Identification and validation of real hub genes in hepatocellular carcinoma based on weighted gene co-expression network analysis Article Swipe
Yu Qiao
,
Fahu Yuan
,
Xin Wang
,
Jun Hu
,
Yurong Mao
,
Zhigang Zhao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3233/cbm-220151
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3233/cbm-220151
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common liver malignancies in the world. With highly invasive biological characteristics and a lack of obvious clinical manifestations, hepatocellular carcinoma usually has a poor prognosis and ranks fourth in cancer mortality. The etiology and exact molecular mechanism of primary hepatocellular carcinoma are still unclear. OBJECTIVE: This work aims to help identify biomarkers of early HCC diagnosis or prognosis based on weighted gene co-expression network analysis (WGCNA). METHODS: Expression data and clinical information of HTSeq-Counts were downloaded from The Cancer Genome Atlas (TCGA) database, and gene expression map GSE121248 was downloaded from Gene Expression Omnibus (GEO). By differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) searched for modules in the two databases that had the same effect on the biological characteristics of HCC, and extracted the module genes with the highest positive correlation with HCC from two databases, and finally obtained overlapping genes. Then, we performed functional enrichment analysis on the overlapping genes to understand their potential biological functions. The top ten hub genes were screened according to MCC through the string database and Cytoscape software and then subjected to survival analysis. RESULTS: High expression of CDK1, CCNA2, CDC20, KIF11, DLGAP5, KIF20A, ASPM, CEP55, and TPX2 was associated with poorer overall survival (OS) of HCC patients. The DFS curve was plotted using the online website GEPIA2. Finally, based on the enrichment of these genes in the KEGG pathway, real hub genes were screened out, which were CDK1, CCNA2, and CDC20 respectively. CONCLUSIONS: High expression of these three genes was negatively correlated with survival time in HCC, and the expression of CDK1, CCNA2, and CDC20 were significantly higher in tumor tissues of HCC patients than in normal liver tissues as verified again by the HPA database. All in all, this provides a new feasible target for early and accurate diagnosis of HCC, clinical diagnosis, treatment, and prognosis.
Related Topics
Concepts
Hepatocellular carcinoma
Gene co-expression network
Gene
KEGG
Cyclin-dependent kinase 1
Computational biology
Gene expression
Biology
Bioinformatics
Cell cycle
Cancer research
Genetics
Gene ontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3233/cbm-220151
- OA Status
- green
- Cited By
- 4
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4296328641
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4296328641Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3233/cbm-220151Digital Object Identifier
- Title
-
Identification and validation of real hub genes in hepatocellular carcinoma based on weighted gene co-expression network analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-16Full publication date if available
- Authors
-
Yu Qiao, Fahu Yuan, Xin Wang, Jun Hu, Yurong Mao, Zhigang ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3233/cbm-220151Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://figshare.com/articles/figure/Identification_and_Validation_of_Real_Hub_Genes_in_Hepatocellular_Carcinoma_Based_on_Weighted_Gene_Co-expression_Network_Analysis/19388294Direct OA link when available
- Concepts
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Hepatocellular carcinoma, Gene co-expression network, Gene, KEGG, Cyclin-dependent kinase 1, Computational biology, Gene expression, Biology, Bioinformatics, Cell cycle, Cancer research, Genetics, Gene ontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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51Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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