Department of Nephrology, China-Japan Friendship Hospital, Beijing, China.
Department of Nephrology, China-Japan Friendship Hospital, Beijing, China; Department of Nephrology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
Int Immunopharmacol. 2024 Sep 30;139:112783. doi: 10.1016/j.intimp.2024.112783. Epub 2024 Jul 27.
This study performs a detailed bioinformatics and machine learning analysis to investigate the genetic foundations of membranous nephropathy (MN) in lung adenocarcinoma (LUAD).
In this study, the gene expression profiles of MN microarray datasets (GSE99339) and LUAD dataset (GSE43767) were downloaded from the Gene Expression Omnibus database, common differentially expressed genes (DEGs) were obtained using the limma R package. The biological functions were analyzed with R Cluster Profiler package according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Machine learning algorithms, including LASSO regression, support vector machine (SVM), Random Forest, and Boruta analysis, were applied to identify hubgenes linked to LUAD-associated MN. These genes' prognostic values were evaluated in the TCGA-LUAD cohort and validated through immunohistochemistry on renal biopsy specimens.
A total of 36 DEGs in common were identified for downstream analyses. Functional enrichment analysis highlighted the involvement of the Toll-like receptor 4 pathway and several immune recognition pathways in LUAD-associated MN. COL3A1, PSENEN, RACGAP1, and TNFRSF10B were identified as hub genes in LUAD-associated MN using machine learning algorithms. ROC analysis demonstrated their effective discrimination of MN with high accuracy. Survival analysis showed that lung adenocarcinoma patients with higher expression of these genes had significantly reduced overall survival. In patients with lung adenocarcinoma-associated MN, RACGAP1, COL3A1, PSENEN, and TNFRSF10B were higher expressed in the glomerular, especially RACGAP1, indicating an important role in the pathogenesis of LUAD-associated membranous nephropathy.
Our study underscores the critical role of RACGAP1, COL3A1, PSENEN, and TNFRSF10B in the development of LUAD-associated MN, providing important insights for future research and the development of potential therapeutic strategies.
本研究通过详细的生物信息学和机器学习分析，研究肺腺癌（LUAD）中膜性肾病（MN）的遗传基础。
本研究从基因表达综合数据库中下载 MN 微阵列数据集（GSE99339）和 LUAD 数据集（GSE43767）的基因表达谱，使用 limma R 包获得共同差异表达基因（DEGs）。根据基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析，使用 R Cluster Profiler 包进行生物功能分析。应用 LASSO 回归、支持向量机（SVM）、随机森林和 Boruta 分析等机器学习算法，识别与 LUAD 相关的 MN 相关的枢纽基因。在 TCGA-LUAD 队列中评估这些基因的预后价值，并通过肾活检标本的免疫组织化学进行验证。
共鉴定出 36 个用于下游分析的共同 DEGs。功能富集分析强调了 Toll 样受体 4 途径和几种免疫识别途径在 LUAD 相关 MN 中的参与。使用机器学习算法，COL3A1、PSENEN、RACGAP1 和 TNFRSF10B 被确定为 LUAD 相关 MN 的枢纽基因。ROC 分析表明，它们可以有效地以较高的准确性区分 MN。生存分析表明，这些基因表达水平较高的肺腺癌患者总生存率显著降低。在肺腺癌相关 MN 患者中，肾小球中 RACGAP1、COL3A1、PSENEN 和 TNFRSF10B 的表达水平更高，尤其是 RACGAP1，表明其在 LUAD 相关膜性肾病的发病机制中具有重要作用。
本研究强调了 RACGAP1、COL3A1、PSENEN 和 TNFRSF10B 在 LUAD 相关 MN 发展中的关键作用，为未来的研究和潜在治疗策略的开发提供了重要的见解。
