Oncotarget

Clinical Research Papers:

The identification of trans-associations between prostate cancer GWAS SNPs and RNA expression differences in tumor-adjacent stroma

Xin Chen _, Michael McClelland, Zhenyu Jia, Farah Rahmatpanah, Anne Sawyers, Jeffrey Trent, David Duggan and Dan Mercola

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Oncotarget. 2015; 6:1865-1873. https://doi.org/10.18632/oncotarget.2763

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Abstract

Xin Chen1, Michael McClelland2,3, Zhenyu Jia2,4,5, Farah B. Rahmatpanah2, Anne Sawyers2, Jeffrey Trent6, David Duggan7, Dan Mercola2

1Genomics Center, Loma Linda University, Loma Linda, California, 92354, United States of America

2Department of Pathology and Laboratory Medicine, University of California, Irvine, California, 92697, United States of America

3Department of Microbiology and Molecular Genetics, University of California, Irvine, California, 92697, United States of America

4Department of Statistics, The University of Akron, Akron, Ohio, 44325, United States of America

5Department of Family & Community Medicine, Northeast Ohio Medical University, Rootstown, Ohio, 44272, United States of America

6Genetic Basis of Human Disease Division, The Translational Genomics Research Institute, Phoenix, Arizona, 85004, United States of America

7Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, Arizona, 85004, United States of America

Correspondence to:

Xin Chen, e-mail: xchen@llu.edu

Dan Mercola, e-mail: dmercola@uci.edu

Keywords: SNPs, eQTL, prostate cancer

Received: August 14, 2014     Accepted: November 17, 2014     Published: February 09, 2015

ABSTRACT

Here we tested the hypothesis that SNPs associated with prostate cancer risk, might differentially affect RNA expression in prostate cancer stroma. The most significant 35 SNP loci were selected from Genome Wide Association (GWA) studies of ~40,000 patients. We also selected 4030 transcripts previously associated with prostate cancer diagnosis and prognosis. eQTL analysis was carried out by a modified BAYES method to analyze the associations between the risk variants and expressed transcripts jointly in a single model. We observed 47 significant associations between eight risk variants and the expression patterns of 46 genes. This is the first study to identify associations between multiple SNPs and multiple in trans gene expression differences in cancer stroma. Potentially, a combination of SNPs and associated expression differences in prostate stroma may increase the power of risk assessment for individuals, and for cancer progression.


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