Prognostic four-gene signature for overall survival in patients with hepatocellular carcinoma

Yokosuka, Osamu; Moriyama, Mitsuhiko

doi:10.1007/s12072-019-09976-x

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Recently, research on genetic markers used to predict the prognosis of human cancers has been reported. A 4-genes signature, associated with the OS of patients with HCC, were identified and an independent prognostic factor particularly for Asian patients with serum α-fetoprotein (AFP) >20 ng/ml [23]. An immune-related gene expression pattern in liver tissues of patients with early-stage HCC that is associated with risk of HCC development in patients with cirrhosis [24].…”

Section: Discussionmentioning

confidence: 99%

Identification of hepatocellular carcinoma prognostic markers based on 10-immune gene signature

Zhao

et al. 2020

Bioscience Reports

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Background: Due to the heterogeneity of Hepatocellular Carcinoma (HCC), hepatocelluarin-associated differentially expressed genes were analyzed by bioinformatics methods to screen the molecular markers for HCC prognosis and potential molecular targets for immunotherapy. Methods: RNA-seq data and clinical follow-up data of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis and Lasso regression were used to identify robust immunity-related genes. Finally, a risk prognosis model of immune gene pairs was established and verified by clinical features, test set and Gene Expression Omnibus (GEO) external validation set. Results: A total of 536 immune-related gene (IRGs) were significantly associated with the prognosis of patients with HCC. 10 robust IRGs were finally obtained and a prognostic risk prediction model was constructed by feature selection of Lasso. The risk score of each sample is calculated based on the risk model and is divided into high risk group (Risk-H) and low risk group (Risk-L). Risk models enable risk stratification of samples in training sets, test sets, external validation sets, staging, and subtypes. The area under the curve (AUC) in the training set and the test set were all greater than 0.67, and there were significant overall survival (OS) differences between the Risk-H and Risk-L samples. Compared with the published four models, the traditional clinical features of Grade, Stage and Gender, the model performed better on the risk prediction of HCC prognosis. Conclusion: This study constructed 10-gene signature as a novel prognostic marker for predicting survival in patients with HCC.

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Section: Discussionmentioning

confidence: 99%

Identification of hepatocellular carcinoma prognostic markers based on 10-immune gene signature

Zhao

et al. 2020

Bioscience Reports

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“…Secondly, the specific functions of the 4 genes in HCC were still missing, even though the molecular characterization for hub genes were predicted, and we still need to perform experiments to explore this in the future. In addition, singlegene expression is insufficient to discover HCC initiation and progression [30,31]. Construction of the molecular network as a whole appears to be a better strategy.…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes in Hepatocellular Carcinoma Related to Progression and Prognosis by Weighted Gene Co-Expression Network Analysis

Wang

Yang

et al. 2020

Med Sci Monit

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Background:Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in the world. Bioinformatics studies have been widely used for screening genes involved in the initiation and progression of HCC. Material/Methods:We obtained liver cancer microarray raw data from the GEO database (GSE54238). Next, weighted gene coexpression network analysis (WGCNA) was used to assess the critical modules. Then, we assessed the gene significance by calculating survival, expression level, and receiver operating characteristic (ROC) in the TCGA database. We also validated the expression of selected genes in the Oncomine database and calculated the relationship between 4 hub genes and immune infiltration. Finally, GSEA enrichment analysis was used to explore the potential mechanism. Results:We identified the red and blue modules as the critical modules, and found 176 candidate genes by assessing gene significance. GO and KEEG results suggested that the candidate genes are involved in the cell cycle. Four hub genes -SOX4, STK39, TARBP1, and TDRKH -were eventually screened after validating their expression and power in diagnosing HCC in the TCGA database. Immune infiltration analysis and GSEA enrichment analysis showed that these 4 hub genes were correlated with the immune cell populations infiltration and that multiple mechanisms were involved, such as angiogenesis and epithelial-mesenchymal transition. Conclusions:Our findings revealed that these 4 genes can be regarded as potential prognosticators and therapeutic targets for HCC.

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Prognostic four-gene signature for overall survival in patients with hepatocellular carcinoma

Cited by 2 publications

References 11 publications

Identification of hepatocellular carcinoma prognostic markers based on 10-immune gene signature

Identification of hepatocellular carcinoma prognostic markers based on 10-immune gene signature

Identification of Hub Genes in Hepatocellular Carcinoma Related to Progression and Prognosis by Weighted Gene Co-Expression Network Analysis

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