We systematically analyzed six publicly available GEO datasets encompassing clear cell renal cell carcinoma (ccRCC) and obesity-related transcriptomic profiles (10). The discovery cohorts included three ccRCC tissue datasets (GSE40435, GSE53757, GSE66272), one obesity-associated white adipose tissue cohort (GSE94752),two independent validation datasets (GSE68417, GSE76351). All datasets met stringent inclusion criteria: histopathological confirmation, availability of raw expression matrices (RNA-seq/microarray), and minimum sample sizes exceeding biological triplicates (11). Detailed dataset characteristics are presented in Table 1 .

Raw expression matrices were preprocessed using the sva package (v3.5.0) for ComBat-based batch correction (12), followed by quantile normalization via preprocessCore (v1.56) (13). RNA-seq data were variance-stabilized using limma’s (v3.56) voom transformation (14). Post-harmonization quality metrics included principal variance component analysis (PVCA, residual batch effect <8.7%) (15) and silhouette width scoring with clusterSim (v0.48) (16), confirming effective technical artifact removal.

Conserved transcriptional signatures were identified through a tiered analytical framework. Disease-specific differentially expressed genes (DEGs) were first extracted using limma (FDR<0.05, |log2FC|>1.0) (17), followed by intersection analysis of ccRCC and obesity DEGs via the VennDiagram package (v1.7.3) (18). Covariate-adjusted hypothesis weighting was implemented using the IHW package (v1.28) to optimize demographic confounder control (19). Visualization of expression patterns included heatmaps and volcano plots generated using R software (20).

Biological interpretation of shared transcriptional signatures integrated Gene Ontology (GO) and KEGG pathway analyses through computational workflows (21, 22). Gene identifiers were standardized via Entrez ID mapping (org.Hs.eg.db) prior to enrichment testing (23). Semantic similarity reduction (SimRel=0.7) consolidated redundant terms (24), while Benjamini-Hochberg correction (FDR<0.05) addressed multiple testing (3). Significant pathways were visualized through stratified ggplot2 workflows (25), separating GO categories into biological processes, molecular functions, and cellular components. Circular genome plots (circlize) highlighted cross-compartment interactions (26), with heatmap annotations reflecting pathway activation z-scores.

Single-cell data (GSE159115) were processed using Seurat (v4.3.0) with SCTransform normalization. Clustering (resolution=0.8) employed shared nearest neighbor modularity optimization (27). Spatial transcriptomics data aligned via SpaceFlow (v0.9.5) with default parameters (28).

Leukocyte fractions were deconvolved through single-sample Gene Set Enrichment Analysis (ssGSEA) using the LM22 reference matrix (29), following voom-ComBat normalization. Gaussian kernel regularization was applied to ensure signal fidelity (30). Post-normalization infiltration metrics underwent moderated t-tests (FDR<0.1) with 95% bootstrap confidence intervals (31), visualized through composite ggplot2 workflows integrating density distributions and Benjamini-Hochberg-adjusted heatmaps (26).

The shared differentially expressed gene (DEG) subset was mapped to the STRING database (v11.5) using high-confidence interaction thresholds (combined score ≥0.7) (32). Network topology was interrogated in Cytoscape (v3.9.1) (33) with the CytoHubba plugin (v0.1), applying Maximal Clique Centrality (MCC) algorithms to identify hub genes (34).

PPI-derived hub genes informed a multi-algorithm diagnostic model combining LASSO-mRMR co-optimization (10-fold λ selection) (35) with UMAP-based manifold learning (15-neighbor local topology) (36). The meta-classifier ensemble integrated 113 combinatorial strategies from 12 base learners, including radial SVM (γ=0.01) (37) and depth-constrained gradient boosting (38), validated through tiered cross-validation (5×3 nested design) (39). Biomarker performance was quantified via permutation-adjusted AUROC (1,000 resamples) with Bonferroni-corrected significance thresholds (40).TCGA data underwent additional preprocessing to harmonize RNA-seq v3 protocols, including UQ normalization and ComBat-Seq batch correction (χ²=3.21, P = 0.073).Proteomic validation was performed using the CPTAC ccRCC dataset (n=110 tumor/normal pairs). Raw mass spectrometry data were log₂-transformed and quantile-normalized. Diagnostic model performance was assessed using logistic regression with leave-one-out cross-validation.

To identify pharmacological agents targeting shared pathological mechanisms in ccRCC and obesity, we analyzed co-dysregulated genes using the Drug Signature Database (DSigDB) (41) through the Enrichr platform (42).

FCGR2A expression patterns were assessed in ccRCC cell lines versus normal renal epithelium using SYBR Green-based qPCR following MIQE guidelines (43). Primer specificity was confirmed through melt curve analysis (44), with β-actin serving as the normalization control (45). Technical replicates demonstrated minimal Ct variability (<0.5 cycles), and statistical comparisons employed Student’s t-test (46).

Analyses utilized R (v4.2.1) (20) and Bioconductor packages for differential expression (47), with Benjamini-Hochberg correction controlling false discoveries (3). Machine learning implementations leveraged caret (48) and glmnet (49). Network analyses employed Cytoscape (v3.9.1) [331] with statistical validation through permutation testing (40). All tests were two-tailed with α=0.05 unless specified.

The experimental workflow delineating cross-cohort integration and analytical procedures is schematized in Figure 1 . Through combinatorial processing of three renal carcinoma transcriptomic cohorts (GSE40435: n=101 tumors/101 normals; GSE53757: n=72/72; GSE66272: n=27/26), we generated an aggregated matrix containing 200 malignant specimens and 199 histologically normal counterparts. ComBat-mediated harmonization effectively resolved platform-specific technical biases (12), as quantified by principal component analysis (PCA) revealing divergent preprocessed clustering patterns (PC1 = 62% variance, PERMANOVA p=1.2×10^-7; Figure 2A ). Post-integration assessment demonstrated mitigated inter-study heterogeneity through two key metrics: reduced principal component variance (PC1 = 38%) and a 63% decrement in Mahalanobis distance distributions between datasets ( Figure 2B ), collectively validating successful batch effect rectification (50).

Comparative analysis identified 1,782 differentially expressed genes (DEGs) in ccRCC (904 upregulated, 878 downregulated) and 323 obesity-associated DEGs (268 upregulated, 55 downregulated), with hierarchical clustering ( Figures 2C–F ) revealing disease-specific transcriptional landscapes. Tumor tissues exhibited marked upregulation of glycolytic effectors (ENO2: +10.3-fold, LDHA: +8.7-fold) (51), while obese adipose demonstrated immune activation signatures (FCGR2A: +6.1-fold, C1QC: +5.4-fold) (3). Intersectional analysis revealed 130 shared DEGs (121 co-upregulated, 9 co-downregulated; hypergeometric p=2.7×10^-18), visualized through Venn diagrams ( Figures 2G, H ). Technical validation confirmed batch effect residuals <8.4% (PVCA) and cross-platform DEG consistency >97% (15).

Figures 2I, J delineates the differential immune infiltration patterns in renal cell carcinoma (RCC). Tumor tissues exhibited selective activation of cytotoxic effectors, with significantly elevated fractions of activated CD8+ T cells (p<0.001) and macrophages (p=0.003) compared to adjacent controls ( Figure 2I ) (29). Paradoxically, regulatory T cells displayed marked depletion in advanced-stage tumors (p=0.008), suggesting potential immunosuppression breakdown ( Figure 2J ) (52). The compositional heatmap ( Figure 2K ) revealed coordinated upregulation of innate immune components (neutrophils: p=0.012; dendritic cells: p=0.004) in obese cohorts, correlating with metabolic inflammation markers (53). Particularly, mast cell infiltration demonstrated a BMI-dependent accumulation pattern (r=0.53, p=0.002) (54). Comparative analysis ( Figure 2L ) highlighted disease-specific signatures: RCC showed predominant cytotoxic/NK cell activation, while obesity exhibited chronic inflammation dominated by monocyte-macrophage axis activation (p<0.01) (55).

To resolve cellular heterogeneity in bulk transcriptomes, we interrogated single-cell RNA sequencing data from 8 primary ccRCC specimens (GSE159115) (27). Unsupervised clustering identified 13 distinct cellular populations ( Figure 3B ), including malignant epithelial cells (CA9+, NDUFA4L2+), myeloid subsets, T cells, fibroblasts, and endothelial compartments. Notably, FCGR2A expression was predominantly localized to myeloid lineages ( Figures 3A, C–E ), with significant enrichment in M2-polarized macrophages (log₂FC=4.6 vs. other cells, adj. p=1.3×10^-7). Co-expression analysis revealed strong correlation between FCGR2A and canonical myeloid markers (CD163: r=0.89; C1QC: r=0.83; both p<0.001). Spatial transcriptomics further demonstrated colocalization of FCGR2A+ macrophages with TREM2+ adipocytes at tumor-adipose interfaces (Pearson r=0.76, p=2.1×10^-5) (28), suggesting direct crosstalk between obese microenvironments and immunosuppressive myeloid populations.

Integrated pathway analysis revealed conserved immunological dysregulation between ccRCC and obesity through two complementary approaches ( Figures 4A–D ). KEGG enrichment identified neutrophil-mediated defense mechanisms as central shared pathways, with Staphylococcus aureus infection (C1QA/B/C, P = 2.2×10^-11) and neutrophil extracellular trap formation (ITGAM/ITGB2, P = 7.3×10^-10) exhibiting strongest associations ( Figures 4A, B ) (21). Leukocyte transendothelial migration (CXCR4/ITGA4, P = 1.6×10^-9) and phagosome activation (CTSS/MSR1, P = 2.5×10^-7) emerged as critical cellular trafficking mechanisms (56).

GO analysis delineated myeloid-specific functional clusters, with biological processes dominated by immune cell chemotaxis (31 genes, P = 3.1×10^-21) and phagocytic regulation (FCGR2A/RAC2, P = 9.8×10^-17) ( Figure 4C ) (22). Cellular component mapping highlighted integrin adhesion complexes (ITGAL/ITGB2, P = 4.8×10^-14), while molecular functions emphasized pattern recognition through CLEC7A/CD300A (P = 3.2×10^-15) ( Figure 4D ) (57). Cross-talk analysis identified the IL-17/MMP9 axis and SPI1-mediated transcriptional networks as conserved regulatory nodes, suggesting coordinated immunometabolic reprogramming across both pathologies (58).

To delineate the functional interplay between common differentially expressed genes (DEGs) in ccRCC and obesity, protein-protein interaction (PPI) networks were constructed using the STRING database (combined score >0.7) (30). The resultant network comprised 86 nodes and 1,428 edges, visualized through Cytoscape (31), revealing dense connectivity clusters centered on complement activation and myeloid cell adhesion modules. CytoHubba analysis (32) identified 30 hub genes with degree centrality ≥10 ( Figures 4E, F ), predominantly involving pattern recognition receptors (C1QA/B/C, FCGR2A/B) and integrin signaling components (ITGAM, ITGB2). Notably, the complement receptor C5AR1 emerged as a topological bottleneck (betweenness centrality=0.158), interacting bidirectionally with chemotaxis regulators (C3AR1, FPR1) and integrin complexes (ITGAX/ITGB2) (59). The IL-6 signaling node exhibited pleiotropic connectivity (degree=20), bridging inflammatory cytokines (CCL5, CXCL10) with leukocyte migration effectors (CXCR4, RAC2) (60). Myeloid-specific transcription factors SPI1 (degree=18) and TYROBP (degree=30) coordinated multiple functional modules, including phagosome formation (CTSS, MSR1) and neutrophil degranulation pathways (NCF2/4) (61).

To establish a robust diagnostic model for ccRCC, we systematically evaluated 113 machine learning algorithms using training cohorts GSE68417 and GSE76351 (n=41), with validation in independent datasets GSE40435, GSE53757, and GSE66272 (n=226). Following rigorous filtering to exclude overfitted models and those exceeding 15-gene complexity, the glmBoost+Stepglm[forward] algorithm (62) emerged as optimal, demonstrating superior predictive accuracy ( Figure 5A ). This parsimonious 14-gene signature (C1QB, CD163, CD48, CYBB, FCER1G, FCGR2A, HCK, IL7R, ITGAX, ITGB2, MMP9, MNDA, PTPRC, TLR7) demonstrated exceptional diagnostic performance across training and validation cohorts, with high AUC values detailed in Figures 5B–F (see legend for cohort-specific results) (63). Further validation confirmed the model’s clinical utility through minimal train-validation AUC variance (<2%) and resistance to overfitting (64). The signature’s biological relevance was underscored by enrichment of myeloid regulators (e.g., FCGR2A, AUC = 0.961) and immunoreceptor tyrosine-based activation motif (ITAM) signaling components, implicating tumor-immune crosstalk in its predictive mechanism (65).

Our glmBoost+Stepglm[forward] diagnostic model demonstrated superior discriminative accuracy compared to existing ccRCC signatures, achieving a mean AUC of 0.995 (95% CI: 0.988–1.000) versus 0.740 for the MAPK-based model (66) in cross-cohort validation (DeLong’s test P = 2×10^-43; Figure 5F ). The model maintained exceptional performance across validation datasets (GSE40435: 0.978, GSE53757: 0.956, GSE66272: 0.991,KIRC:0.992; Figures 5B–E ). Critically, validation in the TCGA-KIRC cohort (n=539) confirmed exceptional performance with an AUC of 0.992 (95% CI: 0.982–0.999), demonstrating remarkable consistency across ethnically diverse populations (17% Asian, 9% African ancestry, Figure 5G ). This parsimonious signature demonstrated enhanced stability versus complex models (>15 genes), showing <2% train-validation AUC variance and superior resistance to overfitting through bootstrap validation (1,000 iterations) (Efron & Tibshirani, 1993).

The glmBoost+Stepglm[forward] model exhibited exceptional performance in clinical staging discrimination, achieving an AUC of 0.990 (95% CI: 0.974–1.000) for distinguishing early-stage (I/II) from advanced (III/IV) ccRCC in the GSE40435 cohort ( Figure 5H ) (2). FCGR2A emerged as the most robust single-gene biomarker, demonstrating exceptional diagnostic accuracy (AUC = 0.998, P = 1.8×10^-16) across all stages ( Figure 5I ), likely reflecting its critical role in Fcγ receptor-mediated myeloid cell activation (67). Bootstrap validation (1,000 iterations) confirmed model stability with <1% AUC variance between training and validation phases, while maintaining interpretability through myeloid-specific transcriptional networks (68).

Experimental validation substantiated the pathogenic role of FCGR2A in ccRCC through multilayered evidence. qPCR quantification across five biological replicates demonstrated consistent transcriptional activation, with ACHN cells exhibiting 3.1 ± 0.4-fold upregulation (p=0.002 vs. HK-2) and 786-O cells showing 2.8 ± 0.3-fold elevation (p=0.003) relative to normal renal epithelium ( Figures 6A, B ) (43). Computational interpretation through Shapley value analysis (69) revealed FCGR2A’s dominant contribution to the diagnostic model, accounting for 23.7% of predictive weight—nearly double that of secondary contributors IL7R (12.4%) and MMP9 (9.8%) ( Figure 3I ). Hierarchical clustering analyses validated FCGR2A’s clinical discriminative power, achieving near-perfect separation of tumor/normal specimens (silhouette width=0.92) and robust stratification of early/late-stage tumors (silhouette width=0.85), with expression patterns strongly correlating with histopathological progression (Spearman’s ρ=0.81, p=1.3×10^-6) (70). Bootstrap resampling (1,000 iterations) confirmed analytical robustness, showing <5% variance in expression fold-changes across experimental replicates (71).

To address the clinical translatability of our diagnostic model, we performed orthogonal validation using mass spectrometry-based proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) clear cell renal cell carcinoma cohort (72). Quantification of FCGR2A protein expression revealed significant elevation in tumor tissues compared to matched normal controls (log₂FC = 2.8, Wilcoxon rank-sum test p = 7.42 × 10^-5; Figure 5J ). The 14-gene diagnostic signature maintained exceptional discriminatory capacity at the protein level (AUC = 0.995, 95% CI: 0.985-1.000; Figure 5K ), with no statistically significant difference in performance compared to transcriptomic validation in TCGA-KIRC (DeLong’s test p = 0.217). Notably, expression patterns between transcriptomic and proteomic platforms showed strong concordance (Spearman’s ρ = 0.81, p < 0.001), confirming cross-platform robustness of our molecular signature.

Network pharmacology analysis identified kinase inhibitors and immunomodulators targeting the 14-gene signature (C1QB, FCGR2A, MMP9, etc.), with dasatinib showing highest enrichment (P = 2.1×10^-8) via SRC kinase HCK inhibition (73). Decitabine inversely correlated with C1QB hypomethylation (ρ=–0.61), supported by its immune-regulatory associations (74). Methotrexate and aspirin demonstrated multi-target activity against myeloid activation (FCGR2A, ITGB2), aligning with recent obesity-cancer immunomodulation studies (75). FCGR2A-centric synergy was observed in 38% of candidates, including off-target effects of rituximab (P = 0.007) (76). DSigDB gaps persisted for STAT3/MMP9-axis drugs, emphasizing incomplete pathway annotations (77). The therapeutic prioritization network ( Figure 7 ) identifies kinase inhibitors (e.g., dasatinib) and immunomodulators as key candidates targeting the FCGR2A-centered pathway.