Identification of Potential Genes in Rheumatoid Arthritis‐Associated Interstitial Lung Disease Using RNA‐seq and In Vitro Analyses

类风湿性关节炎 基因 间质性肺病 生物 信号转导 免疫学 基因表达 癌症研究 计算生物学 医学 遗传学 内科学
作者
Nie Liu-yan,Kun Zhao,Chengfu Xu,Wenjuan Zhang,Xin Huang,Yaling Han
出处
期刊:Cell Biochemistry and Function [Wiley]
卷期号:43 (1)
标识
DOI:10.1002/cbf.70033
摘要

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is an increasingly recognized extra-articular manifestations (EAMs) in the RA, with highly morbidity and mortality. The identification of key molecules involved in RA-ILD has a high requirement in clinic, and the role of their transcriptional regulation in the etiology of RA-ILD is great significant for investigation. In this study, we collected the whole peripheral blood samples of RA-ILD and RA only patients to bulk RNA-sequence. Differential gene expression analysis was employed to identify key genes, common pathways, and potential drug targets for RA-ILD. Furthermore, RT-qPCR was conducted to verify potential biomarkers in RA-ILD. Four hundred seventy-eight differentially expressed genes (DEGs) were identified that related to chromatin-modifying enzymes. A robust correlation with immune and inflammation biological processes and pathways was indicated through enrichment analyses of these shared DEGs, like B cell receptor signaling pathway, complement activation, NF-kappa B signaling pathway. Protein-protein interaction network analysis further emphasized the significance of 12 hub genes, including CHD4, MUS81, CXCL8, NSUN6, RAD9A, CCL4, B3GAT1, KAT2A, TBX21, HDAC2, ERBB2, and ITGAL. Notably, NSUN6 expression was statistically significant in RA-ILD by the machine learning LASSO regression analysis and RT-qPCR. Our study provides novel insights into the molecular mechanisms of RA-ILD, identifies potential biomarkers, and lays the groundwork for future therapeutic strategies.

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