Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma

Purpose Programmed cell death (PCD) mechanisms play crucial roles in cancer progression and treatment response. This study aims to develop a PCD scores prediction model to evaluate the prognosis of hepatocellular carcinoma (HCC) and elucidate the tumor microenvironment differences. Methods We analyzed transcriptomic data from 363 HCC patients in the TCGA database and 221 patients in the GEO database to develop a PCD prediction model. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) data from HCC patients were analyzed to investigate the tumor microenvironment and functional disparities. The oncogenic role of the key gene UBE2E1 in the model was explored in HCC through various in vitro experiments. Results Seventeen PCD-related genes were identified as significant prognostic indicators, forming the basis of our PCD prediction model. High-PCD scores correlated with poorer overall survival (OS) and exhibited significant predictive capabilities. scRNA-seq analysis revealed distinct tumor cell characteristics and immune microenvironment differences between high- and low-PCD groups. High-PCD tumors showed increased cell proliferation and malignancy-associated gene expression. T cells in high-PCD patients were more likely to be exhausted, with elevated expression of exhaustion markers. ST-seq data also confirmed these results. Among the genes associated with the PCD prognostic model, UBE2E1 was identified as a key oncogenic marker in HCC. Conclusions The PCD prediction model effectively predicts prognosis in HCC patients and reveals critical insights into the tumor microenvironment and immune cell exhaustion. This study underscores the potential of PCD-related biomarkers in guiding personalized treatment strategies for HCC.