Rheumatoid arthritis (RA) is a complex autoimmune disorder predominantly affecting joints, with its etiology and response to treatment still not fully understood despite extensive historical documentation. This study aims to shed light on the differentially expressed genes (DEGs) associated with RA progression, potentially identifying new drug targets and management strategies. This study analyzed gene expression data (GSE193193) from the Gene Expression Omnibus (GEO) database, identifying 3672 significant DEGs out of 36107 initially retrieved genes. Among these, 283 genes were up-regulated and 360 were down-regulated. Gene enrichment analysis was performed to uncover relevant gene ontology terms and pathways. Subsequently, network construction and analysis, along with hub gene prediction using Cytoscape's MCODE and CytoHubba plugins, were conducted. Key genes identified in this study include HBB, ALAS2, GATA1, AHSP, HBG1, HBG2, HBD, KLF1, SLC4A1, EPB42, ZMYND10, DNAJC7, HYDIN, LRRC6, FN1, NCAM1, FASLG, CTCF, SMAD4, and STAT1. These genes are implicated not only in RA but also in other diseases, presenting them as potential therapeutic targets. Additionally, three transcription factors (GATA1, NFKB1, and RELA) and one miRNA (has-mir-27a-3p) were identified as key regulators of these hub genes. In conclusion, this study not only enhances our understanding of the molecular mechanisms underlying RA but also identifies several critical DEGs and regulatory factors that could serve as promising targets for therapeutic intervention. The identification of these genes and regulatory elements paves the way for the development of targeted treatments, which could significantly improve disease management and patient outcomes. Future research focusing on these identified targets may lead to innovative strategies for combating RA and potentially other autoimmune disorders, thereby offering new hope to patients affected by these conditions.
