Related Experiment Video
Updated: May 27, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Identification of markers of prostate cancer progression using candidate gene expression
S E T Larkin1, S Holmes, I A Cree
1School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michaels Building, White Swan Road, Portsmouth, PO1 2DT, UK. S.Larkin@soton.ac.uk
Background:
Metastatic prostate cancer (PCa) has no curative treatment options. Some forms of PCa are indolent and slow growing, while others metastasise quickly and may prove fatal within a very short time. The basis of this variable prognosis is poorly understood, despite considerable research. The aim of this study was to identify markers associated with the progression of PCa.
Methods:
Artificial neuronal network analysis combined with data from literature and previous work produced a panel of putative PCa progression markers, which were used in a transcriptomic analysis of 29 radical prostatectomy samples and correlated with clinical outcome.
Results:
Statistical analysis yielded seven putative markers of PCa progression, ANPEP, ABL1, PSCA, EFNA1, HSPB1, INMT and TRIP13. Two data transformation methods were utilised with only markers that were significant in both selected for further analysis. ANPEP and EFNA1 were significantly correlated with Gleason score. Models of progression co-utilising markers ANPEP and ABL1 or ANPEP and PSCA had the ability to correctly predict indolent or aggressive disease, based on Gleason score, in 89.7% and 86.2% of cases, respectively. Another model of TRIP13 expression in combination with preoperative PSA level and Gleason score was able to correctly predict recurrence in 85.7% of cases.
Conclusion:
This proof of principle study demonstrates a novel association of carcinogenic and tumourigenic gene expression with PCa stage and prognosis.
