Identification of hub genes involved in the development of hepatocellular carcinoma by transcriptome sequencing

Zheng, Yongchang; Long, Junyu; Wu, Liangcai; Zhang, Haohai; Li, Lin; Zheng, Ying; Wang, Anqiang; Lin, Jianzhen; Yang, Xiaobo; Sang, Xinting; Hu, Ke; Pan, Jie; Zhao, Haitao

doi:10.18632/oncotarget.19483

Cited by 9 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, analysis of profiling arrays has demonstrated that HCC pathogenesis is a complex biological process that involves genetic and epigenetic changes 17, and DNA hypermethylation is an early event in the development of HCC 18. One meta-analysis offered empirical evidence that abnormal promoter methylation of suppressor of cytokine signaling 1 (SOCS1) might lead to HCC pathogenesis 19.…”

Section: Introductionmentioning

confidence: 99%

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

et al. 2019

Self Cite

View full text Add to dashboard Cite

In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC).Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes.Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively.Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.

show abstract

Section: Introductionmentioning

confidence: 99%

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…RNAseq is a method that is conductive to the application of a systematic comprehensive study of differentially expressed gene interactions and related signaling pathways with high precision. Moreover, protein-protein interaction (PPI) networks are useful for distinguishing hub genes, which are defined as genes with a high degree of connectivity that play an essential role in stabilizing the PPI network structure (13,14). There are numerous oncology studies based on TCGA.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic analysis and identification of prognostic biomarkers in cholangiocarcinoma

Long

Xie

et al. 2019

Oncol Rep

Self Cite

View full text Add to dashboard Cite

Cholangiocarcinoma (CCA) is acknowledged as the second most commonly diagnosed primary liver tumor and is associated with a poor patient prognosis. The present study aimed to explore the biological functions, signaling pathways and potential prognostic biomarkers involved in CCA through transcriptomic analysis. Based on the transcriptomic dataset of CCA from The Cancer Genome Atlas (TCGA), differentially expressed protein-coding genes (DEGs) were identified. Biological function enrichment analysis, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was applied. Through protein-protein interaction (PPI) network analysis, hub genes were identified and further verified using open-access datasets and qRT-PCR. Finally, a survival analysis was conducted. A total of 1,463 DEGs were distinguished, including 267 upregulated genes and 1,196 downregulated genes. For the GO analysis, the upregulated DEGs were enriched in ‘cadherin binding in cell-cell adhesion’, ‘extracellular matrix (ECM) organization’ and ‘cell-cell adherens junctions’. Correspondingly, the downregulated DEGs were enriched in the ‘oxidation-reduction process’, ‘extracellular exosomes’ and ‘blood microparticles’. In regards to the KEGG pathway analysis, the upregulated DEGs were enriched in ‘ECM-receptor interactions’, ‘focal adhesions’ and ‘small cell lung cancer’. The downregulated DEGs were enriched in ‘metabolic pathways’, ‘complement and coagulation cascades’ and ‘biosynthesis of antibiotics’. The PPI network suggested that CDK1 and another 20 genes were hub genes. Furthermore, survival analysis suggested that CDK1, MKI67, TOP2A and PRC1 were significantly associated with patient prognosis. These results enhance the current understanding of CCA development and provide new insight into distinguishing candidate biomarkers for predicting the prognosis of CCA.

show abstract

“…There are 11 topological analysis methods in the cytoHubba plug-in of Cytoscape software platform. In the current study, four comparatively accurate methods of topological analysis, including the MCC, MNC, DMNC and degree methods (8), were selected to construct the subnetworks of the PPI network (Fig. 2B-E).…”

Section: Resultsmentioning

confidence: 99%

“…The Cancer Genome Atlas (TCGA) database contains a large number of gene microarray data collected from cancer patients. Several previous studies have used bioinformatics analysis of such gene microarray data in order to identify key genes in HCC (6–8). In contrast to these earlier studies, the present study focuses on analysing the protein-protein interaction (PPI) networks using a number of different topological analysis methods to identify the hub genes.…”

Section: Introductionmentioning

confidence: 99%

Identification of cancer/testis antigen�2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis

Liu

Sun

et al. 2019

Oncol Lett

View full text Add to dashboard Cite

The 5-year survival rate of hepatocellular carcinoma (HCC) is <20%; thus, identifying new potential therapeutic targets or novel biomarkers for prognosis prediction is crucial. The present study aimed to screen hub genes by constructing protein-protein interaction (PPI) subnetworks using topological analysis methods, as well as reveal their clinical significance through big data analytics and their association with the clinicopathological features. Firstly, the PPI subnetworks were constructed using four topological analysis methods, including the MCC, DMNC, MNC and degree methods, to obtain 6 hub genes. Subsequently, the hub gene cancer/testis antigen 2 (CTAG2), which affects the prognosis of HCC (overall survival, P=0.035), was acquired by analysing clinical data in The Cancer Genome Atlas database. Meanwhile, CTAG2 expression was significantly associated with the age at diagnosis (P=0.003), T stage (P=0.028), TNM stage (P=0.028) and α-fetoprotein (AFP) expression (P=0.045). Further immunohistochemical analysis of samples collected in our hospital revealed that the expression level of CTAG2 in 46 HCC tissues was significantly higher in comparison with that in paired adjacent tissues. The clinical data indicated that the expression of CTAG2 was significantly correlated with the hepatitis B virus status (P=0.010) and AFP expression (P=0.004). These results were then found to be consistent with the results of big data analytics. Furthermore, Gene Set Enrichment Analysis demonstrated that the function of CTAG2 in HCC may be associated with the cell cycle. Taken together, these findings suggest that CTAG2 may serve as a new potential therapeutic target for HCC patients.

show abstract

Identification of hub genes involved in the development of hepatocellular carcinoma by transcriptome sequencing

Cited by 9 publications

References 45 publications

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

Transcriptomic analysis and identification of prognostic biomarkers in cholangiocarcinoma

Identification of cancer/testis antigen�2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis

Contact Info

Product

Resources

About