ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
A radiogenomics biomarker based on tumor angiogenesis for non-invasive prognosis of clear cell renal cell carcinoma.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTumor angiogenesis drives prognostic heterogeneity in clear cell renal cell carcinoma (ccRCC), but macroscopic imaging cannot predict angiogenesis-related gene dysregulation. We aimed to develop a noninvasive radiogenomic model for assessing angiogenesis-associated gene signatures.
methodsTranscriptomic profiles from TCGA-KIRC were analyzed via "ConsensusClusterPlus" to identify angiogenesis subtypes. Univariate Cox, LASSO and Multivariate Cox regression selected prognostic angiogenesis-related genes, constructing a signature-based risk model. Prognostic nomograms integrated genomic markers with clinical variables. Radiomic features from TCIA CT images identified biomarkers stratifying angiogenesis expression, forming a radiogenomic prognostic nomogram. Performance was validated using receiver operating characteristic curves, calibration plots, and decision curve analysis.
resultsThe ccRCC patients were stratified into two angiogenesis-based molecular subtypes. An eight-gene angiogenesis signature predicted overall survival in TCGA, categorizing patients into low-/high-risk groups. Six radiomic features predicting signature expression were identified (Training AUC = 0.753; Testing AUC = 0.814). The combined radiogenomic-clinical nomogram achieved time-dependent survival AUCs of 0.870 (1-year), 0.811 (3-year), and 0.784 (5-year).
conclusionThe radiogenomics model correlates significantly with angiogenesis-related gene expression and enables prognostic stratification in ccRCC, supporting precision treatment selection and advancing personalized theranostics.
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