Angiogenesis-Informed Preoperative CT Radiogenomics Predicts Overall Survival in Clear Cell Renal Cell Carcinoma:...

We aimed to identify angiogenesis-related prognostic biomarkers and develop a radiogenomics model to predict overall survival (OS) in clear cell renal cell carcinoma (ccRCC), supporting risk stratification and potential therapeutic target discovery.

Bulk transcriptomes from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma cohort (TCGA-KIRC), seven Gene Expression Omnibus (GEO) microarrays, and a single-cell RNA sequencing (scRNA-seq) dataset were integrated to identify angiogenesis-related prognostic genes. Preoperative contrast-enhanced computed tomography (CT) images from The Cancer Imaging Archive Kidney Renal Clear Cell Carcinoma collection (TCIA-KIRC) were used for radiomics feature extraction, and a radiogenomics signature was constructed by linking radiomic features with transcriptomic risk patterns. Nine machine learning models were trained to predict OS; the best model was further evaluated in an independent external retrospective cohort. PDLIM1 (PDZ and LIM domain protein 1) was validated at the protein level, and conditioned medium from stable ccRCC cell lines was applied to human umbilical vein endothelial cells (HUVECs) for Matrigel tube formation assays.

Five angiogenesis-related hub genes (PDLIM1, EMCN, ARPC1B, PLAT, and TIMP1) were identified. The extreme gradient boosting (XGBoost)-based radiogenomics model showed the best performance, with time-dependent concordance index (C-index) values of 0.880, 0.816, and 0.789 at 1, 3, and 5 years in the training set and 0.864, 0.758, and 0.736 in the internal validation set, respectively. In the external validation cohort, C-index values were 0.800, 0.726, and 0.703 at 1, 3, and 5 years. PDLIM1 protein was upregulated in ccRCC versus normal tissues. Functionally, PDLIM1 overexpression suppressed, whereas PDLIM1 knockdown promoted tube formation.

This study developed and validated an angiogenesis-related radiogenomics model that accurately predicts OS in ccRCC patients and provides potential therapeutic targets for anti-angiogenic therapy.