Identification of immunogenic cell death signature genes in hepatocellular carcinoma: from single-cell transcriptomics to in vitro mechanistic validation and comprehensive prognostic modeling with hundreds of machine learning algorithms
Hongliang Liu, Zhenni Sun, Xi Wang, Bin Zhou, Lichao Cha
TL;DR
The study identifies genes linked to immunogenic cell death in liver cancer, creating a model to predict patient outcomes and guide treatment choices.
Contribution
A novel ICD-based risk score and framework for HCC prognosis and immunotherapy selection using multi-omics data and machine learning.
Findings
The ICD-based risk score (ICDRS) showed strong prognostic accuracy (C-index=0.839) across multiple HCC cohorts.
Low-risk patients exhibited anti-tumor immunity with CD8+ T cells and M1 macrophages, while high-risk patients had immunosuppressive environments.
CLIC1 and NAP1L1 overexpression in HepG2 cells confirmed their role in promoting HCC malignant behaviors.
Abstract
Hepatocellular carcinoma (HCC) lacks reliable prognostic biomarkers for immunotherapy. Immunogenic cell death (ICD) represents a promising therapeutic target, but its comprehensive characterization in HCC remains unexplored. We performed multi-omics integration of single-cell RNA sequencing data from 7 HCC samples (GSE112271, 44,461 cells) with bulk transcriptomics from three independent cohorts (TCGA-HCC [n=371], GSE14520 [n=242], ICGC [n=445]). ICD activity was quantified using ssGSEA. We identified HCC-specific ICD-related (HCC-ICDR) genes via WGCNA and optimized a prognostic model by benchmarking machine learning algorithms. Experimental validation included functional assays using CLIC1 and NAP1L1 overexpression in HepG2 cells. The ICD-based risk score (ICDRS) demonstrated superior prognostic accuracy (C-index=0.839), validated across cohorts. Single-cell profiling revealed…
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Taxonomy
TopicsFerroptosis and cancer prognosis · Immune cells in cancer · Cancer Immunotherapy and Biomarkers