Comprehensive characterization of the molecular feature of acetylation in colorectal cancer using integrated single-cell and bulk RNA sequencing.

Colorectal cancer (CRC) remains a major global health burden with high mortality rates, underscoring the need for effective therapies. This study explores the acetylation characteristics in CRC using single-cell RNA sequencing (scRNA-seq) and weighted gene co-expression network analysis (WGCNA), assessing their relationship with prognosis and the immune microenvironment. We analyzed two scRNA-seq datasets from the GEO database to identify distinct cell subtypes. Acetylation activity scores were calculated using the ssGSEA method. A WGCNA was constructed to identify gene modules associated with acetylation. An acetylation-related prognostic signature (ARPS) was developed, and its clinical significance was evaluated through survival analysis and immune landscape characterization. Acetylation activity was significantly elevated in epithelial, endothelial, and stromal cells. Based on the results of scRNA-seq, WGCNA identified 169 acetylation-related genes. Intersection with 1,691 acetylation-related differentially expressed genes (DEGs) yielded 131 common genes. Combining clinical data with the expression profiles of these genes, we employed 101 machine learning algorithms to develop an ARPS that accurately predicts the prognosis of CRC patients. Low-risk patients showed increased infiltration of immune cells, enhanced immune function, and better responses to immunotherapy. These findings underscore the clinical significance of acetylation features in CRC prognosis and immune response, highlighting their potential as biomarkers and therapeutic targets.