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Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma

文献类型： 外文期刊

作者： Zheng, Yujia 1 ; Liu, Yulin 1 ; Zhao, Songfeng 2 ; Zheng, Zhetian 3 ; Shen, Chunyi 1 ; An, Li 4 ; Yuan, Yongliang 2 ;

作者机构： 1.Zhengzhou Univ, Affiliated Hosp 1, Biotherapy Ctr, Zhengzhou, Henan, Peoples R China

2.Zhengzhou Univ, Affiliated Hosp 1, Dept Pharm, 1 Jianshe East Rd, Zhengzhou 450052, Henan, Peoples R China

3.Yangtze Univ, Sch Comp Sci, Jingzhou, Hubei, Peoples R China

4.Henan Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Zhengzhou 450002, Henan, Peoples R China

关键词： hepatocellular carcinoma; GEO; TCGA; biomarker; differentially expressed genes

期刊名称：CANCER MANAGEMENT AND RESEARCH （ 影响因子：3.989； 五年影响因子：3.947 ）

ISSN： 1179-1322

年卷期： 2018 年 10 卷

页码：

收录情况： SCI

摘要： Background: Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers. Materials and methods: One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between FICC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KIRKI) pathway, Gene ontology enrichment analyses, and protein-protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored. Results: Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein-protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1 , TXNR1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states. Condusion: This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

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