ArticleLiver cancer2026
Clinical Prediction of Glycolysis-Driven Molecular Subclass of Hepatocellular Carcinoma without Transcriptomic Profiling.
Article in Liver cancer, 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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21 authors.
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Abstract
Introduction: Glycolysis-driven hepatocellular carcinoma (HCC) has been associated with resistance to immune checkpoint inhibitors; however, clinical implementation of glycolysis-targeted therapies remains limited by the reliance on transcriptomic subclassification. Methods: We developed a logistic regression model using clinical and pathological features to predict transcriptome-defined glycolysis subclass (training cohort, Results: The integer logistic model (cutoff ≥3), incorporating serum α-fetoprotein, serum albumin, arterial enhancement pattern, and histopathological phenotypes (macrotrabecular-massive, compact, and pseudoglandular), achieved an area under the receiver operating characteristic curve (AUROC) of 0.905, accuracy of 0.850, and F1 score of 0.80 in the training cohort, with consistent performance across subgroups. External validation using the fully annotated subset of the TCGA-LIHC cohort ( Conclusion: We provide a transcriptomics-independent logistic regression model for identifying glycolysis-enhanced HCC. This approach enables practical metabolic subclassification of HCC using routinely available clinical data. Macrotrabecular-massive and compact histopathological patterns, inhomogeneous arterial hyperenhancement, elevated serum α-fetoprotein levels, and
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