Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Zhipeng Zhu; Lulu Li; Jiuhua Xu; Weipeng Ye; Borong Chen; Junjie Zeng; Zhengjie Huang

doi:10.7717/peerj.9201

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

PeerJ. 2020 May 26:8:e9201. doi: 10.7717/peerj.9201. eCollection 2020.

Authors

Zhipeng Zhu^#¹, Lulu Li^#¹, Jiuhua Xu², Weipeng Ye², Borong Chen¹, Junjie Zeng¹, Zhengjie Huang^{1

2}

Affiliations

¹ Department of Gastrointestinal Surgery, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
² Department of Clinical Medicine, Fujian Medical University, Xiamen, Fujian, China.

^# Contributed equally.

Abstract

Background: Due to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC).

Methods: Differentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in four types of testing cohorts. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance. Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group.

Results: Ten prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis. Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide new insights into the development of HCC.

Conclusion: The study have identified ten metabolic prognostic genes and developed a prognostic gene signature to provide more powerful prognostic information and improve the survival prediction for HCC.

Keywords: Bioinformatics; Biomarker; Diagnosis; Gene signature; Hepatocellular carcinoma; Metabolism; Prognosis; Survival.

Grants and funding

This work was supported by Xiamen Scientific and Technological Plan (No. 3502Z20194005, 3502Z20184020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.