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Identification of aberrantly methylated differentially expressed

genes targeted by differentially expressed miRNA in osteosarcoma

Ting-Xuan Wang

1#

, Wen-Le Tan

2#

, Jin-Cheng Huang

3#

, Zhi-Fei Cui

1

, Ri-Dong Liang

4

, Qing-Chu Li

5

,

Hai Lu

1

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: luhai01@163.com; 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: lqc16@263.net.

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

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

(2)

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

(3)

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% CO

2

at 37 ℃. 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.

Cell proliferation assay

MG63 cells were seeded into a 96 well plate at the density of

3×10

3

/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 1×10

4

/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

(4)

4,313 DMGs from the GSE36002

dataset

835 DMGs from the GSE36002

dataset

3,441 hypermethylated

mRNAs

187 hypermethylated

down-regulated mRNAs

47 hypermethylated down-regulated genes

targeted by DEMs combined with GSEA

8 hypermethylated down-regulated tumor

suppressor genes

combined with GSEA analysis

combined with TSGs database

correlation analysis

Validation of qR

T-PCR

aberrant methylation can contributed to OS pr

ogr ession

survival analysis

3 hypermethylated up-regulated

mRNAs 877

hypermethylated

mRNAs 574 down-regulated mRNAs 261 up-regulated mRNAs

18,809 target genes predicted

[image:4.595.143.449.83.635.2]

by the RNA22 program 15 DEMs from the GSE28423 and GSE65071

(5)

A

C

D

B

−4 −2 0 2 color

−1·log10 (p value)

down no up

Type Tumor

Tumor Normal

Normal Type

Type Type

logFC 15

10

5

0

4

2

0

−2

−4 3

2 1 0 −1 −2 −3

2 1 0 −1 −2

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.

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 (Figure 3A) and only 3 hypomethylated up-regulated

genes (Figure 3B). 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 Tables S3,S4.

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 (Figure 4A). The most

significantly enriched KEGG gene set in OS was the

KEGG_SPLICEOSOME (Figure 4B), and its constituent

DEGs are visualized in Figure 4C. Other significant gene

sets included KEGG_CELL CYCLE and KEGG_DNA

REPLICATION (Figure S1). 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 (Figure 4D) that are likely involved in

OS. The list of 47 hypermethylated down-regulated genes

was shown in Table S5.

Functional enrichment analysis and PPI network

construction

(6)

Hypermethylation Hypomethylation

637 (3.3%)

6

(0%) (0.1%)13

192

(1%) (0%)0 (0.1%)19

239 (1.3%) 3 (0%) 677 (3.6%)

17890 (94.1%) 368

(1.9%) 187 (1%) 2611 (13.4%)

15643 (80.4%)

down-regulated up-regulated

Targets Targets

A

B

A

B

0.5 0.4 0.3 0.2 0.1 0.0

0.5 0.0 −0.5 −1.0

Enrichment PLOT: KEGG_SPLICEOSOME

'tumor' (positively correlated)

'normal' (negatively correlated)

Zero cross at 8264

Enrichment profile Hits Ranking metric scores 0 2,500 5,000 7,500 10.000 12,500 15,000

Rank in ordered dataset

Enrichment scor

e (ES)

Ranked list metric (Signal2Noise)

D

Hypermethylation GSEA 636

(3.3%) 5 (0%)

13 (0.1%)

306 (1.6%)

15292 (78.6%)

351 (1.8%)

1 (0%)

0 (0%) 62 (0.3%)

47 (0.2%) 140 (0.7%)

2535 (13%)

1 (0%) 76 (0.4%) (0%)1

down-regulated Targets

[image:6.595.131.466.82.257.2]

C

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.

[image:6.595.49.543.322.607.2]
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[image:7.595.52.537.86.361.2]

Figure 5 Functional analysis and PPI network of the hypermethylated 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

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 Figure 5B, 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 (Figure 5D).

The PPI network further showed 40 nodes with strong

correlations and 2 nodes with weak correlations (Figure 5A),

indicating complex interactions between the downregulated

and hypermethylated mRNAs at the protein level.

Function analysis and PPI network construction of

aberrantly methylated TSGs involved in OS

(8)

TSGs 47 mRNAs

1,209

(96.3%) (0.6%)8 (3.1%)39

[image:8.595.112.479.77.275.2]

A

B

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; non-TSGs, tumor suppressor genes; PPI, protein−protein interaction.

(Table 1), and pathway enrichment analysis indicated

that they were significantly enriched in the NF-kappa

B signaling pathway, chemokine signaling pathway and

viral carcinogenesis (Table 2). Therefore, these aberrantly

methylated TSGs are likely involved in OS progression, and

are potential biomarkers.

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 (Figure 7), indicating

that these genes have a significant impact on patient

prognosis.

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 (Figure 8). The methylation sites and correlation

coefficient of the significant TSGs are shown in Table 3. 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).

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 Figure 9,

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.

Aberrant methylation contributed to OS progression

(9)
[image:9.595.49.547.96.695.2]

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

(10)
[image:10.595.43.550.99.167.2]

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

invasion capacities (Figure 10C) 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.

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

142-5p (12), 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.

[image:10.595.44.560.241.405.2]
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[image:11.595.50.548.83.350.2]

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

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

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 3500 3000 2500 2000 1500 1000 500 0 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 1100 1000 900 800 700 600 500 400 300 200 100 3000 2500 2000 1500 1000 500 0 3000 2500 2000 1500 1000 500 0 2000 1800 1600 1400 1200 1000 800 600 400 200 8000 7000 6000 5000 4000 3000 2000 1000 0 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 PYCARD

GPX3 BTK CDH5 GSN

STAT5A CXCL12 CXCL14

Kaplan curve

Kaplan curve Kaplan curve Kaplan curve Kaplan curve

Kaplan curve Kaplan curve Kaplan curve

Expression Graph Expression Graph

Expression Graph Expression Graph

Expression Graph Expression Graph

Expression Graph Expression Graph

Event = No Event = No Event = No 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)

Expr ession Expr ession Expr ession Expr ession Expr ession Expr ession Expr ession Expr ession

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

sorted by expression sorted by expression sorted by expression sorted by expression sorted by expression sorted by expression sorted by expression sorted by expression

p value p value p value

p value p value

p value

p value

p value

Follow up in months Follow up in months Follow up in months

Follow up in months Follow up in months Follow up in months Follow up in months

Follow up in months

0 0 0

0 0 0 0

0

24 24 24

24 24 24 24

24

48 48 48

48 48 48 48

48

72 72 72

72 72 72 72

72

96 96 96

96 96 96 96

96

120 120 120

120 120 120 120

120

144 144 144

144 144 144 144

144

168 168 168

168 168 168 168

168

192 192 192

192 192 192 192

192

216 216 216

216 216 216 216

216

240 240 240

240 240 240 240

240

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

metastasisfr

ee survival pr

obability

Event = No high (n=52) Event = Yes

low (n=36)

Event = No high (n=48) Event = Yes

low (n=40)

Event = No high (n=9) Event = Yes

low (n=79)

Event = No high (n=77) Event = Yes

low (n=11)

raw p 0.082 raw p 0.199

raw p 0.014 raw p 0.011

bonf p 1.000 bonf p 1.000

(12)

11

10

9

8

7

12

11

10

9

8

7

14

12

10

8

6

11.0

10.5

10.0

9.50

9.00

8.50

8.00

7.50

7.00

11

10

9

8

7 11

10

9

8

7

13

12

11

10

9

8

7

13

12

11

10

9

8

7

ST

AT5A expr

ession

CXCL14 expr

ession

CXCL12 expr

ession

CDH5 expr

ession

BTK expr

ession

PYCARD expr

ession

GSN expr

ession

GPX3 expr

ession

Cor =−0.826 (P value =5.207e−20)

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

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

STAT5A methylation PYCARD methylation GSN methylation GPX3 methylation

CXCL14 methylation CXCL12 methylation CDH5 methylation BTK methylation

Cor =−0.376 (P value =1.058e−19)

Cor =−0.887 (P value =1.468e−18)

Cor =−0.431 (P value =1.733e−15)

Cor =−0.498 (P value =1.510e−23)

Cor =−0.571 (P value =6.547e−21)

Cor =−0.573 (P value =8.270e−18)

[image:12.595.52.547.79.337.2]

Cor =−0.323 (P value =1.074e−23)

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/

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.

[image:12.595.44.553.404.554.2]
(13)
[image:13.595.55.538.81.330.2]

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.

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.

4

3

2

1

0

0.8

0.6

0.4

0.2

0.0

80

60

40

20

0 0 h

24 h

OD value

MG63

MG63

MG63

MG63

MG63

***

** 0 1 2 3 4 5 6

days **

** * *

*

DMSO

DMSO

DMSO

DMSO

invasive cells perfield

DMSO

W

ound healing per

centage

5-Aza

5-Aza

5-Aza

5-Aza

5-Aza

A

C

B

100

80

60

40

20

0

3

2

1

0

3

2

1

0

2.5

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0 2.0

1.5

1.0

0.5

0.0 1.5

1.0

0.5

0.0

5

4

3

2

1

0

DMSO DMSO DMSO DMSO

DMSO DMSO

DMSO DMSO

5-Aza 5-Aza 5-Aza 5-Aza

5-Aza 5-Aza

5-Aza 5-Aza

MG63 MG63 MG63

MG63 MG63 MG63 MG63

** ***

***

*** *

PYCARD expr

ession level

GSN expr

ession level

CDH5 expr

ession level

GPX3 expr

ession level

BTK expr

ession level

ST

AT5A expr

ession level

CXCL14 expr

ession level

CXCL12 expr

ession level

PYCARD

GSN

STAT5A

CDH5

CXCL14

GPX3

CXCL12

[image:13.595.84.511.389.631.2]
(14)

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 NPM1-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.

Acknowledgments

(15)

Funding: This study was supported by grants from the

National Natural Science Foundation of China (Grant

number 81572628, 81372869), The Science and Technology

Project of Guangzhou (Grant No. 201704020033) and

The Science and Technology Project of Tianhe District,

Guangzhou (Grant No. 201604KW012).

Footnote

Conflicts of Interest: All authors have completed the ICMJE

uniform disclosure form and declare: the authors have no

conflicts of interest to declare.

Ethical Statement: The authors are accountable for all

aspects of the work in ensuring that questions related

to the accuracy or integrity of any part of the work are

appropriately investigated and resolved.

Open Access Statement: This is an Open Access article

distributed in accordance with the Creative Commons

Attribution-NonCommercial-NoDerivs 4.0 International

License (CC BY-NC-ND 4.0), which permits the

non-commercial replication and distribution of the article with

the strict proviso that no changes or edits are made and the

original work is properly cited (including links to both the

formal publication through the relevant DOI and the license).

See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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[image:17.595.49.546.92.377.2]

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

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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

Table S2 (continued)

Gene logFC P value Expression status

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

<

Figure

Figure 1 Workflow of how aberrantly methylated differentially expressed genes were identified.
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
Figure 5 Functional analysis and PPI network of the hypermethylated down-regulated genes
Figure 6 Identification of TSGs. (A) Venn diagram showing the aberrantly methylated DEGs in the TSGs database
+7

References

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