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

Zhenzhen Huang,Huilong Duan,Haomin Li

doi:10.1155/2015/878546

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.

Highlights

Breast cancer is one of the most common cancers and the leading cause of cancer death among women all over the world, with 2.6 women being diagnosed every minute and more than 52 women died every hour in 2008 [1]
TCGA4U provides an interactive interface for users to query distribution of gene expression values and corresponding survival curves of two gene expression patterns
The results of the Log-Rank test of 14,811 genes were published at http://www.tcga4u.org:8888/SurvivalLogRank

Summary

Introduction

Breast cancer is one of the most common cancers and the leading cause of cancer death among women all over the world, with 2.6 women being diagnosed every minute and more than 52 women died every hour in 2008 [1]. With the public availability of genomic data such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), a plenty of bioinformatics researchers analyzed gene expression data with clinical data to attempt to predict the prognosis and find biomarkers for therapy [2,3,4,5]. These researches have gained obvious achievements in prediction of cancer prognosis. Through integrating gene expression data and clinical outcome data of breast cancer from TCGA datasets on a web-based genomic analysis platform (TCGA4U), breast cancer survival related gene expression patterns were identified and analyzed

Methods

Results

Discussion

Conclusion