Work

Failure to Suppress Progression in Clear Cell Renal Cell Carcinoma Associated with NCOA7 Low Expression Revealed Through Radiogenomic Analysis

Year: 2025

Type: article

Abstract: Background/Objectives: Radiogenomics investigates the relationship between imaging features and genomic characteristics, offering a non-invasive approach to studying tumor biology. Nuclear receptor coactivator 7 (NCOA7) is a conserved nuclear receptor coactivator with potential prognostic relevance in clear cell renal cell carcinoma (ccRCC). This study aims to evaluate the radiogenomic features associated with NCOA7 low expression in ccRCC patients and its correlation with tumor aggressiveness. Methods: A cohort of 209 ccRCC patients was analyzed using genomic data from The Cancer Genome Atlas and imaging features from The Cancer Imaging Archive. Imaging characteristics were assessed through computed tomography scans, focusing on tumor size, margins, necrosis, growth patterns, and other radiological features. Statistical analyses, including Student’s t-test and Pearson’s Chi-square test, were used to examine associations between NCOA7 expression and clinicopathological or radiological features, with significance set at p < 0.05. Results: NCOA7 low expression was identified in 66.03% of patients and significantly associated with older age (p = 0.001), higher tumor grade (p = 0.015), advanced American Joint Committee of Cancer stage (p = 0.006), collateral vascular supply (p = 0.014), ill-defined margins (p = 0.016), tumor necrosis (p = 0.002), exophytic growth pattern ≥50% (p = 0.002), and perinephric fat stranding (p = 0.027). Conclusions: These findings indicate a radiologic phenotype suggestive of increased tumor aggressiveness. NCOA7 low expression correlates with aggressive radiologic and clinicopathological features, underscoring its potential as a biomarker for poor prognosis in ccRCC. Radiogenomic integration provides insights into tumor behavior and aids in developing therapeutic strategies.

Source: Genes

DOI: 10.3390/genes16040386

Authors: Federico Greco, Marco Cataldo, Bruno Beomonte Zobel, Carlo Augusto Mallio

Institutions: Università Campus Bio-Medico, Campus Bio Medico University Hospital

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137