Integrated bioinformatics analysis and experimental validation reveals hub genes of rheumatoid arthritis

Kun Luo; Yumei Zhong; Yanding Guo; Jingwei Nie; Yimei Xu; Haiyan Zhou

doi:10.3892/etm.2023.12179

Integrated bioinformatics analysis and experimental validation reveals hub genes of rheumatoid arthritis

Exp Ther Med. 2023 Aug 25;26(4):480. doi: 10.3892/etm.2023.12179. eCollection 2023 Oct.

Authors

Kun Luo¹, Yumei Zhong², Yanding Guo¹, Jingwei Nie¹, Yimei Xu¹, Haiyan Zhou¹

Affiliations

¹ Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P.R. China.
² Department of Painology, Chengdu Integrated TCM & Western Medicine Hospital/Chengdu First People's Hospital, Chengdu, Sichuan 610095, P.R. China.

Abstract

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation, especially synovitis, leading to joint damage. It is important to explore potential biomarkers and therapeutic targets to improve the clinical treatment of RA. However, the potential underlying mechanisms of action of available treatments for RA have not yet been fully elucidated. The present study investigated the potential biomarkers of RA and identified specific targets for therapeutic intervention. A comprehensive analysis was performed using mRNA files downloaded from the Gene Expression Omnibus. Differences in gene expression were analyzed and compared between the normal and RA groups. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on differentially expressed genes (DEGs). A protein-protein interaction network, Molecular Complex Detection and cytoHubba network were evaluated to identify hub genes. Finally, using an experimental RA rat model induced by Freund's complete adjuvant (FCA), the expression of potential biomarkers or target genes in RA were verified through reverse transcription-quantitative PCR. The results of the mRNA dataset processing revealed 195 DEGs in patients with RA when compared with the healthy controls. Moreover, 10 hub genes were identified in patients with RA and four candidate mRNAs were identified, as follows: Discs large homolog-associated protein 5 (DLGAP5), kinesin family member 20A (KIF20A), maternal embryonic leucine zipper kinase (MELK) and nuclear division cycle 80 (NDC80). Finally, the bioinformatics analysis results were validated by quantifying the expression of the DLGAP5, KIF20A, MELK and NDC80 genes in the FCA-induced experimental RA rat model. The findings of the present study suggested that the treatment of RA may be successful through the inhibition of DLGAP5, KIF20A, MELK and NDC80 expression. Therefore, the targeting of these genes may result in more effective treatments for patients with RA.

Keywords: autoimmune disease; bioinformatics; biomarkers; gene expression omnibus; rheumatoid arthritis.

Grants and funding

Funding: This work was supported by The National Natural Science Foundation of China (grant no. 81973959), The National Key R&D Program of China (grant no. 2019YFC1709001), The Science and Technology Innovation Seedling Project of Sichuan Province (grant no. 2022037) and The Foundation of Sichuan Provincial Administration of Traditional Chinese Medicine (grant no. 2018JC007).