Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models

Rheumatoid arthritis (RA) is a systemic immune-mediated disease characterized by synovitis and joint cartilage destruction. Although many studies have shown that mitophagy is crucial in the development of bone metabolism disorders, its exact function in rheumatoid arthritis (RA) is still not well understood. This study analysed the GSE77298 dataset from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between rheumatoid arthritis (RA) patients and healthy controls. Mitophagy-related genes (MRGs) were extracted from the literature and screened using bioinformatics techniques, resulting in differentially expressed MRGs (DE-MRGs). The diagnostic value of these genes was assessed using receiver operating characteristic (ROC) curves, and an ANN model was constructed. In the GSE77298 dataset, 267 differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) identified 2191 key module genes, leading to 63 DE-MRGs. Two MRGs, TMEM45A and ZBTB25, were identified as hub genes with areas under the curve (AUC) of 0.991 and 0.911, respectively. The nomogram model demonstrated high diagnostic value. Mitophagy plays a critical role in the progression of rheumatoid arthritis (RA). Identifying two genes associated with mitophagy may aid in the early diagnosis, mechanistic understanding, and treatment of RA.

类风湿关节炎（Rheumatoid arthritis, RA）是一种以滑膜炎和关节软骨破坏为特征的全身性免疫介导疾病。尽管多项研究表明线粒体自噬（mitophagy）在骨代谢紊乱的发生发展中至关重要，但其在类风湿关节炎（RA）中的确切功能仍未完全阐明。本研究分析了来自基因表达综合数据库（Gene Expression Omnibus, GEO）的GSE77298数据集，以鉴定类风湿关节炎（RA）患者与健康对照者之间的差异表达基因（differentially expressed genes, DEGs）。从文献中提取线粒体自噬相关基因（mitophagy-related genes, MRGs），并通过生物信息学技术进行筛选，最终获得差异表达的线粒体自噬相关基因（differentially expressed MRGs, DE-MRGs）。采用受试者工作特征（receiver operating characteristic, ROC）曲线评估这些基因的诊断价值，并构建了人工神经网络（Artificial Neural Network, ANN）模型。在GSE77298数据集中，共鉴定出267个差异表达基因（DEGs）。加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）鉴定出2191个关键模块基因，最终得到63个差异表达的线粒体自噬相关基因（DE-MRGs）。筛选出TMEM45A与ZBTB25这2个线粒体自噬相关基因作为核心基因，其受试者工作特征曲线下面积（area under the curve, AUC）分别为0.991与0.911。列线图模型展现出优异的诊断价值。线粒体自噬在类风湿关节炎（RA）的疾病进展中发挥关键作用。本研究鉴定出的2个线粒体自噬相关基因，可为类风湿关节炎的早期诊断、机制阐释与治疗提供辅助思路。