Expression profiling identifies genes involved in neoplastic transformation of serous ovarian cancer
© Merritt et al; licensee BioMed Central Ltd. 2009
Received: 8 October 2008
Accepted: 23 October 2009
Published: 23 October 2009
The malignant potential of serous ovarian tumors, the most common ovarian tumor subtype, varies from benign to low malignant potential (LMP) tumors to frankly invasive cancers. Given the uncertainty about the relationship between these different forms, we compared their patterns of gene expression.
Expression profiling was carried out on samples of 7 benign, 7 LMP and 28 invasive (moderate and poorly differentiated) serous tumors and four whole normal ovaries using oligonucleotide microarrays representing over 21,000 genes.
We identified 311 transcripts that distinguished invasive from benign tumors, and 20 transcripts that were significantly differentially expressed between invasive and LMP tumors at p < 0.01 (with multiple testing correction). Five genes that were differentially expressed between invasive and either benign or normal tissues were validated by real time PCR in an independent panel of 46 serous tumors (4 benign, 7 LMP, 35 invasive). Overexpression of SLPI and WNT7A and down-regulation of C6orf31, PDGFRA and GLTSCR2 were measured in invasive and LMP compared with benign and normal tissues. Over-expression of WNT7A in an ovarian cancer cell line led to increased migration and invasive capacity.
These results highlight several genes that may play an important role across the spectrum of serous ovarian tumorigenesis.
In Australia, the age-standardized incidence of ovarian cancer was 11 cases per 100,000 women in 2005, and approximately 8 deaths per 100,000 women resulted from this disease in the same time period . The difficulties associated with making improvements in early diagnosis of epithelial ovarian cancer partly result from a lack of knowledge regarding the pathway to tumor development. It is believed that the ovarian surface epithelium (OSE) is a common site for the initiation of ovarian carcinogenesis and most studies have identified genes involved in ovarian tumorigenesis by comparing gene expression profiles with normal OSE [2–8]. The major histological subtypes of epithelial ovarian cancer resemble neoplasms arising from other organs of the female genital tract that are derived from the Mullerian ducts during embryogenesis . Thus it has been suggested that the comparison of Mullerian-appearing ovarian tumors with a tissue exhibiting mesothelial characteristics (OSE) may preferentially identify markers of Mullerian differentiation rather than true markers of neoplastic transformation [10, 11].
To identify genes associated with neoplastic progression in the serous subtype of ovarian tumors, we compared gene expression in tissues that exhibited the spectrum of tumor behavior, namely benign, low malignant potential (LMP) and invasive. Compared with invasive tumors, benign tumors lack evidence of cellular atypia and are non-invasive, while LMP tumors display atypical proliferation but do not usually invade below the basement membrane of the ovarian surface epithelium . Benign and LMP tumors usually result in an excellent prognosis for the patient.
In order to clarify the molecular relationships among the spectrum of serous ovarian tumors, we compared gene expression profiles of 7 LMP and 28 invasive (moderate and poorly differentiated or Grade 2 and Grade 3) serous ovarian tumors with two different reference groups: 7 serous benign ovarian tumors and four normal whole ovary specimens.
Tumor grade and stage details of low malignant potential and invasive tissues analyzed by microarray hybridization
Tumor grade and stage details of low malignant potential and invasive tissues analyzed by real time PCR
RNA was extracted from all tissues for oligonucleotide microarray analysis (set 1 only) and gene expression quantitation by real time PCR. Approximately 100 mg of fresh frozen tumor and normal ovary tissue was used for extraction following the protocol outlined by Newton et al. . All RNA was treated with RNase-free DNase (Roche, Castle Hill, New South Wales, Australia) and only those samples with 28S:18S ratios ≥ 1.7 were selected to ensure high quality. RNA concentrations were estimated using the Nanodrop ND-1000 (NanoDrop Technologies Inc, Wilmington, DE).
Human Genome v 2.1 microarrays representing 21,329 genes (Qiagen Operon oligo set) were purchased from the Prostate Centre at the Vancouver General Hospital. Total RNA (20 μg) from individual tumor tissues and Universal Human Reference RNA (Stratagene, La Jolla, CA) was labeled with Cy5 and Cy3, respectively, using an amino allyl (indirect) method with the CyScribe Post-Labeling kit (Amersham Biosciences, Piscataway, NJ) as per the manufacturer's instructions. Purification of amino allyl-modified and CyDye-labeled cDNA was achieved using the CyScribe GFX Purification kit (Amersham Biosciences). For hybridization, test and reference cDNA were pooled and mixed with 20 μg Cot-1 DNA (Invitrogen, Carlsbad, CA), 20 μg of poly dA (Sigma, St. Louis, MO), 40 μg of Salmon Testis DNA (Sigma) and 60 μl DIG Easy hybridization solution (Roche). Hybridization mix was placed on the microarray slide, coverslipped and incubated overnight at 37°C in humidified hybridization chambers (TeleChem International, Sunnyvale, CA). Microarrays were washed twice in 1× SSC, 0.1% SDS for 5 min, once in 1× SSC and once in 0.1× SSC for 3 min. Microarrays were dried by centrifugation at 100 × g for 5 min and immediately scanned using the GMS-418 confocal scanner (Genetic MicroSystems/Affymetrix, Santa Clara, CA). Images were imported into ImaGene 5 (BioDiscovery, Marine Del Rey, CA) for data extraction. Mean pixel intensities (test to reference) in the Cy5 and Cy3 channels were imported into GeneSpring 7 for analysis (Agilent/Silicon Genetics, Redwood City, CA). All elements were reviewed manually and those of poor quality were removed from subsequent analysis. The data discussed in this publication have been deposited in the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO [GEO: GSE17308].
Microarray data normalization and analysis
Fluorescent intensities were normalized to the mean expression level in each array and for each element (Lowess normalization). Data were additionally normalized to the median for each gene, or expressed relative to the median fluorescence ratios for a particular gene in all 46 samples. Gene expression data were calculated as the ratio of the test to reference fluorescent intensity. To eliminate low quality elements, those with intensities between 50 and 65,000 fluorescent units in the test (signal) channel in 75% of arrays were selected, resulting in 17,244 genes for analyses. Unsupervised hierarchical clustering methods were applied to 3,197 genes exhibiting greatest variance (≥ 0.5 SD) across all samples. Clustering trees were constructed using average linkage algorithms and Pearson's correlation. ANOVA analysis was applied to identify genes that were differentially expressed between all tumor groups and normal ovaries. Student's t-tests were applied to pair-wise comparisons between invasive and LMP or benign tumors. The Benjamini and Hochberg FDR multiple testing correction  was applied to all ANOVA analyses and tests were conducted at a significance level of p < 0.01. Thus, using the FDR multiple testing correction it is estimated that 1 in every 100 genes (1%) will be due to chance.
Quantitative real time PCR
Primer sets were designed to amplify chromosome 6 open reading frame 31 (C6orf31; F: 5'-CACACGACTACATGCCCATC-3'; R: 5'-CGGTGAGGATGGTACAGAGC-3'), glioma tumor suppressor candidate region gene 2 (GLTSCR2; F: 5'-CGGTTCAAGAGCTTCCAGAG-3'; R: 5'-CTGATGGCAGCTACAACTGG-3'), platelet-derived growth factor receptor, alpha polypeptide (PDGFRA; F: 5'-GCGCTGACAGTGGCTACAT-3'; R: 5'-TTCAGAGGTCTGCGAGCTG-3'), secretory leukocyte protease inhibitor (SLPI; F: 5'-GGCTCTGGAAAGTCCTTCAAAGC-3'; R:-5' CATAAGTCACTGGGCACTT CC-3'), TCF3 (E2A) fusion partner (TFPT; F:-5' CGGAAGTGGAGTTTGTGTCA-3'; R: 5'-CTCGTTCACCTGCTCGATCT-3') and wingless-type MMTV integration site family member 7A (WNT7A)  for real time PCR. First strand cDNA was produced from total RNA as previously described . The reaction included 5 μl of a 1/50 dilution of cDNA, 1× QuantiTect SYBR Green PCR Master Mix (Qiagen, Clifton Hill, Victoria, Australia), 0.5 μM of forward and reverse primers and 3 μl of water. PCR reactions were performed using the Corbett RotorGene 6000 (Corbett Research, Sydney, New South Wales, Australia). Gene expression levels were normalized using Beta-2-microglobulin (B2M) . Product quantitation was determined as previously described . Extreme outliers exhibiting expression levels beyond those expected were noted and removed from statistical analyses.
Analysis of formalin-fixed paraffin-embedded sections from serous tumors and normal ovaries previously analyzed by microarrays was performed using antibodies directed against SLPI (NCL-SLPI 1/100 dilution; Novocastra Laboratories Ltd, Newcastle, UK)). The experimental protocol was adapted from manufacturer's instructions. Antigen retrieval was performed by autoclaving in 0.01 M trisodium citrate buffer (pH 6.0) at 105°C. Endogenous peroxidase activity was blocked with 0.5% hydrogen peroxide. Sections were blocked with 10% normal goat serum (Vector Laboratories, Burlingame, CA) and incubated overnight with primary antibody at 4°C. The DAKO Envision plus secondary antibodies (DakoCytomation, Carpinteria, CA) were applied and slides were stained using AEC+ substrate chromogen and counterstained with hematoxylin. Scoring was done by the study pathologist (D.J.P.) who was blinded to the study objectives. The percentage of cells staining and intensity of staining in both epithelial and stromal cells were recorded. Staining positivity was determined when any positive stain was noted. Negative controls (no primary antibody added) were carried out to confirm antibody specificity.
Correlations between microarray and real time PCR experiments were assessed using Spearman's Rank Correlation. To assess differences in mRNA and protein expression of selected genes between the groups, the Kruskal Wallis test was applied to microarray and real time PCR analyses and Fisher's Exact test was used to analyze immunohistochemistry data. A p-Value < 0.05 was considered statistically significant.
WNT7A vector construction and transfection
Full length WNT7A cDNA was amplified by RT-PCR from a malignant ovarian tumor using the primers 5' CGCGAATTCACTATGAACCGGAAAGCGCGGCGCTGCCTG 3' and 5' GCGACCGGTCTTGCACGTGTACATCTCCGTGCGCTC 3'. PCR products were detected and then cloned into the EcoRI and AgeI sites of the pcDNA3.1/V5-His A plasmid (Invitrogen). The PCR product was verified by sequencing. OVCAR-3 cells were seeded at 2 × 105/ml in 60 mm dishes and transfected the following day with 2 μg of plasmid DNA and 8 μl of Metafectine (Biontex Laboratories, Munich, Germany) as per the manufacturer's instructions. As a control, OVCAR-3 cells were transfected with the pcDNA3.1/V5-His A empty vector. Cells were selected in media containing 400 μg/ml geneticin (G-418; Invitrogen) for 3 weeks, and individual stable clones were picked for further analysis. Thereafter, clones were maintained on media containing 300 μg/ml G-418. Increased expression of WNT7A protein was confirmed by western blotting of whole cell lysates with Anti-WNT7A antibody (Q-12, Santa Cruz). Expression of beta actin served as the loading control.
Cell growth assay
Cells were seeded in triplicate at 5,000 per microtiter well and allowed to attach. A separate plate was seeded for each time point at which cells were fixed and growth was estimated by the intensity of sulforhodamine B protein staining . Experiments were repeated in triplicate and the mean ± SE values were determined in Prism 3.0 (GraphPad Software, San Diego, CA).
Scratch wound migration and Matrigel invasion chamber system (MICS) assays
Assays were conducted as described by Pavey et al. . Cells were seeded at 2.5 × 104 cells per well. At 0 and 24 h after introduction of the scratch wound, cells were fixed with methanol and stained with 1% crystal violet for 5 min at RT. Cells were washed with distilled water and allowed to dry before being photographed, and the width of the wound was measured. The experiment was performed on three independent occasions, with triplicate measurements each time. MICS assays were conducted using the BD BioCoat Matrigel Invasion Chamber system (BD Biosciences, Bedford, MA) as described by the manufacturer.
Within the group of invasive tumors, four were classified as of peritoneal rather than ovarian origin, however no distinction between gene expression profiles of ovarian and primary peritoneal tumors was observed by unsupervised clustering or ANOVA analysis (data not shown). Similarly, no differences in gene expression profiles were observed between invasive tumors of different stages (I - IV) and grades (G2 versus G3) when analyzed by unsupervised clustering or supervised ANOVA with Benjamini and Hochberg FDR multiple testing correction applied (data not shown). Further, hierarchical clustering was not affected by epithelial or tumor percentage content (data not shown).
The mean age was lowest for women with normal ovarian samples (49 years, range 41 - 57) followed by those with LMP tumors (52 years, range 25 - 76) compared with benign (61 years, range 46 - 69) and invasive tumors (62 years, range 25 - 80), although these differences did not quite reach statistical significance (p = 0.08, Kruskal-Wallis test). However, age had no observable effect on gene expression profiles in unsupervised clustering analysis or in supervised ANOVA analyses (with Benjamini and Hochberg FDR multiple testing correction applied) and was therefore not considered further.
Genes differentially expressed between serous invasive and low malignant potential tumors
Genbank Acc. No.
Immunoglobulin kappa constant
Ferritin, light polypeptide-like 1
Heterogeneous nuclear ribonucleoprotein A1
DIRAS family, GTP-binding RAS-like 2
Slit homolog 3
Wingless-type MMTV integration site family member 2
Interferon-induced protein 35
Brain specific protein
BRCA2 and CDKN1A interacting protein
Spermatogenesis associated 2-like
Zinc finger protein 222
Zinc finger, matrin type 4
Solute carrier family 28
Cholinergic receptor, nicotinic, alpha 9
Surfactant, pulmonary-associated protein A2
G protein-coupled receptor 108
Olfactory receptor, family 10, subfamily C, member 1
Solute carrier family 6, member 9
Endothelial differentiation, lysophosphatidic acid G-protein-coupled receptor, 4
We were interested in investigating markers associated with the development of an invasive serous tumor and therefore a total of six genes were selected for further analysis, including two genes from the normal ovary vs invasive tumors comparison (SLPI, GLTSCR2), two genes from the benign vs invasive comparison (WNT7A, PDGFRA) and two genes from the overlap of these two analyses (TFPT, C6orf31) (Fig. 1C). Analyses of the microarray data for each of the six selected genes showed significant differences in expression between the groups (Kruskal Wallis p < 0.05, Fig. 1D).
Differences in gene expression as judged from the microarray data were compared with those from real time PCR experiments for these six genes using RNA extracted from the initial 46 tissues. Strong correlations were found between the microarray and real time PCR data for four of the candidate genes, namely C6orf31, GLTSCR2, PDGFRA and SLPI (Spearman's rank correlation coefficient ≥ 0.6, p < 0.001). Although similar trends in WNT7A expression were measured using microarray and real time PCR analyses, a poor correlation was observed (Spearman's correlation = 0.4) due to the large difference in scale of results between the two techniques. The microarray data minimized apparent differences in gene expression, possibly as they are not optimized on a single gene basis. The real time PCR data did not confirm the microarray analysis for the sixth gene (TFPT).
Validation of selected genes in an independent set of 47 tissues by real time PCR
Fold change (± SD)1
Kruskal Wallis p-Values
Genbank Acc. No.
0.37 ± 0.52
0.19 ± 0.49
NS, p = 0.13
p < 0.01
p < 0.05
0.51 ± 0.61
0.17 ± 0.22
NS, p = 0.17
p < 0.01
p < 0.01
0.31 ± 0.51
0.15 ± 0.22
P < 0.05
p < 0.01
NS, p = 0.13
3.86 ± 1.80
5.70 ± 4.20
P < 0.05
p < 0.05
NS, p = 0.95
180.0 ± 250.0
150.0 ± 205.0
P < 0.01
p < 0.001
NS, p = 0.67
Cytoplasmic expression of SLPI as detected by immunohistochemistry in 411 tissues evaluated by microarray analysis
NS, p-Value = 1.0
Expression profiling data of WNT family members
1.02 ± 0.16
1.24 ± 0.22
1.03 ± 0.23
0.94 ± 0.28
0.94 ± 0.31
1.17 ± 0.20
1.83 ± 0.44
0.94 ± 0.24
1.46 ± 0.74
1.10 ± 0.68
1.03 ± 0.37
0.90 ± 0.38
0.01 ± 0.01
0.08 ± 0.22
2.28 ± 5.64
1.32 ± 2.64
1.07 ± 0.21
1.20 ± 0.35
1.01 ± 0.10
0.95 ± 0.23
0.97 ± 0.13
1.10 ± 0.24
0.94 ± 0.15
0.98 ± 0.20
1.00 ± 0.08
1.14 ± 0.16
1.10 ± 0.26
0.98 ± 0.20
0.91 ± 0.62
1.36 ± 0.44
1.10 ± 0.33
0.79 ± 0.35
1.13 ± 0.22
0.91 ± 0.16
0.96 ± 0.23
1.05 ± 0.19
0.87 ± 0.57
1.16 ± 0.16
0.93 ± 0.11
1.05 ± 0.19
0.61 ± 0.19
0.60 ± 0.13
0.71 ± 0.28
1.26 ± 0.47
0.52 ± 0.69
0.09 ± 0.23
2.08 ± 3.51
2.02 ± 4.72
1.27 ± 0.40
1.29 ± 0.33
1.55 ± 1.44
1.03 ± 0.43
1.14 ± 0.13
1.06 ± 0.20
1.09 ± 0.34
0.99 ± 0.31
1.00 ± 0.12
0.92 ± 0.49
0.99 ± 0.35
1.15 ± 0.71
1.23 ± 0.17
1.19 ± 0.28
1.02 ± 0.19
0.95 ± 0.20
0.54 ± 0.62
0.65 ± 0.63
1.04 ± 1.17
0.73 ± 0.69
0.91 ± 0.19
1.00 ± 0.20
0.96 ± 0.10
0.97 ± 0.27
1.01 ± 0.38
0.74 ± 0.36
1.22 ± 0.38
1.02 ± 0.41
To determine whether WNT7A enhances tumorigenic potential, in vitro functional studies were applied to test the influence of WNT7A on migration and invasion capabilities in OVCAR-3 cells. Scratch wound assays showed that WNT7A over-expressing clones displayed a statistically significant increase in wound healing abilities as compared with control clones (Fig. 3C). WNT7A over-expressing clones also had significantly greater invasion ability than their mock-transfected counterparts (Fig. 3D).
The aim of the current study was to identify potential markers of serous ovarian neoplastic transformation. We applied gene expression profiling to evaluate the molecular relationships among serous ovarian tumors exhibiting distinct differences in tumor behavior (LMP and invasive tumors) and compared these with two reference groups, benign serous ovarian tumors and normal ovarian tissue. Unsupervised hierarchical clustering analysis demonstrated that LMP and invasive tumors exhibited similar gene expression profiles that were distinct from normal ovarian and benign tumor samples. A Student's t-test with the Benjamini and Hochberg FDR multiple testing correction applied confirmed that the gene expression profiles of invasive and LMP tumors were similar, with only 20 genes differing (at p < 0.01) between the two tumor types. This varied substantially from the number of genes that differed between invasive and benign tumors (311 transcripts at p < 0.01).
The finding of similar gene expression profiles in LMP and invasive tumors was unexpected as it is widely accepted that these tumors arise via distinct pathways [20, 21]. Our results are in agreement, however, with previous findings in serous ovarian tumors [22, 23] but contrast with other studies [21, 24–26]. In support of the current findings of similar gene expression profiles in LMP and invasive tumors, minimal differences in gene expression profiles have been reported previously in studies that compared well-differentiated with poorly-differentiated serous invasive tumors [6, 26]. Thus, the evidence currently available regarding serous ovarian cancer highlights a relatively small number of genes that may be associated with the acquisition of invasive potential. This is not unlike some corresponding findings for breast cancer where similar gene expression profiles were observed among distinct pathological stages - premalignant atypical ductal hyperplasia, pre-invasive ductal carcinoma in situ and invasive ductal carcinoma . Further, our data may also suggest that benign ovarian lesions are a distinct entity to LMP and invasive tumors, given the differences or similarities in gene expression profile between the tissues.
We included both normal ovarian tissue and benign tumor samples for comparison with LMP and invasive serous tumors. In contrast, the majority of previous studies compared ovarian cancer (various subtypes) with the ovarian surface epithelium [2–8, 24]. Contrary to our expectations, there was much overlap between differentially expressed transcripts from invasive tumor versus 'normal' tissue, regardless of whether benign tumors or normal ovaries served as the reference. Since there were significant differences between frankly malignant tumors and the above-mentioned controls, these comparisons could have minimized the differences between benign tumors and normal ovarian tissues. It is also possible that similar expression profiles in benign tumors and normal ovaries may reflect the stromal content in these samples.
From the analyses of global gene expression profiles, we focused on six genes (both novel and well-known) that were identified as differentially expressed between either normal ovarian tissue or benign tumors and invasive ovarian cancer. Five of these genes were validated as having differential expression in an independent set of serous tumors. Increased expression of SLPI was measured in LMP and invasive tumors at the mRNA and protein level. These findings support previous studies showing over-expression of SLPI in serous as well as other subtypes of ovarian cancer [28–31].
Decreased expression of C6orf31, PDGFRA and GLTSCR2 was measured in LMP and invasive tumors compared with high mRNA levels in benign tumors and normal ovarian tissues. To our knowledge, this is the first report of the potential involvement of C6orf31 (located on chromosome 6p21) in cancer development. PDGFRA encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor family. Similar observations of decreased PDGFRA expression in invasive serous tumors compared with benign tumors or normal ovaries have been reported in previous microarray studies [22, 32]. In contrast, PDGFRA was also identified as overexpressed in a poor prognosis gene expression signature in ovarian cancer and it was suggested that PDGFRA could be associated with the occurrence of an epithelial/mesenchymal transition .
A trend of decreasing GLTSCR2 expression with increasingly aggressive tumor behavior was observed in the current study. Previous results demonstrated that down-regulation of GLTSCR2 directly enhanced degradation of wild-type PTEN in breast cancer (MCF7) cells . Thus it was hypothesized that dysfunctional GLTSCR2 could lead to activation of PI3K signaling via rapid turnover of PTEN . In serous ovarian cancer, amplification of multiple components of the PI3K pathway have been reported [36, 37] although PTEN mutation has rarely been identified .
The finding of increased WNT expression in LMP and invasive tumors, in particular WNT7A, WNT3 and WNT8A, was of interest because it has been hypothesized that aberrant Wnt activation is associated with the initiation and growth of cancer in tissues since Wnt signaling is normally involved in growth and patterning . Wnt activation, specifically Wnt7a, is required for the development of the female reproductive tract from the Mullerian ducts into the oviduct (fallopian tubes), uterus, cervix and upper vagina during murine embryogenesis [40, 41]. WNT7A has previously been shown to play a role in the migration of normal cornea cells in response to wounding . The function of WNT7A has not previously been examined in ovarian cancer. In the current study, overexpression of WNT7A in the ovarian cancer cell line OVCAR-3 resulted in increased cell migration and invasive capacity. The data presented here is limited by the use of a single cell line. Further analyses of additional ovarian cancer cell lines with either overexpression or ablation of WNT7A would be needed to precisely identify its role in ovarian cancer progression. Likewise, the role of WNT3 and WNT8A in ovarian cancer needs to be further addressed. The data presented here may suggest a widespread activation of the WNT pathway in LMP and invasive tumors, although initial pathway analysis did not indicate this to be the case. A preliminary study reported possible activation of canonical Wnt signaling in high grade serous tumors  but another study did not support this suggestion .
This study showed that an unexpectedly small number of genes distinguished serous LMP and invasive tumors. Further studies of these genes may highlight those with an important role in the acquisition of invasive potential and could lead to the development of improved therapies for ovarian cancer. Molecular profiling of serous ovarian tumors exhibiting differences in behavior identified several genes potentially involved in neoplastic transformation. In vitro functional studies of one of these genes suggested a role for WNT7A in promoting migration and invasion in ovarian cancer cells. Further understanding of how serous ovarian tumors develop will contribute towards an improvement in strategies for the prevention and early detection of ovarian cancer.
This study formed part of the Australian Cancer Study. We are thankful to all of the study participants, without whom this study would not have been possible. We are grateful to the staff of the Royal Brisbane and Women's Hospital for allowing us to collect tissue samples used in this study. Tissues were collected with the assistance of Kaltin Ferguson. Additional technical assistance in the laboratory was kindly provided by Julie Pedley. We gratefully acknowledge the contribution of the study nurses and research assistants involved in the Australian Cancer Study, and particularly Susan Brown who recruited many of the study participants from the Royal Brisbane and Women's Hospital. Financial support was provided by The National Health and Medical Research Council of Australia Program Grant Number 199600. MAM was supported by an Australian Postgraduate Award; PMW was funded by a fellowship from the Cancer Council Queensland.
- AIHW, ACCR: Cancer in Australia: an overview, 2008. AIHW, Cancer Series no 46, Cat no CAN 42. Canberra. 2008, 10.1016/S0378-1119(99)00342-X.Google Scholar
- Schummer M, Ng WV, Bumgarner RE, Nelson PS, Schummer B, Bednarski DW, Hassell L, Baldwin RL, Karlan BY, Hood L: Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas. Gene. 1999, 238 (2): 375-385. 10.1038/sj.onc.1205785.View ArticlePubMedGoogle Scholar
- Matei D, Graeber TG, Baldwin RL, Karlan BY, Rao J, Chang DD: Gene expression in epithelial ovarian carcinoma. Oncogene. 2002, 21 (41): 6289-6298. 10.1038/sj.onc.1205785.View ArticlePubMedGoogle Scholar
- Shridhar V, Lee J, Pandita A, Iturria S, Avula R, Staub J, Morrissey M, Calhoun E, Sen A, Kalli K, et al: Genetic analysis of early- versus late-stage tumors. Cancer Research. 2001, 61: 5895-5904. 10.1073/pnas.98.3.1176.PubMedGoogle Scholar
- Welsh JB, Zarrinkar PP, Sapinoso LM, Kern SG, Behling CA, Monk BJ, Lockhart DJ, Burger RA, Hampton GM: Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci USA. 2001, 98 (3): 1176-1181. 10.1073/pnas.98.3.1176.View ArticlePubMedPubMed CentralGoogle Scholar
- Jazaeri AA, Yee CJ, Sotiriou C, Brantley KR, Boyd J, Liu ET: Gene expression profiles of BRCA1-linked, BRCA2-linked, and sporadic ovarian cancers. J Natl Cancer Inst. 2002, 94 (13): 990-1000.View ArticlePubMedGoogle Scholar
- Bayani J, Brenton JD, Macgregor PF, Beheshti B, Albert M, Nallainathan D, Karaskova J, Rosen B, Murphy J, Laframboise S, et al: Parallel analysis of sporadic primary ovarian carcinomas by spectral karyotyping, comparative genomic hybridization, and expression microarrays. Cancer Res. 2002, 62 (12): 3466-3476. 10.1002/ijc.20408.PubMedGoogle Scholar
- Santin AD, Zhan F, Bellone S, Palmieri M, Cane S, Bignotti E, Anfossi S, Gokden M, Dunn D, Roman JJ, et al: Gene expression profiles in primary ovarian serous papillary tumors and normal ovarian epithelium: Identification of candidate molecular markers for ovarian cancer diagnosis and therapy. Int J Cancer. 2004, 112 (1): 14-25. 10.1006/gyno.1998.5275.View ArticlePubMedGoogle Scholar
- Dubeau L: The cell of origin of ovarian epithelial tumors and the ovarian surface epithelium dogma: does the emperor have no clothes?. Gynecol Oncol. 1999, 72 (3): 437-442. 10.1016/j.humpath.2004.04.014.View ArticlePubMedGoogle Scholar
- Drapkin R, Crum CP, Hecht JL: Expression of candidate tumor markers in ovarian carcinoma and benign ovary: evidence for a link between epithelial phenotype and neoplasia. Hum Pathol. 2004, 35 (8): 1014-1021. 10.1158/0008-5472.CAN-04-3924.View ArticlePubMedGoogle Scholar
- Drapkin R, von Horsten HH, Lin Y, Mok SC, Crum CP, Welch WR, Hecht JL: Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res. 2005, 65 (6): 2162-2169. 10.1158/0008-5472.CAN-04-3924.View ArticlePubMedGoogle Scholar
- Chen V, Ruiz B, Killeen J, Cote T, Wu X, Correa C: Pathology and classification of ovarian tumors. Cancer Supplement. 2003, 97 (10): 2631-2642. 10.1002/ijc.21823.View ArticleGoogle Scholar
- Newton TR, Parsons PG, Lincoln DJ, Cummings MC, Wyld DK, Webb PM, Green AC, Boyle GM: Expression profiling correlates with treatment response in women with advanced serous epithelial ovarian cancer. Int J Cancer. 2006, 119 (4): 875-883. 10.1002/sim.4780090710.View ArticlePubMedGoogle Scholar
- Hochberg Y, Benjamini Y: More powerful procedures for multiple significance testing. Stat Med. 1990, 9 (7): 811-818. 10.1073/pnas.97.23.12776.View ArticlePubMedGoogle Scholar
- Calvo R, West J, Franklin W, Erickson P, Bemis L, Li E, Helfrich B, Bunn P, Roche J, Brambilla E, et al: Altered HOX and WNT7A expression in human lung cancer. Proc Natl Acad Sci USA. 2000, 97 (23): 12776-12781. 10.1038/sj.onc.1207563.View ArticlePubMedPubMed CentralGoogle Scholar
- Pavey S, Johansson P, Packer L, Taylor J, Stark M, Pollock PM, Walker GJ, Boyle GM, Harper U, Cozzi SJ, et al: Microarray expression profiling in melanoma reveals a BRAF mutation signature. Oncogene. 2004, 23 (23): 4060-4067. 10.1093/nar/29.9.e45.View ArticlePubMedGoogle Scholar
- Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29 (9): e45-10.1093/jnci/82.13.1107.View ArticlePubMedPubMed CentralGoogle Scholar
- Skehan P, Storeng R, Scudiero D, Monks A, McMahon J, Vistica D, Warren JT, Bokesch H, Kenney S, Boyd MR: New colorimetric cytotoxicity assay for anticancer-drug screening. J Natl Cancer Inst. 1990, 82 (13): 1107-1112. 10.1097/01.cmr.0000222589.30117.f2.View ArticlePubMedGoogle Scholar
- Pavey S, Zuidervaart W, van Nieuwpoort F, Packer L, Jager M, Gruis N, Hayward N: Increased p21-activated kinase-1 expression is associated with invasive potential in uveal melanoma. Melanoma Res. 2006, 16 (4): 285-296. 10.1097/01.cmr.0000222589.30117.f2.View ArticlePubMedGoogle Scholar
- Shih L, Kurman R: Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. Am J Pathol. 2004, 164 (5): 1511-1518.View ArticlePubMedPubMed CentralGoogle Scholar
- Singer G, Kurman RJ, Chang HW, Cho SK, Shih Ie M: Diverse tumorigenic pathways in ovarian serous carcinoma. Am J Pathol. 2002, 160 (4): 1223-1228. 10.1186/1476-4598-3-27.View ArticlePubMedPubMed CentralGoogle Scholar
- Warrenfeltz S, Pavlik S, Datta S, Kraemer ET, Benigno B, McDonald JF: Gene expression profiling of epithelial ovarian tumours correlated with malignant potential. Mol Cancer. 2004, 3 (1): 27-44. 10.1016/j.ygyno.2004.11.039.View ArticlePubMedPubMed CentralGoogle Scholar
- Gilks CB, Vanderhyden BC, Zhu S, Rijn van de M, Longacre TA: Distinction between serous tumors of low malignant potential and serous carcinomas based on global mRNA expression profiling. Gynecol Oncol. 2005, 96 (3): 684-694. 10.1158/0008-5472.CAN-05-2240.View ArticlePubMedGoogle Scholar
- Bonome T, Lee JY, Park DC, Radonovich M, Pise-Masison C, Brady J, Gardner GJ, Hao K, Wong WH, Barrett JC, et al: Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. Cancer Res. 2005, 65 (22): 10602-10612. 10.1038/sj.onc.1208298.View ArticlePubMedGoogle Scholar
- Meinhold-Heerlein I, Bauerschlag D, Hilpert F, Dimitrov P, Sapinoso LM, Orlowska-Volk M, Bauknecht T, Park TW, Jonat W, Jacobsen A, et al: Molecular and prognostic distinction between serous ovarian carcinomas of varying grade and malignant potential. Oncogene. 2005, 24 (6): 1053-1065. 10.1200/JCO.2005.02.2541.View ArticlePubMedGoogle Scholar
- Sieben NL, Oosting J, Flanagan AM, Prat J, Roemen GM, Kolkman-Uljee SM, van Eijk R, Cornelisse CJ, Fleuren GJ, van Engeland M: Differential Gene Expression in Ovarian Tumors Reveals Dusp 4 and Serpina 5 as Key Regulators for Benign Behavior of Serous Borderline Tumors. J Clin Oncol. 2005, 23 (29): 7257-7264. 10.1073/pnas.0931261100.View ArticlePubMedGoogle Scholar
- Ma XJ, Salunga R, Tuggle JT, Gaudet J, Enright E, McQuary P, Payette T, Pistone M, Stecker K, Zhang BM, et al: Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA. 2003, 100 (10): 5974-5979. 10.1038/sj.bjc.6603346.View ArticlePubMedPubMed CentralGoogle Scholar
- Biade S, Marinucci M, Schick J, Roberts D, Workman G, Sage EH, O'Dwyer PJ, Livolsi VA, Johnson SW: Gene expression profiling of human ovarian tumours. Br J Cancer. 2006, 95: 1092-1100. 10.1038/sj.bjc.6603346.View ArticlePubMedPubMed CentralGoogle Scholar
- Hough CD, Cho KR, Zonderman AB, Schwartz DR, Morin PJ: Coordinately up-regulated genes in ovarian cancer. Cancer Res. 2001, 61 (10): 3869-3876.PubMedGoogle Scholar
- Hough CD, Sherman-Baust CA, Pizer ES, Montz FJ, Im DD, Rosenshein NB, Cho KR, Riggins GJ, Morin PJ: Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Res. 2000, 60: 6281-6287. 10.1046/j.1525-1438.2001.01062.x.PubMedGoogle Scholar
- Shigemasa K, Tanimoto H, Underwood LJ, Parmley TH, Arihiro K, Ohama K, O'Brien TJ: Expression of the protease inhibitor antileukoprotease and the serine protease stratum corneum chymotryptic enzyme (SCCE) is coordinated in ovarian tumors. Int J Gynecol Cancer. 2001, 11 (6): 454-461. 10.1038/sj.onc.1207959.View ArticlePubMedGoogle Scholar
- Donninger H, Bonome T, Radonovich M, Pise-Masison CA, Brady J, Shih JH, Barrett JC, Birrer MJ: Whole genome expression profiling of advance stage papillary serous ovarian cancer reveals activated pathways. Oncogene. 2004, 23 (49): 8065-8077. 10.1200/JCO.2004.04.070.View ArticlePubMedGoogle Scholar
- Spentzos D, Levine DA, Ramoni MF, Joseph M, Gu X, Boyd J, Libermann TA, Cannistra SA: A gene expression signature with independent prognostic significance in epithelial ovarian cancer. J Clin Oncol. 2004, 22 (23): 4700-4710. 10.1074/jbc.C400377200.View ArticlePubMedGoogle Scholar
- Okahara F, Ikawa H, Kanaho Y, Maehama T: Regulation of PTEN phosphorylation and stability by a tumor suppressor candidate protein. J Biol Chem. 2004, 279 (44): 45300-45303. 10.1042/BST0320343.View ArticlePubMedGoogle Scholar
- Maehama T, Okahara F, Kanaho Y: The tumour suppressor PTEN: involvement of a tumour suppressor candidate protein in PTEN turnover. Biochem Soc Trans. 2004, 32 (Pt 2): 343-347. 10.1016/S0093-7754(01)90290-8.View ArticlePubMedGoogle Scholar
- Mills GB, Lu Y, Fang X, Wang H, Eder A, Mao M, Swaby R, Cheng KW, Stokoe D, Siminovitch K, et al: The role of genetic abnormalities of PTEN and the phosphatidylinositol 3-kinase pathway in breast and ovarian tumorigenesis, prognosis, and therapy. Semin Oncol. 2001, 28 (5 Suppl 16): 125-141. 10.1016/S0093-7754(01)90290-8.View ArticlePubMedGoogle Scholar
- Nakayama K, Nakayama N, Kurman RJ, Cope L, Pohl G, Samuels Y, Velculescu VE, Wang TL, Shih Ie M: Sequence Mutations and Amplification of PIK3CA and AKT2 Genes in Purified Ovarian Serous Neoplasms. Cancer Biol Ther. 2006, 5 (7): 779-785.View ArticlePubMedGoogle Scholar
- Obata K, Morland SJ, Watson RH, Hitchcock A, Chenevix-Trench G, Thomas EJ, Campbell IG: Frequent PTEN/MMAC mutations in endometrioid but not serous or mucinous epithelial ovarian tumors. Cancer Res. 1998, 58 (10): 2095-2097. 10.1038/35077219.PubMedGoogle Scholar
- Taipale J, Beachy PA: The Hedgehog and Wnt signalling pathways in cancer. Nature. 2001, 411 (6835): 349-354. 10.1038/nrg1225.View ArticlePubMedGoogle Scholar
- Kobayashi A, Behringer RR: Developmental genetics of the female reproductive tract in mammals. Nat Rev Genet. 2003, 4 (12): 969-980. 10.1038/27221.View ArticlePubMedGoogle Scholar
- Parr BA, McMahon AP: Sexually dimorphic development of the mammalian reproductive tract requires Wnt-7a. Nature. 1998, 395 (6703): 707-710. 10.1074/jbc.M500374200.View ArticlePubMedGoogle Scholar
- Lyu J, Joo CK: Wnt-7a up-regulates matrix metalloproteinase-12 expression and promotes cell proliferation in corneal epithelial cells during wound healing. J Biol Chem. 2005, 280 (22): 21653-21660. 10.1016/S0090-8258(02)00015-X.View ArticlePubMedGoogle Scholar
- Lee CM, Shvartsman H, Deavers MT, Wang SC, Xia W, Schmandt R, Bodurka DC, Atkinson EN, Malpica A, Gershenson DM, et al: Beta-catenin nuclear localization is associated with grade in ovarian serous carcinoma. Gynecol Oncol. 2003, 88 (3): 363-368. 10.1016/j.ejca.2005.01.022.View ArticlePubMedGoogle Scholar
- Kildal W, Risberg B, Abeler VM, Kristensen GB, Sudbo J, Nesland JM, Danielsen HE: beta-catenin expression, DNA ploidy and clinicopathological features in ovarian cancer: a study in 253 patients. Eur J Cancer. 2005, 41 (8): 1127-1134. 10.1016/j.ejca.2005.01.022.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/9/378/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.