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

S. Shi,

Xin Hong,

Yue Zhang

et al.

Journal of Inflammation Research, Journal Year: 2025, Volume and Issue: Volume 18, P. 3449 - 3468

Published: March 1, 2025

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

Language: Английский

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

S. Shi,

Xin Hong,

Yue Zhang

et al.

Journal of Inflammation Research, Journal Year: 2025, Volume and Issue: Volume 18, P. 3449 - 3468

Published: March 1, 2025

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

Language: Английский

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