Construction and Validation of Multi-Omics Predictive Models for Colorectal Cancer Using Machine-Learning Approaches

Objective: To construct and externally validate a multi-omics nomogram that uses only routine clinicopathological variables to predict tumor mutational burden (TMB), microsatellite instability (MSI), NTRK/PIK3CA mutation status and overall survival (OS) in colorectal cancer (CRC). Methods: TCGA data (n=398) served as the training set and 120 consecutive CRC patients who underwent radical resection at Yuebei People's Hospital formed the prospective validation set. After z-score normalization, 21demographic, clinical and pathological features were screened for multicollinearity (VIF<5) and redundancy via least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. Optimal hyper-parameters for each algorithm were tuned by nested 10-fold grid search. Four machine-learning algorithms, logistic regression (LR), support-vector machine (SVM), decision tree (DT) and random forest (RF), were compared by area under the receiver-operating-characteristic curve (AUC), F1 score and decision-curve analysis. The best model was externally validated and calibrated with bootstrapping. Results: gene status prediction model, which included three indicators of TMB and MSI status, had the best power when constructed using the RF method. Conclusion: The prediction model constructed in this study can help clinicians quickly identify high-risk patients and provide a basis for formulating a reasonable treatment plan. Further optimization of the model and expansion of the sample size are required to verify its power in the future.