Identification and validation of crotonylation-related diagnostic markers for lung adenocarcinoma via weighted correlation network analysis and machine learning

Science and Research

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common tumors in terms of incidence and mortality worldwide. Posttranslational modifications, including crotonylation, play a crucial role in various biological processes and diseases. However, the role of crotonylation in LUAD remains unclear. Our research focuses on identifying key genes in LUAD that are linked to crotonylation and prognosis. We also aim to clarify their role in the LUAD microenvironment to advance clinical translation of related targets. METHODS: We used RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to identify differentially expressed genes (DEGs) related to crotonylation in LUAD. Weighted correlation network analysis (WGCNA) was applied to construct gene networks, and hub genes were identified using protein-protein interaction (PPI) analysis. The prognostic value of hub genes was assessed using Kaplan-Meier plots, and the correlation with immune infiltration was analyzed via Tumor Immune Estimation Resource (TIMER) and other algorithms. We then verified these genes through clinical samples and confirmed the role of MMACHC in LUAD. RESULTS: We identified GAPDH, SLC25A13, MMACHC, and HDAC1 as potential crotonylation-related biomarkers for LUAD. These genes were found to be overexpressed in LUAD and were associated with poor prognosis. They also showed significant correlations with immune cell infiltration and immune-inflammatory pathways. Functional experiments confirmed that MMACHC knockdown inhibited cell proliferation and migration, induced apoptosis, and enhanced the efficacy of immunotherapy in LUAD. CONCLUSIONS: Our study suggests that crotonylation-related genes, particularly MMACHC, may serve as novel therapeutic targets and diagnostic markers for LUAD.