ABSTRACT
There is a potential link between rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). The aim of this study is to investigate the molecular processes that underlie the development of these two conditions by bioinformatics methods. The gene expression samples for RA (GSE77298) and IPF (GSE24206) were retrieved from the Gene Expression Omnibus (GEO) database. After identifying the overlapping differentially expressed genes (DEGs) for RA and IPF, we conducted functional annotation, protein-protein interaction (PPI) network analysis, and hub gene identification. Finally, we used the hub genes to predict potential medications for the treatment of both disorders. We identified 74 common DEGs for further analysis. Functional analysis demonstrated that cellular components, biological processes, and molecular functions all played a role in the emergence and progression of RA and IPF. Using the cytoHubba plugin, we identified 7 important hub genes, namely COL3A1, SDC1, CCL5, CXCL13, MMP1, THY1, and BDNF. As diagnostic indicators for RA, SDC1, CCL5, CXCL13, MMP1, and THY1 showed favorable values. For IPF, COL3A1, SDC1, CCL5, CXCL13, THY1, and BDNF were favorable diagnostic markers. Furthermore, we predicted 61 Chinese and 69 Western medications using the hub genes. Our research findings demonstrate a shared pathophysiology between RA and IPF, which may provide new insights for more mechanistic research and more effective treatments. These common pathways and hub genes identified in our study offer potential opportunities for developing more targeted therapies that can address both disorders.
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.
