Integrating transcriptomics and machine learning for immunotherapy assessment in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is a highly prevalent and lethal malignancy worldwide. Although immunotherapy has substantially improved CRC outcomes, intolerance remains a major concern among most patients. Considering the pivotal role of the tumor microenvironment (TME) in tumor progression and treatment outcomes, profiling the TME at the transcriptomic level can provide novel insights for developing CRC treatment strategies. Seventy-seven TME-associated signatures were acquired from previous studies. To elucidate variations in prognosis, clinical features, genomic alterations, and responses to immunotherapy in CRC, we employed a non-negative matrix factorization algorithm to categorize 2595 CRC samples of 27 microarrays from the Gene Expression Omnibus database. Three machine learning technique

SUBMITTER: Xiang J 

PROVIDER: S-EPMC10987483 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature