Integrated Multi-Omics Analysis Reveals Folate Metabolism Related Genes as Prognostic Markers and Therapeutic Targets in Clear Cell Renal Cell Carcinoma

Background Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor with high metastatic potential and therapeutic resistance, yet the role of folate metabolism in its pathogenesis and immune evasion remains unclear. This study aims to develop and validate a folate metabolism related gene (FMRG) scoring system to stratify patients by prognostic risk and immune phenotypes, and to explore the functional role of key FMRGs in ccRCC progression. Methods Using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA), we developed a folate metabolism related gene (FMRG) scoring system via integrative machine learning and validated it in an external cohort. We analyzed associations of the FMRG score with clinicopathological features, biological pathways, immune infiltration, therapeutic responsiveness, and drug sensitivity. Single-cell RNA sequencing and spatial transcriptomics mapped candidate gene expression, and in vitro experiments validated the functional role of NGF. Results A ten gene prognostic model based on the FMRG score stratified ccRCC patients into groups with distinct clinical outcomes, immune profiles, and therapeutic responses. NGF was upregulated in ccRCC, with heterogeneous spatial expression. Functional assays showed that NGF enhances proliferation, migration, and invasion, and contributes to an immunosuppressive tumor microenvironment. Conclusions This folate metabolism-based scoring framework facilitates prognostic stratification, tumor microenvironment characterization, and prediction of immunotherapy response in ccRCC. NGF is identified as a functional mediator of tumor progression and immune modulation, offering potential therapeutic targets and insights into metabolic-immune crosstalk.