Identification of DNA methylation-regulated differentially expressed genes in RA by integrated analysis of DNA methylation and RNA-Seq data

Runrun Zhang; Cen Chang; Yehua Jin; LingXia Xu; Ping Jiang; Kai Wei; Linshuai Xu; Shicheng Guo; Songtao Sun; Dongyi He

doi:10.1186/s12967-022-03664-5

Identification of DNA methylation-regulated differentially expressed genes in RA by integrated analysis of DNA methylation and RNA-Seq data

J Transl Med. 2022 Oct 22;20(1):481. doi: 10.1186/s12967-022-03664-5.

Authors

Runrun Zhang^#^{1

2}, Cen Chang^#^{1

3

4}, Yehua Jin¹, LingXia Xu^{1

3}, Ping Jiang^{1

3}, Kai Wei^{1

3}, Linshuai Xu^{1

3}, Shicheng Guo⁵, Songtao Sun⁶, Dongyi He^{7

8

9}

Affiliations

¹ Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
² Department of Rheumatology, The Second Affiliated Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, China.
³ Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁴ Arthritis Institute of Integrated Traditional and Western Medicine, Shanghai Chinese Medicine Research Institute, Shanghai, China.
⁵ Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. Shicheng.Guo@wisc.edu.
⁶ Department of Orthopaedics, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China. sstever0156258@aliyun.com.
⁷ Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China. hedongyi1967@shutcm.edu.cn.
⁸ Shanghai University of Traditional Chinese Medicine, Shanghai, China. hedongyi1967@shutcm.edu.cn.
⁹ Arthritis Institute of Integrated Traditional and Western Medicine, Shanghai Chinese Medicine Research Institute, Shanghai, China. hedongyi1967@shutcm.edu.cn.

^# Contributed equally.

Abstract

Objective: To identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data.

Methods: The transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software.

Results: A total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10^{- 05}), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10^{- 16}. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA.

Conclusions: RGS1 can be used as novel methylated biomarkers for RA.

Keywords: DNA methylation; Differentially expressed genes; RNA-seq; Rheumatoid arthritis; Synovial tissue.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Arthritis, Rheumatoid* / genetics
Arthritis, Rheumatoid* / metabolism
Biomarkers / metabolism
Calcium / metabolism
DNA Methylation* / genetics
Gene Expression Profiling
Humans
NF-kappa B / metabolism
RNA, Messenger / genetics
RNA, Messenger / metabolism
RNA-Seq
Receptors, Antigen, T-Cell / metabolism

Substances

Biomarkers
Calcium
NF-kappa B
Receptors, Antigen, T-Cell
RNA, Messenger
RGS1 protein, human