A novel machine learning-based immune prognostic signature for improving clinical outcomes and guiding therapy in colorectal cancer: an integrated bioinformatics and experimental study.

ABSTRACT: Immune cells are pivotal components in the tumor microenvironment (TME), which can interact with tumor cells and significantly influence cancer progression and therapeutic outcomes. Therefore, classifying cancer patients based on the status of immune cells within the TME is increasingly recognized as an effective approach to identify prognostic biomarkers, paving the way for more effective and personalized cancer treatments. Considering the high incidence and mortality of colorectal cancer (CRC), in this study, an integrated machine learning survival framework incorporating 93 different algorithmic combinations was utilized to determine the optimal strategy for developing an immune-related prognostic signature (IRPS) based on the average C-index across the four CRC cohorts. Notably, IRPS w

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11724613 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature