Identification of 13 Key Genes Correlated With Progression and Prognosis in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

Gu, Yang; Li, Jun; Guo, Deliang; Chen, Baiyang; Liu, Pengpeng; Xiao, Yusha; Yang, Kuo‐Chung; Liu, Zhisu; Liu, Quanyan

doi:10.3389/fgene.2020.00153

Cited by 38 publications

(30 citation statements)

References 36 publications

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“…Weighted gene co-expression network and bioinformatic-based analyses revealed that SPC25 plays a role in the cell cycle, mitosis, and organelle organization, common pathways of tumor growth [ 32 , 33 ]. SPC25 colocalized with kinetochores from prometaphase through anaphase, the knockdown of which resulted in mitotic arrest [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

SPC25 overexpression promotes tumor proliferation and is prognostic of poor survival in hepatocellular carcinoma

Zhang

Zhou

Xie

et al. 2020

Aging

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Background: The nuclear division cycle 80 (NDC80) complex assures proper chromosome segregation during the cell cycle progression. SPC25 is a crucial component of NDC80, and its role in hepatocellular carcinoma (HCC) has been explored recently. This study characterized the differential expression of SPC25 in HCC patients of different races and HBV infection status. Methods: Expression patterns of SPC25 were evaluated in TCGA and Chinese HCC patients. Kaplan-Meier analysis was applied to examine the predictive value of SPC25. In vitro and in vivo functional assays were conducted to explore the role of SPC25 in HCC. Bioinformatics methods were applied to investigate the regulatory mechanisms of SPC25. Findings: The mRNA levels of SPC25 were up-regulated in HCC. SPC25 has a significantly higher transcriptional level in Asian patients than Caucasian patients. SPC25 promoted HCC cell proliferation in vitro and tumor growth in vivo by accelerating the cell cycle. We identified transcription factors, miRNAs, and immune cells that may interact with SPC25. Interpretation: The findings suggest that increased expression of SPC25 is associated with poor prognosis of HCC and enhances the proliferative capacity of HCC cells. SPC25 could serve as a valuable prognostic marker and a novel treatment target for HCC.

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Section: Resultsmentioning

confidence: 99%

SPC25 overexpression promotes tumor proliferation and is prognostic of poor survival in hepatocellular carcinoma

Zhang

Zhou

Xie

et al. 2020

Aging

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“…In 2018, Zhang L. et al (2018) used bioinformatics methods to select 10 genes from the GEO dataset GSE64041 for the identification of hub genes and pathways of HCC. Gu et al (2020) recognized 13 crucial genes correlated with progression and prognosis of HCC from the TCGA-LIHC dataset by weighted gene coexpression network analysis. Compared with previous similar studies, our study not only integrated a large sample size of mRNA expression data from the GEO database but also analyzed RNA sequencing result and clinical data from the TCGA-LIHC database to screen out potential hub genes in HCC.…”

Section: Discussionmentioning

confidence: 99%

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

Lin

Cheng

et al. 2021

Front. Genet.

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BackgroundHepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.MethodsThe bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.ResultsWe ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.ConclusionThe present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

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“…SPC25 encodes a protein that may be involved in kinetochore–microtubule interaction and spindle checkpoint activity. Gu et al screened 13 key genes including SPC25 in HCC by WGCNA [ 28 ]. Guo et al identified SPC25 as a prognostic biomarker of HCC via random-forest algorithm [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020

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This study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with Weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

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Identification of 13 Key Genes Correlated With Progression and Prognosis in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

Cited by 38 publications

References 36 publications

SPC25 overexpression promotes tumor proliferation and is prognostic of poor survival in hepatocellular carcinoma

SPC25 overexpression promotes tumor proliferation and is prognostic of poor survival in hepatocellular carcinoma

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

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