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Abstract
Background
Hepatocellular carcinoma (HCC) is considered one of the most common cancers, characterized by low early detection and high mortality rates, and is a global health challenge. Immunogenic cell death (ICD) is defined as a specific type of regulated cell death (RCD) capable of reshaping the tumor immune microenvironment by releasing danger signals that trigger immune responses, which would contribute to immunotherapy.
Methods
The ICD gene sets were collected from the literature. We collected expression data and clinical information from public databases for the HCC samples in our study. Data processing and mapping were performed using R software to analyze the differences in biological characteristics between different subgroups. The expression of the ICD representative gene in clinical specimens was assessed by immunohistochemistry, and the role of the representative gene in HCC was evaluated by various in vitro assays, including qRT-PCR, colony formation, and CCK8 assay. Lasso-Cox regression was used to screen prognosis-related genes, and an ICD-related risk model (ICDRM) was constructed. To improve the clinical value of ICDRM, Nomograms and calibration curves were created to predict survival probabilities. Finally, the critical gene of ICDRM was further investigated through pan-cancer analysis and single-cell analysis.
Results
We identified two ICD clusters that differed significantly in terms of survival, biological function, and immune infiltration. As well as assessing the immune microenvironment of tumors in HCC patients, we demonstrate that ICDRM can differentiate ICD clusters and predict the prognosis and effectiveness of therapy. High-risk subpopulations are characterized by high TMB, suppressed immunity, and poor survival and response to immunotherapy, whereas the opposite is true for low-risk subpopulations.
Conclusions
This study reveals the potential impact of ICDRM on the tumor microenvironment (TME), immune infiltration, and prognosis of HCC patients, but also a potential tool for predicting prognosis.
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