ArticleDiscover oncology2025
Integrative machine learning identifies lactylation-related gene signature prognostic for chemotherapeutic efficacy in colorectal carcinoma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLactylation, 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.
methodGene 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 analysis (WGCNA). Intersecting genes were determined by aligning DEGs with WGCNA module genes. A predictive model was developed utilizing 11 machine learning algorithms and 92 algorithm combinations. Furthermore, the correlation between lactylation-related gene score and immune infiltration as well as drug sensitivity in CRC were also investigated with "CIBERSORT" and "oncoPredict" package.
resultsEight lactylation-related genes in CRC were identified and used to construct a predictive model employing Random Forest (RF) and Gradient Boosting Machine (GBM) algorithms. The model demonstrated strong predictive efficacy for chemotherapy response in CRC patients. Using lactylation gene scores, we effectively stratified patients into high- and low-score groups, which showed distinct patterns in immune cell infiltration, tumor mutational profile, and response to conventional antitumor drugs. Notably, the high-lactylation score group exhibited reduced Treg immune characteristics and increased sensitivity to 5-Fluorouracil.
conclusionsIn summary, our findings demonstrate that machine learning-driven analysis of lactylation biomarkers represents a promising approach for advancing personalized therapy and optimizing clinical management in CRC.
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