Abstract
BACKGROUNDS: Protein crotonylation is a novel post-translational modification implicated in tumorigenesis and progression. Its putative roles and mechanisms in lung adenocarcinoma (LUAD), however, remain incompletely elucidated. METHODS: We analyzed the expression of crotonylation-related genes (CRGs) in LUAD samples and identified differentially expressed genes for gene set variation analysis (GSVA). Using GSVA scores as phenotypic traits, weighted gene co-expression network analysis (WGCNA) was applied to identify key module genes. A putative prognostic model was subsequently constructed via Lasso-Cox regression. Functional enrichment, gene mutation analysis, and immune infiltration analyses were conducted to compare high- and low-risk groups. Furthermore, cellular experiments were performed to validate the putative role of the hub gene FAM83A and its regulation of key glycolytic enzymes PKM2 and LDHA. RESULTS: We established a putative crotonylation-related prognostic model for LUAD, which effectively stratified patients into high- and low-risk groups with significantly different overall survival. Functional analysis suggested putative disparities in metabolic pathways between the two groups. Mutation landscape analysis revealed distinct genomic variation patterns, while immune infiltration assessment indicated a putative immune-evasion phenotype in high-risk patients. Cellular assays demonstrated that FAM83A enhances lung cancer cell proliferation, putatively through promoting glycolysis. Our findings establish that FAM83A integrates histone H3K27 crotonylation signaling to drive transcriptional reprogramming of glycolytic metabolism, specifically upregulating key enzymes PKM2 and LDHA. CONCLUSION: This study proposes a putative prognostic model based on CRGs for LUAD outcome prediction. The hub gene FAM83A may facilitate lung cancer cell growth by regulating glycolysis via PKM2 and LDHA, offering a novel theoretical foundation and a potential target for prognostic assessment and targeted therapy in LUAD patient.