Abstract
Rheumatoid arthritis (RA) and cervical cancer (CC) are major global health challenges, yet the molecular connections between these two conditions remain poorly understood. To bridge this gap, our study employs bioinformatics approaches to explore shared genetic pathways and potential biomarkers. We started by identifying differentially expressed genes in RA and CC and then applied WGCNA to detect functionally related gene clusters using gene expression data from the GEO database. Additionally, we constructed protein-protein interaction (PPI) networks and examined the role of the immune microenvironment. To assess the prognostic relevance of key genes in CC, we leveraged survival data from TCGA. Our analysis identified 55 key genes common to RA and CC, with four-CXCL1, CXCL13, ZWINT, and PTTG1-emerging as significant. ROC curve validation confirmed their diagnostic potential, and a model incorporating these genes was associated with poorer prognosis in CC. Among them, CXCL1 stood out as especially crucial. Our findings suggest a potential link between chronic inflammation, immune dysregulation, and chemokine-related pathways in RA patients, which may contribute to an increased susceptibility to CC.