ArticleEuropean journal of medical research2013
Identification of biomarkers for hepatocellular carcinoma using network-based bioinformatics methods.
Article in European journal of medical research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Key hub genes and pathways associated with HCV-related hepatocellular carcinoma as potential diagnostic biomarkers.Journal, genetic engineering & biotechnology · 2026Article
- Article
- A Novel Four-Gene Signature as a Potential Prognostic Biomarker for Hepatocellular Carcinoma.Journal of oncology · 2021Article
- Enrichment and identification of differentially expressed genes in hepatocellular carcinoma stem-like cells.Oncology letters · 2020Article
- Transcriptome profiling identifies a recurrent CRYL1-IFT88 chimeric transcript in hepatocellular carcinoma.Oncotarget · 2017Article
- Identification and functional analysis of differentially expressed genes in poorly differentiated hepatocellular carcinoma using RNA-seq.Oncotarget · 2017Article
- Article
- Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma.BioMed research international · 2015Article
- Derivative component analysis for mass spectral serum proteomic profiles.BMC medical genomics · 2014Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. Despite several efforts to elucidate molecular mechanisms involved in this cancer, they are still not fully understood.
methodsTo acquire further insights into the molecular mechanisms of HCC, and to identify biomarkers for early diagnosis of HCC, we downloaded the gene expression profile on HCC with non-cancerous liver controls from the Gene Expression Omnibus (GEO) and analyzed these data using a combined bioinformatics approach.
resultsThe dysregulated pathways and protein-protein interaction (PPI) network, including hub nodes that distinguished HCCs from non-cancerous liver controls, were identified. In total, 29 phenotype-related differentially expressed genes were included in the PPI network. Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples. Among these genes, CDC2 (Cell division control protein 2 homolog), MMP2 (matrix metalloproteinase-2) and DCN (Decorin were the hub nodes in the PPI network.
conclusionsThis study provides a portfolio of targets useful for future investigation. However, experimental studies should be conducted to verify our findings.
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