AUTHOR=He Zhongyuan , Wang Zheng , Lai Shang , Yin Xunfei , Zheng Dawang , Liu Shuai , Liu Wenjie , Guo Guiying 

TITLE=Machine learning-driven dissection of the obesity-ccRCC interface: FCGR2A emerges as a central coordinator of tumor-immune crosstalk

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1598007

DOI=10.3389/fonc.2025.1598007

ISSN=2234-943X

ABSTRACT=IntroductionObesity is a well-established risk modifier for clear cell renal cell carcinoma (ccRCC), yet the molecular mechanisms linking these conditions remain incompletely characterized.  MethodsWe developed a dual-disease analytical framework integrating transcriptomic harmonization (5 ccRCC cohorts, n=876; obesity adipose profiles) with machine learning. Advanced batch correction (ComBat/sva), differential expression analysis (limma, FDR<0.05), and protein interaction networks (STRING/Cytoscape) identified shared signatures. Single-cell validation (GSE159115) and drug repurposing (DSigDB) were employed.  ResultsCross-platform harmonization identified 130 co-dysregulated genes enriched in myeloid immune functions, with FCGR2A emerging as the central hub gene exhibiting robust diagnostic power (AUC=0.998 for tumor staging), significant overexpression in ccRCC versus normal epithelium (3.1-fold, p=0.002), and specific localization to M2 macrophages in single-cell analyses (log₂FC=4.6, adj.p=1.3×10⁻⁷). The optimized machine learning model (glmBoost+Stepglm) generated a parsimonious 14-gene signature demonstrating exceptional cross-cohort accuracy (mean AUC=0.991), while pharmacological screening prioritized kinase inhibitors (e.g., dasatinib, p=2.1×10⁻⁸) and immunomodulators as therapeutic candidates.DiscussionOur study establishes FCGR2A-mediated myeloid reprogramming as a critical interface between metabolic dysfunction and ccRCC progression, serving as both a prognostic biomarker and therapeutic target. This dual-disease modeling paradigm provides actionable insights for precision management of obesity-associated malignancies.