CT-based radiogenomic prediction of ICAM1 and RAET1E as biomarkers of NK cytotoxicity in clear cell renal cell carcinoma

Introduction This study aims to develop a noninvasive CT-based radiogenomic framework to estimate imaging-associated biomarkers related to natural killer (NK) cell cytotoxicity in clear cell renal cell carcinoma (ccRCC). By linking imaging features to underlying molecular pathways, we address the limitations of invasive tissue sampling in assessing tumor heterogeneity and immune microenvironment. Methods This study analyzed preoperative contrast-enhanced CT images from 143 patients with histologically confirmed ccRCC and transcriptomic data from 538 TCGA-KIRC tumor samples. Radiomic features (3,176 per tumor) were extracted using PyRadiomics from manually segmented 3D tumor volumes (validated by two radiologists), spanning seven feature classes and eight image filters. Feature selection included variance filtering, the Mann-Whitney U test for gene-expression stratification (high/low groups), and redundancy removal via Pearson correlation analysis (|r| > 0.9), with AUC prioritization. An L 1 -penalized support vector machine model with leave-one-out cross-validation was developed to estimate expression levels of ICAM1 and RAET1E, two NK cytotoxicity-related biomarkers. External validation was performed using tissue microarrays and immunohistochemistry from 26 independent ccRCC cases. Differential gene expression was assessed using edgeR (FDR < 0.05, fold-change > 2) and survival analysis via Cox regression. Results Transcriptomic analysis identified 835 imaging-associated genes enriched in immune-related pathways, with significant representation of the NK cell-mediated cytotoxicity pathway (KEGG, P < 0.05). Among candidate genes, ICAM1 and RAET1E demonstrated the strongest radiogenomic associations (AUC = 75.7% and 67.3%, respectively). Immunohistochemistry confirmed increased ICAM1 and decreased RAET1E expression in ccRCC tissues. Higher ICAM1 and lower RAET1E expression were associated with advanced tumor stage and unfavorable survival outcomes. External validation of the L 1 -SVM model achieved predictive accuracies of 76.92% for ICAM1 and 73.08% for RAET1E, supporting the preliminary feasibility of this radiogenomic approach. Conclusions Our findings suggest that CT-derived radiomic features may provide noninvasive imaging correlates of biomarkers related to NK cytotoxicity-in ccRCC. Radiogenomic analysis of ICAM1 and RAET1E may provide a complementary exploratory framework for noninvasive immune characterization and biomarker research in ccRCC.