Journal
NEOPLASIA
Volume 8, Issue 1, Pages 59-68Publisher
ELSEVIER SCIENCE INC
DOI: 10.1593/neo.05664
Keywords
metastasis; cancer; proteomics; prostate cancer; bioinformatics
Categories
Funding
- NCI NIH HHS [R01 CA097063, U01 CA111275, P50 CA069568, U01 CA111275-01, P50CA90381, P50CA69568, 5P30 CA46592, P30 CA046592, P50 CA090381, CA97063] Funding Source: Medline
- NIA NIH HHS [R01AG21404, R01 AG021404] Funding Source: Medline
- NATIONAL CANCER INSTITUTE [P50CA069568, R01CA097063, P50CA090381, P30CA046592, U01CA111275] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [R01AG021404] Funding Source: NIH RePORTER
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The critical clinical question in prostate cancer research is: How do we develop means of distinguishing aggressive disease from indolent disease? Using a combination of proteomic and expression array data, we identified a set of 36 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Another five prostate cancer biomarkers were included using linear discriminant analysis, we determined that the optimal model used to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, transcriptional levels of the 12 genes encoding for these proteins predicted prostate-specific antigen failure in 79 men following surgery for clinically localized prostate cancer (P=.0015). This study demonstrates that cross-platform models can lead to predictive models with the possible advantage of being more robust through this selection process.
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