Dong Guoqi, Gao Hui, Chen Yingqi, Yang Huayuan
School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Genet. 2023 Sep 13;14:1238407. doi: 10.3389/fgene.2023.1238407. eCollection 2023.
Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients. The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases. In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to "macroautophagy" and "autophagy-animal," DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988-0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts. This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.
尽管类风湿关节炎（RA）是一种常见的自身免疫性疾病，但其确切的发病机制仍不清楚。最近的研究揭示了自噬在RA发展中的作用。本研究旨在探索RA患者外周血中与自噬相关的诊断生物标志物。从基因表达综合数据库（GEO）中检索GSE17755的基因表达谱。通过插入自噬相关基因和差异表达基因（DEG）来鉴定差异表达的自噬相关基因（DE-ARG），用于后续研究。采用包括随机森林、支持向量机递归特征消除（SVM-RFE）和最小绝对收缩和选择算子（LASSO）在内的三种机器学习算法来识别诊断生物标志物。构建列线图模型以评估生物标志物的诊断价值。使用CIBERSORT算法研究诊断生物标志物与免疫细胞和免疫因子的相关性。最后，在包含不同组织和疾病的多个队列中验证诊断生物标志物的诊断效能和差异表达趋势。在本研究中，在RA患者和健康个体之间鉴定出25个DE-ARG。根据基因本体（GO）和京都基因与基因组百科全书（KEGG）分析，除了“巨自噬”和“自噬-动物”外，DE-ARG还与几种类型的程序性细胞死亡和免疫相关途径有关。通过随机森林、SVM-RFE和LASSO鉴定出三种诊断生物标志物，即EEF2、HSP90AB1和TNFSF10。列线图模型在GSE17755中显示出优异的诊断价值（AUC = 0.995，95% CI：0.988 - 0.999）。此外，免疫浸润分析表明EEF2、HSP90AB1和TNFSF10的表达与各种免疫细胞和免疫因子之间存在显著关联。这三种诊断生物标志物在多个验证队列中也表现出良好的诊断效能，并呈现出相同的差异表达趋势。本研究基于三种机器学习算法鉴定出与自噬相关的诊断生物标志物，为RA的诊断和治疗提供了有前景的靶点。
