Background <p>Hepatocellular carcinoma (HCC) is a globally serious malignant tumor with high incidence and mortality. Ferroptosis, a newly discovered form of regulated cell death, is significant in tumor initiation and growth. Herein, we performed bioinformatics analysis in order to investigate the expression heterogeneity of ferroptosis-related genes as well as its correlation with HCC clinical outcomes.</p> Methods <p>The gene expression data of HCC were downloaded from the TCGA and GEO databases. Weighted Gene Co-expression Network Analysis (WGCNA) was deployed to build gene co-expression networks and explore ferroptosis-related gene modules. We used single-cell RNA sequencing data to analyze the expression of these genes in different cell types. Survival analysis and functional enrichment analysis were utilized to study the biological function and clinical significance of these genes in HCC.</p> Results <p>Several ferroptosis-related gene modules were identified by WGCNA, one of which was significantly linked with the clinical characteristics of HCC. Analysis of single-cell sequencing data revealed distinct expression of these core genes in different cell types. Survival analysis revealed that certain ferroptosis-related gene expressions were strongly correlated with patient survival outcomes. Functional enrichment analysis indicated that these genes mainly participate in oxidative stress response, iron metabolism, and apoptosis.</p> Conclusions <p>This study reveals the expression heterogeneity of ferroptosis-related genes in HCC and may provide new molecular targets for HCC prognosis and therapy.</p>

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WGCNA and single-cell analysis reveal ferroptosis-related gene signatures for hepatocellular carcinoma prognosis and therapy

  • Can Peng,
  • Fujian Yan,
  • Mingli Xu,
  • Yueyue Zhao,
  • Jiaxing Sun

摘要

Background

Hepatocellular carcinoma (HCC) is a globally serious malignant tumor with high incidence and mortality. Ferroptosis, a newly discovered form of regulated cell death, is significant in tumor initiation and growth. Herein, we performed bioinformatics analysis in order to investigate the expression heterogeneity of ferroptosis-related genes as well as its correlation with HCC clinical outcomes.

Methods

The gene expression data of HCC were downloaded from the TCGA and GEO databases. Weighted Gene Co-expression Network Analysis (WGCNA) was deployed to build gene co-expression networks and explore ferroptosis-related gene modules. We used single-cell RNA sequencing data to analyze the expression of these genes in different cell types. Survival analysis and functional enrichment analysis were utilized to study the biological function and clinical significance of these genes in HCC.

Results

Several ferroptosis-related gene modules were identified by WGCNA, one of which was significantly linked with the clinical characteristics of HCC. Analysis of single-cell sequencing data revealed distinct expression of these core genes in different cell types. Survival analysis revealed that certain ferroptosis-related gene expressions were strongly correlated with patient survival outcomes. Functional enrichment analysis indicated that these genes mainly participate in oxidative stress response, iron metabolism, and apoptosis.

Conclusions

This study reveals the expression heterogeneity of ferroptosis-related genes in HCC and may provide new molecular targets for HCC prognosis and therapy.