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

BioMed Research International. 2015;2015 DOI 10.1155/2015/878546

 

Journal Homepage

Journal Title: BioMed Research International

ISSN: 2314-6133 (Print); 2314-6141 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Medicine

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Zhenzhen Huang (College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhouyiqing Building No. 510, Yuquan Campus, Hangzhou 310027, China)
Huilong Duan (College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhouyiqing Building No. 510, Yuquan Campus, Hangzhou 310027, China)
Haomin Li (The Children’s Hospital, Zhejiang University, Zhouyiqing Building No. 510, Yuquan Campus, Hangzhou 310003, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 19 weeks

 

Abstract | Full Text

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.