the lack of prostate specificity and intercellular localiza-
tion of its gene product make this gene a poor serum
biomarker. FN1 (fibronectin 1) and VEGF (vascular endo-
thelial growth factor) are associated with prostate cancer
and encode secreted proteins; however, these genes lack
prostate-specific tissue specificity (15–18). The lack of
prostate tissue specificity in the expression of these 2
genes may be a major reason why they are not yet used
clinically as prostate cancer serum biomarkers.
We have developed a bioinformatics protocol for
screening candidate serum biomarker sets to identify
high-quality markers for experimental evaluation. The in
silico secreted protein pipeline provides a rapid screen for
identifying biomarkers found extracellularly and is likely
to be detectable by serum assays. Tissue specificity pro-
filing compliments secreted protein prediction by identi-
fying the originating tissue components of a biomarker’s
serum signal and by allowing investigators to select
candidate markers with a higher probability of having
distinguishable signals. We hope that the use of intelligent
bioinformatics analysis before costly experimental evalu-
ation will accelerate the selection of candidate biomarkers
that can be successfully translated into novel, clinically
This work was supported under a grant by Invitrogen.
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Representational Fragment Amplification: Exponential
Amplification of Fragmented cDNA Enables Multimil-
lion-Fold Expression Testing Gregory D. Sgarlato and
Howard H. Sussman*(Department of Pathology, Stanford
University, Stanford, CA; * address correspondence to
this author at: Stanford University, Lane Building, L217,
Microarray analysis, which enables the comprehensive
examination of many thousands of genes in a single
experiment, is a promising method for furthering under-
standing of disease states. Because of the large amounts of
probe required, however, microarray analysis has not
Table 1. Localization predictions and annotations of prostate cancer-associated proteins.
Type II membrane
We analyzed 7 genes by the secretion and specificity prediction methods described above. KLK3 and ACPP (positive controls) possess the secretion and prostate
specificity characteristics that we describe as key for serum biomarkers. Negative controls include ZWINT, AMACR, and HPN, which fail to encode secreted proteins,
and FN1 and VEGF, which lack prostate specific tissue expression.
aModerate refers to genes expressed in prostate and several other tissues.
Abstracts of Oak Ridge Posters