Biomarker-Based Precision Prediction of Immunotherapy Response in Hepatocellular Carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) remains a major global health challenge with limited treatment options for advanced disease. Although immune checkpoint inhibitors (ICIs) have shown clinical benefits, response rates remain low, emphasizing the need for reliable biomarkers to guide patient selection. Given the critical role of metabolic reprogramming in immune modulation, this study aimed to identify a metabolic gene signature predictive of immunotherapy response in HCC. Methods: Three independent transcriptomic datasets (GSE279750, GSE215011, and GSE235863) comprising 35 ICI-treated HCC samples were integrated after quality control and ComBat batch correction. Differentially expressed genes were identified using DESeq2 and limma, followed by integration of the meta-analysis results. Machine learning models, including LASSO regression and random forest algorithms, were applied for feature selection, and a logistic regression model was developed for predictive scoring. Results: A five-gene metabolic signature (PLPPR1, CNTN3, HOXA10, HAGLR, and ENPP3) demonstrated good discriminative ability between responders and non-responders, with consistent performance observed across internal validation analyses. Functional enrichment analysis revealed significant involvement of metabolic pathways, with HOXA10 linked to immune evasion and CNTN3 associated with immune activation. Conclusions: This five-gene signature represents a biologically interpretable biomarker panel with potential utility for immunotherapy response stratification in HCC. The integrative analytical framework provides preliminary evidence supporting its value, warranting further validation in larger, independent clinical cohorts before clinical translation.