ArticleFrontiers in immunology2025
Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spatially organized regulated cell death-immune coupling in solid tumors: integrating spatial omics with actionable regulated cell death biology.Molecular cancer · 2026Review
- Insights into programmed cell death from multiple imaging modalities.Journal of translational medicine · 2026Review
- Mechanisms and therapeutic strategies of bidirectional crosstalk between hepatic stellate cell-derived cancer-associated fibroblasts and T cells in immune evasion and therapeutic resistance of hepatocellular carcinoma.Frontiers in immunology · 2026Review
- Predictive Modeling and Mendelian Randomization for Identifying HCC Patients with High Response to TACE with Atezolizumab and Bevacizumab.Journal of hepatocellular carcinoma · 2026Article
- Ubiquitin-conjugating Enzymes in Cancer.International journal of biological sciences · 2026Review
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Authors and funding
11 authors.
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Abstract
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 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.
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