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

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.

Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.

Background: Colorectal cancer (CRC) is a common intestinal malignancy worldwide, posing a serious threat to public health. Due to its high heterogeneity, prognosis and drug response of different CRC patients vary widely, limiting the effectiveness of traditional treatment. Therefore, this study aims to construct a novel CRC prognostic signature using machine learning algorithms to assist in making informed clinical decisions and improving treatment outcomes.