Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis

doi:10.1007/s11684-016-0440-4

Front. Med.

2016, Vol. 10

Issue (2) : 183-190 https://doi.org/10.1007/s11684-016-0440-4

RESEARCH ARTICLE

Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis

Xinsen Xu,Yanyan Zhou,Runchen Miao,Wei Chen,Kai Qu,Qing Pang,Chang Liu(

)

Department of Hepatobiliary Surgery, the First Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an 710061, China

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Abstract

We performed weighted gene coexpression network analysis (WGCNA) to gain insights into the molecular aspects of hepatocellular carcinoma (HCC). Raw microarray datasets (including 488 samples) were downloaded from the Gene Expression Omnibus (GEO) website. Data were normalized using the RMA algorithm. We utilized the WGCNA to identify the coexpressed genes (modules) after non-specific filtering. Correlation and survival analyses were conducted using the modules, and gene ontology (GO) enrichment was applied to explore the possible mechanisms. Eight distinct modules were identified by the WGCNA. Pink and red modules were associated with liver function, whereas turquoise and black modules were inversely correlated with tumor staging. Poor outcomes were found in the low expression group in the turquoise module and in the high expression group in the red module. In addition, GO enrichment analysis suggested that inflammation, immune, virus-related, and interferon-mediated pathways were enriched in the turquoise module. Several potential biomarkers, such as cyclin-dependent kinase 1 (CDK1), topoisomerase 2α (TOP2A), and serpin peptidase inhibitor clade C (antithrombin) member 1 (SERPINC1), were also identified. In conclusion, gene signatures identified from the genome-based assays could contribute to HCC stratification. WGCNA was able to identify significant groups of genes associated with cancer prognosis.

Keywords hepatocellular carcinoma coexpression module microarray prognosis

Corresponding Author(s): Chang Liu

Just Accepted Date: 23 March 2016 Online First Date: 05 April 2016 Issue Date: 27 May 2016

Cite this article:

Xinsen Xu,Yanyan Zhou,Runchen Miao, et al. Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis[J]. Front. Med., 2016, 10(2): 183-190.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-016-0440-4
https://academic.hep.com.cn/fmd/EN/Y2016/V10/I2/183

Fig.1 Flow chart for the coexpression network analysis.

Fig.2 Network analysis of gene expression in hepatocellular carcinoma (HCC) identified modules of coexpressed genes. (A) The dendrogram was produced by unsupervised hierarchical clustering of genes on the basis of topological overlap. (B) We calculated their eigengenes and clustered them according to their correlation to quantify the coexpression similarity of entire modules. We chose the height cut of 0.4, resulting in eight merged coexpressed modules.

Fig.3 Correlation analysis of the identified modules with clinicopathological variables of hepatocellular carcinoma. Pearson’s correlation coefficient between modules and clinicopathological variables are shown, accompanied by the corresponding P value in brackets.

Tab.1 Correlation between gene coexpression modules and survival time

Fig.4 Survival analysis based on the gene expression pattern in the turquoise module. Impacts of the turquoise module expression on the (A) overall survival and (B) tumor-free survival. (C) GO enrichment analysis for the 1046 probe sets included in the turquoise module. The original significance were transformed to “−log₁₀(FDR)” to plot the curve. (D) Visualization of the network connections among the most connected genes in the turquoise module, generated by the VisANT software.

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