Machine learning-driven dissection of the obesity-ccRCC interface: FCGR2A emerges as a central coordinator of...

Obesity is a well-established risk modifier for clear cell renal cell carcinoma (ccRCC), yet the molecular mechanisms linking these conditions remain incompletely characterized.

We 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.

Cross-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.

Our 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.