Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis. Issue 7 (18th February 2019)

• Record Type: Journal Article
• Authors: Xu, Baojin; Lv, Wu; Li, Xiaoyan; Zhang, Lina; Lin, Jie
• Is Part Of: Journal of cellular biochemistry. Volume 120:Issue 7(2019)
• Page Start: 11616
• Page End: 11623
• Publication Date: 2019-02-18
• Subjects: biomarker -- hepatocellular carcinoma -- malignant progression -- weighted gene coexpression network analysis; Cytochemistry -- Periodicals; 572
• DOI: 10.1002/jcb.28441
• Languages: English
• ISSNs: 0730-2312

Abstract

Abstract: Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients. Abstract : In our paper, on the basis of weighted gene coexpression network analysis, we have promoted the method of recognizing hub genes and identify 10 unknown genes who play a vital role in the cancer development in hepatocellular carcinoma (HCC). These findings may contribute to the improvement of therapeutic decision, risk stratification, and prognosis prediction for HCC patients.