Evaluation of JNK- and Hypoxia-Related Gene Signatures for Predicting 2-Year Overall Survival in Colorectal Cancer Using Machine Learning

Background: Colorectal cancer remains a major cause of cancer-related mortality worldwide, with poor outcomes in patients with advanced-stage disease. The c-Jun N-terminal kinase (JNK) pathway and hypoxia are important regulators of tumor progression, but their combined prognostic value in colorectal cancer has not been systematically evaluated using machine learning. Methods: Transcriptomic and clinical data from 346 patients with colorectal cancer were divided into training (n = 242) and testing (n = 104) cohorts. Pathway-specific model selection used nested cross-validation with five outer and five inner folds. Within each inner-training fold, minimum redundancy maximum relevance (mRMR) feature selection was repeated, and gene counts of 1–15 were evaluated across nine classifiers, retaining age and sex. An exploratory integrated analysis evaluated combinations selected from the resulting pathway-specific gene pool. An age-and-sex-only model served as a demographic baseline. Results: Results: Training-based selection identified random forest with eight JNK-related genes and histogram-based gradient boosting with 12 hypoxia-related genes. Their testing ROC-AUCs were 0.658 (95% CI: 0.538–0.774) and 0.600 (95% CI: 0.492–0.717), respectively; pooled outer out-of-fold ROC-AUCs were 0.549 and 0.536. The exploratory integrated analysis selected a common 12-gene set. Following parameter exploration and testing-based comparison, K-nearest neighbors achieved a testing ROC-AUC of 0.690 (95% CI: 0.584–0.786), compared with 0.483 (95% CI: 0.368–0.602) for the age-and-sex baseline. Conclusions: Machine learning analysis of JNK- and hypoxia-associated gene signatures suggests potential for predicting 2-year survival in colorectal cancer. The exploratory integrated findings warrant independent external validation, assessment of calibration, and comparison with clinicopathological models before clinical application.