Combining nomogram and microarray data for predicting prostate cancer recurrence

Yijun Sun, Yunpeng Cai, Steve Goodison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The derivation of molecular signatures indicative of disease status and behavior are required to facilitate the optimal choice of treatment for prostate cancer patients. We conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether an advanced computational algorithm can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. Our study demonstrates the possibility of utilizing both genetic and clinical information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of microarray and clinical data is warranted before clinical application of predictive signatures is considered.

Original languageEnglish (US)
Title of host publication8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
DOIs
StatePublished - Dec 1 2008
Event8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008 - Athens, Greece
Duration: Oct 8 2008Oct 10 2008

Publication series

Name8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008

Other

Other8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
CountryGreece
CityAthens
Period10/8/0810/10/08

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering

Fingerprint Dive into the research topics of 'Combining nomogram and microarray data for predicting prostate cancer recurrence'. Together they form a unique fingerprint.

Cite this