Research article: Identifying novel prostate cancer associated pathways based on integrative microarray data analysis

  • Authors:

  • Ying Wang;Jiajia Chen;Qinghui Li;Haiyun Wang;Ganqiang Liu;Qing Jing;Bairong Shen

  • Affiliations:

  • Center for Systems Biology, Soochow University, No. 1. Shizi Street, Suzhou 215006, China and Laboratory of Gene and Viral Therapy, Eastern Hepatobiliary Surgical Hospital, the Second Military Med ...;Center for Systems Biology, Soochow University, No. 1. Shizi Street, Suzhou 215006, China and School of Chemistry and Biological Engineering, Suzhou University of Science and Technology, 215009, C ...;Center for Systems Biology, Soochow University, No. 1. Shizi Street, Suzhou 215006, China;School of Life Science and Technology, Tongji University, Shanghai 200092, China;Center for Systems Biology, Soochow University, No. 1. Shizi Street, Suzhou 215006, China and School of Life Science and Technology, Tongji University, Shanghai 200092, China and Institute for Mol ...;School of Life Science and Technology, Tongji University, Shanghai 200092, China and Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shangha ...;Center for Systems Biology, Soochow University, No. 1. Shizi Street, Suzhou 215006, China

  • Venue:
  • Computational Biology and Chemistry
  • Year:
  • 2011

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Abstract

The development and diverse application of microarray and next generation sequencing technologies has made the meta-analysis widely used in expression data analysis. Although it is commonly accepted that pathway, network and systemic level approaches are more reproducible than reductionism analyses, the meta-analysis of prostate cancer associated molecular signatures at the pathway level remains unexplored. In this article, we performed a meta-analysis of 10 prostate cancer microarray expression datasets to identify the common signatures at both the gene and pathway levels. As the enrichment analysis result of GeneGo's database and KEGG database, 97.8% and 66.7% of the signatures show higher similarity at pathway level than that at gene level, respectively. Analysis by using gene set enrichment analysis (GSEA) method also supported the hypothesis. Further analysis of PubMed citations verified that 207 out of 490 (42%) pathways from GeneGo and 48 out of 74 (65%) pathways from KEGG were related to prostate cancer. An overlap of 15 enriched pathways was observed in at least eight datasets. Eight of these pathways were first described as being associated with prostate cancer. In particular, endothelin-1/EDNRA transactivation of the EGFR pathway was found to be overlapped in nine datasets. The putative novel prostate cancer related pathways identified in this paper were indirectly supported by PubMed citations and would provide essential information for further development of network biomarkers and individualized therapy strategy for prostate cancer.