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

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. 2020 Annals of Translational Medicine. All rights reserved.

Introduction

Osteosarcoma (OS) is the most common primary malignant bone tumors in children and young adults. It originates from mesenchymal cells and is characterized by the appearance of spindle cells and aberrant osteoid formation. OS can occur in any bone, although the distal femur, proximal tibia and the proximal humerus are the most common sites, accounting for respectively 43%, 23% and 10% of the cases. Within the bone, OS typically affects the metaphysis close to the growth plate (1). OS also has a high metastasis rate of 20%, and commonly invades the lungs and other bones (2).

Studies in recent years have shown that epigenetic changes play an important role in tumorigenesis and progression. DNA methylation involves addition of methyl groups (-CH3) to cytosines in the CpG dinucleotides, and both hypermethylation or hypomethylation can result in long-term silencing of imprinted genes, transposons and the inactive X chromosome (3). Aberrant methylation is frequently observed in tumors, and silencing of the promoter regions of tumor suppressor genes (TSGs) by hypermethylation of CpG islands (4) affects the cell cycle checkpoint, apoptosis, signal transduction, cell adhesion and angiogenesis genes (5). MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression at the post-translational level by binding to the 3'-UTR of target mRNA, and are also aberrantly expressed in various cancers (6). Abnormal DNA methylation patterns have been detected in OS that interfere with the TSGs, and promote tumor initiation and progression (7). Although the DNA methylation patterns in OS have gained attention as vital biomarkers and therapeutic targets, the regulatory network between DNA methylation, miRNAs and target genes remains elusive.

Previous studies have identified several differentially methylated genes (DMGs) and differentially expressed genes (DEGs) in OS through bioinformatics analysis (8). However, the regulatory networks of OS-related miRNAs, mRNAs and methylation patterns in OS are largely unknown. To this end, we screened the mRNA, miRNA and gene methylation profiles from OS and normal bone microarray datasets, in order to identify the DEGs, DMGs, TSGs and differentially expressed miRNAs (DEMs) related to OS. The aim of our study was to identify and validate the aberrantly methylated TSGs in OS. Our findings provide new insights into the potential tumorigenic role of abnormal DNA methylation, and identify potential biomarkers and therapeutic targets for OS.

Methods

Identification of DEGs, DEMs and DMGs

The miRNA datasets GSE28423 and GSE65071, the mRNA dataset GSE36001 and the methylation dataset GSE36002 were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The GSE28423 was registered on the GPL8227 microarray platform (Agilent-019118 Human miRNA Microarray 2.0 G4470B) and contained data from 19 OS and 4 normal bone samples. GSE65071 was registered on the microarray platform of GPL19631 (Exiqon human V3 microRNA PCR panel I+II) and included data of 20 OS and 15 normal bone samples. GSE36001 and GSE36002 were from the GPL6102 microarray platform (Illumina human-6 v2.0 expression beadchip), and included data of 19 OS cell lines and 6 normal osteoblasts and bones samples.

GEO2R was used to screen for the DEGs, DEMs and DMGs from the respective datasets using |logFC| >1 or log|β| >0.2 (for DMGs) and P value <0.05 as the thresholds. The putative target genes of the overlapping DEMs of GSE28423 and GSE65071 datasets were predicted using the RNA22 tool (https://cm.jefferson.edu/rna22/Interactive/). The expression matrix of the DEGs was analyzed by the GSEA tool according to P values <0.05 and q values <0.05, and the putative TSGs were identified from the TSGene database (https://bioinfo.uth.edu/TSGene/download.cgi). Finally, the Venn diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to determine (I) overlapping hypomethylated mRNAs, up-regulated genes and targets to identify the up-regulated hypomethylated mRNAs; (II) overlapping hypermethylated mRNAs, down-regulated mRNAs and targets for downregulated hypermethylated mRNAs; (III) overlapping down-regulated mRNAs, hypermethylated mRNAs, targets and TSGs to identify the down-regulated hypermethylated TSGs.

Functional analyses of the relevant genes

Biological process (BP) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using the ClusterProfiler package of R software, with P<0.05 as the threshold. Gene set enrichment analysis (GSEA) was conducted using the GSEA desktop tool. The Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) database was used to construct the PPI networks, which were then visualized using CYTOSCAPE.

Validation of TSGs

The prognostic value of the 8 TSGs was determined by survival analysis using the online database R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl). The clinical data and the expression data were extracted from a dataset, the Mixed Osteosarcoma–Kuijjer–127–vst–ilmnhwg6v2. The patients were stratified into the respective low- and high-expression groups using the R2 scan method. The correlation between TSGs methylation and expression was determined by Spearman’s rank correlation analysis, with |Cor|>0.3 and P<0.05 as the thresholds.

Cell culture and treatment

The OS cell line MG63 was purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA), and cultured in DMEM (Gibco, USA) supplemented with 10% FBS under 5% CO2 at 37 °C. The DNA methyltransferase inhibitor 5-azacytidine (5-Aza) was purchased from MedChemExpress. The MG63 cells were treated with 5 µM 5-Aza or DMSO (vehicle) for 48 h.

RNA isolation and quantitative RT-PCR

Total RNA was extracted from the cells using Total RNA Extraction Reagent (Vazyme, China), and reverse transcribed 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, Switzerland). The relative mRNA expression levels were determined by the 2-ΔΔCT method. All tests were performed three times, and GAPDH was used as the internal control. The primer sequences are shown in .

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

Cell proliferation assay

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 supplemented with 10 µL Cell Counting Kit-8 (CCK-8) reagent/well (DOJINDO, Japan), and the cells were incubated for another hour. The absorbance at 450nm was measured using a microplate reader (Bio.Tek, USA), and the proliferation rates were calculated after subtracting the background absorbance. The experiment was performed thrice.

Wound-healing assay

MG63 cells were seeded into 6 well plates at 80% density and treated with 5 µM 5-Aza or DMSO for 48 h. The monolayer was scratched perpendicular to the plate with a sterile 200µl pipette tip. The dislodged cells were removed by washing twice with PBS, and serum-free medium was added. The cells were cultured for 24 h, and the wound edges were photographed at the same position at 0, 24 h under an inverted microscope. The migration rate was determined based on the width of the wound edges. The assay was performed in triplicate.

Invasion assay

In vitro invasion was analyzed using 24 well Invasion Chambers (8 µm pore size; Costar, Corning, USA). For the invasion assay, the transwell chambers were pre-coated with 50 µL Matrigel (Corning, USA) for 3 h. MG63 cells were seeded into the upper chambers at the density of 1×104/100 µL in serum free medium, and the lower chambers were filled with 500 µL DMEM supplemented with 10% FBS. After incubating for 24 h, the cells were washed twice with PBS and fixed with 4% paraformaldehyde for 30 minutes at room temperature. The PET membranes were air-dried and stained with 0.1% crystal violet for 15 minutes. The non-invaded cells were wiped off, and the invaded cells were photographed and counted under an inverted microscope in five random fields per membrane. Both assays were performed in triplicate.

Statistical analyses

All statistical analyses were performed using GRAPHPAD PRISM7 (GraphPad Prism Software Inc., San Diego, CA, USA) or SPSS 23.0. Two groups were compared using Student’s t-test with Welch correction in case of significantly different variance, or multiple groups were compared using one-way ANOVA analysis. P<0.05 was considered statistically significant.

Results

Identification of DEGs and DEMs in microarray

Workflow of how aberrantly methylated differentially expressed genes were identified was shown in . A total of 15 DEMs were identified in the OS samples from the GSE28423 and GSE65071 microarray datasets (see methods), and the heat maps are shown in . In addition, 835 DEGs () were also identified from the GSE36001 dataset, of which 574 were down-regulated and 261 were up-regulated (). Finally, 4,313 DMGs were detected in the GSE36002 dataset (), of which 3,441 were hypermethylated and 872 were hypomethylated.

Figure 1

Workflow of how aberrantly methylated differentially expressed genes were identified.

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.

Table S2

Differentially expressed genes in GSE36001

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

Identification of aberrantly methylated DEGs targeted by the DEMs

The RNA22 program predicted 18,809 target genes of the 15 DEMs. The overlap between these targets, DEGs and DMGs revealed 187 hypermethylated down-regulated genes () and only 3 hypomethylated up-regulated genes (). The latter included BMP4, PYCR1 and PRAME, which are involved in BP promoting cancers and other diseases. We surmised therefore that BMP4, PYCR1 and PRAME were significant in OS progression. The DEMs and 187 hypermethylated down-regulated genes are respectively shown in .

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.

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

Identification of hypermethylated down-regulated genes targeted by DEMs combined with GSEA

To further identify the DEGs in OS, we conducted GSEA based on the GSE36001 dataset. Ten KEGG gene sets was significantly enriched in OS compared to normal tissues, including a total of 539 mRNAs according to P values <0.05 and q values <0.05 (). The most significantly enriched KEGG gene set in OS was the KEGG_SPLICEOSOME (), and its constituent DEGs are visualized in . Other significant gene sets included KEGG_CELL CYCLE and KEGG_DNA REPLICATION (). Overlapping of the 539 mRNAs of KEGG gene sets, DEGs, DMGs and 18,809 target genes of the 15 DEMs, revealed 47 hypermethylated down-regulated genes () that are likely involved in OS. The list of 47 hypermethylated down-regulated genes was shown in .

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 S1

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

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

Functional enrichment analysis and PPI network construction

Gene ontology (GO) analysis and KEGG analysis were next performed to determine the BP and pathways associated with the 47 hypermethylated downregulated genes in OS (). As shown in , the most enriched BP were positive regulation of response to external stimulus (GO:0032103) and phagocytosis (GO:0006909). Furthermore, KEGG pathway enrichment analysis showed that most genes were enriched in platelet activation (hsa04611), osteoclast differentiation (hsa04380), natural killer cell mediated cytotoxicity (hsa04650), phagosome (hsa04145), and chemokine signaling pathway (hsa04062). The top 10 significant pathways for the screened genes are shown in , indicating that the DEGs are likely involved in some osteoclast differentiation pathways and several cancer-related pathways, such as the JAK-STAT and PI3K-Akt. Furthermore, the clue GO analysis showed that MAPK13 and FCGR2A were significantly enriched in more pathways compared to the other DEGs (). The PPI network further showed 40 nodes with strong correlations and 2 nodes with weak correlations (), indicating complex interactions between the downregulated and hypermethylated mRNAs at the protein level.

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.

Function analysis and PPI network construction of aberrantly methylated TSGs involved in OS

Our previous study showed that down-regulated hypermethylated mRNAs play an important role in tumorigenesis. Therefore, we screened for known TSGs among the 47 OS-related DEGs, and identified eight (). This indicated that aberrant methylation of these TSGs downregulated their expression levels in OS and promoted tumorigenesis. PPI network construction showed that 7 down-hypermethylated TSGs were strongly correlated at the protein level (). Further GO analysis showed that the key genes were mainly associated with cell regulation, migration and apoptosis (), and pathway enrichment analysis indicated that they were significantly enriched in the NF-kappa B signaling pathway, chemokine signaling pathway and viral carcinogenesis (). Therefore, these aberrantly methylated TSGs are likely involved in OS progression, and are potential biomarkers.

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

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
GO:0002532	Production of molecular mediator involved in inflammatory response	0.00032	PYCARD/BTK	2
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
GO:0008630	Intrinsic apoptotic signaling pathway in response to DNA damage	0.00083	PYCARD/CXCL12	2
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
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.

Prognostic assessment of the down-regulated hypermethylated TSGs in OS

To determine the prognostic value of the above 8 TSGs in OS, we assessed the metastasis free survival of patients from the dataset, Mixed OS (Mesenchymal)–Kuijjer–127–vst–ilmnhwg6v2, after stratifying them into the respective low- and high-expressing groups. Patients expressing low levels of BTK, GPX3, CXCL12, CXCX14, PYCARD and STAT5A had worse metastasis free survival compared to the corresponding high expression groups (), indicating that these genes have a significant impact on patient prognosis.

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.

Correlation between methylation and gene expression

Correlation analysis of the gene expression and DNA methylation data revealed a significant inverse correlation between DNA methylation and the respective gene expression levels (). The methylation sites and correlation coefficient of the significant TSGs are shown in . The most significant effect of DNA methylation was seen on the expression levels of STAT5A and PYCARD (Cor =−0.826, P value =5.207e-20; Cor =−0.887, P value =1.468e-18).

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.

Inhibition of DNA methylation can upregulate the key genes

To further validate the impact of methylation on the expression of the TSGs, we treated MG63 cells with the methyltransferase inhibitor 5-Aza, and examined the expression levels of the relevant genes. As shown in , PYCARD, STAT5A, CDH5, CXCL12 and CXCL14 mRNA levels were significantly increased after 5-Aza treatment, thereby confirming the in silico data. In contrast, BTK and GPX3 expression was not significantly affected by inhibiting DNA methylation.

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.

Aberrant methylation contributed to OS progression

The findings so far indicated that methylated TSGs were involved in cancer-related pathways and contributed to OS progression. To further elucidate the effect of aberrant methylation on OS progression, the MG63 cells were treated with 5-Aza and various functional assays were performed. Blocking DNA methylation not only decreased the survival of the OS cells in vitro (), but also markedly diminished their migration () and invasion capacities () compared to the DMSO controls. Taken together, aberrant DNA methylation is conducive to OS progression, and inhibiting DNA methyltransferases can decrease proliferation, migration and invasion of OS cells.

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.

Discussion

OS is a primary bone malignancy with a high rate of recurrence. Despite advancements in surgery and chemotherapy, the overall survival rates have been dismal over the past 20 years (9). Aberrant DNA methylation is an epigenetic modification that is frequently observed in most cancers, and is a major risk factor that drives tumorigenesis via gene silencing (7). Since epigenetic modifications affect gene functions without altering the DNA sequence, they result in more diverse gene expression profiles (10). Novel molecular biology techniques and bioinformatics integration analysis in recent years have enabled identification of aberrant methylation patterns in various cancers, and provided insights into the underlying mechanisms.

We identified 187 hypermethylated genes and 3 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 hsa-miR-142-5p (12), hsa-miR-338-3p (13) and hsa-miR-542-5p were in particular strongly associated with OS, and can help identify the key genes involved in OS progression.

BMP4, PYCR1 and PRAME were the 3 hypomethylated upregulated genes in OS. BMP4 is a secreted ligand of the TGF-β superfamily that recruits and activates SMAD family transcription factors, and is involved in cardiac development and adipogenesis (14). BMP4 promoter hypomethylation is associated with worse prognosis in gastric cancer (15), indicating that the aberrant methylation of BMP4 in OS is likely oncogenic. PYCR1 catalyzes NAD(P)H-dependent conversion of pyrroline-5-carboxylate to proline during cell proliferation and metabolism, and is up-regulated in a wide range of cancers. Overexpression of PYCR1 contributes to poor prognosis in prostate cancer (16), and based on our results, may have a pro-tumorigenic role in OS. PRAME is overexpressed in melanoma, myeloid leukemia, neuroblastoma (17), and head and neck cancer, and its hypomethylated form promotes the progression of chronic myeloid leukemia (18). In OS as well, PRAME overexpression is associated with poor prognosis, and increases tumor cell proliferation by attenuating cell cycle arrest (19). Taken together, these genes likely promote OS progression, although the underlying mechanisms need to be elucidated. Furthermore, they are potential prognostic biomarkers and therapeutic targets in OS.

Forty-seven hypermethylated down-regulated genes in OS were enriched in functions like response to external stimulus, cell migration and cell proliferation, chemotaxis and inflammatory response, all of which contribute to tumor metastasis (20). Aberrant migration and proliferation is an important cellular program in tumors, and are mediated by dysregulated signaling pathways (21). KEGG analysis revealed that the OS-related hypermethylated genes were significantly associated with the PI3K-Akt and JAT-STAT pathways. The former is the most frequently mutated network in human cancers, and is also dysregulated in tumors due to methylation (22). It promotes cancer cell survival by inhibiting pro-apoptotic and activating anti-apoptotic genes, Furthermore, the downstream mTOR kinase also promotes cell growth and protein synthesis, and downregulates this pathway through a feedback loop. Constitutive activation of the PI3K-Akt pathway is often accompanied by loss or mutations in the tumor suppressor PTEN (23). It also plays a critical role in OS genesis by inhibiting apoptosis and activating pro-survival pathways (24). The JAK/STAT pathway is also dysregulated in many solid tumors and increases tumor cell proliferation and angiogenesis, resulting in worse prognosis (25). Tyrosine phosphorylation and nuclear localization of the STATs have been observed in the tumor tissues across a range of cancers, indicating that JAK/SAT activation correlates with worse prognosis. We next established a clue GO network of the hypermethylated genes and these pathways, and observed significant enrichment of MAPK13 and FCGR2A. The latter is an IgG receptor, and methylation of its promoter decreases binding affinity to the human IgG2, which is associated with higher susceptibility to Kawasaki Disease (26). The p38 MAP kinase MAPK13 is aberrantly expressed in several tumors, and its promoter methylation contributes to melanoma progression (27). However, the role of MAPK13 and FCGR2A in OS remains to be elucidated.

The hypermethylated downregulated genes included 8 TSGs—CDH5, BTK, GPX3, PYCARD, CXCL12, CXCL14, STAT5A and GSN. PYCARD is a signaling factor consisting of a PYD and CARD domain, and mediates the apoptotic pathway by activating caspases (28). Studies show that hypermethylation-mediated silencing of PYCARD enables tumor cell survival by blocking apoptosis (29). We showed a strong correlation between PYCARD hypermethylation and down-regulation in OS, and verified that its low expression levels indicated worse prognosis based on clinical data. Furthermore, 5-Aza treatment markedly upregulated PYCARD, which strongly suggests that the aberrant methylation of PYCARD drives OS genesis. CXCL12 is a chemokine of the intercrine family, and binds with CXCR4 to initiate divergent pathways related to chemotaxis and cell survival (30). High expression levels of CXCL12 is associated with poor prognosis in ovarian cancer (31), and increased migration and invasiveness of adamantinomatous craniopharyngiomas (32). In contrast, down-regulation of CXCL12 have also been detected in OS via promoter hypermethylation by DNA methyltransferase 1 (DNMT1) (33). According to our findings, low expression and hypermethylation of CXCL12 indicates worse prognosis in OS, which make it a potential therapeutic target. CXCL14 is another chemokine involved in immunoregulatory and inflammatory processes (34), as well as tumor migration and invasion. It inhibited colorectal cancer cell migration by suppressing NF-kB signaling, whereas hypermethylation-mediated silencing promoted migration (35). BTK is a component of the Toll-like receptors (TLR) pathway, and promotes inflammatory responses (36). In addition, it is a modulator of p53 that can be induced in response to DNA damage and p53 activation, and phosphorylates the latter to enhance apoptosis (37). We found that low expression levels of BTK due to hypermethylation was associated with poor prognosis in OS.

CDH5 belongs to the cadherin superfamily, and is involved in the vasculogenic mimicry of glioblastoma stem-like cells under hypoxic conditions (38). Low expression levels of CDH5 mediated by promoter methylation was strongly associated with poor overall survival in neuroblastomas (39). In agreement with this, CDH5 was expressed at low levels in OS tissues compared to normal samples, and predicted poor prognosis. STAT5A is a transcription factor that is frequently dysregulated in cancer. Methylation-dependent promoter region silencing of STAT5A inhibited NPM1-ALK expression in ALK + TCL cell lines (40). One study reported low levels of STAT5A in OS and correlated it with increased tumor progression and worse overall survival (41). Consistent with this, the hypermethylation status of STAT5A was strongly associated with its low expression levels as well as poor prognosis in our study. Interestingly, BTK, CXCL12, CDH5 and STAT5A showed strong functional connectivity at the protein level, although the molecular mechanisms remain to be elucidated.

GPX3 is a glutathione peroxidase that protects cells against ROS and DNA damage, and is an established tumor suppressor in various cancers (42). Gene silencing of GPX3 by promoter hypermethylation has been reported in hepatocellular carcinoma (43), and similar trends were observed in the OS samples as well. GSN is an actin-binding protein that regulates actin filament formation and disassembly, as well as apoptosis via DNase I binding and release (44). GSN downregulation via promoter methylation predicts poor survival in gastric cancer (45), whereas overexpression of GSN promoted growth and invasion of OS cells (46). This contradicts our findings that hypermethylation and down regulation of GSN likely drive OS progression.

To summarize, we identified several TSGs that were hypermethylated and downregulated in the OS tissues, indicating a strong relationship between DNA methylation and tumorigenesis. Although our findings have to be validated in experimental studies and the underlying mechanisms also need to be elucidated, we can conclude that the aforementioned genes are significantly involved in OS progression, and are potential prognostic markers and/or therapeutic targets. There are however several limitations in this study. Since the CpG sites information was not available, the significance of specific methylation sites of these hub genes could not be determined. Furthermore, we did not correlate the expression data with the clinical parameters in the same datasets. Further studies are needed to validate the role of these TSGs in OS.

Conclusions

We identified 47 hypermethylated down-regulated mRNAs targeted by significant miRNAs in OS, of which 8 are established tumor suppressors. The aberrantly methylated TSGs may be the potential prognostic biomarkers and therapeutic targets for OS. Our findings provide new insights into the role of methylation in OS progression.