Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression

BMC Cancer

Research Article

Amy Xia¹  Xin Qi²  Ravil Khaybullin²  Junjie Fu²  Yanping Zhang³ 

[1] Columbia University, 10027, New York, NY, USA;Department of Medicinal Chemistry, College of Pharmacy, University of Florida, 1600 SW Archer Rd, Health Science Center P5-31, 32610, Gainesville, FL, USA;Gene Expression and Genotyping, Interdisciplinary Center for Biotechnology Research, University of Florida, 32610, Gainesville, FL, USA;

关键词: Breast cancer;    Gene expression profiling;    Biomarker;    MMP11;    HPSE2;   

DOI  :  10.1186/s12885-015-1410-y

 received in 2014-10-02, accepted in 2015-04-30,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundIn order to identify biomarkers involved in breast cancer, gene expression profiling was conducted using human breast cancer tissues.MethodsTotal RNAs were extracted from 150 clinical patient tissues covering three breast cancer subtypes (Luminal A, Luminal B, and Triple negative) as well as normal tissues. The expression profiles of a total of 50,739 genes were established from a training set of 32 samples using the Agilent Sure Print G3 Human Gene Expression Microarray technology. Data were analyzed using Agilent Gene Spring GX 12.6 software. The expression of several genes was validated using real-time RT-qPCR.ResultsData analysis with Agilent GeneSpring GX 12.6 software showed distinct expression patterns between cancer and normal tissue samples. A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05. In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration. Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.ConclusionsOur findings identified these 2 genes as a novel breast cancer biomarker gene set, which may facilitate the diagnosis and treatment in breast cancer clinical therapies.

【 授权许可】

Unknown   
© Fu et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.