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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Prostate. Author manuscript; available in PMC 2009 August 1.
Published in final edited form as:
Prostate. 2008 August 1; 68(11): 1248–1256.
doi:  10.1002/pros.20792
PMCID: PMC2516917

Identification of candidate prostate cancer genes through comparative expression-profiling of seminal vesicle



Prostate cancer is the most frequently diagnosed cancer among men in the United States. In contrast, cancer of the seminal vesicle is exceedingly rare, despite that the prostate and seminal vesicle share similar histology, secretory function, androgen dependency, blood supply, and (in part) embryonic origin. We hypothesized that gene-expression differences between prostate and seminal vesicle might inform mechanisms underlying the higher incidence of prostate cancer.


Whole-genome DNA microarrays were used to profile gene expression of 11 normal prostate and 7 seminal vesicle specimens (including 6 matched pairs) obtained from radical prostatectomy. Supervised analysis was used to identify genes differentially expressed between normal prostate and seminal vesicle, and this list was then cross-referenced to genes differentially expressed between normal and cancerous prostate. Expression patterns of selected genes were confirmed by immunohistochemistry using a tissue microarray.


We identified 32 genes that displayed a highly statistically-significant expression pattern with highest levels in seminal vesicle, lower levels in normal prostate, and lowest levels in prostate cancer. Among these genes was the known candidate prostate tumor suppressor GSTP1 (involved in xenobiotic detoxification). The expression pattern of GSTP1 and four other genes, ABCG2 (xenobiotic transport), CRABP2 (retinoic acid signaling), GATA3 (lineage-specific transcription) and SLPI (immune response), was confirmed by immunohistochemistry.


Our findings identify candidate prostate cancer genes whose reduced expression in prostate (compared to seminal vesicle) may be permissive to prostate cancer initiation. Such genes and their pathways may inform mechanisms of prostate carcinogenesis, and suggest new opportunities for prostate cancer prevention.

Keywords: prostate cancer, seminal vesicle, expression profiling, microarray


Anatomically, the glandular prostate can be divided into the peripheral zone (PZ), central zone (CZ) and transitional zone (TZ), arising embryologically from the urogenital sinus (PZ, TZ) and Wolffian (mesonephric) duct (CZ) [1]. Approximately 60% of prostate cancers arise in the PZ, while another ~20% arise in each of the CZ and TZ [2]. The normal prostate functions to secrete components of the seminal fluid, including acid phosphatase, proteases, polyamines, citric acid and zinc [3]. Histologically, prostate ducts are lined by two layers of epithelium, a columnar secretory layer surrounded by a cuboidal basal layer, embedded in a fibromuscular stroma. Both epithelial growth and function of the prostate are androgen dependent [4].

In close anatomic proximity to the prostate is another (paired) accessory sex gland, the seminal vesicle. The seminal vesicle also secretes components of the seminal fluid, including fructose, semen coagulators, sperm motility enhancers, antioxidants and immune response modulators [5]. In addition to its seminal secretory function, the seminal vesicle shares other features with the prostate, including an embryonic origin from the Wolffian duct (like prostate CZ), a bilayered tubuloalveolar histology, androgen dependent growth and function, and a common blood supply (and therefore carcinogen exposure) from branches of the internal iliac artery [5,6]. While seminal vesicle histology resembles that of the prostate, particularly CZ [1], differences are noted including that the basal cell layer is discontinuous and neuroendocrine cells are lacking in seminal vesicle epithelium [7].

Despite the overall similarities, the incidence of cancer of the respective organs is strikingly different. While in 2007 an estimated 219,000 new cases of prostate cancer will have been diagnosed in the United States alone [8], only about 50 cases of primary adenocarcinoma of the seminal vesicle have ever been documented in the medical literature [9]. Though the volume of the paired seminal vesicle is less than that of the prostate, this alone cannot account for the more than 100,000-fold difference in cancer incidence between the two organs. Understanding the basis of the disparity in cancer incidence may provide new insights into the development of prostate cancer, and may point towards new strategies for prevention [10].

We hypothesize that the tissue-specific differences in cancer incidence might stem from differences in expressed genes, discoverable by gene expression profiling. Specifically, we propose that certain genes expressed at higher levels in seminal vesicle might be protective, while at lower levels in prostate would be permissive, to cancer initiation. Here, we carry out whole-genome microarray-based expression profiling to discover such genes.

Materials and Methods

Profiled specimens

Frozen tissue from prostate and seminal vesicle specimens was obtained from radical prostatectomy specimens with IRB approval and patient informed consent. The absence of cancer or necrosis was confirmed by H&E-stained frozen section. Tissues were homogenized and total RNA isolated by Trizol (Invitrogen, Carlsbad, CA) extraction, and RNA quality confirmed by rRNA integrity using gel electrophoresis.

Expression profiling

cDNA microarrays were obtained from the Stanford Functional Genomics Facility (SFGF) and contained 39,632 non-redundant cDNA clones representing 14,613 named genes, 4,925 additional UniGene clusters [11], and 3,068 expressed sequence tags (ESTs) not mapping to UniGene clusters. Expression profiling was performed as previously described [12]. Briefly, 50μg total RNA from each sample and 50μg “universal” reference RNA (derived from 11 different established human cell lines) were differentially labeled with Cy5 and Cy3, respectively, and co-hybridized to cDNA microarrays. Following overnight hybridization and washing, arrays were imaged using a GenePix 4000B scanner (Molecular Devices, Sunnyvale, CA). Fluorescence ratios were extracted using SpotReader software (Niles Scientific, Portola Valley, CA), and the data uploaded into the Stanford Microarray Database (SMD) [13] for storage, retrieval and analysis. The complete microarray dataset is available at SMD and at GEO (GSE000).

Microarray data analysis

Background-subtracted fluorescence ratios were normalized for each array (i.e. global normalization), and then each gene normalized to the average expression of that gene across all samples (i.e. mean-centered). For subsequent analysis, we used only the 971 cDNAs on the microarray whose expression was both well measured and highly variable among samples. Well-measured genes were defined as those with signal intensity/background >1.5 for either the Cy5-labeled test sample or the Cy3-labeled reference, in at least 75% of samples. Variably expressed genes were those with expression at least 3-fold higher or lower from the sample average, in at least two samples. Genes differentially expressed between seminal vesicle and normal prostate were identified with the two-class Significance Analysis of Microarrays (SAM) method [14], which uses a modified t-statistic and provides a false discovery rate (FDR) estimate by comparison to randomly permuted data. SAM analysis was also used to identify genes differentially expressed between normal prostate and prostate cancer, using publicly-available cDNA microarray data from our previously published study [12]. Cross-tissue comparisons were also made using previously published expression profiles [15] for normal seminal vesicle, prostate, breast, colon and lung.

Tissue microarray construction

A tissue microarray (TMA) was constructed from formalin-fixed paraffin embedded blocks comprising 15 matched specimen sets of seminal vesicle, normal prostate and prostate cancer obtained from radical prostatectomy (including the six matched cases profiled for gene expression, plus nine additional cases), together with 7 matched sets of seminal vesicle and normal prostate from radical cystectomy cases where prostate cancer was not present. Each specimen was represented by a single 1.5 mm core.


A 4 μm section was cut from the TMA block, deparaffinized in Citrisolv (Fisher Scientific, Hampton, NH), and hydrated in a graded series of alcohol solutions. Heat-induced antigen retrieval was performed by microwave pretreatment in citrate (1mM, pH 6.0) for 15min before staining. Endogenous peroxidase was blocked by preincubation with 1% hydrogen peroxide in phosphate-buffered saline. Primary antibodies were used as follows: anti-ABCG2 mouse monoclonal antibody (ab3380 Novus Biologicals, Littleton, CO) at 1:100 dilution incubated 1hr at room temp; anti-CRABP2 rabbit polyclonal antibody [16] at 1:500 for 16hrs at 4°C; anti-GATA3 mouse monoclonal antibody (sc-268, Santa Cruz Biotechnology, Santa Cruz, CA) at 1:10 for 16hrs at 4°C; anti-SLPI rabbit polyclonal antibody (ALX-210-376, Alexis Biochemicals, San Diego, CA) at 1:50 for 30min at RT; anti-GSTP1 rabbit polyclonal antibody (AB8902, Chemicon/Millipore, Billerica, MA) at 1:500 16hrs at 4°C. Chromogenic detection was carried out using the appropriate peroxidase-conjugated secondary antibody and DAB reagents provided with the Envision detection kit (Dako). Staining intensity was scored as absent (-), weak (+), or strong (++).


To explore gene-expression patterns in normal prostate and seminal vesicle, we used whole-genome cDNA microarrays to profile a set of 11 normal prostate and 7 seminal vesicle specimens, including 6 matched pairs. Unsupervised hierarchical clustering of expression profiles distinguished two main groups, one containing the prostate and the other the seminal vesicle specimens (data not shown). To directly identify genes expressed at higher levels in seminal vesicle, we carried out a two-class Significance Analysis of Microarrays (SAM) analysis. In total, we identified 215 cDNAs with significantly higher expression levels in seminal vesicle, using a stringent false discovery rate cutoff (FDR=0.045%) (Fig. 1A).

Figure 1
Comparative profiling to identify candidate prostate cancer genes

Based on the rarity of seminal vesicle compared to prostate cancer, we had hypothesized that genes protective against cancer might be embedded within this set of seminal vesicle tissue-specific expressed genes. An additional characteristic of such genes might be their further decreased expression in prostate cancer compared to normal prostate. That is, further decreased expression in a subpopulation of precancerous prostate epithelial cells would further favor prostate cancer initiation (and the low expression levels would be retained in the outgrowing prostate tumor). Therefore, to enrich for such candidate cancer genes, we cross-referenced the list of 215 cDNAs exhibiting higher-level expression in seminal vesicle compared to normal prostate with a SAM-derived list of 1,235 cDNAs (FDR=0.018%) exhibiting significantly lower-level expression in prostate cancer compared to normal prostate (Fig. 1A), as determined from our previously published prostate cancer microarray dataset [12]. In total, the intersection of these two SAM lists comprised 38 cDNAs representing 32 different genes, each displaying highest levels of expression in seminal vesicle, lower levels in normal prostate, and lowest levels in prostate cancer (Fig. 1A, 1B and Table 1). These genes were also generally expressed at lower levels in normal breast, colon and lung, other epithelial tissues where cancer commonly develops (Fig. 3).

Figure 3
Expression of candidate prostate cancer genes across other normal tissues
Table 1
Candidate prostate cancer genes (decreased in prostate vs. seminal vesicle)

An additional feature of candidate tumor suppressor genes is that their expression in cancer can be silenced by promoter DNA hypermethylation [17]. We had previously identified 271 genes whose expression was upregulated in at least one of four cultured prostate cancer cell lines assayed following treatment with 5-Aza-2′-deoxycytidine (5-aza-dC), an inhibitor of DNA methyltransferase [18]. Such genes are enriched for those transcriptionally silenced by promoter hypermethylation in prostate cancer. It is therefore notable that 8 of the 32 candidate prostate cancer genes identified above were among the genes upregulated in prostate cancer cells upon treatment with 5-aza-dC, a highly statistically significant enrichment (P<0.001, hypergeometric test) (genes indicated by asterisk in Table 1).

To verify and extend our microarray findings, we sought to assess corresponding protein expression of these genes by immunohistochemistry using a tissue microarray (TMA) containing matched sets of seminal vesicle, normal prostate and prostate cancer, from the original cases profiled for gene expression as well as additional cases. We characterized protein expression for 5 of the 32 genes for which antibodies functioning on paraffin tissue were available: ABCG2, CRABP2, GATA3, SLPI and GSTP1 (Fig. 2A). Immunostaining of ABCG2 and CRABP2 was predominantly cytoplasmic, while GATA3 staining was nuclear, and SLPI and GSTP1 stained both nuclei and cytoplasm. Consistent with the microarray findings, all five proteins exhibited a statistically significant expression gradient with highest levels in the epithelia of seminal vesicle, intermediate levels in normal prostate, and lowest levels in prostate cancer (P≤0.01; X2-test) (Fig. 2B).

Figure 2
Immunostain patterns of candidate prostate cancer genes


The prostate and seminal vesicle share similar histology, secretory function, androgen dependency, blood supply, and (in part) embryonic origin. Despite these commonalities, cancer of the prostate is very common while cancer of the seminal vesicle is exceedingly rare. We hypothesized that the difference in cancer incidence might reflect differences in expressed genes. In particular, genes expressed at higher level in seminal vesicle might protect against cancer, while those same genes when expressed at lower levels in prostate would provide more permissive conditions for cancer initiation. By expression profiling, we identified 32 genes exhibiting highest expression levels in seminal vesicle, lower levels in normal prostate, and lowest levels in prostate cancer. For a subset of these genes, we then confirmed this expression pattern across tissues for the corresponding proteins by immunohistochemistry.

Several lines of evidence suggest that the set of 32 genes is likely enriched for prostate cancer genes. First, the genes are significantly enriched for those upregulated by 5-aza-dC treatment of prostate cancer cells, consistent with promoter hypermethylation, a feature of tumor suppressor genes. Second, among the genes is a well-studied candidate tumor suppressor, GSTP1. Hypermethylation of GSTP1 is among the earliest molecular alterations documented in prostate tumorigenesis [19]. It has been proposed that GSTP1 functions as a “caretaker”, protecting against oxidative damage to DNA (e.g. from dietary carcinogens) [20]. Loss of GSTP1 would therefore be permissive to cancer initiation, and indeed studies of cultured prostate cells have established that GSTP1 protects against carcinogen-associated DNA damage [21]. Here, we find that GSTP1 is expressed at highest levels in seminal vesicle, which might in part explain the rare occurrence of cancer in that organ.

Additional evidence supporting an enrichment of tumor suppressors among the 32 genes is that many of their known functions are consistent with tumor suppression. For example, ABCG2 (ATP-binding cassette, sub-family G, member 2) functions as a xenobiotic transporter [22], and like GSTP1 may protect against dietary carcinogens. TGFBR3 (transforming growth factor, beta receptor III) is a co-receptor mediating growth inhibitory TGF-β signaling, and its decreased expression has recently been linked to prostate tumorigenesis [23,24]. GATA3 (GATA binding protein 3) is a transcriptional regulator that mediates cell lineage/differentiation-specific transcriptional programs, and has been found mutated, implying a tumor suppressive role, in some estrogen receptor-positive breast cancers [25].

CRABP2 (cellular retinoic acid binding protein 2), previously identified as down-regulated in prostate cancer [26], mediates the activities of retinoic acid in cell growth, differentiation, apoptosis, and anti-carcinogenesis [27]. GPRC5B (G protein-coupled receptor, family C, group 5, member B), another of the 32 genes, has also been characterized as retinoic acid inducible gene 2 (RAIG2) [28], providing another connection to retinoic acid signaling. SLPI (Secretory leukocyte peptidase inhibitor) has anti-inflammatory, as well as pathogen defense and tissue repair activities [29]. We can speculate that decreased expression of SLPI may be permissive to inflammation, a proposed etiologic factor for prostate carcinogenesis [30].

Of course the higher incidence of prostate cancer, compared to cancer of the seminal vesicle, might result from factors other than differentially expressed genes. For example, chronic prostatitis is common and inflammation can be found on most radical prostatectomy specimens (though typically without an identified bacterial or viral source) [30], while inflammation of the seminal vesicle is not frequently described. Nevertheless, there likely exists interplay between gene expression and such tissue-specific environments. For example, the differential expression of genes like SLPI may contribute to the tissue-specific differences in inflammation. It is likely therefore that gene expression differences contribute in large part to the disparate rates of carcinogenesis.

Prostate cancer has a relatively strong hereditary component. Linkage analysis has identified at least ten probable loci harboring prostate cancer susceptibility genes [31]. It is noteworthy that three of the 32 candidate prostate cancer genes identified in our study reside within such loci, including CRABP2 at 1q23.1 (CAPB susceptibility locus), C8orf13 at 8p23.1, and SLPI at 20q13.2 (HPC20 locus). These three genes should therefore also be considered candidates for the resident hereditary susceptibility genes, where specific allelic variants might modify hereditary prostate cancer risk.

It might also be that genes expressed at higher levels in normal prostate compared to seminal vesicle promote the development of prostate cancer. A similar but “reverse” analysis identified 14 genes (Table 2) with significantly elevated expression both in normal prostate compared to seminal vesicle, and in prostate cancer compared to normal prostate, including ENTPD5, SIM2, TARP, previously linked to prostate tumorigenesis [32-34]. However, since cancer develops commonly in many different epithelial tissues, parsimony would support the relative importance of tissue-specific protective genes expressed at higher levels in seminal vesicle.

Table 2
Candidate prostate cancer genes (increased in prostate vs. seminal vesicle)

In summary, our microarray analysis has identified a set of candidate prostate cancer genes whose decreased expression in prostate compared to seminal vesicle might be permissive to prostate cancer initiation. Further studies are needed to establish a mechanistic connection to prostate carcinogenesis, through modifying the expression of these genes in cultured prostate cells, or in animal models, and assessing the impact on carcinogenesis. Nonetheless, our findings identify new potential links to prostate tumorigenesis, and highlight promising avenues for prostate cancer prevention [35,36]. In particular they underscore the potential roles of antioxidants, anti-inflammatory agents, retinoids, and possibly new strategies to modify TGF-β signaling. More generally, our findings underscore the potential for leveraging insights from the seminal vesicle to inform and possibly in the future reduce the risk of prostate cancer.


We wish to thank the SFGF for DNA microarray manufacture, the SMD for microarray database support, Kelli Montgomery and Veronica Mason for advice on TMA construction, and members of the Pollack lab for helpful discussion.

Grant support: NIH CA111782 (J.D.B), Stanford VPUE (J.R.P)


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