Vol 19: Network analysis of ChIP-Seq data reveals key genes in prostate cancer.Report as inadecuate



 Vol 19: Network analysis of ChIP-Seq data reveals key genes in prostate cancer.


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This article is from European Journal of Medical Research, volume 19.AbstractBackground: Prostate cancer PC is the second most common cancer among men in the United States, and it imposes a considerable threat to human health. A deep understanding of its underlying molecular mechanisms is the premise for developing effective targeted therapies. Recently, deep transcriptional sequencing has been used as an effective genomic assay to obtain insights into diseases and may be helpful in the study of PC. Methods: In present study, ChIP-Seq data for PC and normal samples were compared, and differential peaks identified, based upon fold changes with P-values calculated with t-tests. Annotations of these peaks were performed. Protein–protein interaction PPI network analysis was performed with BioGRID and constructed with Cytoscape, following which the highly connected genes were screened. Results: We obtained a total of 5,570 differential peaks, including 3,726 differentially enriched peaks in tumor samples and 1,844 differentially enriched peaks in normal samples. There were eight significant regions of the peaks. The intergenic region possessed the highest score 51%, followed by intronic 31% and exonic 11% regions. The analysis revealed the top 35 highly connected genes, which comprised 33 differential genes such as YWHAQ, tyrosine 3-monooxygenase-tryptophan 5-monooxygenase activation protein and θ polypeptide from ChIP-Seq data and 2 differential genes retrieved from the PPI network: UBA52 ubiquitin A-52 residue ribosomal protein fusion product 1 and SUMO2 SMT3 suppressor of mif two 3 homolog 2 . Conclusions: Our findings regarding potential PC-related genes increase the understanding of PC and provides direction for future research. Electronic supplementary material: The online version of this article doi:10.1186-s40001-014-0047-7 contains supplementary material, which is available to authorized users.



Author: Zhang, Yu; Huang, Zhen; Zhu, Zhiqiang; Liu, Jianwei; Zheng, Xin; Zhang, Yuhai

Source: https://archive.org/







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