ArticleTranslational cancer research2025
Development and validation of a lipid metabolism-related prognostic model for gastric adenocarcinoma.
Article in Translational cancer research, 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
Background: Lipid metabolism plays a critical role in the development, progression, and immune regulation of gastric adenocarcinoma (GA). However, its prognostic significance and relationship with the tumor microenvironment (TME) remain unclear. This study aimed to construct a GA lipid metabolism-related prognostic model and evaluate its clinical relevance in GA. Methods: Lipid metabolism-related differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA) database. A prognostic lipid metabolism-related signature for GA (GA-LMRS) was developed via least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The model's predictive performance was validated in multiple cohorts. Functional enrichment, tumor mutation burden (TMB), and immune correlation analyses were performed. Drug sensitivity analysis was conducted to assess the immunotherapy response. Results: A 14-gene GA-LMRS was established, effectively stratifying patients into high- and low-risk groups. High-risk patients exhibited significantly poorer survival (P<0.001), and the model demonstrated robust predictive ability [area under the curve (AUC) >0.71]. Functional analysis revealed enrichment of the high-risk genes in extracellular matrix remodeling, immune evasion, and cancer-related pathways, whereas low-risk genes were associated with DNA repair and metabolic processes. High-risk patients had higher TMB, upregulated immune checkpoint expression, and lower sensitivity to CTLA4 inhibitors, suggesting immunotherapy resistance. In contrast, low-risk and C2 subtype patients were more likely to benefit from immune checkpoint inhibitors (ICIs). Conclusions: GA-LMRS serves as a reliable prognostic tool and reflects the immune status of patients with GA. Targeting lipid metabolism may improve immunotherapy efficacy. Future studies should integrate single-cell sequencing and multicenter clinical data to enhance model applicability and therapeutic strategies.
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