SynthesisBioMed research international2014
A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma.
Synthesis in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Fibulin-2 Facilitates Malignant Progression of Hepatocellular Carcinoma.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Article
- Integrated Proteomics and Bioinformatics to Identify Potential Prognostic Biomarkers in Hepatocellular Carcinoma.Cancer management and research · 2021Article
- CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.International journal of general medicine · 2021Article
- Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine.Scientific reports · 2019Article
- Protein-Protein Interaction Network Analysis of Salivary Proteomic Data in Oral Cancer CasesAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.Scientific reports · 2017Article
- Decreased expression and clinical significance of miR-148a in hepatocellular carcinoma tissues.European journal of medical research · 2014Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC.
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