Survival prediction of colorectal cancer using 101 machine learning methods based on immune-related genes: A machine learning study.
Summary
This study developed a prognostic model for colorectal cancer (CRC) using 101 machine learning algorithms based on immune-related genes. Analyzing CRC patient data, the Ridge regression model demonstrated the best performance, generating an immune-related gene risk score (IRGRS) significantly associated with lower survival rates. The IRGRS was identified as an independent prognostic factor and correlated with immune cell infiltration and stromal activity. This score could also predict response to immune checkpoint inhibitors and sensitivity to certain chemotherapies.
Analysis
This work is crucial as it offers a robust machine learning tool for early identification of high-risk colorectal cancer patients. The IRGRS's ability to predict survival and potentially guide responses to immunotherapies and chemotherapies paves the way for personalized treatment strategies. This could enable more targeted interventions, thereby improving clinical outcomes and reducing mortality in CRC patients.