ArticleInternational journal of molecular sciences2026
XGBoost-SHAP Interpretable Modeling Identifies and Validates an Eight-Gene Biomarker for Hepatic Encephalopathy Risk Prediction in Cirrhosis.
Article in International journal of molecular sciences, 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
Cirrhosis, accounting for 2.4% of global mortality in 2019, represents a leading cause of death in chronic liver disease. Hepatic encephalopathy (HE), a decompensated complication of cirrhosis, is associated with a median survival of only 0.92 years post-diagnosis. Current screening methods relying on neuropsychological tests (e.g., Psychometric Hepatic Encephalopathy Score, PHES) have limitations such as time-consuming procedures and subjective interpretation, potentially delaying diagnosis. To address this, we integrated four cirrhotic transcriptomic cohorts (GSE41919, GSE57193, GSE139602, and GSE15654) and employed an integrated algorithm (LASSO [Least Absolute Shrinkage and Selection Operator]-RFE [Recursive Feature Elimination]-random forest) to identify HE-specific biomarker genes. Ultimately, we developed an HE risk-prediction system centered on eight HE-specific marker genes, namely,
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