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¹ Department of Gynaecological Oncology, Westmead Hospital² Westmead Institute for Cancer Research, ³ Department of Medicine and ⁴ University of Sydney at Westmead Millennium Institute, Westmead, New South Wales 2145, Australia

(Correspondence should be addressed to A deFazio Email: anna_defazio{at}wmi.usyd.edu.au)

^* *C Bye is now at Brain Injury and Repair, Howard Florey Institute, University of Melbourne, Parkville, Victoria 3010 Australia

	Abstract

Top Abstract Introduction Materials and methods Results Discussion References

 
Epithelial ovarian cancer, the leading cause of death from gynecological malignancy in Western countries, is thought to arise from the ovarian surface epithelium (OSE). It has been postulated that the constant rounds of proliferation and repair following ovulation contributes to neoplastic transformation. However, there is little information on the genes and pathways which are involved in the normal functions of the ovarian epithelium, in particular genes that are hormone responsive and those central to functions such as proliferation and apoptosis during ovulation. We used laser microdissection and cDNA microarrays to profile gene expression specifically in mouse ovarian epithelial cells, first compared with other ovarian cells, and secondly between ovarian epithelium collected at different physiological stages. We identified over 1000 transcripts that were consistently more highly expressed in the ovarian epithelium compared with remaining ovarian cell types, including genes involved in cell growth, transcription, and cell adhesion. At the various physiological stages examined, the highest number of regulated genes was found during the estrous cycle, specifically on the evening of proestrus, coincident with the ovulatory surge of hormones and just prior to ovulation. The expression of several selected genes, identified by the microarray analysis, including Villin 2, Keratin 8, Arginine-rich mutated in epithelial tumors, and Tumor-associated calcium signal transducer 1, was validated by independent methods. The identification of genes expressed and regulated in the OSE, and characterization of the pathways involved, will contribute to a more detailed understanding of the ovarian epithelium transcriptome and ultimately lead to a better understanding of the aberrations leading to malignant transformation in the ovarian epithelium.

	Introduction

Top Abstract Introduction Materials and methods Results Discussion References

 
Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in Western women, and is generally thought to arise in the ovarian epithelium either on the surface of the ovary or in inclusion cysts. However, the underlying mechanisms leading to malignant transformation are not known. Gonadotrophins that initiate each ovulation stimulate ovarian surface epithelium (OSE) proliferation and have been implicated in OSE cell transformation (Riman et al. 2004). By contrast, progesterone, a component of the oral contraceptive pill, produced at high levels during pregnancy, is thought to be protective against EOC (Risch 1998, Ho 2003), potentially by stimulating apoptosis (Rodriguez et al. 1998).

The ovarian epithelium is a single cell layer that covers the entire ovarian surface (Gillett et al. 1991), continuous with the peritoneal mesothelium, and derived from the coelomic epithelium. This epithelial layer varies morphologically from simple flattened, to cuboidal, and to low pseudostratified columnar, in association with the cyclic changes in the ovary (Clement 1987, Clow et al. 2002, Gaytan et al. 2005). It also has the capacity to remodel the ovarian cortex (Woessner et al. 1989, Auersperg et al. 1991) and participate in gonadotrophin-induced follicular rupture. Prior to ovulation, OSE proliferation increases at sites adjacent to follicular development (Bjersing & Cajander 1974, Burdette et al. 2006) and ovulation-related desquamation of the surface epithelial cells involves programmed cell death or apoptosis (Bjersing & Cajander 1975, Ackerman & Murdoch 1993, Murdoch 1994, 1995). Following ovulation, the wound at the ovarian surface is rapidly repaired by proliferation of the OSE from the perimeter of the ruptured follicle, and ovarian epithelial cells have also been postulated to be involved in the deposition and restructuring of the extracellular matrix of the tunica albuginea (Motta 1980, Osterholzer et al. 1985, Auersperg et al. 1991, Kruk & Auersperg 1992, Gaytan et al. 2005, Burdette et al. 2006). The constant rounds of proliferation and repair following ovulation provide the opportunity for replication errors and dysregulation of the genes involved in the normal processes of proliferation, apoptosis, and DNA repair, and thus may contribute to neoplastic progression in the OSE (Fathalla 1971). However, the specific molecular pathways underlying the development of EOC remain unclear.

A number of studies have documented the expression of known genes in normal OSE (reviewed in Auersperg et al. (2001)). Human OSE express cell adhesion-related proteins including integrins, collagens, fibronectin, and vitronectin, and secrete chymotrypsin- and elastase-like peptidases, metalloproteases, and plasminogen activator inhibitor (Carreiras et al. 1996, Auersperg et al. 1998). Normal OSE also express - and β-catenin (Davies et al. 1998), as well as a number of growth factors, including amphiregulin, and express receptors for epidermal growth factor, ovarian hormones, gonadotrophins, and hepatocyte growth factor/scatter factor (Johnson et al. 1991, Gulati & Peluso 1997, Auersperg et al. 2001). Although extensive genomic studies of the whole ovary (Espey & Richards 2002, Rinn et al. 2004, Zhang et al. 2004, Herrera et al. 2005) or various components, such as oocytes (Kocabas et al. 2006) and granulosa cells at various stages of development (Sasson et al. 2004) have been conducted, the limited material and delicate nature of the OSE has been a major barrier to studying OSE genomics using high throughput techniques, such as microarrays. Attempts have been made to address these problems using ovarian epithelial cells maintained in short-term cultures or immortalized cell lines, however Zorn et al. (2003) found that the gene expression profiles of cultured and immortalized cell lines are quite different compared with normal ovarian epithelial cell brushings, and thus may not accurately represent the phenotype of their cell of origin.

While there is a growing list of genes that are expressed by the OSE, most of these data have been gathered from ovarian epithelial cells grown in culture, and although some studies have been reported using tissue samples, there is almost no information on the regulation of these gene products under normal physiological circumstances in vivo. Since ovulation and cyclical ovulatory hormones have been implicated in the development of EOC, we examined gene expression profiles specifically in the OSE at defined ovulatory stages. We used laser microbeam microdissection and cDNA microarrays to profile gene expression in ovarian epithelial cells compared with the other cell types within the mouse ovary and identified gene sets differentially expressed in the OSE at different stages of the estrous cycle and different stages of development.

	Materials and methods

Top Abstract Introduction Materials and methods Results Discussion References

 
Animals and tissue preparation

BALB/c mice were housed in humidity- and temperature-controlled rooms under a 12 h light:12 h darkness cycle, with food and water provided ad libitum. Ovaries were obtained from mice at 27 days of age (immature, n=4) and 10–13 weeks of age during the estrous cycle, namely proestrus (n=4) and estrus (n=4), and between 10 and 14 days of pregnancy (mid-pregnancy, n=4).

For cycling mice, vaginal smears were taken daily from the age of 8 weeks and used to monitor progression through the estrous cycle (Nelson et al. 1982). Mice exhibiting two consecutive 4- to 5-day cycles were killed on either the evening of proestrus (2200 h) or the morning of estrus (1000 h). All mice were anesthetized with ketamine (100 µg/g body weight) and xylazine (10 µg/g body weight) and then culled by cervical dislocation. Both ovaries were collected, removed from the ovarian bursa, snap frozen, and stored in liquid nitrogen until use. All experiments were approved by the Institutional Animal Care and Ethics Committee.

Laser microdissection, RNA isolation, and amplification

One ovary chosen randomly from each mouse was embedded in Tissue-Tek OCT medium (Sakura Finetek, Torrance, CA, USA) and 10 µm sections were mounted on RNase- and UV-treated PALM MembraneSlides (PALM Microlaser Technologies AG, Bernried, Germany), frozen on dry ice, and stored at –80 °C until use. Prior to microdissection, the slides were fixed in 70% ethanol for 2 min, stained with Mayers hematoxylin (Sigma–Aldrich) for 1.5 min, rinsed with deionized water, and sequentially dehydrated in graded alcohols (70, 95, and 100% ethanol). The OSE were then laser microdissected from 20 ovarian sections per mouse using the PALM Robot-Microbeam system (PALM Microlaser Technologies) under 200x magnification. In addition to collecting the OSE, the remaining ovarian tissue (including follicles, stroma, corpora lutea, and blood vessels) was also collected and placed separately in lysis buffer containing 0.7% β-mercaptoethanol. Samples were vortexed for 5 s, centrifuged, incubated for 10 min at 55 °C, snap frozen on dry ice, and stored at –80 °C.

Total RNA was extracted from each mouse ovarian epithelial cell and residual ovarian tissue sample using the Stratagene Absolutely RNA Nanoprep or Microprep kit (Stratagene, La Jolla, CA, USA) respectively, following the manufacturer's instructions. RNA was eluted in 15 (Nanoprep kit) or 30 µl (Microprep kit) elution buffer and stored at –80 °C. RNA quality/integrity was measured using the RNA 6000 Nano LabChip kit in combination with the Agilent 2100 Bioanalyzer. For each mouse, 10 µl OSE and 500 ng residual ovarian tissue total RNA were amplified using the MessageAmp aRNA kit (Ambion, Austin, TX, USA), following the manufacturer's instructions. Two rounds of amplification were performed on each sample.

cDNA microarrays

Microarray slides were obtained from the Australian Genome Research Facility (Melbourne, Australia) and consisted of 15 000 expressed sequence tags from the National Institute of Ageing 15K mouse clone library. More details about this clone set, as well as further gene annotation and informatics, are available from http://lgsun.grc.nia.nih.gov/cDNA/15k.html. Printed on all microarray slides was a range of positive, negative, and calibration controls, including the Amersham Lucidea scorecard (Amersham Biosciences). All arrays were hybridized with 3 µg Cy3- and Cy5-labeled cDNA generated from 3 µg double-amplified RNA using a modified protocol from http://brownlab.stanford.edu/protocols.html, as described by Graham et al. (2005).

Microarray comparisons

Thirty-six microarray hybridizations were performed using amplified RNA prepared from the OSE and residual ovarian tissue collected from 16 mice (4 replicate mice per group). Two main comparisons were performed. The first comparison examined OSE-specific gene expression, consisting of a direct comparison between OSE and residual ovarian tissue from immature (IM), proestrus evening (PE), and mid-pregnant (P) mice (12 slides, Fig. 1B). The second comparison examined developmental and estrous stage-specific gene expression profiles, consisting of a direct comparison between OSE from IM, PE, estrus morning (EM), and mid-pregnant mice (24 slides, Fig. 1C). Each group comparison compared RNA from four independent biological replicates, with two hybridizations labeled with each fluorescent dye to account for dye effects.

View larger version (11K): [in this window] [in a new window]   Figure 1 (A) Fluctuating hormone levels during the estrous cycle in rodents. The cycle can be divided into four main stages: diestrus, proestrus, estrus, and metestrus. Black bars indicate the dark period, with each phase commencing at midnight. The dotted line represents when ovulation begins, after the surge of hormones on the evening of proestrus. Adapted from Smith et al. (1975). (B and C) Microarray design. (B) OSE obtained from immature (IM), proestrus evening (PE), and pregnant (P) mice (n=4/stage) were compared directly with the residual ovarian (RO) tissue, making a total of 12 hybridizations (4 hybridizations/group). (C) OSE from IM, PE, estrus morning (EM), and P mice (n=4/stage) were directly compared, making a total of 24 hybridizations (4 hybridizations/group). Direction of the arrow head represents the sample labeled with Cy5.

Hybridized arrays were scanned using a GenePix 4000B scanner and the images analyzed with GenePix PRO 6.0 software (Molecular Devices Corp., Sunnyvale, CA), using the median-fixed circles for foreground and the morphological opening method to calculate the local background for each spot. Spots not found by the image analysis software or not significantly different to local background (background plus twice the S.D. of the background) in either channel were defined as low quality and weighted 0.1 out of 1. The results were analyzed in the statistical computing environment R (www.r-project.org) through the Bioconductor Project (Dudoit et al. 2003), using the marray and limma packages (Gentleman et al. 2004). The expression values were calculated as the log ratio of dye-normalized red (Cy5) and green (Cy3) channel signals. All microarrays were normalized using the weighted robust splines normalization method. The empirical Bayes linear modeling approach was then used to rank genes in order for differential expression (Smyth 2004, see Supplementary information for the details in the online version of the Journal of Endocrinology at http://jme.endocrinology-journals.org/content/vol40/issue6/). Briefly, data were divided into each physiological condition (four arrays per group) and an average ratio (M-value) for each gene was calculated, by fitting a simple linear model for each gene. Genes were only included if they were weighted as high quality in 75% of the arrays (i.e., in three out of four arrays per group) and not defined as a control. Next, empirical Bayes statistics were calculated for differential expression, using moderated t- and B-statistics (Smyth 2004). For these experiments, genes displaying B>0 (log posterior odds) and at least a 1.2-fold change were considered differentially expressed. For comparison, analysis of normalized data was also conducted using Serial Analysis of Microarray (SAM) software (Tusher et al. 2001), freely available from the TM4 package (http://www.tm4.org/ Saeed et al. 2003).

Gene identification and function (ontology) was assigned based on the SOURCE database (http://source.stanford.edu). The Kyoto Encyclopedia of Genes and Genomes pathway database (Ogata et al. 1999, Dennis et al. 2003) was used to identify whether any genes were involved in any known cellular pathways and the GOstat program (http://gostat.wehi.edu.au/) was used to identify whether any gene ontologies were statistically over-represented in particular gene profiles (Beissbarth & Speed 2004).

Real-time quantitative RT-PCR

Expression levels of arginine-rich mutated in epithelial tumors (Armet) and tumor-associated calcium signal transducer (Tacstd1) were analyzed by real-time quantitative RT-PCR (RT-qPCR). For each RNA sample, cDNA was synthesized using Superscript III reverse transcriptase (Invitrogen), following the manufacturer's instructions. RT-qPCR was performed using Platinum SYBR Green Master Mix (Invitrogen) and the Rotor-Gene 3000 (Corbett Research, Sydney, NSW, Australia). For each gene, RT-qPCR reactions were conducted using RNA that was used for the microarray analysis. All quantifications were normalized to 18S RNA. Primer sequences used were as follows: Armet forward, 5'-CACCAGCCACTATTGAAGAAGAA-3'; Armet reverse, 5'-TCCAATGTAGTAGCACAACCG-3'; Tacstd1 forward, 5'-CCGAAGAACCGACAAGGACAC-3'; Tacstd1 reverse, 5'-AGTAGGTCCTCACGCGCTCG-3'; 18S RNA forward, 5'-GTAACCCGTTGAACCCCATT-3'; and 18S RNA reverse, 5'-CCATCCAATCGGTAGTAGCG-3'.

Immunohistochemistry

Ovaries were embedded in Tissue-Tek OCT compound (Sakura Finetek, Torrance, CA, USA) and 10 µm serial sections were cut on a cryotome (Microm HM 505E) using disposable blades, at –25 °C. The sections were mounted on Superfrost Plus slides and stored at –20 °C until use. The sections were fixed in either cold acetone for 10 min (TROMA-1) or cold 3.7% formaldehyde in PBS for 30 min (Ezrin). Endogenous peroxidase was blocked by incubation in 0.003% hydrogen peroxide solution for 10 min and then rinsed with distilled water. To block for endogenous biotin, the sections were treated with the Dako Biotin Blocking system (DakoCytomation, Carpinteria, CA, USA), according to the manufacturer's instructions. After a brief rinse with 1x PBS, the sections were incubated for 30 min with normal goat serum and diluted 1:1 in PBS. Excess normal goat serum was removed before incubation with the primary antibody.

The TROMA-1 rat anti-mouse monoclonal antibody that recognizes Keratin 8 (Brulet et al. 1980) was obtained from the Developmental Studies Hybridoma Bank (The University of Iowa, Department of Biological Sciences, Iowa City, IA, USA). The sections were incubated with 4.5 ng/µl TROMA-1 antibody diluted in PBT (PBS with 0.5% (v/v) Triton X-100 (Amresco, Solon, OH, USA)) at 37 °C for 1 h. Villin 2, also known as Ezrin, was detected using rabbit anti-human Ezrin polyclonal antibody (Upstate, supplied by Auspep Pty Ltd, Parkville, Australia), which cross-reacts with mouse Villin 2/Ezrin. The sections were incubated with 5 ng/µl Villin 2/Ezrin antibody diluted in PBT at room temperature for 1 h.

After incubation with the primary antibody, the sections were rinsed with PBT, and PBS and the appropriate biotinylated secondary antibody was added. After rinsing, the sections were then incubated with streptavidin-biotinylated peroxidase according to the manufacturer's instructions (Zymed Laboratories, Inc., San Francisco, CA, USA) and then rinsed, as described above. Bound antibody was visualized using diaminobenzidine (DAB; DakoCytomation) and the reaction was stopped in distilled water. The sections were counterstained with Harris hematoxylin, differentiated with 1% acid alcohol, and allowed to air-dry before mounting with histolene and normount. To control for non-specific staining, adjacent sections were stained as above, except the primary antibody was replaced with PBT.

	Results

Top Abstract Introduction Materials and methods Results Discussion References

 
RNA quality and microarray design

In laboratory mice, the estrous cycle generally lasts for 4–6 days. It can be divided into four major phases (diestrus, proestrus, estrus, and metestrus), with each stage distinguished by changes in structures and function of the sex organs. Generally, proestrus and estrus constitute the follicular phase of the cycle, whereas metestrus and diestrus constitute the luteal phase of the ovarian cycle (Fig. 1A). To investigate global gene expression changes in the OSE under various hormonal conditions (Fig. 1A), we used laser microdissection to isolate pure populations of ovarian epithelial cells (Fig. 2) from ovaries collected from mice at four distinct physiological stages: immaturity, proestrus, estrus, and mid-pregnancy. Individual ovarian epithelial cell samples were arrayed rather than using pooled samples, to reduce identifying genes with high biological variability in their expression levels (Seltmann et al. 2005). RNA from the laser microdissected material was amplified twice to obtain sufficient material. Previous studies have shown that RNA amplification does not significantly change the expression profile (Wang et al. 2000, Baugh et al. 2001, Feldman et al. 2002). To further confirm this, we compared hybridization data between total RNA and amplified RNA obtained from two cell lines to verify that the amplification process was not biasing our microarray results. Analysis of these cDNA microarrays resulted in correlation coefficients greater than 0.8 for first round amplification and greater than 0.7 between total RNA and double amplified RNA (data not shown).

View larger version (156K): [in this window] [in a new window]   Figure 2 Laser microdissection of the epithelial cells from the ovarian surface using the Robot-Microbeam system. (A) Ten µm ovarian sections were mounted on PALM MembraneSlides and stained with hematoxylin. Inset represents area in (B). (B) The laser was used to cut and separate the OSE from the underlying ovarian cortex. (C) Ovarian section with underlying cells removed, separating the OSE from the other ovarian cells. Original magnification, (A and C) 200x and (B) 400x.

We applied a further statistical method, SAM (Tusher et al. 2001), to confirm the accuracy of the linear model approach to identify differentially expressed genes. Applying SAM to the same normalized data set identified the same top two transcripts as in the linear model analysis and ranked the remaining transcripts in a similar order (Table 1), with a median FDR of 0.000% and a 90th percentile FDR of 0.046%. Only 116 (4%) transcripts were identified as being differentially expressed in the SAM analysis and were not by the linear model analysis (data not included). These results indicate that both methods of analysis are able to identify very similar sets of differentially expressed genes and further confirm the reliability of the linear model approach.

inhibin-

View larger version (18K): [in this window] [in a new window]   Figure 3 OSE genes involved in the regulation of the actin cytoskeleton, focal adhesion, cell cycle, apoptosis, MAPK signaling, and Wnt signaling pathways. Analysis of all 1111 genes identified as highly expressed in mouse OSE compared with RO, irrespective of the estrous developmental stage, using the KEGG pathway analysis program (Ogata et al. 1999) found multiple genes (as listed) involved in the actin cytoskeleton, focal adhesion, cell cycle, apoptosis, MAPK signaling, and Wnt signaling pathways.

View larger version (135K): [in this window] [in a new window]   Figure 4 Validation of differential expression of Keratin 8 and Villin 2 by immunohistochemical staining. Cryostat sections (e.g. 4 µm) were stained for (A) Keratin 8 in an ovary from a pregnant mouse and (C) for Villin 2 in an ovary removed on the evening of proestrus. (B and D) No primary antibody negative controls. Staining was visualized using biotin–avidin amplification and horseradish peroxidase/diaminobenzidine and counterstained with hematoxylin. Original magnification, 600x.

View larger version (15K): [in this window] [in a new window]   Figure 5 Number of genes identified as being differentially expressed in the OSE at each physiological stage. OSE were isolated from immature (IM), proestrus evening (PE), estrus morning (EM), and mid-pregnant (P) mice and amplified RNA from each stage were compared with all other physiological stages by cDNA microarrays. The number of differentially expressed genes/ESTs identified (up- and down-regulated) in the OSE between each of the six cDNA microarray comparisons (Fig. 1C) are indicated in the inset. Results from these microarrays were also combined to identify subsets of genes that were specifically associated with each physiological stage or had restricted patterns of expression. The numbers in the diagram represent the number of genes differentially expressed at each physiological stage compared with the other stages.

When the OSE from PE mice were compared with the three other stages, 161 genes/ESTs were found to be specifically induced in the OSE just prior to ovulation (PE), whereas 35 genes/ESTs were down-regulated (Fig. 5). A subset of genes up- and down-regulated in epithelial cells collected on the evening of proestrus is represented in Table 3, and the full data set is supplied in Supplementary Table 2 in the online version of the Journal of Molecular Endocrinology at http://jme.endocrinology-journals.org/content/vol40/issue6/. Out of the 161 transcripts more highly expressed in PE-OSE, 105 were genes with known function (65%), 8 were genes with unknown function (5%), and 48 were ESTs (30%). We identified over-representation of several processes using GOstat (Beissbarth & Speed 2004), including those involved in protein binding (P<0.0001, Fisher's exact test) and protein folding (P<0.02). Four genes (calmodulin 1 (Calm1), cyclin B1 (Ccnb1), jun oncogene (Jun), and heat shock protein 8 (Hspb8)) were found to be involved in the cell cycle and two genes (tumour necrosis factor receptor super family, member 12 (Tnfrsf12a) and huntington disease gene homolog (Hdh)) were found to be involved in apoptosis. We identified 10 genes, (10/105 with known function, 9%) that have previously been shown to have induced expression in response to ovulatory levels of LH, human chorionic gonadotrophin, or follicle-stimulating hormone, in various ovarian cell types in vitro (Table 3). In addition to providing support for data obtained by microarray analysis, this suggests that the remaining up-regulated transcripts may represent novel hormone-regulated genes. Out of the 161 transcripts, 21 genes/ESTs were also more highly expressed in the OSE compared with the remaining ovarian tissue.

View larger version (13K): [in this window] [in a new window]   Figure 6 Validation of gene expression levels in the OSE using RT-qPCR. The relative expression of (A) Tacstd1 and (B) Armet in the OSE from IM, PE, EM, and P mice, as measured using RT-qPCR. Inset, expression levels of Tacstd1 and Armet in PE-OSE compared with IM, EM, and P-OSE by microarray analysis. FC, fold change.

	Discussion

Top Abstract Introduction Materials and methods Results Discussion References

Using gene expression profiling of microdissected tissue, we showed first that there were significant differences in gene expression between the OSE and other cell types within the ovary. These results are not surprising considering the numerous cell types that are present in the ovary and the high level of physiological activity but indicate that the OSE are involved in many cellular processes. Many of the differentially regulated genes identified are well characterized, and several have been previously identified to be involved in ovarian epithelial cell biology, including Villin 2 (Ezrin) (Moilanen et al. 2003), Keratins 8, 18, and 19 (Auersperg et al. 2001), and ESR1 (Sar & Welsch 1999, Saunders et al. 2000).

The marked difference in gene expression between the OSE and the residual ovarian tissue has significant implications considering that several microarray studies have examined EOC gene expression compared with the whole ovary. These results provide at least some insight into why EOC gene expression profiles are not consistent between studies, particularly between studies where EOC specimens are compared with whole ovarian tissue rather than the OSE (Zorn et al. 2003).

In addition to profiling gene expression changes between the OSE and the residual ovary, we also examined microarray profiles of the OSE under specific hormonal conditions. Interestingly, no genes were identified to be differentially expressed between the OSE from mid-pregnant and IM mice. This was an unexpected result, given the very different hormonal milieu associated with these phases, immaturity being associated with low systemic hormone levels and pregnancy with high hormone levels. The relative similarity in transcripts between the OSE from IM and mid-pregnant mice may reflect the similar ‘dormant’ nature of the epithelium during these anovulatory phases.

Several hypotheses have been proposed to explain altered risk of ovarian cancer related to reproductive factors (Fathalla 1971, Risch 1998). The first argues that repeated cycles of ovulation-induced proliferation, trauma, and repair of the OSE at the site of ovulation contributes to ovarian cancer development (Fathalla 1971), and that pregnancies and oral contraceptives protect by suppressing ovulation (Casagrande et al. 1979). A second hypothesis is that circulating levels of gonadotrophins increase the risk of malignancy, and that pregnancies and oral contraceptives protect by suppressing secretion of these hormones (Zheng et al. 2000, Ho 2003, Riman et al. 2004). These two hypotheses are not mutually exclusive and features of our data are consistent with both.

The most extensive differential expression was observed on the evening of proestrus directly prior to ovulation when hormone levels are high and OSE proliferation is stimulated. This subset included genes involved in the cell cycle and apoptosis, although the processes most over-represented in proestrus were protein folding/binding and transcription factors, indicative of the rapid changes that are occurring in these cells in response to ovulation. We identified several genes that were specifically induced in the OSE on the evening of proestrus, which have been implicated in the early events of ovarian tumorigenesis, including two integral membrane proteins, claudin 3 (Cldn3) and Tacstd1 (Donninger et al. 2004, Heinzelmann-Schwarz et al. 2004). Cldn3 has previously been shown to be regulated in response to ovulatory hormones, which is consistent with our expression array data (Rimon et al. 2004).

Cldn3 has been found to be one of the most highly expressed genes in EOC, identified by several microarray and SAGE studies (Hough et al. 2000, Rangel et al. 2003, Adib et al. 2004, Heinzelmann-Schwarz et al. 2004, Lu et al. 2004, Santin et al. 2004). Cldn3 is expressed at low levels in the normal human OSE and epithelial cells lining inclusion cysts, its expression is increased in ovarian adenocarcinomas compared with benign and low malignant potential (LMP) tumors (Heinzelmann-Schwarz et al. 2004, Zhu et al. 2006), and it has been shown to be expressed in all EOC subtypes (Heinzelmann-Schwarz et al. 2004, Lu et al. 2004, Zhu et al. 2006). Recently, small interfering RNA (siRNA) analysis has shown CLDN3 expression to increase invasion and survival of ovarian tumor cells (Agarwal et al. 2005).

Similarly, immunohistochemical analysis has revealed that Tacstd 1 (commonly known as epithelial cell adhesion/activating molecule, EpCAM/CD326)) is also expressed in all EOC subtypes (Hough et al. 2000, Connor et al. 2004, Drapkin et al. 2004, Heinzelmann-Schwarz et al. 2004). While it is expressed at low levels in the normal OSE, its expression increases significantly in ovarian epithelial inclusion cysts and further in ovarian adenocarcinomas compared with benign and LMP tumors (Drapkin et al. 2004, Heinzelmann-Schwarz et al. 2004). Recently, Cheng et al. (2005) demonstrated the expression of Tacstd1 and Cldn3 in the serous-like tumors that developed from inoculation of nude mice with transformed mouse ovarian epithelial cells transfected with various members of the homeo box (HOX) family (HOX genes normally regulate mullerian duct differentiation, but are not expressed in normal mouse OSE (Cheng et al. 2005)). Although their exact roles in tumorigenesis are still being investigated (Agarwal et al. 2005, Baeuerle & Gires 2007), results indicate that both Cldn3 and Tacstd1 are widely expressed in ovarian cancer and represent promising targets for detection, diagnosis, and therapy.

Related to the hormonal hypothesis of ovarian cancer development is the proposal that progesterone is protective against ovarian cancer with increased progesterone synthesis during pregnancy, and progestins contained in formulations of oral contraceptives, conferring protection to the ovarian epithelium (Risch 1998, Rodriguez et al. 1998, Schildkraut et al. 2002). Various mechanisms have been proposed to explain the progestin-mediated reduction in ovarian cancer risk including induction of apoptosis (Murdoch 1995), promotion of repair of ovulation-induced genomic damage (Murdoch & Martinchick 2004), and inhibition of proliferation by progestin-mediated induction of alternative expression of transforming growth factor-β (TGF-β) isoforms (Ho 2003).

Although very few genes were differentially expressed in the OSE during pregnancy compared with the OSE from cycling mice in our study, the expression of one gene, Tsc22d1 (also known as TGF-β-stimulated clone 22 homolog) is of potential interest in the light of these proposed effects of pregnancy on ovarian epithelial cells. Tsc22d1 expression has been shown to be stimulated by both TGF-β (Shibanuma et al. 1992, Jay et al. 1996) and progesterone (Kester et al. 1997), and Tsc22d1 has features consistent with tumor suppression in prostate cancer, salivary gland cancer, and leukemia; however, further investigation is required to determine whether Tsc22d1 has a role in reduction of ovarian cancer risk.

Overall, we have identified genes that were consistently more highly expressed in ovarian epithelial cells compared with the other ovarian cells, illustrating their specific role in ovarian physiology. We also identified many known and unknown genes that were differentially expressed only during the estrous cycle, indicating stage-specific roles. Our findings show clear evidence of the highly complex and tightly regulated processes affected by the fluctuations in hormone levels that occur through the estrous cycle and with pregnancy. Analysis of these gene profiles may also help to further elucidate some of these pathways and also help to identify genes that are involved in ovarian epithelial tumorigenesis.

	Acknowledgements

 
We would like to thank Dr Dinny Graham and Dr Lucy Webster for help with the cDNA microarray experiments. We also thank the Westmead Millennium Foundation and the Westmead Gynaecological Oncology Research Fund, (Westmead Hospital, Westmead, NSW, Australia) for funding. The authors of this manuscript have nothing to declare.

	References

Top Abstract Introduction Materials and methods Results Discussion References

 
Ackerman RC & Murdoch WJ 1993 Prostaglandin-induced apoptosis of ovarian surface epithelial cells. Prostaglandins 45 475–485.[CrossRef][Web of Science][Medline]

Adib TR, Henderson S, Perrett C, Hewitt D, Bourmpoulia D, Ledermann J & Boshoff C 2004 Predicting biomarkers for ovarian cancer using gene-expression microarrays. British Journal of Cancer 90 686–692.[CrossRef][Web of Science][Medline]

Agarwal R, D'Souza T & Morin PJ 2005 Claudin-3 and claudin-4 expression in ovarian epithelial cells enhances invasion and is associated with increased matrix metalloproteinase-2 activity. Cancer Research 65 7378–7385.[Abstract/Free Full Text]

Auersperg N, Maclaren IA & Kruk PA 1991 Ovarian surface epithelium: autonomous production of connective tissue-type extracellular matrix. Biology of Reproduction 44 717–724.[Abstract]

Auersperg N, Edelson MI, Mok SC, Johnson SW & Hamilton TC 1998 The biology of ovarian cancer. Seminars in Oncology 25 281–304.[Web of Science][Medline]

Auersperg N, Wong AS, Choi KC, Kang SK & Leung PC 2001 Ovarian surface epithelium: biology, endocrinology, and pathology. Endocrine Reviews 22 255–288.[Abstract/Free Full Text]

Baeuerle PA & Gires O 2007 EpCAM (CD326) finding its role in cancer. British Journal of Cancer 96 1491[CrossRef][Web of Science]

Baugh LR, Hill AA, Brown EL & Hunter CP 2001 Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Research 29 E29[CrossRef][Medline]

Beissbarth T & Speed TP 2004 GOstat: find statistically overrepresented gene ontologies within a group of genes. Bioinformatics 20 1464–1465.[Abstract/Free Full Text]

Bjersing L & Cajander S 1974 Ovulation and the mechanism of follicle rupture. II. Scanning electron microscopy of rabbit germinal epithelium prior to induced ovulation. Cell Tissue Research 149 301–312.[Web of Science][Medline]

Bjersing L & Cajander S 1975 Ovulation and the role of the ovarian surface epithelium. Experientia 31 605–608.[CrossRef][Web of Science][Medline]

Brulet P, Babinet C, Kemler R & Jacob F 1980 Monoclonal antibodies against trophectoderm-specific markers during mouse blastocyst formation. PNAS 77 4113–4117.[Abstract/Free Full Text]

Burdette JE, Kurley SJ, Kilen SM, Mayo KE & Woodruff TK 2006 Gonadotropin-induced superovulation drives ovarian surface epithelia proliferation in CD1 mice. Endocrinology 147 2338–2345.[Abstract/Free Full Text]

Carreiras F, Denoux Y, Staedel C, Lehmann M, Sichel F & Gauduchon P 1996 Expression and localization of alpha v integrins and their ligand vitronectin in normal ovarian epithelium and in ovarian carcinoma. Gynecologic Oncology 62 260–267.[CrossRef][Web of Science][Medline]

Casagrande JT, Louie EW, Pike MC, Roy S, Ross RK & Henderson BE 1979 Incesssant ovulation and ovarian cancer. Lancet 2 170–173.[CrossRef][Web of Science][Medline]

Cheng W, Liu J, Yoshida H, Rosen D & Naora H 2005 Lineage infidelity of epithelial ovarian cancers is controlled by HOX genes that specify regional identity in the reproductive tract. Nature Medicine 11 531–537.[CrossRef][Web of Science][Medline]

Clement PB 1987 Histology of the ovary. American Journal of Surgical Pathology 11 277–303.[CrossRef][Web of Science][Medline]

Clow OL, Hurst PR & Fleming JS 2002 Changes in the mouse ovarian surface epithelium with age and ovulation number. Molecular and Cellular Endocrinology 191 105–111.[CrossRef][Web of Science][Medline]

Connor JP, Felder M, Hank J, Harter J, Gan J, Gillies SD & Sondel P 2004 Ex vivo evaluation of anti-EpCAM immunocytokine huKS-IL2 in ovarian cancer. Journal of Immunotherapy 27 211–219.[CrossRef][Web of Science][Medline]

Davies BR, Worsley SD & Ponder BA 1998 Expression of E-cadherin, alpha-catenin and beta-catenin in normal ovarian surface epithelium and epithelial ovarian cancers. Histopathology 32 69–80.[CrossRef][Web of Science][Medline]

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC & Lempicki RA 2003 DAVID: Database for annotation, visualization, and integrated discovery. Genome Biology 4 P3[CrossRef][Medline]

Donninger H, Bonome T, Radonovich M, Pise-Masison CA, Brady J, Shih JH, Barrett JC & Birrer MJ 2004 Whole genome expression profiling of advance stage papillary serous ovarian cancer reveals activated pathways. Oncogene 23 8065–8077.[CrossRef][Web of Science][Medline]

Drapkin R, Crum CP & Hecht JL 2004 Expression of candidate tumor markers in ovarian carcinoma and benign ovary: evidence for a link between epithelial phenotype and neoplasia. Human Pathology 35 1014–1021.[CrossRef][Medline]

Drummond AE, Dyson M, Mercer JE & Findlay JK 1996 Differential responses of post-natal rat ovarian cells to FSH and activin. Molecular and Cellular Endocrinology 122 21–32.[CrossRef][Web of Science][Medline]

Dudoit S, Gentleman RC & Quackenbush J 2003 Open source software for the analysis of microarray data. Biotechniques Mar Suppl 45–51.

Espey LL & Richards JS 2002 Temporal and spatial patterns of ovarian gene transcription following an ovulatory dose of gonadotropin in the rat. Biology of Reproduction 67 1662–1670.[Abstract/Free Full Text]

Fathalla MF 1971 Incessant ovulation-a factor in ovarian neoplasia? Lancet 2 163[CrossRef][Web of Science][Medline]

Feldman AL, Costouros NG, Wang E, Qian M, Marincola FM, Alexander HR & Libutti SK 2002 Advantages of mRNA amplification for microarray analysis. Biotechniques 33 906–912. 914[Web of Science][Medline]

Gaytan M, Sanchez MA, Morales C, Bellido C, Millan Y, Martin de las Mulas J, Sanchez-Criado JE & Gaytan F 2005 Cyclic changes of the ovarian surface epithelium in the rat. Reproduction 129 311–321.[Abstract/Free Full Text]

Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J et al. 2004 Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 5 R80[CrossRef][Medline]

Gillett WR, Mitchell A & Hurst PR 1991 A scanning electron microscopic study of the human ovarian surface epithelium: characterization of two cell types. Human Reproduction 6 645–650.[Abstract/Free Full Text]

Graham JD, Yager ML, Hill HD, Byth K, O'Neill GM & Clarke CL 2005 Altered progesterone receptor isoform expression remodels progestin responsiveness of breast cancer cells. Molecular Endocrinology 19 2713–2735.[Abstract/Free Full Text]

Gulati R & Peluso JJ 1997 Opposing actions of hepatocyte growth factor and basic fibroblast growth factor on cell contact, intracellular free calcium levels, and rat ovarian surface epithelial cell viability. Endocrinology 138 1847–1856.[Abstract/Free Full Text]

Heinzelmann-Schwarz VA, Gardiner-Garden M, Henshall SM, Scurry J, Scolyer RA, Davies MJ, Heinzelmann M, Kalish LH, Bali A, Kench JG et al. 2004 Overexpression of the cell adhesion molecules DDR1, claudin 3, and Ep-CAM in metaplastic ovarian epithelium and ovarian cancer. Clinical Cancer Research 10 4427–4436.[Abstract/Free Full Text]

Herrera L, Ottolenghi C, Garcia-Ortiz JE, Pellegrini M, Manini F, Ko MS, Nagaraja R, Forabosco A & Schlessinger D 2005 Mouse ovary developmental RNA and protein markers from gene expression profiling. Developmental Biology 279 271–290.[CrossRef][Web of Science][Medline]

Ho SM 2003 Estrogen, progesterone and epithelial ovarian cancer. Reproductive Biology and Endocrinology 1 73[CrossRef]

Hough CD, Sherman-Baust CA, Pizer ES, Montz FJ, Im DD, Rosenshein NB, Cho KR, Riggins GJ & Morin PJ 2000 Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Research 60 6281–6287.[Abstract/Free Full Text]

Jay P, Ji JW, Marsollier C, Taviaux S, Berge-Lefranc JL & Berta P 1996 Cloning of the human homologue of the TGF beta-stimulated clone 22 gene. Biochemical and Biophysical Research Communications 222 821–826.[CrossRef][Web of Science][Medline]

Johnson GR, Saeki T, Auersperg N, Gordon AW, Shoyab M, Salomon DS & Stromberg K 1991 Response to and expression of amphiregulin by ovarian carcinoma and normal ovarian surface epithelial cells: nuclear localization of endogenous amphiregulin. Biochemical and Biophysical Research Communications 180 481–488.[CrossRef][Web of Science][Medline]

Kalli KR, Chen BK, Bale LK, Gernand E, Overgaard MT, Oxvig C, Cliby WA & Conover CA 2004 Pregnancy-associated plasma protein-A (PAPP-A) expression and insulin-like growth factor binding protein-4 protease activity in normal and malignant ovarian surface epithelial cells. International Journal of Cancer 110 633–640.[CrossRef][Web of Science][Medline]

Kester HA, van der Leede BM, van der Saag PT & van der Burg B 1997 Novel progesterone target genes identified by an improved differential display technique suggest that progestin-induced growth inhibition of breast cancer cells coincides with enhancement of differentiation. Journal of Biological Chemistry 272 16637–16643.[Abstract/Free Full Text]

Kocabas AM, Crosby J, Ross PJ, Otu HH, Beyhan Z, Can H, Tam WL, Rosa GJ, Halgren RG, Lim B et al. 2006 The transcriptome of human oocytes. PNAS 103 14027–14032.[Abstract/Free Full Text]

Kruk PA & Auersperg N 1992 Human ovarian surface epithelial cells are capable of physically restructuring extracellular matrix. American Journal of Obstetrics and Gynecology 167 1437–1443.[Web of Science][Medline]

Lu KH, Patterson AP, Wang L, Marquez RT, Atkinson EN, Baggerly KA, Ramoth LR, Rosen DG, Liu J, Hellstrom I et al. 2004 Selection of potential markers for epithelial ovarian cancer with gene expression arrays and recursive descent partition analysis. Clinical Cancer Research 10 3291–3300.[Abstract/Free Full Text]

Moilanen J, Lassus H, Leminen A, Vaheri A, Butzow R & Carpen O 2003 Ezrin immunoreactivity in relation to survival in serous ovarian carcinoma patients. Gynecologic Oncology 90 273–281.[CrossRef][Web of Science][Medline]

Motta PM 1980 Morphodynamic changes of the mammalian ovary as revealed by scanning electron microscopy. Folia Morphologica 28 79–81.

Murdoch WJ 1994 Ovarian surface epithelium during ovulatory and anovulatory ovine estrous cycles. Anatomical Record 240 322–326.[CrossRef][Medline]

Murdoch WJ 1995 Programmed cell death in preovulatory ovine follicles. Biology of Reproduction 53 8–12.[Abstract]

Murdoch WJ & Martinchick JF 2004 Oxidative damage to DNA of ovarian surface epithelial cells affected by ovulation: carcinogenic implication and chemoprevention. Experimental Biology and Medicine 229 546–552.[Abstract/Free Full Text]

Nelson JF, Felicio LS, Randall PK, Sims C & Finch CE 1982 A longitudinal study of estrous cyclicity in aging C57BL/6J mice: I. Cycle frequency, length and vaginal cytology. Biology of Reproduction 27 327–339.[Abstract]

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H & Kanehisa M 1999 KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research 27 29–34.[Abstract/Free Full Text]

Oksjoki S, Soderstrom M, Vuorio E & Anttila L 2001 Differential expression patterns of cathepsins B, H, K, L and S in the mouse ovary. Molecular Human Reproduction 7 27–34.[Abstract/Free Full Text]

Osterholzer HO, Johnson JH & Nicosia SV 1985 An autoradiographic study of rabbit ovarian surface epithelium before and after ovulation. Biology of Reproduction 33 729–738.[Abstract]

Rangel LBA, Agarwal R, D'Souza T, Pizer ES, Alo PL, Lancaster WD, Gregoire L, Schwartz DR, Cho KR & Morin PJ 2003 Tight junction proteins claudin-3 and claudin-4 are frequently overexpressed in ovarian cancer but not in ovarian cystadenomas. Clinical Cancer Research 9 2567–2575.[Abstract/Free Full Text]

Riman T, Nilsson S & Persson IR 2004 Review of epidemiological evidence for reproductive and hormonal factors in relation to the risk of epithelial ovarian malignancies. Acta Obstetricia et Gynecologica Scandinavica 83 783–795.[CrossRef][Web of Science][Medline]

Rimon E, Sasson R, Dantes A, Land-Bracha A & Amsterdam A 2004 Gonadotropin-induced gene regulation in human granulosa cells obtained from IVF patients: modulation of genes coding for growth factors and their receptors and genes involved in cancer and other diseases. International Journal of Oncology 24 1325–1338.[Web of Science][Medline]

Rinn JL, Rozowsky JS, Laurenzi IJ, Petersen PH, Zou K, Zhong W, Gerstein M & Snyder M 2004 Major molecular differences between mammalian sexes are involved in drug metabolism and renal function. Developmental Cell 6 791–800.[CrossRef][Web of Science][Medline]

Risch HA 1998 Hormonal etiology of epithelial ovarian cancer, with a hypothesis concerning the role of androgens and progesterone. Journal of the National Cancer Institute 90 1774–1786.[Abstract/Free Full Text]

Robker RL, Russell DL, Espey LL, Lydon JP, O'Malley BW & Richards JS 2000 Progesterone-regulated genes in the ovulation process: ADAMTS-1 and cathepsin L proteases. PNAS 97 4689–4694.[Abstract/Free Full Text]

Rodriguez GC, Walmer DK, Cline M, Krigman H, Lessey BA, Whitaker RS, Dodge R & Hughes CL 1998 Effect of progestin on the ovarian epithelium of macaques: cancer prevention through apoptosis? Journal of the Society of Gynecologic Investigations 5 271–276.[CrossRef]

Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M et al. 2003 TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34 374–378.[Web of Science][Medline]

Santin A, Zhan F, Bellone S, Palmieri M, Cane S, Bignotti E, Anfossi S, Gokden M, Dunn D, Roman J et al. 2004 Gene expression profiles in primary ovarian serous papillary tumors and normal ovarian epithelium: identification of candidate molecular markers for ovarian cancer diagnosis and therapy. International Journal of Cancer 112 14–25.[CrossRef][Web of Science][Medline]

Sar M & Welsch F 1999 Differential expression of estrogen receptor-beta and estrogen receptor-alpha in the rat ovary. Endocrinology 140 963–971.[Abstract/Free Full Text]

Sasson R, Rimon E, Dantes A, Cohen T, Shinder V, Land-Bracha A & Amsterdam A 2004 Gonadotrophin-induced gene regulation in human granulosa cells obtained from IVF patients. Modulation of steroidogenic genes, cytoskeletal genes and genes coding for apoptotic signalling and protein kinases. Molecular Human Reproduction 10 299–311.[Abstract/Free Full Text]

Saunders PT, Millar MR, Williams K, Macpherson S, Harkiss D, Anderson RA, Orr B, Groome NP, Scobie G & Fraser HM 2000 Differential expression of estrogen receptor- and -β and androgen receptor in the ovaries of marmosets and humans. Biology of Reproduction 63 1098–1105.[Abstract/Free Full Text]

Schildkraut JM, Calingaert B, Marchbanks PA, Moorman PG & Rodriguez GC 2002 Impact of progestin and estrogen potency in oral contraceptives on ovarian cancer risk. Journal of the National Cancer Institute 94 32–38.[Abstract/Free Full Text]

Seltmann M, Horsch M, Drobyshev A, Chen Y, de Angelis MH & Beckers J 2005 Assessment of a systematic expression profiling approach in ENU-induced mouse mutant lines. Mammalian Genome 16 1–10.[CrossRef][Web of Science][Medline]

Shi F & LaPolt PS 2003 Relationship between FoxO1 protein levels and follicular development, atresia, and luteinization in the rat ovary. Journal of Endocrinology 179 195–203.[Abstract]

Shibanuma M, Kuroki T & Nose K 1992 Isolation of a gene encoding a putative leucine zipper structure that is induced by transforming growth factor beta 1 and other growth factors. Journal of Biological Chemistry 267 10219–10224.[Abstract/Free Full Text]

Smyth GK 2004 Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3 3

Tusher VG, Tibshirani R & Chu G 2001 Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98 5116–5121.[Abstract/Free Full Text]

Wang E, Miller LD, Ohnmacht GA, Liu ET & Marincola FM 2000 High-fidelity mRNA amplification for gene profiling. Nature Biotechnology 18 457–459.[CrossRef][Web of Science][Medline]

Woessner JF Jr, Morioka N, Zhu C, Mukaida T, Butler T & LeMaire WJ 1989 Connective tissue breakdown in ovulation. Steroids 54 491–499.[CrossRef][Medline]

Zhang W, Morris QD, Chang R, Shai O, Bakowski MA, Mitsakakis N, Mohammad N, Robinson MD, Zirngibl R, Somogyi E et al. 2004 The functional landscape of mouse gene expression. Journal of Biology 3 21[CrossRef][Medline]

Zheng W, Lu JJ, Luo F, Zheng Y, Feng Y, Felix JC, Lauchlan SC & Pike MC 2000 Ovarian epithelial tumor growth promotion by follicle-stimulating hormone and inhibition of the effect by luteinizing hormone. Gynecologic Oncology 76 80–88.[CrossRef][Web of Science][Medline]

Zhu Y, Brannstrom M, Janson PO & Sundfeldt K 2006 Differences in expression patterns of the tight junction proteins, claudin 1, 3, 4 and 5, in human ovarian surface epithelium as compared to epithelia in inclusion cysts and epithelial ovarian tumours. International Journal of Cancer 118 1884–1891.[CrossRef][Web of Science][Medline]

Zorn KK, Jazaeri AA, Awtrey CS, Gardner GJ, Mok SC, Boyd J & Birrer MJ 2003 Choice of normal ovarian control influences determination of differentially expressed genes in ovarian cancer expression profiling studies. Clinical Cancer Research 9 4811–4818.[Abstract/Free Full Text]

Received in final form 26 February 2008
Accepted 31 March 2008
Made available online as an Accepted Preprint 31 March 2008

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