Association between <i>AUP1</i> expression and lipid-rich phenotypes in clear cell renal cell carcinoma: a CT-based radiogenomic study

Abstract Objectives Clear cell renal cell carcinoma (ccRCC) is characterized by abnormal lipid accumulation, which may influence tumor behavior and imaging appearance. However, the relationship between lipid metabolism–related genes, such as ancient ubiquitous protein 1 ( AUP1 ), and computed tomography (CT) imaging features remains unclear. This study aims to investigate the association between AUP1 expression and CT features in ccRCC patients, using artificial intelligence (AI)-based radiogenomic analysis. Methods Genomic and imaging data from 208 patients were analyzed. Regions of interest (ROIs) were manually placed on solid tumor tissue, excluding necrotic areas. An AI-based workflow, implemented in Python with the scikit-learn library, extracted first-order radiogenomic features, median, minimum, and maximum Hounsfield unit (HU) values, as indirect surrogates of intracellular lipid content, with lower HU values indicating higher lipid accumulation. Distributional descriptors were also computed to capture intra-tumoral heterogeneity. Associations between imaging features, AUP1 expression, and histological grade were tested using non-parametric statistics (Wilcoxon, Kruskal-Wallis tests, Pearson’s Chi-square test, and Fisher’s exact test, p&lt;0.05). Results Among the 208 patients, 25 (12 %) expressed AUP1 . AI-based analysis showed that AUP1-positive tumors exhibited significantly lower median and minimum HU values compared with AUP1-negative tumors (p=0.025), consistent with AUP1-driven intracellular lipid droplet accumulation. In AUP1 -positive patients, high-grade tumors showed significantly higher median HU (p=0.036) and minimum HU values (p=0.037) than low-grade tumors. Interobserver agreement for ROI placement and HU measurement was excellent (k=0.91). Conclusions Radiogenomic feature extraction with AI-supported statistical analysis identified lower HU values as radiogenomic biomarkers of AUP1 expression, suggesting that AUP1 positivity is linked to a lipid-rich metabolic phenotype detectable by CT.