ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1630836
This article is part of the Research TopicInflammation, Immunity, and Cancer: New Pathways Towards Therapeutic InnovationView all 9 articles
Identification and validation of biomarkers in Gastric cancer-associated membranous nephropathy: Insights from comprehensive bioinformatics analysis and machine learning
Provisionally accepted
- China-Japan Friendship Hospital, Beijing, China
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Background: This study explores the genetic basis of membranous nephropathy (MN) in gastric adenocarcinoma (GC) through bioinformatics and machine learning analyses. Methods: Gene expression profiles from MN (GSE108109) and GC (GSE54129) datasets were obtained from the Gene Expression Omnibus. Common differentially expressed genes (DEGs) were identified using the limma R package. Biological functions were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with the Cluster Profiler package. LASSO regression and Random Forest algorithms were used to identify hub genes associated with GC-related MN. The area under the curve (AUC) of ROC analysis validated these genes for their diagnostic potential. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted, with hub genes validated through immunohistochemistry on renal and gastric cancer tissues. Results: We identified 40 common DEGs between GC and MN datasets. Using protein-protein interaction networks, 20 significant hub genes were selected, primarily involved in inflammatory and immune response regulation. Key hub genes identified were CCND1, CEBPD, COL10A1, and BMP2, which demonstrated high accuracy in discriminating MN. Notably, CCND1, CEBPD, and BMP2 were significantly overexpressed in glomerular and gastric cancer tissues. Conclusions: Our findings highlight the crucial roles of CCND1, CEBPD, and BMP2 in the pathogenesis of GC-associated MN, providing insights for future research and potential therapeutic strategies.
Keywords: gastric cancer, Membranous nephropathy, Immunohistochemistry, Bioinformatics analysis, machine learning
Received: 18 May 2025; Accepted: 25 Sep 2025.
Copyright: © 2025 Xu, Yang, Zhang, Tan, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wenge Li, wenge_lee2024@126.com
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