Feed

Single-cell transcriptomics and machine learning methods are increasingly used to identify immune-related biomarkers in solid tumors, yet their combined application to microenvironment-related drivers of therapeutic resistance in clear cell renal cell carcinoma (ccRCC) is still limited. Here, we investigated the biological and clinical significance of myeloid-derived growth factor (MYDGF) through an integrative strategy spanning single-cell profiling, bulk multiomics, and functional validation. Analysis of scRNA-seq data (GSE156632) revealed that MYDGF is preferentially detected in malignant epithelial subpopulations and associated with the composition of myeloid and lymphoid compartments. Integration with TCGA-KIRC transcriptomic and clinical datasets demonstrated strong associations between MYDGF expression and immune-checkpoint activation, immune dysfunction signatures, and PI3K/AKT-MAPK pathway activity. Tumors with high MYDGF expression exhibited an immune-infiltrated yet functionally impaired microenvironment and were predicted to show reduced responsiveness to immune checkpoint blockade. Differential expression and enrichment analyses further highlighted MYDGF-associated genes involved in inflammatory, extracellular, and receptor-binding functions. A machine learning pipeline using LASSO Cox regression identified a preliminary 19-gene MYDGF-related prognostic gene set that requires further validation. Functional experiments confirmed that MYDGF knockdown suppressed proliferation, migration, and invasion in ccRCC cells. Overall, our analyses characterize MYDGF as a microenvironment-related biomarker linked to immune-associated features, signaling-associated alterations, and adverse prognosis in ccRCC. These results nominate MYDGF as a candidate prognostic biomarker and show the value of pairing single-cell resolution with computational modeling for biomarker discovery in renal cancer.