github link
Accession IconGSE25136

Optimizing molecular signatures for prostate cancer recurrence

Organism Icon Homo sapiens
Sample Icon 79 Downloadable Samples
Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Submitter Supplied Information

Description
The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. In this study, 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 advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic 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 feasibility of utilizing gene expression information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of tissue-derived microarray data is warranted before clinical application of molecular signatures is considered.
PubMed ID
Total Samples
79
Submitter’s Institution

Samples

Show of 0 Total Samples
Filter
Add/Remove
Accession Code
Title
Specimen part
Processing Information
Additional Metadata
No rows found
Loading...