Qiu Jiao, Xu Yalin, Tong Luyuan, Yang Xingchun, Wu Xiao
Preventive Medical Center of Traditional Chinese Medicine, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, China.
Pengan County People's Hospital, Nanchong, China.
Sci Rep. 2025 Jul 1;15(1):21686. doi: 10.1038/s41598-025-05757-9.
This study aimed to identify the potential pathogenic genes associated with the comorbidity of rheumatoid arthritis (RA) and renal fibrosis (RF). Transcriptomic data related to RA and RF were retrieved from the GEO database. Differential expression gene analysis (DEGs) and weighted gene co-expression network analysis (WGCNA) were carried out to identify the RA-RF-DEGs. Subsequently, functional enrichment analysis was performed to clarify the biological functions of these genes. Machine learning algorithms were used to screen for the hub RA-RF differential expression genes, and then a Logistic Regression (LR) model was constructed. The accuracy of the model was evaluated using the ROC curve. At the same time, single-sample gene set enrichment analysis (ssGSEA) was applied to conduct immune infiltration analysis on the RF dataset. Gene set enrichment analysis (GSEA) was further performed on the hub genes to explore their underlying mechanisms in RF. Finally, a miRNA-TF-mRNA regulatory network centered around the hub genes was constructed.The results showed that 10 RA-RF-DEGs were identified through a comprehensive screening process. Enrichment analysis indicated that these differential expression genes were mainly involved in inflammatory responses and immune regulation. Subsequently, two hub genes, namely BIRC3 and PSMB9, were identified. A LR model was developed, and its predictive accuracy was verified using the ROC curve derived from an external independent dataset. Immune infiltration analysis revealed a significant correlation between the two hub genes and immune dysregulation in RF. Gene set enrichment analysis (GSEA) clarified the potential biological pathways through which BIRC3 and PSMB9 might function in RF. The constructed miRNA-TF-mRNA regulatory network provided a comprehensive overview of the post-transcriptional and transcriptional regulatory mechanisms. In conclusion, this study identified two candidate risk genes for RA-RF, providing new insights for the early diagnosis and treatment of RA complicated with RF.
本研究旨在鉴定与类风湿关节炎（RA）和肾纤维化（RF）合并症相关的潜在致病基因。从基因表达综合数据库（GEO数据库）中检索与RA和RF相关的转录组数据。进行差异表达基因分析（DEGs）和加权基因共表达网络分析（WGCNA）以鉴定RA-RF-DEGs。随后，进行功能富集分析以阐明这些基因的生物学功能。使用机器学习算法筛选枢纽RA-RF差异表达基因，然后构建逻辑回归（LR）模型。使用ROC曲线评估模型的准确性。同时，应用单样本基因集富集分析（ssGSEA）对RF数据集进行免疫浸润分析。对枢纽基因进一步进行基因集富集分析（GSEA）以探索它们在RF中的潜在机制。最后，构建了以枢纽基因为中心的miRNA-TF-mRNA调控网络。结果显示，通过全面筛选过程鉴定出10个RA-RF-DEGs。富集分析表明，这些差异表达基因主要参与炎症反应和免疫调节。随后，鉴定出两个枢纽基因，即BIRC3和PSMB9。开发了一个LR模型，并使用来自外部独立数据集的ROC曲线验证了其预测准确性。免疫浸润分析揭示了这两个枢纽基因与RF中的免疫失调之间存在显著相关性。基因集富集分析（GSEA）阐明了BIRC3和PSMB9在RF中可能发挥作用的潜在生物学途径。构建的miRNA-TF-mRNA调控网络提供了转录后和转录调控机制的全面概述。总之，本研究鉴定出两个RA-RF候选风险基因，为RA合并RF的早期诊断和治疗提供了新的见解。
J Matern Fetal Neonatal Med. 2025-12
J Affect Disord. 2025-1-15
Nucleic Acids Res. 2025-1-6
Front Genet. 2022-11-29
Zotero 插件