Gastric Cancer (GC) has become one of the most important causes of cancer-related deaths worldwide due to its intractability. Studying the mechanisms of gastric carcinogenesis, recurrence, and metastasis, and searching for new therapeutic targets have become the main directions of today's gastric cancer research. Lactate is considered a metabolic by-product of tumor aerobic glycolysis, which can regulate tumor development through various mechanisms, including cell cycle regulation, immunosuppression, and energy metabolism. However, the effects of genes related to lactate metabolism on the prognosis and tumor microenvironmental characteristics of GC patients are unknown.
Method: In this study, we have collected gene expression data of gastric cancer from The Cancer Genome Atlas (TCGA) and identified differentially expressed genes in gastric cancer using the "Limma" software package.
Result: 76 differentially expressed lactate metabolism-related genes were screened, and then the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analysis were employed that identified 8 genes, constructed Lactate Metabolism-related gene signals (LMRs), and verified the reliability of the prognostic risk mapping by using TCGA training set and TCGA internal test set. Finally, the functional enrichment analysis was employed to identify the molecular mechanism.
Conclusion: Eight lactate metabolism-related genes were constructed into a new predictive signal that better predicted the overall survival of gastric cancer patients and can guide clinical decisions for more precise and personalized treatment.
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| http://dx.doi.org/10.2174/0115665240290237240424054233 | DOI Listing |
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