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Background <p>Coronavirus disease 2019 (COVID-19) outbreak has widespread impacts on the patients with pre-existing conditions. It is well-established that liver cirrhosis (LC) is a significant risk factor in the etiopathogenesis of hepatocellular carcinoma (HCC). This study was undertaken to investigate the potential gene signatures and the gene regulatory networks between COVID-19 and LC-HCC.</p> Methods <p>Relevant gene signatures were identified through the shared differentially expressed genes (DEGs) based on COVID-19 and LC-HCC utilizing the bioinformatics analysis. Subsequently, the gene functional enrichment analysis (including Kyoto encyclopedia of genes and genomes (KEGG) and Gene Ontology (GO)) and protein-protein interaction (PPI) network were performed to identify the hub gene as the key biomarker. Finally, receiver operating characteristic (ROC) analysis, along with biological function and gene expression regulatory network analyses were systematically performed.</p> Results <p>The study successfully identified 78 common candidate gene signatures distinguishing COVID-19 from liver cirrhosis-hepatocellular carcinoma (LC-HCC). Subsequent KEGG and GO enrichment analyses revealed that these gene signatures were predominantly associated with the cell cycle. In constructing the PPI network, the core gene CDK1 was successfully identified based on the ten scoring methods. Furthermore, ROC analysis demonstrated that CDK1 possesses excellent predictive efficacy for both COVID-19 (AUC = 0.955) and LC-HCC (AUC = 0.946) cohorts. Moreover, it was observed that HCC patients with elevated CDK1 expression had poorer prognoses. Finally, the comprehensive gene regulatory networks were established.</p> Conclusion <p>This study successfully identified the key biomarker and gene regulatory networks between COVID-19 and LC-HCC, thereby contributing to the prediction of clinical outcomes and the identification of novel therapeutic targets.</p>

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Gene signatures and networks: linking COVID-19 to liver cirrhosis and hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Ning Wang,
  • Xue Wang,
  • Xue Zhou,
  • Guoyue Yang,
  • Qing Ye

摘要

Background

Coronavirus disease 2019 (COVID-19) outbreak has widespread impacts on the patients with pre-existing conditions. It is well-established that liver cirrhosis (LC) is a significant risk factor in the etiopathogenesis of hepatocellular carcinoma (HCC). This study was undertaken to investigate the potential gene signatures and the gene regulatory networks between COVID-19 and LC-HCC.

Methods

Relevant gene signatures were identified through the shared differentially expressed genes (DEGs) based on COVID-19 and LC-HCC utilizing the bioinformatics analysis. Subsequently, the gene functional enrichment analysis (including Kyoto encyclopedia of genes and genomes (KEGG) and Gene Ontology (GO)) and protein-protein interaction (PPI) network were performed to identify the hub gene as the key biomarker. Finally, receiver operating characteristic (ROC) analysis, along with biological function and gene expression regulatory network analyses were systematically performed.

Results

The study successfully identified 78 common candidate gene signatures distinguishing COVID-19 from liver cirrhosis-hepatocellular carcinoma (LC-HCC). Subsequent KEGG and GO enrichment analyses revealed that these gene signatures were predominantly associated with the cell cycle. In constructing the PPI network, the core gene CDK1 was successfully identified based on the ten scoring methods. Furthermore, ROC analysis demonstrated that CDK1 possesses excellent predictive efficacy for both COVID-19 (AUC = 0.955) and LC-HCC (AUC = 0.946) cohorts. Moreover, it was observed that HCC patients with elevated CDK1 expression had poorer prognoses. Finally, the comprehensive gene regulatory networks were established.

Conclusion

This study successfully identified the key biomarker and gene regulatory networks between COVID-19 and LC-HCC, thereby contributing to the prediction of clinical outcomes and the identification of novel therapeutic targets.