determined by mRNA dataset GSE36001 and the methylation dataset survival analysis using the online database R2: Genomics

Analysis and Visualization Platform (http://r2.amc.nl). pipette tip. The dislodged cells were removed by washing twice The clinical data and the expression data were extracted with PBS, and serum-free medium was added. The cells were from a dataset, the Mixed Osteosarcoma–Kuijjer–127–vst– cultured for 24 h, and the wound edges were photographed ilmnhwg6v2. The patients were stratified into the respective at the same position at 0, 24 h under an inverted microscope. low- and high-expression groups using the R2 scan method. The migration rate was determined based on the width of the The correlation between TSGs methylation and expression wound edges. The assay was performed in triplicate. was determined by Spearman’s rank correlation analysis, with |Cor|>0.3 and P<0.05 as the thresholds. Invasion assay

In vitro invasion was analyzed using 24 well Invasion Chambers (8 Cell culture and treatment μm pore size; Costar, Corning, USA). For the invasion assay, the The OS cell line MG63 was purchased from the American transwell chambers were pre-coated with 50 µL Matrigel (Corning, Type Culture Collection (ATCC, Manassas, VA, USA), and USA) for 3 h. MG63 cells were seeded into the upper chambers cultured in DMEM (Gibco, USA) supplemented with 10% at the density of 1×104/100 µL in serum free medium, and the FBS under 5% CO2 at 37 ℃. The DNA methyltransferase lower chambers were filled with 500 µL DMEM supplemented inhibitor 5-azacytidine (5-Aza) was purchased from with 10% FBS. After incubating for 24 h, the cells were MedChemExpress. The MG63 cells were treated with washed twice with PBS and fixed with 4% paraformaldehyde 5 µM 5-Aza or DMSO (vehicle) for 48 h. for 30 minutes at room temperature. The PET membranes were air-dried and stained with 0.1% crystal violet for RNA isolation and quantitative RT-PCR 15 minutes. The non-invaded cells were wiped off, and the invaded cells were photographed and counted under an Total RNA was extracted from the cells using Total RNA inverted microscope in five random fields per membrane. Extraction Reagent (Vazyme, China), and reverse transcribed Both assays were performed in triplicate. into cDNA using the HiScript II Q RT SuperMix (Vazyme, China). RT-PCR was conducted using ChamQ SYBR qPCR Master Mix (Vazyme, China) in a Lightcycler 96 (Roche, Statistical analyses Switzerland). The relative mRNA expression levels were All statistical analyses were performed using GRAPHPAD determined by the 2- CT method. All tests were performed ΔΔ PRISM7 (GraphPad Prism Software Inc., San Diego, three times, and GAPDH was used as the internal control. CA, USA) or SPSS 23.0. Two groups were compared The primer sequences are shown in Table S1. using Student’s t-test with Welch correction in case of significantly different variance, or multiple groups were Cell proliferation assay compared using one-way ANOVA analysis. P<0.05 was considered statistically significant. MG63 cells were seeded into a 96 well plate at the density of 3×103/well, and cultured with DMSO or 5-Aza for 24, 48, 72, 96 and 120 h. The medium was replaced with fresh medium Results supplemented with 10 µL Cell Counting Kit-8 (CCK-8) Identification of DEGs and DEMs in microarray reagent/well (DOJINDO, Japan), and the cells were incubated for another hour. The absorbance at 450nm was measured Workflow of how aberrantly methylated differentially using a microplate reader (Bio.Tek, USA), and the proliferation expressed genes were identified was shown in Figure 1. A rates were calculated after subtracting the background total of 15 DEMs were identified in the OS samples from absorbance. The experiment was performed thrice. the GSE28423 and GSE65071 microarray datasets (see methods), and the heat maps are shown in Figure 2A,B. In addition, 835 DEGs (Table S2) were also identified Wound-healing assay from the GSE36001 dataset, of which 574 were down- MG63 cells were seeded into 6 well plates at 80% density and regulated and 261 were up-regulated (Figure 2C). Finally, treated with 5 µM 5-Aza or DMSO for 48 h. The monolayer 4,313 DMGs were detected in the GSE36002 dataset was scratched perpendicular to the plate with a sterile 200μl (Figure 2D), of which 3,441 were hypermethylated and 872

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 4 of 16 Wang et al. Identification of aberrantly methylated genes in OS

15 DEMs from 4,313 DMGs 835 DMGs the GSE28423 from the from the and GSE65071 GSE36002 GSE36002 dataset dataset

18,809 target 3,441 877 genes predicted hypermethylated hypermethylated 574 down-regulated 261 up-regulated by the RNA22 mRNAs mRNAs mRNAs mRNAs program

3 hypermethylated up-regulated 187 mRNAs hypermethylated down-regulated mRNAs

combined with GSEA analysis

47 hypermethylated down-regulated genes targeted by DEMs combined with GSEA

survival analysis combined with TSGs database

correlation analysis 8 hypermethylated down-regulated tumor suppressor genes Validation of qRT-PCR

can contributed to OS progression

aberrant methylation

Figure 1 Workflow of how aberrantly methylated differentially expressed genes were identified.

A C D

3 Type 15 Tumor Type 2 Normal Type 1 4 Tumor 0 Normal −1 2 −2 −3 0

−2 10

−4 color down no up B

−1·log10 (p value) Type 2 5 1 0 −1 −2

−4 −2 0 2 logFC

Figure 2 Identification of DEGs, DEMs and DMGs in OS. (A) Heatmap of 15 DEMs in GSE28423; (B) heatmap of 15 DEMs in GSE65071; (C) volcano plot showing DEG distribution in GSE36001; (D) heatmap of DMGs in GSE36002. The black area, green area and the red area represent the non-DEGs, down-regulated mRNAs, and up-regulated mRNAs, respectively. DEGs, differentially expressed genes; DEMs, differentially expressed miRNAs; DMGs, differentially methylated genes. were hypomethylated. tissues, including a total of 539 mRNAs according to P values <0.05 and q values <0.05 (Figure 4A). The most significantly enriched KEGG gene set in OS was the Identification of aberrantly methylated DEGs targeted by KEGG_SPLICEOSOME (Figure 4B), and its constituent the DEMs DEGs are visualized in Figure 4C. Other significant gene The RNA22 program predicted 18,809 target genes of sets included KEGG_CELL CYCLE and KEGG_DNA the 15 DEMs. The overlap between these targets, DEGs REPLICATION (Figure S1). Overlapping of the 539 and DMGs revealed 187 hypermethylated down-regulated mRNAs of KEGG gene sets, DEGs, DMGs and 18,809 genes (Figure 3A) and only 3 hypomethylated up-regulated target genes of the 15 DEMs, revealed 47 hypermethylated genes (Figure 3B). The latter included BMP4, PYCR1 and down-regulated genes (Figure 4D) that are likely involved in PRAME, which are involved in BP promoting cancers and OS. The list of 47 hypermethylated down-regulated genes other diseases. We surmised therefore that BMP4, PYCR1 was shown in Table S5. and PRAME were significant in OS progression. The DEMs and 187 hypermethylated down-regulated genes are Functional enrichment analysis and PPI network respectively shown in Tables S3,S4. construction

Gene ontology (GO) analysis and KEGG analysis were next Identification of hypermethylated down-regulated genes performed to determine the BP and pathways associated targeted by DEMs combined with GSEA with the 47 hypermethylated downregulated genes in To further identify the DEGs in OS, we conducted GSEA OS (Figure 5). As shown in Figure 5C, the most enriched based on the GSE36001 dataset. Ten KEGG gene sets BP were positive regulation of response to external was significantly enriched in OS compared to normal stimulus (GO:0032103) and phagocytosis (GO:0006909).

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 6 of 16 Wang et al. Identification of aberrantly methylated genes in OS

A Hypermethylation down-regulated B Hypomethylation up-regulated

637 6 13 192 0 19 (3.3%) (0%) (0.1%) (1%) (0%) (0.1%)

187 3 (1%) (0%) 2611 368 677 239 (13.4%) (1.9%) (3.6%) (1.3%)

15643 17890 (80.4%) (94.1%)

Targets Targets

Figure 3 Identification of aberrantly methylated DEGs. (A) A total of 193 hypermethylated down-regulated genes were identified, of which 187 genes were targets of DEMs; (B) three hypomethylated up-regulated genes targeted by DEMs were identified. DEGs, differentially expressed genes; DEMs, differentially expressed miRNAs.

A B C down-regulated Targets

13 15292 (0.1%) (78.6%) 5 306 351 (0%) (1.6%) (1.8%) 636 62 1 (3.3%) 140 (0.7%) (0.3%) (0%) 47 2535 (0.2%) 0 (13%) 76 1 (0%) (0.4%) (0%) 1 (0%) Hypermethylation GSEA

D Enrichment PLOT: KEGG_SPLICEOSOME 0.5 0.4 0.3 0.2 0.1

Enrichment score (ES) Enrichment score 0.0

'tumor' (positively correlated) 0.5 0.0 Zero cross at 8264 −0.5

−1.0 'normal' (negatively correlated) 0 2,500 5,000 7,500 10.000 12,500 15,000 Rank in ordered dataset

Enrichment profile Hits Ranking metric scores Ranked list metric (Signal2Noise)

Figure 4 GSEA results. (A) Heatmap of DEGs from GSE36001; (B) heatmap of the “KEGG_SPLICEOSOME” gene set; (C) Venn diagram showing the overlap between hypermethylated down-regulated genes and GSEA results; (D) enrichment plot of KEGG_SPLICEOSOME. The top portion of plots show the enrichment scores for each gene, and the bottom portion shows the ranked genes. Y-axis: ranking metric, X-axis: individual ranks for all genes. GSEA, gene set enrichment analysis; DEGs, differentially expressed genes.

Figure 5 Functional analysis and PPI network of the hypermethylated down-regulated genes. (A) PPI network of 47 hypermethylated down- regulated genes visualized by the Cytoscape software. The size of the dots and the gradation of color indicate the strength of interaction; (B) bubble chart shows the significant pathways. The color depth indicates statistical significance, Y-axis represents the KEGG pathway, X-axis represents the proportion of enriched genes, and the size of the points indicates the number of genes; (C) bar graph showing the significantly enriched biological processes in the DEGs. The color depth indicates statistical significance, the Y-axis shows the GO-BP terms and X-axis represents the proportion of enriched genes; (D) clue GO analysis results. The large points and small points represent the significant KEGG pathways the enriched genes, respectively. PPI, protein−protein interaction; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; GO, gene ontology; BP, biological process.

Furthermore, KEGG pathway enrichment analysis showed and hypermethylated mRNAs at the protein level. that most genes were enriched in platelet activation (hsa04611), osteoclast differentiation (hsa04380), natural Function analysis and PPI network construction of killer cell mediated cytotoxicity (hsa04650), phagosome aberrantly methylated TSGs involved in OS (hsa04145), and chemokine signaling pathway (hsa04062). The top 10 significant pathways for the screened genes are Our previous study showed that down-regulated hypermethylated shown in Figure 5B, indicating that the DEGs are likely mRNAs play an important role in tumorigenesis. Therefore, we involved in some osteoclast differentiation pathways and screened for known TSGs among the 47 OS-related DEGs, several cancer-related pathways, such as the JAK-STAT and identified eight Figure( 6A). This indicated that aberrant and PI3K-Akt. Furthermore, the clue GO analysis showed methylation of these TSGs downregulated their expression that MAPK13 and FCGR2A were significantly enriched in levels in OS and promoted tumorigenesis. PPI network more pathways compared to the other DEGs (Figure 5D). construction showed that 7 down-hypermethylated TSGs The PPI network further showed 40 nodes with strong were strongly correlated at the protein level (Figure 6B). correlations and 2 nodes with weak correlations (Figure 5A), Further GO analysis showed that the key genes were mainly indicating complex interactions between the downregulated associated with cell regulation, migration and apoptosis

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 8 of 16 Wang et al. Identification of aberrantly methylated genes in OS

A B

TSGs 47 mRNAs

1,209 8 39 (96.3%) (0.6%) (3.1%)

Figure 6 Identification of TSGs. (A) Venn diagram showing the aberrantly methylated DEGs in the TSGs database. The overlapping area includes 8 hypermethylated down-regulated TSGs; (B) hub genes highlighted in the PPI network of hypermethylated down-regulated genes. The 7 key genes are highlighted and shown as the hub genes in the network. Red region and green region indicate TSGs, and non- TSGs, respectively. DEGs, differentially expressed genes; TSGs, tumor suppressor genes; PPI, protein−protein interaction.

(Table 1), and pathway enrichment analysis indicated coefficient of the significant TSGs are shown in Table 3. The that they were significantly enriched in the NF-kappa most significant effect of DNA methylation was seen on the B signaling pathway, chemokine signaling pathway and expression levels of STAT5A and PYCARD (Cor =−0.826, P viral carcinogenesis (Table 2). Therefore, these aberrantly value =5.207e-20; Cor =−0.887, P value =1.468e-18). methylated TSGs are likely involved in OS progression, and are potential biomarkers. Inhibition of DNA methylation can upregulate the key genes Prognostic assessment of the down-regulated To further validate the impact of methylation on the hypermethylated TSGs in OS expression of the TSGs, we treated MG63 cells with To determine the prognostic value of the above 8 TSGs the methyltransferase inhibitor 5-Aza, and examined the in OS, we assessed the metastasis free survival of patients expression levels of the relevant genes. As shown in Figure 9, from the dataset, Mixed OS (Mesenchymal)–Kuijjer–127– PYCARD, STAT5A, CDH5, CXCL12 and CXCL14 vst–ilmnhwg6v2, after stratifying them into the respective mRNA levels were significantly increased after 5-Aza low- and high-expressing groups. Patients expressing low treatment, thereby confirming the in silico data. In contrast, levels of BTK, GPX3, CXCL12, CXCX14, PYCARD and BTK and GPX3 expression was not significantly affected by STAT5A had worse metastasis free survival compared to the inhibiting DNA methylation. corresponding high expression groups (Figure 7), indicating that these genes have a significant impact on patient Aberrant methylation contributed to OS progression prognosis. The findings so far indicated that methylated TSGs were involved in cancer-related pathways and contributed to Correlation between methylation and gene expression OS progression. To further elucidate the effect of aberrant Correlation analysis of the gene expression and DNA methylation on OS progression, the MG63 cells were methylation data revealed a significant inverse correlation treated with 5-Aza and various functional assays were between DNA methylation and the respective gene expression performed. Blocking DNA methylation not only decreased levels (Figure 8). The methylation sites and correlation the survival of the OS cells in vitro (Figure 10A), but also

Table 1 Significant biological processes in which the 8 hub genes were mainly involved ID Description P value Gene Count

GO:2000403 Positive regulation of lymphocyte migration 3.77E-07 PYCARD/CXCL12/CXCL14 3

GO:2000401 Regulation of lymphocyte migration 1.71E-06 PYCARD/CXCL12/CXCL14 3

GO:0072676 Lymphocyte migration 9.76E-06 PYCARD/CXCL12/CXCL14 3

GO:0002687 Positive regulation of leukocyte migration 1.49E-05 PYCARD/CXCL12/CXCL14 3

GO:2000114 Regulation of establishment of cell polarity 3.42E-05 CDH5/GSN 2

GO:0032878 Regulation of establishment or maintenance of cell polarity 4.50E-05 CDH5/GSN 2

GO:0008064 Regulation of actin polymerization or depolymerization 4.74E-05 PYCARD/CXCL12/GSN 3

GO:0030832 Regulation of actin filament length 4.82E-05 PYCARD/CXCL12/GSN 3

GO:0002685 Regulation of leukocyte migration 4.90E-05 PYCARD/CXCL12/CXCL14 3

GO:2000406 Positive regulation of T cell migration 7.08E-05 PYCARD/CXCL12 2

GO:0008154 Actin polymerization or depolymerization 7.32E-05 PYCARD/CXCL12/GSN 3

GO:0098760 Response to interleukin-7 7.57E-05 STAT5A/BTK 2

GO:0098761 Cellular response to interleukin-7 7.57E-05 STAT5A/BTK 2

GO:2000404 Regulation of T cell migration 0.00013 PYCARD/CXCL12 2

GO:0110053 Regulation of actin filament organization 0.00015 PYCARD/CXCL12/GSN 3

GO:0032103 Positive regulation of response to external stimulus 0.00022 CXCL12/CXCL14/BTK 3

GO:0072678 T cell migration 0.00028 PYCARD/CXCL12 2

GO:0032956 Regulation of actin cytoskeleton organization 0.0003 PYCARD/CXCL12/GSN 3

Production of molecular mediator involved in inflammatory GO:0002532 0.00032 PYCARD/BTK 2 response

GO:1902903 Regulation of supramolecular fiber organization 0.00033 PYCARD/CXCL12/GSN 3

GO:0032535 Regulation of cellular component size 0.0004 PYCARD/CXCL12/GSN 3

GO:0032970 Regulation of actin filament-based process 0.00043 PYCARD/CXCL12/GSN 3

GO:2001233 Regulation of apoptotic signaling pathway 0.00049 PYCARD/CXCL12/GSN 3

GO:0007015 Actin filament organization 0.00051 PYCARD/CXCL12/GSN 3

GO:0002690 Positive regulation of leukocyte chemotaxis 0.00056 CXCL12/CXCL14 2

GO:2000106 Regulation of leukocyte apoptotic process 0.0006 CXCL12/BTK 2

GO:0050727 Regulation of inflammatory response 0.00062 PYCARD/CDH5/BTK 3

GO:0030838 Positive regulation of actin filament polymerization 0.00076 PYCARD/GSN 2

Intrinsic apoptotic signaling pathway in response to DNA GO:0008630 0.00083 PYCARD/CXCL12 2 damage

GO:0051249 Regulation of lymphocyte activation 0.00084 PYCARD/GSN/BTK 3

GO:0050900 Leukocyte migration 0.00087 PYCARD/CXCL12/CXCL14 3

GO:0002821 Positive regulation of adaptive immune response 0.00087 PYCARD/BTK 2

GO:0071887 Leukocyte apoptotic process 0.00091 CXCL12/BTK 2

GO:0002688 Regulation of leukocyte chemotaxis 0.00094 CXCL12/CXCL14 2

Table 1 (continued)

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 10 of 16 Wang et al. Identification of aberrantly methylated genes in OS

Table 1 (continued) ID Description P value Gene Count

GO:0015696 Ammonium transport 0.00094 CXCL12/BTK 2

GO:0090066 Regulation of anatomical structure size 0.00098 PYCARD/CXCL12/GSN 3

GO:0001938 Positive regulation of endothelial cell proliferation 0.00099 STAT5A/CXCL12 2 GO, Gene Ontology; PYCARD, PYD and CARD domain containing; STAT5A, signal transducer and activator of transcription 5A; CDH5, Cadherin 5; CXCL12, C-X-C Motif Chemokine Ligand 12; CXCL14, C-X-C Motif Chemokine Ligand 14; GSN, Gelsolin; BTK, Bruton tyrosine kinase; GPX3, glutathione peroxidase 3.

Table 2 Significant KEGG pathways in which the 8 hub genes were mainly involved. ID Description P value Gene Count

hsa04064 NF-kappa B signaling pathway 0.004273 CXCL12/BTK 2

hsa04670 Leukocyte transendothelial migration 0.005333 CDH5/ CXCL12 2

hsa04217 Necroptosis 0.010906 STAT5A/PYCARD 2

hsa04062 Chemokine signaling pathway 0.0148017 CXCL12/CXCL14 2

hsa05203 Viral carcinogenesis 0.0164769 STAT5A/GSN 2

hsa04810 Regulation of actin cytoskeleton 0.0185588 CXCL12/GSN 2

hsa04060 Cytokine-cytokine receptor interaction 0.033662 CXCL12/CXCL14 2

hsa05340 Primary immunodeficiency 0.0370792 BTK 1

hsa04672 Intestinal immune network for IgA production 0.0488436 CXCL12 1 KEGG, Kyoto Encyclopedia of Genes and Genomes; PYCARD, PYD And CARD Domain Containing; STAT5A, Signal Transducer And Activator Of Transcription 5A; CDH5, Cadherin 5; CXCL12, C-X-C Motif Chemokine Ligand 12; CXCL14, C-X-C Motif Chemokine Ligand 14; GSN, Gelsolin; BTK, Bruton Tyrosine Kinase; GPX3, Glutathione Peroxidase 3.

markedly diminished their migration (Figure 10B) and molecular biology techniques and bioinformatics integration invasion capacities (Figure 10C) compared to the DMSO analysis in recent years have enabled identification of controls. Taken together, aberrant DNA methylation aberrant methylation patterns in various cancers, and is conducive to OS progression, and inhibiting DNA provided insights into the underlying mechanisms. methyltransferases can decrease proliferation, migration We identified 187 hypermethylated genes and 3 and invasion of OS cells. hypomethylated genes in OS based on mRNA, miRNA and methylation datasets, along with 15 DEMs that are relevant in OS and other cancers except has-miR-331-5p (11). The Discussion hsa-miR-142-5p (12), hsa-miR-338-3p (13) and hsa-miR- OS is a primary bone malignancy with a high rate 542-5p were in particular strongly associated with OS, and of recurrence. Despite advancements in surgery and can help identify the key genes involved in OS progression. chemotherapy, the overall survival rates have been dismal BMP4, PYCR1 and PRAME were the 3 hypomethylated over the past 20 years (9). Aberrant DNA methylation is an upregulated genes in OS. BMP4 is a secreted ligand of the epigenetic modification that is frequently observed in most TGF-β superfamily that recruits and activates SMAD family cancers, and is a major risk factor that drives tumorigenesis transcription factors, and is involved in cardiac development via gene silencing (7). Since epigenetic modifications affect and adipogenesis (14). BMP4 promoter hypomethylation gene functions without altering the DNA sequence, they is associated with worse prognosis in gastric cancer (15), result in more diverse gene expression profiles (10). Novel indicating that the aberrant methylation of BMP4 in OS is

PYCARD STAT5A CXCL12 CXCL14

Kaplan curve Expression Graph Kaplan curve Expression Graph Kaplan curve Expression Graph Kaplan curve Expression Graph 1.0 3500 Event = No 1.0 2000 Event = No 1.0 8000 Event = No 1.0 9000 Event = No high (n=29) Event = Yes high (n=50) Event = Yes high (n=29) Event = Yes high (n=57) Event = Yes low (n=59) low (n=38) low (n=59) low (n=31) 0.9 0.9 1800 0.9 0.9 8000 3000 7000 0.8 0.8 0.8 0.8 1600 7000 6000 0.7 2500 0.7 0.7 0.7 1400 6000 5000 0.6 0.6 0.6 0.6 2000 1200 5000 0.5 0.5 0.5 4000 0.5 1500 1000 4000 0.4 Expression 0.4 Expression 0.4 Expression 0.4 Expression 3000 800 3000 0.3 1000 0.3 0.3 0.3 2000 metastasisfree survival probability metastasisfree survival probability metastasisfree 600 survival probability metastasisfree survival probability metastasisfree 2000 0.2 0.2 0.2 0.2 500 1000 0.1 0.1 400 0.1 0.1 1000 raw p3.8e-03 raw p 1.2e-03 raw p 4.7e-03 raw p 0.044 bonf p 0.274 bonf p 0.086 bonf p 0.343 bonf p 1.000 0.0 0 0.0 200 0.0 0 0.0 0 0 24 48 72 96 120 144 168 192 216 240 sorted by 0 24 48 72 96 120 144 168 192 216 240 sorted by 0 24 48 72 96 120 144 168 192 216 240 sorted by 0 24 48 72 96 120 144 168 192 216 240 sorted by expression expression expression expression

Follow up in months p value Follow up in months p value Follow up in months p value Follow up in months p value

GPX3 Expression Graph BTK Expression Graph CDH5 Expression Graph GSN Expression Graph 1.0 3000 1.0 Kaplan curve 9000 Event = No 1.0 Kaplan curve 1100 Event = No 1.0 Kaplan curve 3000 Event = No Kaplan curve Event = No high (n=52) high (n=48) high (n=9) high (n=77) Event = Yes Event = Yes Event = Yes Event = Yes low (n=11) low (n=36) low (n=40) low (n=79) 0.9 0.9 8000 0.9 1000 0.9 2500 2500 0.8 0.8 900 0.8 0.8 7000

0.7 0.7 800 0.7 0.7 6000 2000 2000 0.6 0.6 700 0.6 0.6 5000 0.5 0.5 600 0.5 1500 0.5 1500 4000 Expression Expression Expression 0.4 0.4 500 0.4 Expression 0.4 3000 1000 1000 0.3 0.3 400 0.3 0.3 metastasisfree survival probability metastasisfree metastasisfree survival probability metastasisfree 2000 survival probability metastasisfree survival probability metastasisfree 0.2 0.2 300 0.2 0.2 500 500 0.1 1000 0.1 200 0.1 0.1 raw p 0.011 raw p 0.014 raw p 0.199 raw p 0.082 bonf p 0.785 bonf p 1.000 bonf p 1.000 bonf p 1.000 0.0 0 0.0 100 0.0 0 0.0 0 0 24 48 72 96 120 144 168 192 216 240 0 24 48 72 96 120 144 168 192 216 240 0 24 48 72 96 120 144 168 192 216 240 sorted by 0 24 48 72 96 120 144 168 192 216 240 sorted by sorted by sorted by expression expression expression expression p value p value Follow up in months p value Follow up in months p value Follow up in months Follow up in months

Figure 7 Prognostic relevance of hypermethylated TSGs in OS. Kaplan-Meier survival analysis of 8 hub genes were conducted by R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl). The differences were tested using the log-rank test. P values are demonstrated in the lower right corner of each image and the numbers of samples with high expression and low expression are displayed in the higher right corner of each image. TSGs, tumor suppressor genes; Bonf p: Adjusted P value for multiple comparisons (Bonferroni method); Raw P: raw P value. likely oncogenic. PYCR1 catalyzes NAD(P)H-dependent metastasis (20). Aberrant migration and proliferation is an conversion of pyrroline-5-carboxylate to proline during important cellular program in tumors, and are mediated cell proliferation and metabolism, and is up-regulated by dysregulated signaling pathways (21). KEGG analysis in a wide range of cancers. Overexpression of PYCR1 revealed that the OS-related hypermethylated genes were contributes to poor prognosis in prostate cancer (16), and significantly associated with the PI3K-Akt and JAT-STAT based on our results, may have a pro-tumorigenic role pathways. The former is the most frequently mutated in OS. PRAME is overexpressed in melanoma, myeloid network in human cancers, and is also dysregulated in tumors leukemia, neuroblastoma (17), and head and neck cancer, due to methylation (22). It promotes cancer cell survival by and its hypomethylated form promotes the progression inhibiting pro-apoptotic and activating anti-apoptotic genes, of chronic myeloid leukemia (18). In OS as well, PRAME Furthermore, the downstream mTOR kinase also promotes overexpression is associated with poor prognosis, and cell growth and protein synthesis, and downregulates this increases tumor cell proliferation by attenuating cell cycle pathway through a feedback loop. Constitutive activation arrest (19). Taken together, these genes likely promote OS of the PI3K-Akt pathway is often accompanied by loss progression, although the underlying mechanisms need to or mutations in the tumor suppressor PTEN (23). It also be elucidated. Furthermore, they are potential prognostic plays a critical role in OS genesis by inhibiting apoptosis biomarkers and therapeutic targets in OS. and activating pro-survival pathways (24). The JAK/STAT Forty-seven hypermethylated down-regulated genes in pathway is also dysregulated in many solid tumors and OS were enriched in functions like response to external increases tumor cell proliferation and angiogenesis, resulting stimulus, cell migration and cell proliferation, chemotaxis in worse prognosis (25). Tyrosine phosphorylation and and inflammatory response, all of which contribute to tumor nuclear localization of the STATs have been observed in the

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 12 of 16 Wang et al. Identification of aberrantly methylated genes in OS

Cor =−0.826 (P value =5.207e−20) Cor =−0.887 (P value =1.468e−18) Cor =−0.498 (P value =1.510e−23) Cor =−0.573 (P value =8.270e−18) 11 13 13 11 12 10 12 10 11 11 9 9 10 10

STAT5A expression STAT5A 9

8 GSN expression 9 8 GPX3 expression PYCARD expression 8 8 7 7 7 7 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.1 0.2 0.3 0.4 0.5 06 0.7 0.8 0.9 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.0 0.2 0.4 0.6 0.8 1.0 STAT5A methylation PYCARD methylation GSN methylation GPX3 methylation

Cor =−0.376 (P value =1.058e−19) Cor =−0.431 (P value =1.733e−15) Cor =−0.571 (P value =6.547e−21) Cor =−0.323 (P value =1.074e−23) 14 11.0 11 12 10.5

10 11 12 10.0 9.50 10 9.00 9 10 9 8.50 BTK expression

CDH5 expression 8 8.00 CXCL12 expression CXCL14 expression 8 8 7.50 7 7 7.00 6 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.2 0.4 0.6 0.8 1.0 CXCL14 methylation CXCL12 methylation CDH5 methylation BTK methylation

Figure 8 The correlation between methylation values and expression values of 8 hub genes. Higher correlation coefficient indicates stronger association between gene expression and methylation. Cor, correlation coefficient.

Table 3 The methylation sites and correlation coefficient of The TSGs. Gene symbol Methylation site Correlation P value

PYCARD cg09587549 −0.997 1.468e-18

STAT5A cg03001305 −0.826 5.207e-20

CDH5 cg22319147 −0.571 6.547e-21

CXCL12 cg18618334 −0.431 1.733e-15

CXCL14 cg18995088 −0.376 1.058e-19

GSN cg17071957 −0.498 1.510e-23

GPX3 cg17820459 −0.573 8.270e-18

BTK cg03791917 −0.323 1.074e-23 TSGs, tumor suppressor genes; PYCARD, PYD And CARD Domain Containing; STAT5A, Signal Transducer And Activator Of Transcription 5A; CDH5, Cadherin 5; CXCL12, C-X-C Motif Chemokine Ligand 12; CXCL14, C-X-C Motif Chemokine Ligand 14; GSN, Gelsolin; BTK, Bruton Tyrosine Kinase; GPX3, Glutathione Peroxidase 3.

tumor tissues across a range of cancers, indicating that JAK/ to Kawasaki Disease (26). The p38 MAP kinase MAPK13 SAT activation correlates with worse prognosis. We next is aberrantly expressed in several tumors, and its promoter established a clue GO network of the hypermethylated genes methylation contributes to melanoma progression (27). and these pathways, and observed significant enrichment of However, the role of MAPK13 and FCGR2A in OS remains MAPK13 and FCGR2A. The latter is an IgG receptor, and to be elucidated. methylation of its promoter decreases binding affinity to the The hypermethylated downregulated genes included 8 human IgG2, which is associated with higher susceptibility TSGs—CDH5, BTK, GPX3, PYCARD, CXCL12, CXCL14,

PYCARD STAT5A CXCL14 CXCL12 100 3 3 2.5 ** *** ** 80 2.0 *** 2 2 60 1.5

40 1.0 1 1 20 0.5 STAT5A expression level expression STAT5A CXCL12 expression level CXCL12 expression CXCL14 expression level CXCL14 expression PYCARD expression level PYCARD expression 0 0 0 0.0 DMSO 5-Aza DMSO 5-Aza DMSO 5-Aza DMSO 5-Aza MG63 MG63 MG63

GSN CDH5 GPX3 BTK 1.5 5 2.0 2.0

4 *** * 1.5 1.5 1.0 3 1.0 1.0 2 0.5 0.5 0.5 1 BTK expression level BTK expression GSN expression level GSN expression GPX3 expression level GPX3 expression 0.0 level CDH5 expression 0 0.0 0.0 DMSO 5-Aza DMSO 5-Aza DMSO 5-Aza DMSO 5-Aza MG63 MG63 MG63 MG63

Figure 9 Expression levels of the key genes with 5-Aza treatment in MG63 cells. Error bars represent SEM, ***, P<0.001; **, P<0.01; *, P<0.05. 5-Aza, 5-azacytidine.

A B MG63 MG63 DMSO 5-Aza 4 ** DMSO 0.8 *** 5-Aza 3 * 0 h 0.6 2 ** * 0.4 OD value * 1 0.2 24 h 0 0.0 0 1 2 3 4 5 6 healing percentage Wound DMSO 5-Aza days MG63

80 C MG63 ** DMSO 5-Aza 60

invasive cells perfield 0 DMSO 5-Aza MG63

Figure 10 Aberrant methylation contributed to OS progression. (A) After 5-Aza treatment, the proliferation of MG63 cells were significantly decreased; the Wound-healing assay (B) and Transwell Migration Assay (C) evaluated that the migration and invasion of MG63 cells were significantly decreased after 5-Aza treatment compared to those of the control group. 0.1% crystal violet, scale bar, 100 or 200 μm, error bars represent SEM; ***, P<0.001; **, P<0.01; *, P<0.05. 5-Aza, 5-azacytidine.

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 14 of 16 Wang et al. Identification of aberrantly methylated genes in OS

STAT5A and GSN. PYCARD is a signaling factor consisting was strongly associated with its low expression levels as well of a PYD and CARD domain, and mediates the apoptotic as poor prognosis in our study. Interestingly, BTK, CXCL12, pathway by activating caspases (28). Studies show that CDH5 and STAT5A showed strong functional connectivity hypermethylation-mediated silencing of PYCARD enables at the protein level, although the molecular mechanisms tumor cell survival by blocking apoptosis (29). We showed remain to be elucidated. a strong correlation between PYCARD hypermethylation GPX3 is a glutathione peroxidase that protects cells and down-regulation in OS, and verified that its low against ROS and DNA damage, and is an established expression levels indicated worse prognosis based on tumor suppressor in various cancers (42). Gene silencing clinical data. Furthermore, 5-Aza treatment markedly of GPX3 by promoter hypermethylation has been reported upregulated PYCARD, which strongly suggests that the in hepatocellular carcinoma (43), and similar trends were aberrant methylation of PYCARD drives OS genesis. observed in the OS samples as well. GSN is an actin- CXCL12 is a chemokine of the intercrine family, and binding protein that regulates actin filament formation binds with CXCR4 to initiate divergent pathways related and disassembly, as well as apoptosis via DNase I binding to chemotaxis and cell survival (30). High expression levels and release (44). GSN downregulation via promoter of CXCL12 is associated with poor prognosis in ovarian methylation predicts poor survival in gastric cancer (45), cancer (31), and increased migration and invasiveness of whereas overexpression of GSN promoted growth and adamantinomatous craniopharyngiomas (32). In contrast, invasion of OS cells (46). This contradicts our findings that down-regulation of CXCL12 have also been detected in OS hypermethylation and down regulation of GSN likely drive via promoter hypermethylation by DNA methyltransferase 1 OS progression. (DNMT1) (33). According to our findings, low expression To summarize, we identified several TSGs that were and hypermethylation of CXCL12 indicates worse prognosis hypermethylated and downregulated in the OS tissues, in OS, which make it a potential therapeutic target. CXCL14 indicating a strong relationship between DNA methylation is another chemokine involved in immunoregulatory and and tumorigenesis. Although our findings have to be inflammatory processes (34), as well as tumor migration and validated in experimental studies and the underlying invasion. It inhibited colorectal cancer cell migration by mechanisms also need to be elucidated, we can conclude suppressing NF-kB signaling, whereas hypermethylation- that the aforementioned genes are significantly involved in mediated silencing promoted migration (35). BTK is a OS progression, and are potential prognostic markers and/ component of the Toll-like receptors (TLR) pathway, and or therapeutic targets. There are however several limitations promotes inflammatory responses (36). In addition, it is a in this study. Since the CpG sites information was not modulator of p53 that can be induced in response to DNA available, the significance of specific methylation sites of damage and p53 activation, and phosphorylates the latter to these hub genes could not be determined. Furthermore, enhance apoptosis (37). We found that low expression levels we did not correlate the expression data with the clinical of BTK due to hypermethylation was associated with poor parameters in the same datasets. Further studies are needed prognosis in OS. to validate the role of these TSGs in OS. CDH5 belongs to the cadherin superfamily, and is involved in the vasculogenic mimicry of glioblastoma stem- Conclusions like cells under hypoxic conditions (38). Low expression levels of CDH5 mediated by promoter methylation was strongly We identified 47 hypermethylated down-regulated mRNAs associated with poor overall survival in neuroblastomas (39). targeted by significant miRNAs in OS, of which 8 are In agreement with this, CDH5 was expressed at low levels established tumor suppressors. The aberrantly methylated in OS tissues compared to normal samples, and predicted TSGs may be the potential prognostic biomarkers and poor prognosis. STAT5A is a transcription factor that is therapeutic targets for OS. Our findings provide new frequently dysregulated in cancer. Methylation-dependent insights into the role of methylation in OS progression. promoter region silencing of STAT5A inhibited NPM1- ALK expression in ALK + TCL cell lines (40). One study Acknowledgments reported low levels of STAT5A in OS and correlated it with increased tumor progression and worse overall survival (41). The authors thank the GEO databases for the availability of Consistent with this, the hypermethylation status of STAT5A the data.

Funding: This study was supported by grants from the indicates a new therapeutic strategy. J Cancer Res Clin National Natural Science Foundation of China (Grant Oncol 2017;143:2189-200. number 81572628, 81372869), The Science and Technology 8. Kresse SH, Rydbeck H, Skårn M, et al. Integrative analysis Project of Guangzhou (Grant No. 201704020033) and reveals relationships of genetic and epigenetic alterations The Science and Technology Project of Tianhe District, in osteosarcoma. PLoS One 2012;7:e48262. Guangzhou (Grant No. 201604KW012). 9. O'Day K, Gorlick R. Novel therapeutic agents for osteosarcoma. Expert Rev Anticancer Ther 2009;9:511-23. 10. Moore LD, Le T, Fan G. DNA Methylation and Its Basic Footnote Function. Neuropsychopharmacology 2013;38:23-38. Conflicts of Interest: All authors have completed the ICMJE 11. Xie B, Ding Q, Han H, et al. miRCancer: a microRNA- uniform disclosure form and declare: the authors have no cancer association database constructed by text mining conflicts of interest to declare. onliterature. Bioinformatics 2013;29:638-44. 12. Jones KB, Salah Z, Del Mare S, et al. miRNA Signatures Ethical Statement: The authors are accountable for all Associate with Pathogenesis and Progression of aspects of the work in ensuring that questions related Osteosarcoma. Cancer Res 2012;72:1865-77. to the accuracy or integrity of any part of the work are 13. Cao R, Shao J, Hu Y, et al. microRNA-338-3p appropriately investigated and resolved. inhibits proliferation, migration, invasion, and EMT inosteosarcoma cells by targeting activator of 90 kDa Open Access Statement: This is an Open Access article heat shock protein ATPasehomolog 1. Cancer Cell Int distributed in accordance with the Creative Commons 2018;18:49. Attribution-NonCommercial-NoDerivs 4.0 International 14. Gustafson B, Hammarstedt A, Hedjazifar S, et al. BMP4 License (CC BY-NC-ND 4.0), which permits the non- and BMP Antagonists Regulate Human White and Beige commercial replication and distribution of the article with Adipogenesis. Diabetes 2015;64:1670-81. the strict proviso that no changes or edits are made and the 15. Ivanova T, Zouridis H, Wu Y, et al. Integrated original work is properly cited (including links to both the epigenomics identifies BMP4 as a modulator of cisplatin formal publication through the relevant DOI and the license). sensitivity in gastric cancer. Gut 2013;62:22-33. See: https://creativecommons.org/licenses/by-nc-nd/4.0/. 16. Zeng T, Zhu L, Liao M, et al. Knockdown of PYCR1 inhibits cell proliferation and colony formation via cell cycle arrest and apoptosis in prostate cancer. Med Oncol References 2017;34:27. 1. Gill J, Ahluwalia MK, Geller D, et al. New targets 17. Oberthuer A, Hero B, Spitz R, et al. The tumor-associated and approaches in osteosarcoma. Pharmacol Ther antigen PRAME is universally expressed in high-stage 2013;137:89-99. neuroblastoma and associated with poor outcome. Clin 2. Yan GN, Lv YF, Guo QN. Advances in osteosarcoma Cancer Res 2004;10:4307-13. stem cell research and opportunities for novel therapeutic 18. Roman-Gomez J, Jimenez-Velasco A, Agirre X, et al. targets. Cancer Lett 2016;370:268-74. Epigenetic regulation of PRAME gene in chronic myeloid 3. Wu SC, Zhang Y. Active DNA demethylation: many roads leukemia. Leuk Res 2007;31:1521-8. lead to Rome. Nat Rev Mol Cell Biol 2010;11:607-20. 19. Tan P, Zou C, Yong B, et al. Expression and prognostic 4. Cui J, Wang W, Li Z, et al. Epigenetic changes in relevance of PRAME in primary osteosarcoma. Biochem osteosarcoma. Bull Cancer 2011;98:E62-8. Biophys Res Commun 2012;419:801-8. 5. Suzuki H, Maruyama R, Yamamoto E, et al. DNA 20. Lu H, Ouyang W, Huang C. Inflammation, a key event in methylation and microRNA dysregulation in cancer. MOL cancer development. Mol Cancer Res 2006;4:221-33. ONCOL 2012;6:567-78. 21. Zhou N, Si Z, Li T, et al. Long non-coding RNA CCAT2 6. Zhuang M, Qiu X, Cheng D, et al. MicroRNA-524 functions as an oncogene in hepatocellular carcinoma, promotes cell proliferation by down-regulating PTEN regulating cellular proliferation, migration and apoptosis. expression in osteosarcoma. Cancer Cell Int 2018;18:114. Oncol Lett 2016;12:132-8. 7. Li X, Lu H, Fan G, et al. A novel interplay between 22. Chang L, Graham PH, Ni J, et al. Targeting PI3K/ HOTAIR and DNA methylation in osteosarcoma cells Akt/mTOR signaling pathway in the treatment of

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):373 | http://dx.doi.org/10.21037/atm.2020.02.74 Page 16 of 16 Wang et al. Identification of aberrantly methylated genes in OS

prostate cancer radioresistance. Crit Rev Oncol Hematol 36. Liu X, Zhan Z, Li D, et al. Intracellular MHC class 2015;96:507-17. II molecules promote TLR-triggered innate immune 23. Stemke-Hale K, Gonzalez-Angulo AM, Lluch A, et al. responses by maintaining activation of the kinase Btk. Nat Integrative genomic and proteomic analysis of PIK3CA, Immunol 2011;12:416-24. PTEN, and AKT mutations in breast cancer. Cancer Res 37. Althubiti M, Rada M, Samuel J, et al. BTK Modulates p53 2008;68:6084-91. Activity to Enhance Apoptotic and Senescent Responses. 24. Zhang J, Yu XH, Yan YG, et al. PI3K/Akt signaling in Cancer Res 2016;76:5405-14. osteosarcoma. Clin Chim Acta 2015;444:182-92. 38. Mao XG, Xue XY, Wang L, et al. CDH5 is specifically 25. Lai SY, Johnson FM. Defining the role of the JAK-STAT activated in glioblastoma stemlike cells and contributes to pathway in head and neck and thoracic malignancies: vasculogenic mimicry induced by hypoxia. Neuro Oncol Implications for future therapeutic approaches. Drug 2013;15:865-79. Resist Updat 2010;13:67-78. 39. Fujita T, Igarashi J, Okawa ER, et al. CHD5, a tumor 26. Kuo HC, Hsu YW, Wu MS, et al. FCGR2A Promoter suppressor gene deleted from 1p36.31 in neuroblastomas. Methylation and Risks for Intravenous Immunoglobulin J Natl Cancer Inst 2008;100:940-9. Treatment Responses in Kawasaki Disease. Mediators 40. Zhang Q, Wang H, Liu X, et al. STAT5A is epigenetically Inflamm 2015;2015:564625. silenced by the tyrosine kinase NPM1-ALK and acts as a 27. Gao L, Smit MA, van den Oord JJ, et al. Genome- tumor suppressor by reciprocally inhibiting NPM1-ALK wide promoter methylation analysis identifies epigenetic expression. NAT MED 2007;13:1341-8. silencing of MAPK13 in primary cutaneous melanoma. 41. Guo Z, Tang Y, Fu Y, et al. Decreased expression of Pigment Cell Melanoma Res 2013;26:542-54. STAT5A predicts poor prognosis in osteosarcoma. 28. McConnell BB, Vertino PM. Activation of a caspase-9- PATHOL RES PRACT 2019;215:519-24. mediated apoptotic pathway by subcellular redistribution 42. Barrett CW, Ning W, Chen X, et al. Tumor suppressor of the novel caspase recruitment domain protein TMS1. function of the plasma glutathione peroxidase gpx3 in Cancer Res 2000;60:6243-7. colitis-associated carcinoma. Cancer Res 2013;73:1245-55. 29. Conway KE, McConnell BB, Bowring CE, et al. TMS1, a 43. Cao S, Yan B, Lu Y, et al. Methylation of promoter and novel proapoptotic caspase recruitment domain protein, is expression silencing of GPX3 gene in hepatocellular a target of methylation-induced gene silencing in human carcinoma tissue. Clin Res Hepatol Gastroenterol breast cancers. Cancer Res 2000;60:6236-42. 2015;39:198-204. 30. Teicher BA, Fricker SP. CXCL12 (SDF-1)/CXCR4 44. Yeh YL, Hu WS, Ting WJ, et al. Hypoxia Augments Pathway in Cancer. CLIN Cancer Res 2010;16:2927-31. Increased HIF-1α and Reduced Survival Protein p-Akt 31. Guo Q, Gao BL, Zhang XJ, et al. CXCL12-CXCR4 in Gelsolin (GSN)-Dependent Cardiomyoblast Cell Axis Promotes Proliferation, Migration, Invasion, and Apoptosis. Cell Biochem Biophys 2016;74:221-8. Metastasis of Ovarian Cancer. Oncol Res 2014;22:247-58. 45. Wang HC, Chen CW, Yang CL, et al. Tumor-Associated 32. Yin X, Liu Z, Zhu P, et al. CXCL12/CXCR4 promotes Macrophages Promote Epigenetic Silencing of Gelsolin proliferation, migration, and invasion of adamantinomatous through DNA Methyltransferase 1 in Gastric Cancer craniopharyngiomas via PI3K/AKT signal pathway. J Cell Cells. Cancer Immunol Res 2017;5:885-97. Biochem 2019;120:9724-36. 46. Ma X, Sun W, Shen J, et al. Gelsolin promotes cell 33. Li B, Wang Z, Wu H, et al. Epigenetic Regulation of growth and invasion through the upregulation of p-AKT CXCL12 Plays a Critical Role in Mediating Tumor and p-P38 pathway in osteosarcoma. Tumour Biol Progression and the Immune Response In Osteosarcoma. 2016;37:7165-74. Cancer Res 2018;78:3938-53. 34. Schwarze SR, Luo J, Isaacs WB, et al. Modulation of CXCL14 (BRAK) expression in prostate cancer. Prostate Cite this article as: Wang TX, Tan WL, Huang JC, Cui 2005;64:67-74. ZF, Liang RD, Li QC, Lu H. Identification of aberrantly 35. Cao B, Yang Y, Pan Y, et al. Epigenetic Silencing of methylated differentially expressed genes targeted by CXCL14 Induced Colorectal Cancer Migration and differentially expressed miRNA in osteosarcoma. Ann Transl Invasion. Discov Med 2013;16:137-47. Med 2020;8(6):373. doi: 10.21037/atm.2020.02.74

Table S1 Primers used for quantitative RT-PCR No. Primer names Sequence

1 CDH5 Forward AAGCGTGAGTCGCAAGAATG

CDH5 Reverse TCTCCAGGTTTTCGCCAGTG

2 STAT5A Forward GCAGAGTCCGTGACAGAGG

STAT5A Reverse CCACAGGTAGGGACAGAGTCT

3 BTK Forward TCCGAGAAGAGGTGAAGAGTC

BTK Reverse AGAAGACGTAGAGAGGCCCTT

4 GPX3 Forward AGAGCCGGGGACAAGAGAA

GPX3 Reverse ATTTGCCAGCATACTGCTTGA

5 GSN Forward GGTGTGGCATCAGGATTCAAG

GSN Reverse TTTCATACCGATTGCTGTTGGA

6 CXCL12 Forward ATTCTCAACACTCCAAACTGTGC

CXCL12 Reverse ACTTTAGCTTCGGGTCAATGC

7 CXCL14 Forward CTGCGAGGAGAAGATGGTTA

CXCL14 Reverse CTTTGCACAAGTCTCCCAAC

8 PYCARD Forward TGGATGCTCTGTACGGGAAG

PYCARD Reverse CCAGGCTGGTGTGAAACTGAA Table S2 Differentially expressed genes in GSE36001 Table S2 (continued) Gene logFC P value Expression status Gene logFC P value Expression status CBS 2.86863 1.40E-06 up-regulated SEPW1 −1.2235 2.56E-03 TMSB15A 2.51239 1.33E-03 up-regulated LTF −1.2254 6.82E-03 ASNS 2.48438 4.74E-06 up-regulated AMT −1.2395 1.89E-07 PRAME 2.43715 3.85E-03 up-regulated SLC11A1 −1.2422 7.40E-04 PHGDH 2.36205 1.83E-06 up-regulated DES −1.2441 1.33E-02 PSAT1 2.35393 9.79E-04 up-regulated TBC1D10C −1.2446 4.47E-04 HOXB9 2.32252 3.37E-05 up-regulated FOXO4 −1.2525 1.85E-03 FOXF2 2.31887 7.88E-09 up-regulated SNCA −1.2532 3.81E-02 TUBB2B 2.27458 6.73E-03 up-regulated MLKL −1.2533 8.39E-04 RPS27 2.26996 7.67E-09 up-regulated CYTH4 −1.2577 3.12E-04 TRIB3 2.202 1.02E-03 up-regulated ARG1 −1.2622 6.31E-03 KRT18 2.19222 4.04E-02 up-regulated GIMAP6 −1.264 2.38E-05 RPS28 2.16531 2.27E-11 up-regulated TTN −1.2642 9.44E-03 RHPN2 2.14741 5.16E-10 up-regulated DDIT4L −1.2664 4.15E-03 HOXB7 2.0859 7.54E-11 up-regulated PTGIS −1.2665 6.43E-04 UCHL1 2.06058 3.44E-02 up-regulated CYYR1 −1.2676 8.51E-04 SNAR-A1 2.06015 1.81E-02 up-regulated HK3 −1.2693 9.65E-04 LARP6 2.00116 1.90E-04 up-regulated PRSS35 −1.2695 1.09E-04 NDUFB9 1.97682 3.33E-06 up-regulated REC8 −1.2705 1.52E-05 BMP4 1.96386 2.94E-02 up-regulated LTBP2 −1.277 5.84E-05 KRT80 1.93456 2.20E-03 up-regulated SLC25A37 −1.2775 2.81E-02 UQCRHL 1.91456 8.07E-11 up-regulated GPM6B −1.2803 3.35E-05 PYCR1 1.90138 1.45E-08 up-regulated SPOCK2 −1.2814 9.19E-06 TMSB15B 1.89281 1.81E-03 up-regulated TMEM140 −1.2879 1.05E-03 TMEM158 1.87398 3.90E-02 up-regulated TCN1 −1.2932 1.15E-02 RPS23 1.87289 3.49E-08 up-regulated PIK3R1 −1.2952 8.52E-04 CTXN1 1.86829 2.16E-03 up-regulated CD247 −1.2957 7.70E-05 LDHB 1.85533 6.35E-08 up-regulated HCP5 −1.2965 4.17E-02 GGH 1.8369 1.21E-06 up-regulated LBP −1.2966 1.75E-05 CDH2 1.82037 1.67E-04 up-regulated ITPRIP −1.2974 6.09E-05 CBX2 1.81804 1.37E-05 up-regulated ARHGAP15 −1.2975 4.15E-05 CCDC85C 1.79464 5.82E-05 up-regulated MYBPC2 −1.3001 1.27E-02 CD70 1.79194 1.41E-02 up-regulated CA3 −1.3026 3.63E-02 MTHFD2 1.7826 2.26E-06 up-regulated CD37 −1.3042 6.28E-05 HOXC6 1.77691 3.40E-04 up-regulated KEL −1.3112 5.26E-03 NARS 1.71376 8.73E-08 up-regulated ZBTB20 −1.3148 1.66E-02 RPL23 1.70136 1.12E-08 up-regulated HYAL1 −1.3175 1.64E-04 COCH 1.67175 1.18E-02 up-regulated CKMT2 −1.3179 1.64E-02 RPN2 1.66775 1.83E-05 up-regulated STARD8 −1.319 1.22E-04 DLX1 1.66684 8.53E-06 up-regulated RGL4 −1.3196 6.97E-04 LAPTM4B 1.6466 2.60E-04 up-regulated PLIN1 −1.321 5.98E-03 P4HA2 1.622 5.90E-03 up-regulated PRKCH −1.3217 1.87E-03 DKK3 1.62106 1.11E-02 up-regulated GJA4 −1.3244 3.27E-05 SH3BP4 1.6136 1.46E-04 up-regulated ITGAM −1.3245 1.11E-04 INHBE 1.58633 3.15E-03 up-regulated MCEMP1 −1.3257 3.05E-03 FOXD1 1.58097 9.51E-04 up-regulated MMP13 −1.3263 7.07E-03 MARS 1.56382 2.06E-03 up-regulated MAP2K3 −1.328 2.32E-03 CDK4 1.56202 3.82E-03 up-regulated ACTN3 −1.3287 1.80E-02 C8orf59 1.56049 6.93E-07 up-regulated ISLR −1.3305 8.79E-07 TUSC3 1.54348 1.10E-04 up-regulated LYVE1 −1.3332 5.03E-06 TUFT1 1.5424 4.59E-04 up-regulated RNASE3 −1.3361 7.55E-03 NETO2 1.51885 2.62E-03 up-regulated TMEM71 −1.3384 6.29E-03 MRPL13 1.51548 5.36E-05 up-regulated BIN2 −1.3391 4.36E-04 CTHRC1 1.51249 3.13E-02 up-regulated CYP1B1 −1.3393 4.36E-02 PBK 1.50362 3.41E-03 up-regulated ICAM3 −1.341 7.37E-04 LHX2 1.49652 7.64E-05 up-regulated ADAMTS4 −1.3412 1.29E-02 SUMO2 1.4912 1.83E-07 up-regulated CST7 −1.342 9.91E-04 HS3ST3A1 1.49006 1.68E-02 up-regulated GSN −1.3423 2.06E-03 BCAT1 1.49003 1.71E-04 up-regulated ADCY4 −1.3472 2.58E-05 AMY1C 1.47724 1.76E-03 up-regulated NDN −1.3477 4.52E-02 YES1 1.46839 1.51E-06 up-regulated IFI44L −1.3526 2.51E-03 TUBA1A 1.46646 2.09E-03 up-regulated KLF2 −1.3535 1.13E-02 CDC20 1.45371 2.15E-02 up-regulated OAS2 −1.355 3.68E-02 GJA1 1.4512 2.01E-02 up-regulated LDB2 −1.355 1.64E-02 CDKN2A 1.44766 4.14E-02 up-regulated KLF6 −1.3551 2.13E-04 PSMG1 1.44007 2.64E-05 up-regulated MYH2 −1.3572 6.67E-03 CDK1 1.43336 3.51E-04 up-regulated FOLR3 −1.3581 3.19E-03 ADORA2B 1.43215 4.21E-02 up-regulated DPT −1.3595 3.74E-03 PPT1 1.42821 4.80E-05 up-regulated AQP9 −1.3603 9.46E-05 IGF1R 1.41576 7.63E-04 up-regulated THRSP −1.3612 3.46E-03 HOXB5 1.41239 1.40E-04 up-regulated NFIB −1.3669 1.90E-02 RPLP0 1.40545 6.57E-05 up-regulated CYBRD1 −1.3685 1.38E-02 CLEC2D 1.40137 5.23E-05 up-regulated ARHGAP25 −1.369 1.42E-05 IGF2BP3 1.39775 3.08E-04 up-regulated TRAK2 −1.3695 1.08E-04 TSEN15 1.38192 5.97E-06 up-regulated F13A1 −1.3699 8.20E-05 PCNA 1.37241 8.85E-05 up-regulated SLIT2 −1.3727 8.75E-03 FAM60A 1.3608 6.53E-05 up-regulated CLEC12A −1.3736 5.59E-04 MRPS21 1.35778 1.31E-03 up-regulated FERMT3 −1.374 5.95E-04 HIST1H1C 1.3562 4.08E-02 up-regulated RNASE6 −1.3755 3.23E-06 PTGES3 1.35171 8.56E-08 up-regulated PPP1R14A −1.3779 2.04E-04 BAIAP2L1 1.35122 2.40E-02 up-regulated CMYA5 −1.3847 6.02E-03 HOXB8 1.34337 6.75E-04 up-regulated LHFP −1.3855 1.09E-03 UBE2V2 1.3423 4.31E-06 up-regulated APOC1 −1.3882 1.72E-04 TFAP2A 1.34098 2.17E-04 up-regulated SRL −1.389 6.69E-03 RPL39L 1.33544 4.24E-02 up-regulated MTURN −1.39 1.56E-03 PNMA2 1.33329 2.51E-03 up-regulated JSRP1 −1.3901 3.21E-03 MSMO1 1.32728 4.89E-04 up-regulated TNNI2 −1.3929 1.14E-02 43723 1.32217 1.21E-08 up-regulated CFH −1.393 4.75E-03 JPH3 1.32023 6.72E-04 up-regulated PLPP3 −1.3932 2.89E-04 CADM1 1.31959 4.67E-02 up-regulated LEPR −1.3934 1.83E-05 ATF4 1.31573 3.97E-06 up-regulated CD84 −1.3952 1.17E-05 MRGBP 1.31347 1.81E-05 up-regulated IRAK3 −1.3965 3.37E-05 STIL 1.3069 1.79E-04 up-regulated PPP3CB −1.3991 3.93E-07 CD24 1.30188 3.10E-02 up-regulated RETN −1.4011 5.87E-03 FADS1 1.29936 4.22E-04 up-regulated FHL1 −1.4031 1.14E-02 TSPAN3 1.29826 1.58E-04 up-regulated PLIN2 −1.4033 2.34E-02 TMEM97 1.29788 1.21E-03 up-regulated TNFSF10 −1.4067 2.95E-04 SNRPF 1.29673 4.06E-04 up-regulated CEBPA −1.4106 1.83E-02 LMNB2 1.29257 5.44E-04 up-regulated FCER1A −1.4114 9.60E-06 SLC38A2 1.28646 1.17E-06 up-regulated PLPP1 −1.4146 8.26E-04 CTSV 1.2847 1.35E-02 up-regulated GIMAP8 −1.4182 7.54E-06 CENPN 1.2795 7.02E-03 up-regulated CLEC3B −1.4183 4.11E-06 PFN2 1.27903 2.83E-03 up-regulated RPL10A −1.4183 3.78E-05 FSCN1 1.27759 3.83E-03 up-regulated ALAD −1.4193 5.27E-04 KIAA0101 1.27625 3.22E-02 up-regulated IRF8 −1.424 1.20E-03 ITPKA 1.27388 1.83E-03 up-regulated FHDC1 −1.4265 2.21E-04 S100A10 1.27361 1.86E-04 up-regulated PPARG −1.4294 7.19E-03 HEBP2 1.27358 7.11E-05 up-regulated NCKAP1L −1.4316 5.71E-06 USP1 1.2721 1.56E-05 up-regulated GNA15 −1.4322 1.01E-05 SNTB1 1.2702 2.22E-02 up-regulated ARHGAP30 −1.4322 5.76E-05 TOMM34 1.26349 5.32E-04 up-regulated SLA −1.434 2.79E-04 PSPH 1.26287 8.13E-04 up-regulated C1QTNF5 −1.4342 1.64E-03 RPL7 1.26277 7.84E-05 up-regulated HP −1.44 9.24E-06 SLC35B2 1.25915 3.14E-03 up-regulated IFI30 −1.4412 1.44E-02 KCNG1 1.25551 1.67E-02 up-regulated EDNRA −1.4418 1.82E-04 SHROOM3 1.25475 2.97E-05 up-regulated SORBS2 −1.4463 9.11E-07 TRA2B 1.25444 3.53E-06 up-regulated CDH5 −1.4539 3.18E-04 SHMT2 1.25438 5.72E-04 up-regulated RENBP −1.455 2.88E-05 DENND2A 1.25123 1.98E-02 up-regulated CFLAR −1.4558 4.76E-04 P4HA1 1.24989 1.56E-04 up-regulated IKZF1 −1.4561 3.16E-04 NPTN 1.24936 7.71E-07 up-regulated MPP1 −1.4567 9.55E-04 SOX4 1.24746 2.96E-04 up-regulated SULF1 −1.4623 1.14E-03 XRCC6 1.24656 8.32E-06 up-regulated SIRPA −1.4644 1.53E-02 PTK7 1.24626 2.60E-03 up-regulated TUBB1 −1.4649 3.16E-03 SLC25A23 1.24538 6.90E-05 up-regulated CEBPD −1.4668 5.66E-03 YAP1 1.24156 1.83E-03 up-regulated HBQ1 −1.4669 3.48E-02 TSFM 1.24148 2.00E-02 up-regulated XAF1 −1.4671 8.81E-03 RDH10 1.23377 1.03E-02 up-regulated ZBTB16 −1.4696 7.91E-06 AHCY 1.23173 1.93E-04 up-regulated CA4 −1.4729 2.67E-04 DLL3 1.22567 2.91E-04 up-regulated APLNR −1.4736 1.56E-03 BCAS4 1.22392 2.07E-03 up-regulated EBF3 −1.477 1.45E-03 RPS21 1.2177 2.10E-03 up-regulated GYPA −1.4849 7.41E-03 MTRR 1.21718 1.21E-03 up-regulated IFI27 −1.486 1.70E-02 EIF3A 1.21646 1.63E-07 up-regulated SELL −1.4884 1.71E-04 SCD 1.2133 4.26E-03 up-regulated ACACB −1.4886 5.36E-03 SNRPA1 1.21076 1.81E-05 up-regulated C2orf82 −1.4905 7.40E-03 PVT1 1.20649 1.06E-02 up-regulated ARHGAP9 −1.4918 1.69E-03 TNFRSF12A 1.20647 3.45E-02 up-regulated TCIRG1 −1.4996 8.42E-05 MED10 1.20467 1.91E-05 up-regulated SPON1 −1.502 1.76E-02 FOXF1 1.20251 1.41E-02 up-regulated LY86 −1.5021 5.65E-06 AGPAT5 1.19818 8.36E-07 up-regulated HLA-DRB3 −1.5025 5.81E-05 TRIP13 1.19681 9.72E-03 up-regulated GAS1 −1.5052 2.57E-02 FAM64A 1.19622 1.60E-02 up-regulated SELENBP1 −1.5069 4.78E-04 BCAR3 1.19567 8.67E-03 up-regulated C10orf10 −1.5076 1.37E-02 SLC35F2 1.18443 3.98E-04 up-regulated PLCL2 −1.5098 1.00E-06 TMEM14A 1.18308 6.63E-03 up-regulated CAPN3 −1.5113 4.85E-04 DFNA5 1.18302 2.11E-02 up-regulated CEACAM6 −1.5128 5.68E-03 C1orf53 1.18241 1.57E-06 up-regulated MNDA −1.5135 3.29E-04 BOP1 1.18186 4.69E-04 up-regulated SEPP1 −1.514 1.71E-02 TMEM54 1.181 3.62E-04 up-regulated ITGA2B −1.5143 1.86E-02 EPHA2 1.17619 1.05E-02 up-regulated ANPEP −1.5173 8.62E-03 MORF4L1 1.16974 5.49E-05 up-regulated GMFG −1.518 2.55E-04 SLC38A1 1.16964 1.17E-02 up-regulated KLHL41 −1.5223 1.63E-02 KIF20A 1.16683 1.91E-02 up-regulated MYOT −1.5337 8.05E-03 ULBP1 1.16431 9.31E-04 up-regulated CMKLR1 −1.5371 3.12E-13 RPLP1 1.16415 1.09E-05 up-regulated CYBB −1.5396 1.38E-05 TPST1 1.16013 5.00E-03 up-regulated PRKCB −1.5418 8.49E-03 CCNO 1.15859 1.36E-05 up-regulated APOLD1 −1.5531 6.80E-04 MAD2L2 1.15818 6.63E-05 up-regulated CD48 −1.5533 3.35E-05 CCNB2 1.15807 2.76E-02 up-regulated RGS18 −1.554 2.23E-04 NTF3 1.15717 9.22E-03 up-regulated FGD3 −1.556 1.65E-03 METTL21B 1.15654 3.81E-02 up-regulated APBB1IP −1.5657 1.79E-04 HOXB2 1.15621 3.40E-02 up-regulated HSPB2 −1.5668 1.69E-05 PRKCA 1.15608 1.29E-03 up-regulated G0S2 −1.5669 6.93E-03 CKS2 1.15449 9.21E-03 up-regulated LAMA4 −1.5671 5.06E-03 ELOVL5 1.15325 1.65E-05 up-regulated VAMP5 −1.5799 8.51E-03 CNN3 1.15265 8.52E-03 up-regulated LYL1 −1.5847 4.64E-04 WDR54 1.15192 1.99E-03 up-regulated RGS1 −1.5897 1.76E-04 RND3 1.15142 2.44E-02 up-regulated IGFBP7 −1.5976 4.50E-02 UAP1 1.14908 5.14E-05 up-regulated ADAP2 −1.5982 2.90E-05 B4GALNT1 1.14738 2.53E-02 up-regulated OMD −1.5983 5.71E-03 CXorf57 1.14526 2.62E-03 up-regulated FES −1.6038 1.81E-05 PERP 1.14226 4.21E-03 up-regulated GZMK −1.6071 1.41E-05 PIR 1.13938 3.93E-03 up-regulated IRS2 −1.608 8.43E-04 AURKA 1.13846 2.66E-02 up-regulated FGR −1.6097 4.39E-04 MAD2L1 1.13735 8.81E-03 up-regulated EGR1 −1.6118 2.06E-02 TAGLN2 1.13671 2.67E-03 up-regulated P2RY8 −1.6182 1.64E-05 MSH6 1.1361 8.31E-05 up-regulated FGL2 −1.6182 3.63E-05 PIGT 1.13238 6.38E-06 up-regulated EVI2B −1.6256 5.97E-06 AURKB 1.13022 1.13E-02 up-regulated CST3 −1.6281 1.60E-03 AP1S1 1.13018 6.30E-04 up-regulated GIMAP5 −1.6299 5.02E-06 CEP55 1.12981 1.12E-02 up-regulated MFNG −1.6364 7.95E-05 ISL1 1.12874 5.64E-03 up-regulated STRADB −1.642 1.96E-04 MTFR1 1.1271 5.25E-04 up-regulated FCGR2A −1.6441 7.94E-06 TM9SF2 1.12553 3.91E-04 up-regulated TFF3 −1.6444 8.61E-04 NKX2-2 1.1255 8.44E-03 up-regulated PDE7B −1.6495 3.90E-04 43719 1.11697 1.89E-06 up-regulated TCAP −1.6556 7.16E-03 C10orf35 1.11443 6.94E-03 up-regulated SPRY1 −1.6601 6.17E-03 KDELR2 1.11324 7.37E-03 up-regulated NEB −1.6646 4.07E-03 LDOC1 1.11321 1.72E-02 up-regulated C1orf54 −1.6788 1.59E-03 RCN2 1.11287 2.97E-04 up-regulated FOSB −1.6812 8.28E-03 SLC29A4 1.11279 1.22E-02 up-regulated STAT5A −1.6856 2.46E-04 MED30 1.11021 3.60E-04 up-regulated AEBP1 −1.6888 6.45E-03 C14orf166 1.10726 3.13E-04 up-regulated ENG −1.6895 2.30E-04 RSL24D1 1.10265 2.08E-04 up-regulated ABI3BP −1.6917 1.89E-04 43530 1.10214 2.33E-03 up-regulated SLC22A16 −1.6945 2.67E-03 ATAD2 1.09842 7.39E-03 up-regulated CYTL1 −1.6967 3.10E-02 ZFP64 1.09762 3.32E-03 up-regulated ANGPTL2 −1.7015 1.18E-02 PRKDC 1.09743 1.29E-04 up-regulated HMBS −1.7033 6.34E-03 YARS 1.09408 7.90E-05 up-regulated FCGR3B −1.7043 5.02E-04 PDP1 1.09367 3.91E-04 up-regulated S100P −1.707 4.05E-03 F12 1.09256 1.30E-03 up-regulated CLC −1.7108 1.35E-03 PFDN6 1.09154 2.92E-03 up-regulated BPI −1.7221 7.99E-03 MIF 1.08773 9.85E-04 up-regulated TNFRSF14 −1.7226 3.82E-03 E2F7 1.08453 3.54E-03 up-regulated HTRA1 −1.7242 1.88E-02 SDHAF3 1.08319 1.20E-03 up-regulated ATP1A2 −1.726 1.55E-03 HOXC4 1.08245 2.77E-04 up-regulated TNS1 −1.7264 2.52E-05 MTX2 1.07936 3.08E-07 up-regulated CILP −1.7303 3.72E-04 FNBP1L 1.07524 4.44E-04 up-regulated BCL6 −1.7311 1.49E-04 KIF2C 1.0749 6.94E-03 up-regulated RARRES2 −1.7322 1.73E-05 GPX8 1.07488 1.29E-05 up-regulated SERPINA1 −1.7331 1.49E-02 UBE2E1 1.06912 5.50E-06 up-regulated FOLR2 −1.7389 2.39E-05 SREBF2 1.06673 1.99E-05 up-regulated TESC −1.7433 1.72E-03 GPT2 1.06533 1.98E-02 up-regulated COL8A1 −1.7441 3.03E-02 PODXL2 1.06451 3.18E-03 up-regulated RHAG −1.7454 4.89E-03 MTHFD1L 1.06383 2.81E-03 up-regulated TNNC1 −1.7517 4.69E-02 RAE1 1.05617 3.55E-05 up-regulated GP9 −1.7619 2.10E-03 TPI1P2 1.05585 7.46E-04 up-regulated GPIHBP1 −1.7667 1.62E-04 C15orf52 1.05524 4.71E-02 up-regulated RNASE2 −1.7783 1.26E-03 TMED7 1.05358 1.39E-05 up-regulated NFE2 −1.7821 2.16E-02 ASPSCR1 1.05272 1.64E-02 up-regulated CLDN5 −1.7841 1.03E-05 TYMS 1.05271 4.15E-02 up-regulated CYGB −1.7876 1.51E-03 SERF1B 1.05268 9.76E-04 up-regulated ENDOD1 −1.7902 2.43E-07 ZIC2 1.05264 2.88E-02 up-regulated LILRB3 −1.7912 3.11E-05 NUDT14 1.05253 5.59E-03 up-regulated CORO1A −1.7931 2.46E-05 PSMA6 1.0494 2.33E-05 up-regulated SFRP4 −1.8005 6.23E-03 NCOR2 1.04841 4.62E-03 up-regulated ABCA8 −1.8024 1.32E-06 SLC3A2 1.04579 1.14E-04 up-regulated TSC22D3 −1.8079 1.83E-04 FDFT1 1.04297 3.59E-05 up-regulated FZD4 −1.8086 2.40E-04 CNIH1 1.03912 2.00E-04 up-regulated PRG4 −1.8101 2.94E-04 COMMD10 1.03903 1.12E-06 up-regulated DEFA4 −1.8136 4.79E-03 PPIAL4A 1.03873 1.61E-06 up-regulated TIMP4 −1.8156 4.10E-05 CCDC58 1.03669 1.38E-04 up-regulated IFIT1B −1.8174 6.86E-03 NXN 1.03613 6.62E-03 up-regulated SLC4A1 −1.8195 5.51E-03 MCTS1 1.03571 1.97E-07 up-regulated CD36 −1.8207 4.28E-02 BMP7 1.03213 3.34E-03 up-regulated PLA2G16 −1.8207 2.07E-02 PCK2 1.02938 5.85E-03 up-regulated C11orf96 −1.8216 1.14E-02 PRKRA 1.02824 5.65E-06 up-regulated TRIM10 −1.8249 5.86E-03 NFE2L3 1.02822 2.96E-02 up-regulated RASAL3 −1.8301 6.06E-05 HDAC2 1.02712 1.57E-04 up-regulated PHF11 −1.8328 2.59E-04 DHCR7 1.0266 1.03E-03 up-regulated ADH1A −1.8344 1.92E-03 CCNB1IP1 1.02421 9.63E-03 up-regulated C1S −1.8366 3.86E-03 LYPD6B 1.02351 1.54E-02 up-regulated MYOZ1 −1.8378 2.08E-03 IQGAP3 1.02279 5.88E-03 up-regulated BTG2 −1.8546 1.09E-04 PIP4K2C 1.02264 4.04E-02 up-regulated KAT2B −1.8596 2.72E-04 RDH11 1.02229 2.82E-05 up-regulated PRTN3 −1.8604 5.65E-03 MRPL14 1.01797 6.63E-03 up-regulated MS4A7 −1.8621 9.44E-06 YIPF4 1.01761 1.29E-08 up-regulated SPTA1 −1.8625 6.02E-03 SPEG 1.01388 5.49E-03 up-regulated PDK4 −1.8677 3.87E-05 SPDL1 1.01331 3.36E-03 up-regulated NINJ2 −1.8686 6.44E-03 MRPL9 1.01311 3.23E-04 up-regulated DUSP23 −1.8692 1.42E-04 GLRB 1.0118 1.22E-02 up-regulated GYPB −1.8722 6.56E-03 NR2F6 1.0106 8.71E-05 up-regulated SDPR −1.8743 6.97E-04 PIPSL 1.00853 1.49E-06 up-regulated FOS −1.8756 1.01E-02 MEX3B 1.00817 1.25E-03 up-regulated ENO3 −1.8816 1.05E-02 WDYHV1 1.00786 1.17E-03 up-regulated MYL1 −1.8843 1.63E-02 NUDT5 1.00768 6.00E-03 up-regulated PPBP −1.8934 1.95E-03 NME2 1.00582 3.14E-04 up-regulated MYLPF −1.898 3.78E-03 ACOT7 1.00427 2.85E-03 up-regulated FAXDC2 −1.8989 5.14E-05 BDNF 1.00381 1.10E-02 up-regulated GPX3 −1.9002 7.98E-03 BUB1 1.00265 1.79E-03 up-regulated AZU1 −1.9074 6.41E-03 IFNGR1 −1.0006 9.91E-05 CRISPLD2 −1.9076 8.92E-04 STXBP2 −1.0017 1.96E-02 PRG2 −1.9142 1.71E-03 RNASET2 −1.0023 3.72E-02 FKBP5 −1.9163 2.63E-06 VSIG4 −1.0045 1.93E-05 HAVCR2 −1.9199 3.23E-05 HSPB6 −1.005 4.50E-02 SLN −1.9283 2.36E-02 CCL4 −1.0059 4.84E-04 TGM2 −1.9353 1.31E-03 IFITM2 −1.0062 4.72E-02 PGLYRP1 −1.946 4.55E-03 HCK −1.0065 1.74E-05 EMP1 −1.9478 2.98E-02 TMOD4 −1.0081 2.24E-02 PDGFRB −1.9614 5.15E-03 PARVG −1.0083 1.69E-03 CCL8 −1.9626 1.18E-04 CPXM2 −1.0089 4.34E-02 SERPINF1 −1.9717 4.35E-03 CELF2 −1.0101 6.13E-06 PYCARD −1.9757 6.14E-04 APOL3 −1.0124 2.91E-03 PLBD1 −1.981 5.93E-03 KDM5D −1.0166 4.09E-03 EPB42 −1.9867 4.82E-03 SESN1 −1.0167 2.37E-04 TNNT3 −1.9914 6.63E-04 TSPAN7 −1.0196 1.99E-02 PADI4 −1.9984 2.17E-03 PNPLA7 −1.0215 8.98E-03 STAB1 −2.0027 1.40E-05 LPL −1.0237 2.55E-02 NKG7 −2.0053 5.60E-05 FEZ1 −1.0239 4.87E-02 GLDN −2.0065 2.48E-04 ANKRD35 −1.0245 1.58E-04 MXRA5 −2.0086 2.22E-02 SLC44A2 −1.0254 3.21E-03 CYBA −2.0087 1.82E-02 CD59 −1.028 7.01E-03 RASIP1 −2.0139 4.71E-04 UCP2 −1.0282 4.51E-02 CIDEA −2.0173 1.17E-03 LILRA2 −1.0288 5.43E-04 DOCK2 −2.0309 4.08E-05 GPSM3 −1.0291 2.19E-02 MYOM1 −2.0423 2.78E-03 UBAC1 −1.0294 2.85E-03 PTPN6 −2.0493 1.14E-05 TNFSF13B −1.0303 5.62E-05 HLA-DMA −2.0608 5.09E-03 LMOD3 −1.0326 7.47E-04 FRZB −2.0642 3.19E-04 BTK −1.0331 1.42E-03 CRIP1 −2.0653 1.50E-02 PGAM2 −1.0347 2.21E-02 FCGRT −2.0735 2.09E-04 PIK3IP1 −1.0366 2.51E-02 LCN2 −2.0787 8.75E-03 UBA7 −1.0371 1.50E-02 HLA-E −2.0797 4.46E-04 CD79A −1.0373 4.54E-04 CFD −2.096 1.94E-02 DHRS1 −1.0375 5.71E-03 PLAC9 −2.0977 4.74E-05 NNAT −1.038 1.12E-02 ADGRF5 −2.0983 2.10E-04 GZMA −1.039 4.16E-02 SORBS1 −2.0999 3.53E-04 FOXO3B −1.0408 4.64E-04 GYPE −2.1044 5.98E-03 CDC42EP5 −1.041 3.07E-02 DUSP1 −2.1115 9.87E-04 GUCY1A3 −1.0426 7.77E-04 THBS4 −2.1117 5.65E-04 RPL3L −1.0427 2.57E-02 CAT −2.1225 2.36E-05 ACTN2 −1.0434 2.14E-02 LCP2 −2.1291 1.59E-05 JAG1 −1.0437 1.22E-03 SCRG1 −2.1364 9.78E-05 SVIL −1.0447 3.51E-02 RASD1 −2.1522 9.56E-03 HLA-DOA −1.048 8.29E-06 PECAM1 −2.1525 2.70E-05 FAM26F −1.0489 6.45E-03 FPR1 −2.1525 1.20E-04 CXCL16 −1.0512 4.80E-02 MAOA −2.1611 1.30E-04 RBP7 −1.0523 1.23E-02 ICAM2 −2.1614 9.15E-06 GPAM −1.0548 2.10E-03 AKR1C3 −2.1627 9.83E-03 RASGRP3 −1.0574 1.91E-04 FAM46C −2.167 4.42E-03 MEDAG −1.0587 1.18E-03 MMRN1 −2.1728 3.78E-06 PIGQ −1.0594 1.43E-02 COL4A1 −2.1747 3.80E-02 LIPE −1.0602 6.90E-03 WAS −2.1779 4.27E-05 NPL −1.0614 3.84E-03 CD248 −2.183 2.10E-03 GALC −1.0626 4.81E-03 SERPINA3 −2.2073 4.14E-03 NRAP −1.0627 1.86E-02 PRRX1 −2.2099 2.35E-04 LST1 −1.0633 3.30E-05 MEPE −2.2146 1.08E-03 ADARB1 −1.0653 1.23E-02 GYPC −2.2526 1.50E-03 IGF2 −1.0654 2.34E-02 MYL2 −2.2574 2.79E-03 PHKG1 −1.0682 3.72E-03 GIMAP7 −2.2582 2.79E-06 GPX1 −1.0684 3.38E-03 DCN −2.2613 5.45E-04 UGCG −1.0697 1.28E-02 CD163 −2.263 6.82E-06 IL4R −1.0697 2.41E-03 HLA-DMB −2.2633 2.35E-04 COL4A3BP −1.0698 5.34E-06 CECR1 −2.2691 5.73E-08 EBF1 −1.072 8.10E-03 KCTD12 −2.2691 3.88E-05 ACER3 −1.0723 4.22E-06 ATP8B4 −2.2944 5.10E-06 SLC38A5 −1.0734 4.37E-04 SPINT2 −2.3012 3.97E-07 MYH1 −1.0748 2.46E-02 IGFBP4 −2.3038 9.35E-03 FBXO7 −1.0756 1.85E-03 SERPING1 −2.3147 9.86E-06 NTRK2 −1.079 8.53E-04 ALOX5 −2.3181 2.64E-04 PARM1 −1.0803 8.33E-03 CAMP −2.3187 7.55E-03 XIRP2 −1.0813 3.00E-02 BGN −2.3393 1.41E-02 CEACAM8 −1.0816 7.94E-03 JAML −2.3405 1.79E-05 CTSE −1.0834 5.54E-03 MPO −2.3559 4.98E-03 TPP1 −1.0845 1.42E-03 S100A9 −2.3572 6.15E-04 TANGO2 −1.0875 3.40E-03 CD14 −2.3616 4.08E-05 RDH5 −1.089 3.56E-03 SLC2A3 −2.3944 4.47E-03 LPAR6 −1.091 3.66E-03 MB −2.4117 2.70E-03 RAB27B −1.0912 2.94E-02 LAPTM5 −2.4266 4.97E-05 CLK1 −1.092 4.94E-04 TNFRSF1B −2.4272 7.29E-07 TNMD −1.094 2.80E-03 ZFP36 −2.4323 6.44E-05 IL6ST −1.0944 1.54E-03 C1orf162 −2.4329 1.72E-05 ARHGAP4 −1.0963 1.84E-02 MYBPC1 −2.4553 5.01E-04 TRIM22 −1.097 4.04E-03 MS4A6A −2.4632 3.81E-06 ALAS2 −1.0997 7.46E-03 ELANE −2.4632 6.06E-03 XK −1.1016 1.63E-02 RARRES1 −2.4671 2.25E-05 RBP4 −1.104 1.05E-02 IGSF6 −2.4809 8.38E-06 RGS5 −1.105 3.91E-04 DNASE1L3 −2.4988 1.07E-04 IRF9 −1.1056 5.49E-03 DNAJA4 −2.5175 1.04E-04 COQ8A −1.1087 9.28E-03 LXN −2.5234 5.69E-07 COX6A2 −1.1111 2.14E-02 ACP5 −2.5298 1.05E-04 OSTF1 −1.1123 1.30E-04 CA1 −2.5631 5.28E-03 SH2B3 −1.1128 2.51E-02 C2orf40 −2.5704 8.23E-05 PPP2R5A −1.1193 9.74E-04 ACKR1 −2.59 1.68E-05 NR4A3 −1.1194 1.06E-03 MYH11 −2.5992 2.47E-05 PPP6R2 −1.12 5.93E-06 LCP1 −2.616 2.29E-03 PTGER4 −1.1205 4.41E-04 HCST −2.6417 2.37E-05 NFIA −1.1237 1.16E-06 AHSP −2.6584 4.46E-03 PPP1R3C −1.124 4.49E-02 HBD −2.6718 3.49E-03 CLEC4A −1.1248 5.92E-05 FCER1G −2.6727 4.68E-06 FMO2 −1.1259 3.64E-04 MGP −2.6822 1.38E-02 CASQ2 −1.1328 1.56E-03 CKM −2.6824 6.15E-04 KLF1 −1.1347 5.48E-03 HCLS1 −2.6867 1.45E-06 CD209 −1.135 1.05E-03 DEFA1B −2.6891 3.55E-03 SLC7A2 −1.1362 2.56E-02 HLA-DRB4 −2.6958 4.92E-05 CXCL14 −1.1391 3.92E-02 CA2 −2.756 1.43E-03 PYGM −1.1414 1.29E-02 STOM −2.7571 3.16E-05 43526 −1.1418 8.37E-04 PLEK −2.7652 7.59E-06 TPM2 −1.1419 3.33E-02 H19 −2.7657 1.40E-05 S1PR3 −1.1433 1.30E-02 CD93 −2.768 6.90E-06 LYST −1.1437 2.27E-03 RAC2 −2.7844 1.09E-03 ARHGEF3 −1.1437 1.58E-02 HBG1 −2.8219 2.81E-03 RERGL −1.1484 1.07E-03 HBG2 −2.8256 2.60E-03 SIDT2 −1.1493 8.85E-04 FCN1 −2.8322 4.17E-05 NDRG2 −1.1521 8.60E-03 CHAD −2.853 4.87E-06 SLC15A3 −1.153 1.20E-03 ALOX5AP −2.8572 4.71E-05 FOXC1 −1.1554 6.50E-03 MYH7 −2.883 3.03E-04 SLCO2B1 −1.1585 8.56E-06 C1QC −2.8851 2.87E-06 SNRK −1.1601 9.47E-05 CRYAB −2.8997 7.92E-04 CD33 −1.1619 7.32E-04 COMP −2.9056 3.84E-05 GPD1 −1.164 4.56E-03 TNNC2 −2.9107 1.89E-04 CPA3 −1.1643 1.26E-04 GIMAP4 −2.9225 5.09E-06 TGFBR3 −1.1694 7.50E-03 SPARCL1 −2.9283 4.53E-05 CALM1 −1.1707 1.40E-05 PTGS1 −2.9381 1.16E-11 FAM13A −1.1712 1.66E-05 CXCL12 −2.9637 2.77E-04 MYOC −1.1713 7.81E-03 COX7A1 −2.9887 2.81E-07 GVINP1 −1.1734 6.77E-06 CXCR4 −3.0167 8.06E-05 SLCO2A1 −1.1747 5.01E-03 AIF1 −3.0211 3.49E-06 ADD1 −1.1765 1.34E-05 CSF1R −3.0456 4.17E-06 FMO3 −1.1771 2.88E-04 ANGPTL4 −3.059 2.40E-06 FLOT2 −1.1775 2.54E-03 SPP1 −3.0656 2.83E-03 RAB20 −1.1792 1.40E-02 ADIRF −3.0814 7.84E-04 SLC2A5 −1.1846 1.30E-03 RNASE1 −3.1237 1.19E-05 TCF4 −1.1855 1.15E-03 TF −3.1318 7.13E-06 PODN −1.1862 3.33E-04 CTSG −3.146 4.61E-04 INPP5D −1.1876 2.46E-04 C1QB −3.1848 3.57E-06 TRPV2 −1.1884 1.64E-04 JCHAIN −3.2037 6.04E-06 CDO1 −1.1886 7.30E-04 TAGLN −3.2981 3.98E-04 MYL12A −1.1897 6.40E-04 HLA-DPA1 −3.3356 7.11E-06 RAPGEF3 −1.1907 2.10E-03 APOD −3.3434 5.35E-06 YPEL3 −1.1915 3.90E-05 APOE −3.3998 3.44E-07 OSM −1.1929 2.33E-04 PLVAP −3.404 6.54E-08 GCA −1.1965 2.45E-02 VAMP8 −3.4947 1.25E-06 FAM107B −1.1988 1.94E-02 BGLAP −3.5036 8.49E-06 CASQ1 −1.2009 1.77E-02 TYROBP −3.5119 3.56E-06 TEK −1.2011 1.49E-04 C1QA −3.5216 3.18E-06 IL18RAP −1.2014 1.01E-03 FABP4 −3.5553 4.00E-06 LEP −1.2028 1.63E-03 HLA-DRA −3.6005 3.07E-05 RHD −1.2036 7.31E-03 LYZ −3.6115 9.02E-06 FMOD −1.2047 7.63E-03 CTSK −3.6332 8.55E-06 PROS1 −1.208 3.07E-03 VWF −3.6453 2.95E-05 APOBEC2 −1.2121 1.24E-02 S100A8 −3.8846 2.78E-05 MYOZ3 −1.214 1.98E-03 CD74 −3.9079 3.80E-06 NFKBIA −1.216 7.49E-03 MMP9 −4.0695 4.49E-05 C5AR1 −1.2168 2.15E-04 ARHGDIB −4.0756 1.41E-05 PNP −1.2208 7.40E-03 HBA1 −4.8485 4.56E-06 MAPK13 −1.2223 4.00E-05 SRGN −5.1743 5.51E-09 Table S2 (continued) HBB −5.1916 3.03E-06 Table S3 Fifteen differentially expressed miRNAs miRNAs:

hsa-miR-338-3p

hsa-miR-142-5p

hsa-miR-346

hsa-miR-502-5p

hsa-miR-181d

hsa-miR-331-5p

hsa-miR-219-5p

hsa-miR-487a

hsa-miR-501-3p

hsa-miR-330-3p

hsa-miR-301b

hsa-miR-362-3p

hsa-miR-532-3p

hsa-miR-769-5p

hsa-miR-542-5p Table S4 187 hypermethylated down-regulated genes and 3 hypomethylated up-regulated genes Gene Hypomethylated up-regulated genes BMP4 Hypomethylated up-regulated PYCR1 Hypomethylated up-regulated PRAME Hypomethylated up-regulated DES Hypermethylated down-regulated CKMT2 Hypermethylated down-regulated TNFRSF1B Hypermethylated down-regulated PODN Hypermethylated down-regulated TF Hypermethylated down-regulated SPINT2 Hypermethylated down-regulated AMT Hypermethylated down-regulated CMKLR1 Hypermethylated down-regulated SLC15A3 Hypermethylated down-regulated ADCY4 Hypermethylated down-regulated CRIP1 Hypermethylated down-regulated COX7A1 Hypermethylated down-regulated SPOCK2 Hypermethylated down-regulated C1QTNF5 Hypermethylated down-regulated PGLYRP1 Hypermethylated down-regulated DOCK2 Hypermethylated down-regulated TSC22D3 Hypermethylated down-regulated CD248 Hypermethylated down-regulated CD74 Hypermethylated down-regulated LYL1 Hypermethylated down-regulated LTF Hypermethylated down-regulated GPM6B Hypermethylated down-regulated CYGB Hypermethylated down-regulated TGFBR3 Hypermethylated down-regulated STAT5A Hypermethylated down-regulated LXN Hypermethylated down-regulated CA3 Hypermethylated down-regulated SLC22A16 Hypermethylated down-regulated ARHGAP4 Hypermethylated down-regulated TIMP4 Hypermethylated down-regulated HCLS1 Hypermethylated down-regulated ICAM3 Hypermethylated down-regulated FES Hypermethylated down-regulated LEP Hypermethylated down-regulated NDRG2 Hypermethylated down-regulated PTPN6 Hypermethylated down-regulated MAPK13 Hypermethylated down-regulated BPI Hypermethylated down-regulated CA4 Hypermethylated down-regulated HSPB6 Hypermethylated down-regulated CDO1 Hypermethylated down-regulated CD14 Hypermethylated down-regulated TYROBP Hypermethylated down-regulated IRAK3 Hypermethylated down-regulated CHAD Hypermethylated down-regulated PYCARD Hypermethylated down-regulated RAC2 Hypermethylated down-regulated CDH5 Hypermethylated down-regulated MFNG Hypermethylated down-regulated RASIP1 Hypermethylated down-regulated AEBP1 Hypermethylated down-regulated CXCL12 Hypermethylated down-regulated HCK Hypermethylated down-regulated SERPING1 Hypermethylated down-regulated MAP2K3 Hypermethylated down-regulated PTGIS Hypermethylated down-regulated CYP1B1 Hypermethylated down-regulated FRZB Hypermethylated down-regulated STAB1 Hypermethylated down-regulated RBP4 Hypermethylated down-regulated HBQ1 Hypermethylated down-regulated RARRES1 Hypermethylated down-regulated ANGPTL2 Hypermethylated down-regulated HBA1 Hypermethylated down-regulated CPXM2 Hypermethylated down-regulated COMP Hypermethylated down-regulated VAMP8 Hypermethylated down-regulated FMOD Hypermethylated down-regulated CIDEA Hypermethylated down-regulated THBS4 Hypermethylated down-regulated GPX3 Hypermethylated down-regulated SLC2A5 Hypermethylated down-regulated HLA-E Hypermethylated down-regulated LAPTM5 Hypermethylated down-regulated GSN Hypermethylated down-regulated CLEC3B Hypermethylated down-regulated DUSP23 Hypermethylated down-regulated CDC42EP5 Hypermethylated down-regulated SPARCL1 Hypermethylated down-regulated PRG2 Hypermethylated down-regulated CTSK Hypermethylated down-regulated FEZ1 Hypermethylated down-regulated SCRG1 Hypermethylated down-regulated SFRP4 Hypermethylated down-regulated GYPC Hypermethylated down-regulated RBP7 Hypermethylated down-regulated SULF1 Hypermethylated down-regulated CEBPA Hypermethylated down-regulated OAS2 Hypermethylated down-regulated PGAM2 Hypermethylated down-regulated SLCO2A1 Hypermethylated down-regulated PTGS1 Hypermethylated down-regulated PDGFRB Hypermethylated down-regulated MGP Hypermethylated down-regulated FCER1G Hypermethylated down-regulated PLEK Hypermethylated down-regulated FLOT2 Hypermethylated down-regulated LYZ Hypermethylated down-regulated GNA15 Hypermethylated down-regulated KCTD12 Hypermethylated down-regulated VAMP5 Hypermethylated down-regulated ISLR Hypermethylated down-regulated CXCL14 Hypermethylated down-regulated G0S2 Hypermethylated down-regulated TEK Hypermethylated down-regulated NTRK2 Hypermethylated down-regulated RNASET2 Hypermethylated down-regulated TNS1 Hypermethylated down-regulated IL6ST Hypermethylated down-regulated CYTL1 Hypermethylated down-regulated SPON1 Hypermethylated down-regulated SNCA Hypermethylated down-regulated SERPINF1 Hypermethylated down-regulated MYH11 Hypermethylated down-regulated SLA Hypermethylated down-regulated CXCL16 Hypermethylated down-regulated HLA-DMA Hypermethylated down-regulated FCGRT Hypermethylated down-regulated ACTN2 Hypermethylated down-regulated TSPAN7 Hypermethylated down-regulated NNAT Hypermethylated down-regulated CYBA Hypermethylated down-regulated ENO3 Hypermethylated down-regulated RENBP Hypermethylated down-regulated RARRES2 Hypermethylated down-regulated CA2 Hypermethylated down-regulated TNFSF10 Hypermethylated down-regulated DPT Hypermethylated down-regulated NINJ2 Hypermethylated down-regulated LY86 Hypermethylated down-regulated ARHGAP25 Hypermethylated down-regulated RHD Hypermethylated down-regulated TMEM71 Hypermethylated down-regulated IFI44L Hypermethylated down-regulated FCGR2A Hypermethylated down-regulated ARHGDIB Hypermethylated down-regulated GMFG Hypermethylated down-regulated PYGM Hypermethylated down-regulated OSM Hypermethylated down-regulated MCEMP1 Hypermethylated down-regulated ACTN3 Hypermethylated down-regulated GPD1 Hypermethylated down-regulated STXBP2 Hypermethylated down-regulated NFE2 Hypermethylated down-regulated AIF1 Hypermethylated down-regulated MMRN1 Hypermethylated down-regulated BTK Hypermethylated down-regulated CD37 Hypermethylated down-regulated ADARB1 Hypermethylated down-regulated TGM2 Hypermethylated down-regulated RNASE2 Hypermethylated down-regulated WAS Hypermethylated down-regulated LCP2 Hypermethylated down-regulated PRKCH Hypermethylated down-regulated FGL2 Hypermethylated down-regulated RGS5 Hypermethylated down-regulated APOC1 Hypermethylated down-regulated PPP1R14A Hypermethylated down-regulated APOLD1 Hypermethylated down-regulated PRSS35 Hypermethylated down-regulated TNFRSF14 Hypermethylated down-regulated CPA3 Hypermethylated down-regulated BIN2 Hypermethylated down-regulated ALOX5AP Hypermethylated down-regulated C1orf54 Hypermethylated down-regulated RGS1 Hypermethylated down-regulated TBC1D10C Hypermethylated down-regulated TCAP Hypermethylated down-regulated SELENBP1 Hypermethylated down-regulated PDE7B Hypermethylated down-regulated RASD1 Hypermethylated down-regulated APOE Hypermethylated down-regulated TRIM22 Hypermethylated down-regulated PPP1R3C Hypermethylated down-regulated IGF2 Hypermethylated down-regulated APOD Hypermethylated down-regulated VWF Hypermethylated down-regulated ARHGAP15 Hypermethylated down-regulated C1S Hypermethylated down-regulated ALOX5 Hypermethylated down-regulated MXRA5 Hypermethylated down-regulated CLDN5 Hypermethylated down-regulated IGFBP7 Hypermethylated down-regulated CD48 Hypermethylated down-regulated TNNC2 Hypermethylated down-regulated HAVCR2 Hypermethylated down-regulated LDB2 Hypermethylated down-regulated Table S5 Forty-seven hypermethylated down-regulated genes targeted by DEMs combined with GSEA Genes

DES

TNFRSF1B

ADCY4

DOCK2

CD74

STAT5A

ICAM3

LEP

MAPK13

CD14

TYROBP

CHAD

PYCARD

RAC2

CDH5

CXCL12

HCK

SERPING1

PTGIS

COMP

THBS4

GPX3

HLA-E

GSN

PRG2

CTSK

PTGS1

PDGFRB

FCER1G

CXCL14

MYH11

HLA-DMA

ACTN2

CYBA

TNFSF10

FCGR2A

OSM

ACTN3

BTK

CD37

WAS

LCP2

TNFRSF14

CPA3

VWF

CLDN5

CD48 Enrichment plot: Enrichment plot: Enrichment plot: KEGG_CELL_CYCLE Enrichment plot: KEGG_DNA_REPLICATION Enrichment plot: KEGG_AMINOACYL_TRNA_BIOSYNTHESIS KEGG_ARACHIDONIC_ACID_METABOLISM 0.7 KEGG_HEMATOPOIETIC_CELL_LINEAGE 0.5 0.0 0.6 0.7 0.0 0.4 0.5 0.6 −0.1 −0.1 0.4 0.5 −0.2 0.3 −0.2 0.4 0.3 −0.3 −0.3 0.2 0.2 0.3 −0.4 −0.4 0.2 0.1 0.1 −0.5

−0.5 (ES) Enrichment score 0.1 (ES) Enrichment score 0.0 0.0 −0.6 Enrichment score (ES) Enrichment score −0.6 Enrichment score (ES) Enrichment score Enrichment score (ES) Enrichment score 0.0 −0.7

‘tumor’ (positively correlated) ‘tumor’ (positively correlated) ‘tumor’ (positively correlated) ‘tumor’ (positively correlated) ‘tumor’ (positively correlated) 0.5 0.5 0.5 0.5 0.5 0.0 0.0 0.0 0.0 0.0 Zero cross at 8264 Zero cross at 8264 Zero cross at 8264 Zero cross at 8264 Zero cross at 8264 −0.5 −0.5 −0.5 −0.5 −0.5

−1.0 ‘normal’ (negatively correlated) −1.0 ‘normal’ (negatively correlated) −1.0 ‘normal’ (negatively correlated) −1.0 ‘normal’ (negatively correlated) −1.0 ‘normal’ (negatively correlated) 0 2,500 5,000 7,500 10,000 12,500 15,000 0 2,500 5,000 7,500 10,000 12,500 15,000 0 2,500 5,000 7,500 10,000 12,500 15,000 0 2,500 5,000 7,500 10,000 12,500 15,000 0 2,500 5,000 7,500 10,000 12,500 15,000 Rank in Ordered Dataset Rank in Ordered Dataset Rank in Ordered Dataset Rank in Ordered Dataset Rank in Ordered Dataset

Ranked list metric (Signal2Noise) Enrichment profile Hits Ranking metric scores Ranked list metric (Signal2Noise) Enrichment profile Hits Ranking metric scores Ranked list metric (Signal2Noise) Enrichment profile Hits Ranking metric scores Ranked list metric (Signal2Noise) Enrichment profile Hits Ranking metric scores Ranked list metric (Signal2Noise) Enrichment profile Hits Ranking metric scores

Enrichment plot: KEGG_PROTEIN_EXPORT Enrichment plot: KEGG_RENIN_ANGIOTENSIN_SYSTEM Enrichment plot: KEGG_RIBOSOME Enrichment plot: KEGG_RNA_DEGRADATION Enrichment plot: KEGG_SPLICEOSOME 0.7 0.6 0.0 0.5 0.5 0.6 −0.1 0.5 0.5 −0.2 0.4 0.4 0.4 0.4 −0.3 0.3 0.3 0.3 −0.4 0.3 0.2 0.2 −0.5 0.2 0.2 −0.6 0.1 0.1 0.1 0.1

Enrichment score (ES) Enrichment score −0.7 0.0 Enrichment score (ES) Enrichment score Enrichment score (ES) Enrichment score Enrichment score (ES) Enrichment score 0.0 (ES) Enrichment score 0.0 0.0 −0.8 −0.1

Figure S1 the GSEA analysis results of other significant pathways visualized as enrichment plot