Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma
Chen X., Liao L., Li Y., Huang H., Huang Q., Deng S.
Laboratory Study, published in Biomed Res Int (2020) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Laboratory Study
- Journal
- Biomed Res Int (2020)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 33102593
- PMCID
- PMC7568806
- DOI
- 10.1155/2020/7653506
- Citations
- 9
Abstract (original English)
Background The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogene
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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