ArticleBMC medical genomics2014
Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression.
Article in BMC medical genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
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- CBD: a biomarker database for colorectal cancer.Database : the journal of biological databases and curation · 2018Article
- Screening of potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles.Oncology letters · 2017Article
- Emerging proteomics biomarkers and prostate cancer burden in Africa.Oncotarget · 2017Review
- Precision knockdown of EGFR gene expression using radio frequency electromagnetic energy.Journal of neuro-oncology · 2017Article
- Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia.Journal of Cancer · 2017Article
- Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer.Translational cancer research · 2016Article
- Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data.OncoTargets and therapy · 2016Article
- Screening key microRNAs for castration-resistant prostate cancer based on miRNA/mRNA functional synergistic network.Oncotarget · 2015Article
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- Discovery and characterization of long intergenic non-coding RNAs (lincRNA) module biomarkers in prostate cancer: an integrative analysis of RNA-Seq data.BMC genomics · 2015Article
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- New horizons in translational bioinformatics: TBC 2013.BMC medical genomics · 2014Article
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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
backgroundProstate cancer is one of the most common complex diseases with high leading cause of death in men. Identifications of prostate cancer associated genes and biomarkers are thus essential as they can gain insights into the mechanisms underlying disease progression and advancing for early diagnosis and developing effective therapies.
methodsIn this study, we presented an integrative analysis of gene expression profiling and protein interaction network at a systematic level to reveal candidate disease-associated genes and biomarkers for prostate cancer progression. At first, we reconstructed the human prostate cancer protein-protein interaction network (HPC-PPIN) and the network was then integrated with the prostate cancer gene expression data to identify modules related to different phases in prostate cancer. At last, the candidate module biomarkers were validated by its predictive ability of prostate cancer progression.
resultsDifferent phases-specific modules were identified for prostate cancer. Among these modules, transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were shown to be the pathway targets for prostate cancer progression. The identified candidate disease-associated genes showed better predictive ability of prostate cancer progression than those of published biomarkers. In context of functional enrichment analysis, interestingly candidate disease-associated genes were enriched in the nucleus and different functions were encoded for potential transcription factors, for examples key players as AR, Myc, ESR1 and hidden player as Sp1 which was considered as a potential novel biomarker for prostate cancer.
conclusionsThe successful results on prostate cancer samples demonstrated that the integrative analysis is powerful and useful approach to detect candidate disease-associate genes and modules which can be used as the potential biomarkers for prostate cancer progression. The data, tools and supplementary files for this integrative analysis are deposited at http://www.ibio-cn.org/HPC-PPIN/.
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