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BioMed Research International
Volume 2015 (2015), Article ID 878546, 10 pages
http://dx.doi.org/10.1155/2015/878546
Research Article

Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools

1College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhouyiqing Building No. 510, Yuquan Campus, Hangzhou 310027, China
2The Children’s Hospital, Zhejiang University, Zhouyiqing Building No. 510, Yuquan Campus, Hangzhou 310003, China
3The Institute of Translational Medicine, Zhejiang University, Zhouyiqing Building No. 510, Yuquan Campus, Hangzhou 310029, China

Received 3 July 2015; Revised 14 September 2015; Accepted 28 September 2015

Academic Editor: Sílvia A. Sousa

Copyright © 2015 Zhenzhen Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.