Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis.

Hao Song,Na Ding,Shang Li,Caifang Ni,Chunlong Zhang,Jianlong Liao,Youtao Yu,Aimin Xie

doi:10.3389/fgene.2020.00895

Abstract

BackgroundBioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.MethodsWe employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.ResultsTwo key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.ConclusionAnalysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Highlights

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor type and the fourth leading cause of cancerrelated deaths worldwide, with approximately 841,000 new cases and 782,000 deaths each year (Bray et al, 2018)
These single genes affect the phenotype of HCC, it is not known whether they constitute the hub genes
We used the Rank Aggregation (RRA) method to integrate and analyze the nine Gene Expression Omnibus (GEO) datasets to obtain robust differentially expressed genes (DEGs) (Step 1). These DEGs were used to construct a weighted gene co-expression network analysis (WGCNA) network using the GSE14520 dataset, and the key modules displaying a significant correlation with clinical traits were identified (Step 2)

Summary

Introduction

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor type and the fourth leading cause of cancerrelated deaths worldwide, with approximately 841,000 new cases and 782,000 deaths each year (Bray et al, 2018). Multiple therapies have been recently developed for HCC, prognosis remains unsatisfactory due to disease progression, recurrence, and metastasis (Budhu et al, 2006). Abnormal expression of several genes is critical in tumorigenesis and development of HCC. Recent research has shown that tumor necrosis factor-α-induced protein 8 (TNFAIP8) increases HCC cell survival by blocking apoptosis, promoting greater resistance to the anticancer drugs sorafenib and regorafenib (Niture et al, 2020). High expression of ATP/GTP binding protein like 2 (AGBL2) is associated with significantly enhanced survival and proliferation of HCC cells in vitro and tumor growth in vivo To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority

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Discussion

Conclusion