ArticleDNA and cell biology2019
Identifying Hepatocellular Carcinoma Driver Genes by Integrative Pathway Crosstalk and Protein Interaction Network.
Article in DNA and cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches.Discover oncology · 2025Article
- Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma.Communications biology · 2024Article
- P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study.Health science reports · 2023Article
- Relationships Between Indices of Tumor Aggressiveness in Hepatocellular Carcinoma.Journal of gastrointestinal cancer · 2021Article
- System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration.Molecular diagnosis & therapy · 2021Review
- Inflammatory Mechanisms of HCC Development.Cancers · 2020Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we mined out hepatocellular carcinoma (HCC) driver genes from MEDLINE literatures by bioinformatics methods of pathway crosstalk and protein interaction network. Furthermore, the relationship between driver genes and their clinicopathological characteristics, as well as classification effectiveness was verified in the public databases. We identified 560 human genes reported to be associated with HCC in 1074 published articles. Functional analysis revealed that biological processes and biochemical pathways relating to tumor pathogenesis, cancer disease, tumor cell molecule, and hepatic disease were enriched in these genes. Pathway crosstalk analysis indicated that significant pathways could be divided into three modules: cancer disease, virus infection, and tumor signaling pathway. The HCC-related protein-protein interaction network comprised 10,212 nodes, and 56,400 edges were mined out to identify 18 modules corresponding to 14 driver genes. We verified that these 14 driver genes have high classification effectiveness to distinguish cancer samples from normal samples and the classification effectiveness was better than that of randomly selected genes. Present study provided pathway crosstalk and protein interaction network for understanding potential tumorigenesis genes underlying HCC. The 14 driver genes identified from this study are of great translational value in HCC diagnosis and treatment, as well as in clinical study on the pathogenesis of HCC.
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Registered trials
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