↓ Skip to main content

The Nuclear Receptor Superfamily

Overview of attention for book
Attention for Chapter 11: Methods for Identifying and Quantifying mRNA Expression of Androgen Receptor Splicing Variants in Prostate Cancer
Altmetric Badge

Readers on

mendeley
6 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Methods for Identifying and Quantifying mRNA Expression of Androgen Receptor Splicing Variants in Prostate Cancer
Chapter number 11
Book title
The Nuclear Receptor Superfamily
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3724-0_11
Pubmed ID
Book ISBNs
978-1-4939-3722-6, 978-1-4939-3724-0
Authors

Yingming Li, Scott M. Dehm, Li, Yingming, Dehm, Scott M.

Abstract

Constitutively active androgen receptor (AR) variants (AR-Vs) lacking the AR ligand-binding domain have been identified as drivers of prostate cancer resistance to AR-targeted therapies. A definitive understanding of the role and origin of AR-Vs in the natural history of prostate cancer progression requires cataloging the entire spectrum of AR-Vs expressed in prostate cancer, as well as accurate determination of their expression levels relative to full-length AR in clinical tissues and models of progression. Exon constituency differences at the 3' terminus of mRNAs encoding AR-Vs compared with mRNAs encoding full-length AR can be exploited for discovery and quantification-based experiments. Here, we provide methodological details for 3' rapid amplification of cDNA ends (3' RACE) and absolute quantitative RT-PCR, which are cost-effective approaches for identifying new AR-Vs and quantifying their absolute expression levels in conjunction with full-length AR in RNA samples derived from various sources.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Other 2 33%
Student > Doctoral Student 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 2 33%
Biochemistry, Genetics and Molecular Biology 1 17%
Agricultural and Biological Sciences 1 17%
Medicine and Dentistry 1 17%
Unknown 1 17%