Abstract
In this study, we used single-cell sequencing data analysis to explore differentially expressed genes in the polarization process of macrophages in hepatocellular carcinoma. We then integrated these genes with cuproptosis-related genes (CRGs) to identify potential biomarkers. Through a rigorous screening process, including univariate Cox regression analysis and machine learning algorithms, we identified six key risk genes: GLIPR2, ANP32E, LIPT1, ALAD, ARSK, and PGAM1. These genes form the foundation of our prognostic risk prediction model. ROC curve analysis showed that these models had high specificity and accuracy in predicting prognostic characteristics, and Kaplan-Meier curve analysis showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group. In addition, patients stratified by our model showed differences in tumor microenvironment, sensitivity to immunotherapy, and response to chemotherapy. After incorporating patient clinical data, we constructed a nomogram that further improved the accuracy of predicting patient survival. We further analyzed the expression characteristics and spatial distribution of these six risk genes in hepatocellular carcinoma through bulk transcriptomics, single-cell, and spatial transcriptomics data, and validated the expression of risk genes using qPCR. The construction of predictive models in this study helps clinicians to predict the overall survival of patients with hepatocellular carcinoma, which enables patient stratification and has the potential to help personalize patient treatment. The discovery of candidate tumor markers helps to identify potential targeted therapeutic options, which will play a key role in the diagnosis and treatment of hepatocellular carcinoma in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s12672-025-04373-3.