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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

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Published on: November 2, 2013

Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.

S N Thibodeau¹, A J French¹, S K McDonnell²

¹Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905, USA.

Nature Communications

|November 28, 2015

Summary

This study identifies genes linked to prostate cancer (PrCa) risk using normal prostate tissue expression data. It highlights 88 candidate genes associated with known PrCa risk intervals, aiding future research.

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miRNA Expression Analyses in Prostate Cancer Clinical Tissues

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Published on: September 8, 2015

Area of Science:

Genetics
Oncology
Bioinformatics

Background:

Numerous genetic loci are associated with prostate cancer (PrCa) risk, yet the specific genes involved remain largely uncharacterized.
Understanding the functional impact of these risk loci is crucial for elucidating PrCa pathogenesis.

Purpose of the Study:

To identify candidate genes underlying prostate cancer risk by integrating genetic association data with normal prostate tissue gene expression quantitative trait loci (eQTL).
To create a valuable resource for investigating the biological mechanisms of genetic susceptibility to PrCa.

Main Methods:

Construction of a normal prostate tissue-specific eQTL dataset using genotyping and RNA sequencing from 471 samples.
Analysis of 146 prostate cancer risk single nucleotide polymorphisms (SNPs) and their linkage disequilibrium regions, defining 100 unique risk intervals.
Assessment of cis-acting eQTL associations within a 2 Mb window around risk SNP intervals, adjusting for covariates.

Main Results:

A significant eQTL signal was detected in 41.7% of tested SNP-gene combinations after statistical adjustments.
Fifty-one out of 100 prostate cancer risk intervals exhibited significant eQTL signals.
These significant signals were associated with 88 distinct candidate genes.

Conclusions:

This research successfully links prostate cancer risk loci to specific genes through a novel eQTL dataset.
The identified candidate genes provide a foundation for further functional studies into the genetic basis of prostate cancer.
The generated eQTL data serves as a critical resource for understanding PrCa's genetic underpinnings.