Exploring The Role of TOP2A in the Intersection of Pathogenic Mechanisms Between Rheumatoid Arthritis and Idiopathic Pulmonary Fibrosis Based on Bioinformatics

Background: Rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF) share a common pathogenic mechanism, but the underlying mechanisms remain ambiguous. Our study aims at exploring the genetic-level pathogenic mechanism of these two diseases. Methods: We carried out bioinformatics analysis on the GSE55235 and GSE213001 datasets. Machine learning was employed to identify candidate genes, which were further verified using the GSE92592 and GSE89408 datasets, as well as quantitative real-time PCR (qRT-PCR). The expression levels of TOP2A in RA and IPF in vitro models were confirmed using Western blotting and qRT-PCR. Furthermore, we explored the influence of TOP2A on the occurrence and development of RA and IPF by using the selective inhibitor PluriSIn #2 in an in vitro model. Finally, an in vivo model of RA and IPF was constructed to assess TOP2A expression levels via immunohistochemistry. Results: Our bioinformatics analysis suggests a potential intersection in the pathogenic mechanisms of RA and IPF. We have identified 7 candidate genes: CXCL13, TOP2A, MMP13, MMP1, LY9, TENM4, and SEMA3E. Our findings reveal that the expression level of TOP2A is significantly elevated in both in vivo and in vitro models of RA and IPF. Additionally, our research indicates that PluriSIn #2 can effectively restrain inflammatory factors, extracellular matrix deposition, migration, invasion, the expression and nuclear uptake of p-smad2/3 protein in RA and IPF in vitro models. Conclusion: There is a certain correlation between RA and IPF at the genetic level, and the molecular mechanisms of their pathogenesis overlap, which might be the reason for the progression of RA. Among the candidate genes we identified, TOP2A may influence the occurrence and development of RA and IPF through the TGF-β/Smad signal pathway. This could be beneficial to the study of the pathogenesis and treatment of RA and IPF. Keywords: rheumatoid arthritis, idiopathic pulmonary fibrosis, machine learning, TOP2A, fibrosis