ArticleBioscience reports2019
Bioinformatics analysis to identify the key genes affecting the progression and prognosis of hepatocellular carcinoma.
Article in Bioscience reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 30 citations in OpenAlex.
- Analysing cell death patterns to predict outcomes and treatment options in patients with high-grade serous ovarian carcinoma.Scientific reports · 2026Article
- Molecular characterization of the A52 murine hepatocellular carcinoma cell line.Animal models and experimental medicine · 2026Article
- Epigenetic regulation of individual components of combined hepatocellular-cholangiocarcinoma.PloS one · 2025Article
- AGR2: The Covert Driver and New Dawn of Hepatobiliary and Pancreatic Cancer Treatment.Biomolecules · 2024Review
- Polo-like kinase-1 mediates hepatitis C virus-induced cell migration, a drug target for liver cancer.Life science alliance · 2023Article
- Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma.BMC pulmonary medicine · 2022Article
- Data mining of key genes expression in hepatocellular carcinoma: novel potential biomarkers of diagnosis prognosis or progression.Clinical & experimental metastasis · 2022Article
- Immune landscape and prognostic immune-related genes in KRAS-mutant colorectal cancer patients.Journal of translational medicine · 2021Article
- Immune infiltration-associated serum amyloid A1 predicts favorable prognosis for hepatocellular carcinoma.World journal of gastroenterology · 2020Article
- Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma.Current genomics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer, which has poor outcome. The present study aimed to investigate the key genes implicated in the progression and prognosis of HCC. The RNA-sequencing data of HCC was extracted from The Cancer Genome Atlas (TCGA) database. Using the R package (DESeq), the differentially expressed genes (DEGs) were analyzed. Based on the Cluepedia plug-in in Cytoscape software, enrichment analysis for the protein-coding genes amongst the DEGs was conducted. Subsequently, protein-protein interaction (PPI) network was built by Cytoscape software. Using survival package, the genes that could distinguish the survival differences of the HCC samples were explored. Moreover, quantitative real-time reverse transcription-PCR (qRT-PCR) experiments were used to detect the expression of key genes. There were 2193 DEGs in HCC samples. For the protein-coding genes amongst the DEGs, multiple functional terms and pathways were enriched. In the PPI network, cyclin-dependent kinase 1 (CDK1), polo-like kinase 1 (PLK1), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serum amyloid A1 (SAA1), and lysophosphatidic acid receptor 3 (LPAR3) were hub nodes. CDK1 interacting with PLK1 and FOS, and LPAR3 interacting with FOS and SAA1 were found in the PPI network. Amongst the 40 network modules, 4 modules were with scores not less than 10. Survival analysis showed that anterior gradient 2 (
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