ArticleMolecular medicine reports2019
Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis.
Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.PloS one · 2025Article
- ADH4-a potential prognostic marker for hepatocellular carcinoma with possible immune-related implications.BMC cancer · 2024Article
- Identification of metabolism-related key genes as potential biomarkers for pathogenesis of immune thrombocytopenia.Scientific reports · 2024Article
- Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.Scientific reports · 2023Article
- A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system.Cancer science · 2022Article
- Article
- Effect of let-7c on the PI3K/Akt/FoxO signaling pathway in hepatocellular carcinoma.Oncology letters · 2021Article
- Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis.BioMed research international · 2021Article
- Article
- Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma.Frontiers in genetics · 2020Article
- Tacrolimus increases the expression level of the chemokine receptor CXCR2 to promote renal fibrosis progression.International journal of molecular medicine · 2019Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to identify the differentially expressed genes (DEGs) between primary tumor tissue and adjacent non‑tumor tissue of hepatocellular carcinoma (HCC) samples in order to investigate the mechanisms of HCC. The microarray data of the datasets GSE76427, GSE84005 and GSE57957 were downloaded from the Gene Expression Omnibus database. DEGs were identified using the limma package in the R programming language. Following the intersection of the DEGs screened from the three datasets, 218 genes were selected for further study. A protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database. The construction and analysis of modules were performed using Cytoscape and the module with the highest score was selected for further analysis. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted for genes involved in the PPI network and the selected subnetwork. The network of the enriched pathways and their associated genes was constructed using Cytoscape. For the genes in the global PPI network, metabolism‑associated pathways were significantly enriched; whereas, for the genes in the subnetwork, 'cell cycle', 'oocyte meiosis' and 'DNA replication' pathways were significantly enriched. To demonstrate the portability and repeatability of the prognostic value of the weighted genes, a validation cohort was obtained from datasets of The Cancer Genome Atlas and Kaplan‑Meier survival analysis was conducted. Evidence is presented that the expression levels of aldehyde dehydrogenase 2 family member, cytochrome P450 family 2 subfamily C member 8, alcohol dehydrogenase 4 (class II), pi polypeptide, alcohol dehydrogenase 1B (class I), β polypeptide and cytochrome P450 family 2 subfamily C member 9 were associated with the overall survival of patients with HCC and that the expression levels of pituitary tumor‑transforming 1, cell division cycle 20, DNA topoisomerase II α and cyclin B2 were negatively associated with the overall survival of patients with HCC. In conclusion, 9 weighted genes, involved in the development and progression of HCC, were identified using bioinformatics and survival analyses.
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