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Purpose <p>Tumor 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.</p> Methods <p>Transcriptomic 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.</p> Results <p>The 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).</p> Conclusion <p>The radiogenomics model correlates significantly with angiogenesis-related gene expression and enables prognostic stratification in ccRCC, supporting precision treatment selection and advancing personalized theranostics.</p>

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A radiogenomics biomarker based on tumor angiogenesis for non-invasive prognosis of clear cell renal cell carcinoma

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Yuanchao Li,
  • Youting Huang,
  • Haiyun Xu,
  • Shuohui Yang

摘要

Purpose

Tumor 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.

Methods

Transcriptomic 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.

Results

The 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).

Conclusion

The radiogenomics model correlates significantly with angiogenesis-related gene expression and enables prognostic stratification in ccRCC, supporting precision treatment selection and advancing personalized theranostics.