Translating Molecular Subtypes into Cost-Effective Radiogenomic Biomarkers for Prognosis of Colorectal Cancer.

Colorectal cancer (CRC) is currently the third most common cancer worldwide, with high heterogeneity and poor prognosis. Gene expression-based molecular subtypes can effectively dissect tumor heterogeneity, but their clinical translation remains challenging. This study aims to conduct radiogenomic analysis regarding molecular subtypes and establish prognostic signatures for survival prediction of colorectal cancer. : In this retrospective study involving 2948 CRC patients from 8 cohorts, we utilized a supervised deep learning framework to extract quantitative feature representations of molecular subtypes. Through correlation analysis, we selected key gene expression features related to these subtypes to establish a prognostic signature. A similar pipeline was applied to derive a non-invasive radiomic prognostic signature. Finally, we validated the prognostic value of both signatures in multiple cohorts and explored their biological interpretation. : We successfully established a molecular subtype-associated gene signature and a non-invasive radiogenomic signature. The gene signature classified patients into high-risk and low-risk groups with significantly different prognoses. The low-risk group had a better prognosis and showed a greater potential benefit from immunotherapy. Similarly, the radiogenomic signature exhibited characteristics related to molecular subtypes and comparable performance in prognostic prediction. Multivariate analysis confirmed the independent prognostic value of both signatures. In summary, this retrospective study demonstrates that our framework translates molecular subtypes into cost-effective biomarkers for risk stratification and treatment guidance.