ArticleMedicine2026
Construction of an ATP hydrolysis-related 11-gene signature for predicting prognosis and immune response in hepatocellular carcinoma.
Article in Medicine, 2026. 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
The dysregulation of adenosine triphosphate (ATP) hydrolysis, a crucial process in energy metabolism, has been implicated in the complex landscape of tumor development and progression. This study aims to pinpoint crucial genes that characterize the ATP hydrolysis-driven molecular signature of hepatocellular carcinoma (HCC), with a view to examining their potential therapeutic applications in enhancing patient prognosis. Utilizing transcriptomic and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus databases, a systematic framework was developed and validated for model construction and assessment. Univariate Cox regression analysis (P < .05) was performed on all 416 ATP hydrolysis-related genes (ARGs) to identify prognostically relevant genes. Following least absolute shrinkage and selection operator Cox regression with 10-fold cross-validation, an 11-gene signature was established, with the median training cohort risk score used as the cutoff for stratifying patients into high- and low-risk groups. Among 416 investigated ARGs, 85 displayed differential expression in HCC, with 179 demonstrating significant prognostic relevance (univariate Cox P < .05). Two distinct molecular subtypes, characterized by marked differences in prognosis and immune infiltration, were identified through unsupervised clustering. A refined 11-gene signature was constructed via least absolute shrinkage and selection operator Cox regression with 10-fold cross-validation (training area under the curve: 0.826/0.717/0.657 for 1/3/5 year; validation area under the curve: 0.608/0.603/0.621). These risk groups exhibited significantly divergent prognostic features, gene expression patterns, immune microenvironments, and drug sensitivities, suggesting the utility of this risk signature in predicting prognosis, drug responsiveness, and immune therapeutic outcomes. Ultimately, a nomogram incorporating the risk signature was devised, demonstrating favorable predictive performance in estimating HCC patient prognosis compared to other established prognostic factors. This study establishes a classification system and risk model grounded in ARGs, offering valuable insights into the prognosis and treatment responsiveness of HCC.
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