Network analysis of ChIP-Seq data reveals key genes in prostate cancer

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

BackgroundProstate 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.MethodsIn 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.ResultsWe 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 ¿ 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) .ConclusionsOur findings regarding potential PC-related genes increase the understanding of PC and provides direction for future research.