A multi-omics prognostic model and functional validation of

Background: Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, exhibits high molecular heterogeneity and poor clinical prognosis. Current prognostic models often lack functional relevance and fail to reflect the immunometabolic complexity of ccRCC. This study aimed to identify robust, functionally relevant prognostic biomarkers through integrative multi-omics analysis and to develop a clinically applicable risk stratification model. Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in-house proteomic profiles, and Weighted Gene Co-expression Network Analysis (WGCNA)-derived modules were integrated to identify consistently dysregulated genes in ccRCC. A four-gene prognostic signature [15-hydroxyprostaglandin dehydrogenase (HPGD), PSAT1, GLOD5, and SMIM24] was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression. Model performance was evaluated by Kaplan-Meier survival analysis with log-rank tests after median risk-based stratification and by time-dependent concordance indices (C-index) at 1, 3, and 5 years in training and validation cohorts. Gene expression was validated at the mRNA level using TCGA-Kidney Renal Clear Cell Carcinoma (KIRC) RNA sequencing data and quantitative real-time polymerase chain reaction (RT-qPCR) in 10 paired clinical samples, and at the protein level using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Human Protein Atlas (HPA). Functional relevance was explored using gene set enrichment analysis (GSEA) and immune cell infiltration profiling. Based on multi-omics findings, HPGD was selected for functional validation and stably overexpressed in Caki-1 cells, followed by Cell Counting Kit-8 (CCK-8), colony formation, wound-healing, and Transwell assays. Results: All four genes were consistently downregulated in ccRCC at both mRNA and protein levels. The prognostic model effectively stratified patients into high- and low-risk groups with significantly different overall survival (OS) (log-rank P<0.0001; C-index >0.79). GSEA revealed that HPGD, GLOD5, and SMIM24 were negatively enriched in oncogenic pathways, including the cell cycle and epithelial-mesenchymal transition (EMT), while being positively associated with fatty acid metabolism. In contrast, PSAT1 was positively enriched in oncogenic signaling, glycolysis, and cholesterol homeostasis. Immune profiling revealed distinct associations with regulatory T cells, T helper 2 cells, natural killer cells, and mast cells. Conclusions: This study established a novel four-gene signature with robust prognostic significance and biological relevance in ccRCC. The signature is closely associated with lipid metabolism and immune microenvironment remodeling, while functional assays support a tumor-restraining role for HPGD. This integrative multi-omics-based model may improve risk stratification and contribute to personalized clinical decision-making.