ArticleJournal of cancer research and clinical oncology2025
LAC-TME classifier: machine learning-driven model predicts survival and prioritizes targeted therapy in clear cell renal cell carcinoma.
Article in Journal of cancer research and clinical 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
backgroundClear cell renal cell carcinoma (ccRCC) is a major type of kidney cancer, making up about 80% of cases, with advanced stages showing low survival rates. Current treatments face challenges like toxicity and drug resistance. Studies indicate lactate, through the Warburg effect, promotes an immune-suppressive tumor microenvironment (TME), prompting the development of the LAC-TME classifier using machine learning to predict outcomes and personalize treatment.
methodsThe study used data from TCGA-KIRC set and E-MTAB-1980 set, analyzing gene expression, mutations, and clinical data. It employed differential expression analysis, immune infiltration assessment, and 101 machine learning algorithms to build the classifier, integrating lactate-related genes and TME features, with predictive capability verified.
resultsThe LAC-TME classifier, constructed by integrating 9 lactate-related differentially expressed genes and TME cells, demonstrated high predictive accuracy (C-index of 0.92 in the training set and 0.73 in the validation set). Patients were categorized into three groups: Lactate
conclusionThe LAC-TME classifier, integrating metabolic and immune data, offers a new tool for ccRCC prognosis and treatment guidance. Further validation is needed to confirm its clinical potential, reflecting the ongoing need for robust medical research.
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