Machine learning framework for mRNA alternative splicing analysis identifies a signature of progression in colorectal adenocarcinoma.

Despite recent advances in genome-wide profiling and the discovery of novel therapeutic options for colorectal adenocarcinoma (COAD), effective patient classification for the risk of cancer progression remains underdeveloped. Recent research has highlighted the crucial role of mRNA alternative splicing (AS) in the development and progression of COAD, yet a genome-wide comprehensive evaluation of the role of AS in COAD progression has not been implemented. In this study, we present a robust machine-learning framework designed to uncover clinically relevant AS events associated with progression-free survival (PFS) in COAD patients. For this, we analyzed RNA sequencing data from the TCGA-COAD cohort (n = 266). We employed a machine learning approach integrating Cox Proportional Hazards (PH) analysis and Robust Likelihood-Based Survival (RBSURV) modeling that identified a five-event AS-PFS signature (spanning AS events in OR52K1, SPIN3, NDUFV1, BMPR1A, and ARPC4 genes). By leveraging this signature, we defined a risk score for each patient, categorizing them into low and high-risk groups. This signature and its risk score were further validated through Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) analysis in the TCGA-COAD test set and independent patient cohort AC-ICAM (n = 348). Comparison to other markers and methods further confirmed the independent predictive value of the AS-PFS risk signature. We propose that this signature could be utilized in clinical settings to enhance patient stratification at diagnosis and further inform personalized treatment strategies.