Integrative machine learning identifies lactylation-related gene signature prognostic for chemotherapeutic efficacy in colorectal carcinoma.

ABSTRACT:

Background

Lactylation, a recently discovered post-translational modification, has emerged as a critical regulator in cancer biology. Although chemotherapy remains the first-line treatment for metastatic colorectal cancer (CRC), only a subset of patients responds to it. This study aimed to identify key lactylation-related genes in CRC and evaluate their potential as predictive biomarkers for chemotherapy response.

Method

Gene expression profiles and corresponding clinical data from CRC patients were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were identified using the limma R package, and key modules were selected through weighted gene co-expression network

SUBMITTER: Hu H 

PROVIDER: S-EPMC12796083 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature