Gene expression profiling of epithelial ovarian tumours correlated with malignant potential
© Warrenfeltz et al; licensee BioMed Central Ltd. 2004
Received: 25 June 2004
Accepted: 07 October 2004
Published: 07 October 2004
Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression.
Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location.
Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events.
Epithelial ovarian cancer is the fifth leading cause of death for women in the United States . Although early stage ovarian cancer can be effectively treated, symptoms of early disease are sufficiently vague that accurate diagnosis is often delayed until the cancer has progressed into more advanced stages . Treatment of early staged tumours (I through IIa) is associated with a 5-year survival rate of approximately 95% while survival rates drop to less than 30% when diagnosis is delayed until later stages (stage IIb through IV). To improve these statistics, effective early diagnosis and treatment strategies must be developed. Further knowledge of the genes and gene functional pathways involved in ovarian cancer are needed in order to develop these strategies.
Microarray technology has revolutionised the study of gene function by providing "snapshots" of global gene expression patterns from different normal and diseased tissues over multiple stages of development. Nowhere has the impact of this technology been more pronounced than in the field of cancer biology where gene expression profiling has been successfully used to objectively classify tumours and, in some instances, identify novel tumour sub-types . Microarray analyses have also been instrumental in the elucidation of new biological pathways that may be involved in tumour development, as well as, in the identification of new biomarkers of the disease and potential targets of therapeutic intervention.
Previous microarray studies of ovarian cancers have focused on the characterisation of differences between normal ovarian epithelial cells (and cell lines) and various types and stages of ovarian tumours [4–10]. In this study, we focus on characterising differences between benign adenomas, borderline tumours of low malignant potential and malignant adenocarcinomas in order to identify changes associated with the acquisition of malignancy and to avoid the technical difficulties associated with obtaining sufficient amounts of normal ovarian surface epithelium. The ovarian tumour tissue samples used in these microarray studies were chosen to accurately represent the range of malignant potential observed clinically.
We report here the results of applying clustering and statistical analyses to the microarray expression profiles of 18 ovarian tumours. Our findings indicate that gene expression profiling distinguished properly classify 92% of tumours in this study as benign or malignant. Samples taken from ovarian cancer patients who had been treated with chemotherapy prior to surgery were found not to cluster as a distinct group but rather with either the benign or malignant (not pre-treated) tumours. Chemotherapy patients whose tumours clustered with the benign group remained disease free for the duration of the study as evidenced by continued normal serum CA-125 levels. Profiling the functional categories of co-ordinately expressed genes revealed significant correlation between increased malignant potential and loss of IGF binding proteins, and cell adhesion molecules. In several instances co-ordinately expressed genes sharing functional categories also correlated with chromosomal location.
Unsupervised clustering of gene expression profiles can reliably identify ovarian tumour types
Tissue Sample Information
Available Histological Information
Serous papillary adenocarcinoma
Serous papillary adenocarcinoma
Serous papillary adenocarcinoma
Serous papillary adenocarcinoma
Serous papillary adenocarcinoma
Serous papillary/endometroid adenocarcinoma
Serous papillary carcinoma
Serous papillary carcinoma
Serous papillary carcinoma
Serous papillary adenocarcinoma
Serous papillary adenocarcinoma
Since many of the genes in our data set display no differential expression across the 13 tumours, their contribution to the SOM is negligible and can be considered noise. Removing genes whose expression pattern displayed insignificant variation (low standard deviation) across all samples, we reduced the data set to1000 probe sets. After removing probe sets representing the same gene, the reduced data set contained expression values representing 700 genes. The SOM and UHC of the reduced data set yielded identical clusters to those obtained using the entire data set (Figure 1a and 1b).
To determine the genes most highly correlated with each cluster identified by the SOM analysis, we performed a marker analysis (Figure 1c) on the reduced set of 700 genes. Marker analysis helps the user discover which genes are most closely correlated with a cluster and provides a measure of how significant that correlation is for each gene. Marker analysis measures the contribution of each gene to the SOM groupings based on a signal to noise ratio calculated from the difference in each gene's mean expression scaled by the sum of the standard deviations across all samples. To avoid having one cluster containing only one sample in the marker analysis, we grouped clusters cluster 1 and cluster 2 containing the borderline samples together, creating three clusters (Figure 1c) consisting of the benign adenomas and c79 (SOM_a), borderline adenocarcinomas (SOM_b), and the malignant adenocarcinomas (SOM_c). Genes highly correlated with each SOM group were expressed strongly in the tumour type associated with that SOM group and poorly expressed in the other SOM groups. It is interesting to note that the 10 genes highly correlated with SOM_a were expressed at intermediate levels in borderline tumours. Similarly, the 10 genes highly correlated with SOM_b were expressed at intermediate levels in the adenocarcinomas of SOM_c.
Gene expression profiles are correlated with recurrence
Five of the cancer patients in our study were treated with chemotherapy prior to surgery. We added the microarray profiles of these patient samples to our analysis in order to determine if they would cluster into a new distinct group or into one or more of the existing groups. The SOM (Figure 1d) and UHC (Figure 1e) clusters resulting from the analysis of all data (12,590 expression values) from all eighteen samples into four clusters differ only in the stratification of the borderline samples. The addition of the five samples from patients who received chemotherapy prior to surgery did not change the cluster assignments of the original thirteen samples. Clustering of the reduced set of 700 genes (see above), resulted in the same patterns of clustering as determined using the entire set of 12,590 expression values (Figure 1d and 1e). Interestingly, the five samples from patients pre-treated with chemotherapy did not cluster together in a distinct group but rather were dispersed among the existing four clusters. Samples cc_29 and cc_76 clustered with the malignant adenocarcinomas, while samples cc_36 and cc_9 clustered with the benign adenomas. Sample cc_94 clustered with the borderline tumours.
Significant differences in gene expression are associated with different ovarian tumour types
Highest 15 statistically significant genes via ANOVA analysis, their fold change and p-values.
ubiquitin-conjugating enzyme E2C
flap structure-specific endonuclease 1
tumour suppressing subtransferable candidate 3
forkhead box M1
chromosome 20 open reading frame 1
aldehyde dehydrogenase 1 family, member A1
centromere protein A, 17kDa
ubiquitin carrier protein
isocitrate dehydrogenase 2 (NADP+), mitochondrial
WAS protein family, member 3
FYN oncogene related to SRC, FGR, YES
CASP8 and FADD-like apoptosis regulator
Genes expression changes verified with quantitative RT-PCR.
ubiquitin-conjugating enzyme E2C
cadherin 2, type 1, N-cadherin
oviductal glycoprotein 1, 120 kDa
Functionally related genes display correlated changes in expression between benign malignant tumours
Microarray profiles of ovarian tumours are of potential diagnostic and prognostic significance
Gene expression profiling via microarray technology has previously been shown to be an effective tool for the objective classification of established tumour types [18, 19] and in some instances, for the identification of previously unrecognized tumour sub-types . Applied to ovarian cancer, gene expression profiling has aided in distinguishing clear cell carcinomas [8, 9], characterizing advanced stage ovarian cancer [5, 6], and identifying genes differentially expressed between normal and cancerous ovarian tissue [4, 7, 10]. The experiments presented here were designed to elucidate gene expression changes in ovarian tumours of differing malignant potential. In many instances, genes that we identified as differentially expressed across malignant potential were previously determined to be differentially expressed between normal and cancerous ovarian tissue including ERBB3 , ubiquitin carrier protein, and E-cadherin . We also correctly classified 92% of tumours from patients who did not receive chemotherapy prior to surgery into their proper histopathological group. These results are consistent with earlier findings and indicate that gene expression profiling can effectively distinguish between malignant and benign ovarian tumours.
One particularly promising result emerging from our study is that expression profiling may be useful in predicting recurrence in patients treated with chemotherapy prior to surgery. We find that the microarray patterns of ovarian adenocarcinomas obtained from patients treated with chemotherapy prior to surgery clustered either with the benign tumours or with the malignant adenocarcinomas. Serum CA-125 levels indicate that patients whose samples clustered with the benign tumours have remained disease free for more than 3 years after surgery while those patients whose samples clustered with the malignant tumours recurred within 2 years of the initial treatment. Clearly, the testing of additional patient samples will be needed before definitive conclusions can be drawn. However, the preliminary results are consistent with the hypothesis that gene expression profiles of samples removed on the day of surgery may predict recurrence and would therefore be an indicator of the long-term effectiveness of chemotherapy administered to patients prior to surgery.
Expression profiles indicate that borderline tumours are not a distinct disease
Our microarray data are, in general, most consistent with the hypothesis that borderline ovarian tumours represent an intermediate stage between the benign and malignant tumours. Borderline tumours of the ovary display many but not all characteristics of malignancy including nuclear atypia and increased mitotic count, usually in the absence of stromal invasion [21–24]. Whether the borderline tumour is a precursor to the fully malignant ovarian carcinoma or a disease distinct from invasive carcinomas is a topic that has been debated since the International Federation of Gynaecologic Oncology added the borderline tumour to the classification of ovarian tumours in 1972. Distinct disease states are expected to show discrete gene expression patterns when analysed by microarray . Our analysis identified only 5 genes (Figure 3e) with increased expression distinctly correlated with borderline tumours. On the other hand, for 40 of the 163 genes displaying a significant change in expression between benign and malignant ovarian tumours, borderline tumours display an intermediate expression level (Figure 3c). In all other cases, (118 genes) borderline expression mimicked either the benign adenoma (102 genes) or malignant adenocarcinoma (16) tumours. Thus, for these genes, borderline tumours appear to be a transitional state between the benign and malignant state. The five genes identified as characteristic of borderline tumours (Figure 3e) may constitute a reliable marker of borderline tumours. Interestingly, two of these genes, AGR2 and NPTX2, are physically linked to one another, mapping to p21.3 on chromosome 7.
Many genes displaying altered patterns of expression between benign and malignant ovarian tumours are genetically linked
Co-ordinately expressed genes sharing chromosomal location
Expression in cancer
Regulation of cell growth
Regulation of cell growth
Regulation of cell growth
Calcium ion transport
We also observed a co-ordinated increase in gene expression of physically linked genes in the malignant samples in several cases (Table 4). These changes may have been due to regional duplication or amplification events. Examples of such events have been previously documented in cancer cells [33–35]. It is also possible that at least some of these co-ordinated increases in gene expression are the consequence of regional hypomethylation events resulting in a more open chromatin configuration and a consequent increase in transcription factor accessibility. Genes located in proximity to transposable element sequences may be more prone to such epigenetic events .
In a few instances genes that were physically linked displayed opposing changes in gene expression between benign and malignant tumours (Table 4). It is possible that these disparate changes were due to independent mutational events or, perhaps more likely, to a regional relaxation of chromatin structure that permitted increased access of both positive and negative transcription factors.
Our finding that a number of the genes displaying a significant difference in expression among malignant and non-malignant tumours indicates that some caution must be taken in the functional interpretation of microarray results. For example, significant changes in the expression of only one gene in a physically linked group may be of functional significance although correlated changes in gene expression may result from a regional effect.
Malignant ovarian tumours display expression profiles consistent with previously established features of cancer cells
Two common features of malignant cancer cells are increased cell proliferation and loss of cell adhesion . Consistent with these general features, we found that the majority of differentially expressed genes with high expression in the malignant tumours belonged to functional categories associated with DNA metabolism and cell proliferation. We also report here that insulin-like growth factor binding, cell adhesion and calcium ion transport were gene functional categories over-represented among the genes significantly under-expressed in ovarian cancer.
Over-expression of certain members of the IGF system increased sensitivity to IGF1 signaling in breast cancer cells  leading to increased cell proliferation. Insulin receptor substrate 1 (IRS1) is one member of the IGF system whose over-expression potentiated the effects of IGF1. Interestingly, IRS1 was significantly up-regulated in the benign and LMP tumours of our study (Figure 2). Considering the documented ability of the IGFBP's to reduce bioavailability of IGF1, increased expression of IGFBP's would be an appropriate cellular response to increased expression of IRS1.
Loss of cell adhesion molecules (CAM) is one mechanism proposed to induce the tissue invasion and metastatic capabilities acquired by cells during tumourigenesis. Intra-abdominal spread of ovarian cancer via peritoneal implants is a hallmark of advanced stage ovarian cancer and can be linked to loss of cell-cell adhesion . Our findings support the theory that loss of CAM in ovarian cancer is instrumental in cancer progression.
Cell-cell adhesion is often mediated through the cadherins, a family of transmembrane glycoproteins that require calcium to perform their adhesive functions. Well-documented changes in cadherin subtype expression correlate with the progression of breast and prostate cancer . Recently, differences in the profile of cadherin subtypes expressed in normal and cancerous ovarian tissue were also shown to correlate with disease progression . Support for cadherin switching in ovarian tumours is evident in our microarray data. Expression of N-cadherin (N-cad) and cadherin-11 (CDH11), the dominant subtypes in normal ovarian surface epithelium, were significantly higher in the benign and LMP tumours of our study than in the adenocarcinomas (Figure 4a). The intensity of change in expression between the benign adenomas and malignant adenocarcinomas for N-cad and CDH11 were 3.9 and 7.8 fold respectively, and both genes appeared in the list of top 163 differentially expressed genes. The LMP tumours in our study expressed N-cad and CDH11 at levels intermediate to adenomas and adenocarcinomas, suggesting an integral role for these cadherins in transformation to a malignant phenotype. Expression of E-cadherin, a major subtype seen in adenocarcinomas, increased approximately 2 fold from a benign tumour to either LMP or the adenocarcinomas (Figure 6a). This data documents the switch from a normal-like distribution of N-cad and CDH11 in the benign and LMP tumours to a cancerous profile dominated by E-cad expression.
Since cadherins are calcium-dependent cell adhesion molecules and increased dietary intake of calcium correlates with a reduced risk of ovarian cancer , it is also interesting that calcium transport and calcium channel activity are gene functions that we found correlated with genes under-expresses in the adenocarcinomas. Thus it is also possible that altered functionality of the cadherins through changes in calcium availability, a parameter not measurable with microarray, may be involved in increasing a tumours' malignant potential.
Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Genes displaying co-ordinated changes in gene expression were often genetically linked suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic changes. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. A significant portion of the differentially expressed genes exhibited expression levels in borderline samples intermediate between benign adenomas and malignant adenocarcinomas, suggesting the borderline tumours are a transitional state between benign and malignant tumours.
Tumour Samples and RNA Isolation
A set of 18 primary ovarian tumours was obtained from the Ovarian Cancer Institute. This set of tumours was comprised of 4 benign cystadenofibromas, 4 carcinomas of low malignant potential (borderline carcinomas), 5 adenocarcinomas, and 5 adenocarcinomas from patients who received chemotherapy prior to surgery. This study was approved by the Institutional Review Board of the University of Georgia and of Northside Hospital (Atlanta), from which the samples were obtained Tissue was collected at the time of initial surgery and preserved in RNA Later (Ambion) within one minute of collection. For RNA isolation, each tissue (50 ± 25 mg) was homogenized on ice in 1.5 ml Trizol (Molecular Research Corporation) with a polytron homogenizer for about 30 seconds. RNA was isolated from the crude homogenate according to the manufacturer's protocols (Trizol, Molecular Research Corporation) with the following specifics. Linear polyacrylamide (5 μl) was added prior to homogenization to aid in RNA precipitation. Total RNA was further purified over an RNEasy (Qiagen) column using the manufacturer's cleanup protocol.
Biotinylated target cRNA was generated according to the Affymetrix Technical Manual. In brief, 5–10 μg total RNA was converted to double stranded cDNA using Supercript II (Invitrogen). The cDNA was cleaned by phenol/chloroform extraction and ethanol precipitation. In vitro transcription of the cDNA with the High Yield RNA Transcript Labeling Kit (Enzo) yielded 50–100 μg of biotin labeled cRNA target. The cRNA was fragmented in a metal catalyzed acid hydrolysis to a length of 20–200 bp (by electrophoresis) and the fragmented cRNA was hybridized to the Affymetrix array (U95Av2) for 16 hours at 45C. Hybridized arrays were washed, stained and scanned according to the Affymetrix technical manual.
Microarray Data Handling and Manipulation
Signal values were generated in two ways. Affymetrix signal values were generated from the .CEL file using the Affymetrix software MAS 5.0. The overall intensity of each array was scaled to an average intensity of 500. These normalized signal values were exported to Excel (Microsoft) for further analysis labeled the Affy-data set. Robust multi-array analysis (RMA) signal values were generated from the .CEL file using the espresso wrapper in the Affy library of the Bioconductor package in the R-statistical environment. The parameters of PM correction, background correction, normalization, and summary method were set to PM only, RMA, quantile, and median polish, respectively. The normalized signal values were exported to Excel for further analysis and will be referred to in this paper as the RMA-data set. For each data set, Affy-data set and RMA-data set, Pearson correlation coefficients were calculated (Microsoft, Excel) for a_97 vs. all other arrays. Higher correlation and lower standard deviation from the mean within groups was seen with the RMA data set, suggesting higher quality data.
Raw data output from the Affymetrics MicroArray reader is transformed into expression level values using the RMA method [48, 49]of the "affy" package in the Bioconductor suite of the R statistical environment, and a text output file generated. This text file is then transformed into a *.gct file for input into the GeneCluster program. GeneCluster (Whitehead Insitute, http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html) was used to cluster the dataset on both samples and genes. Except where described below, default parameters were used. The SOM feature of GeneCluster was employed, and various values were explored for the "Cluster Range" and "Iterations" parameters. The 'Cluster Range' parameter sets the geometry of the clusterings that will be performed on the data. For instance, if 2–3 is entered, two cluster sets are produced. One has two clusters and the other three clusters. When entering a number, any set of factors of that number will create a clustering. If 9 is entered, a linear set of 9 clusters and a 3 × 3 matrix of clusters are produced. Marker analysis was also performed in GeneCluster, again using the default parameters.
Total RNA (2 μg) from ovarian tissue was converted to cDNA using Superscript III (Invitrogen) primed with random hexamers under conditions described by the supplier. cDNA from this reaction was used directly in the Quantitative RT-PCR analysis. TaqMan probes and gene specific primers for three genes (RPL-29, UBE2C, OVGP1, and CDH2) were obtained from Applied Biosystems' Assay on Demand. The mRNA levels of the three genes were measured in 6 ovarian tumours and one normal ovary on the ABI Prism 7700 Sequence Detection System. PCR was performed using the TaqMan Universal PCR MasterMix (Applied Biosystems), according to the manufacturer's protocols with standard PCR cycling steps. Using RPL29 as a housekeeping gene and the normal human ovary RNA as a reference sample, the expression levels of UBE2C, OVGP1 and CDH2 were calculated according to the 2-ΔΔCt method. The Ct values of triplicate RT-PCR reactions were averaged for each gene in each cDNA sample. For each tissue sample assayed, the average Ct value for the gene of interest (UBE2C, CDH2 and OVGP1) was subtracted from the average Ct value of the housekeeping gene (RPL29) to obtain the ΔCt value. The ΔCt value of the reference sample was subtracted from that of the tumours to obtain the ΔΔCt value.
Data Filtering and Statistical Analysis
The RMA normalised data set was analysed for probe sets likely to be absent in all samples. Probe sets whose maximum RMA normalised value across all samples was less than 5.2 were removed from further analysis. Analysing the remaining 10,520 probe sets, we applied an analysis of variance (ANOVA) to test the hypothesis that the mean expression values for all groups (adenoma, borderline and cancer) are equal. For each gene, the within group and between group variation was calculated and used to generate the F statistic and subsequent p-values . Adjusted p-values were also calculated using Holm's method. The ranking of genes in order of significance was exactly the same for the un-adjusted and adjusted methods. However, the adjusted p-values were 1000 fold higher than the unadjusted p-values and only the top 50 genes were considered significant (p < 0.05). Since RT-PCR confirmed differential expression down to the 739th statistically significant gene (see results section), we continued the analysis on the top 300 statistically significant genes. Genes whose groups means were identified as significantly different (p ≤ 0.001, 299 genes) in the unadjusted ANOVA were further analysed using Fisher's Least Significant Difference multiple comparison method. The differences between group means for all pairwise combinations of groups were calculated and compared to the least significant difference. Genes were declared differentially expressed if the pairwise difference between group means was greater than the least significant difference. Probe sets duplicated between pairwise comparisons and probes sets with a fold change value below 2.0 were removed, leaving 163 unique genes differentially expressed.
Genes found to be differentially expressed in the statistical analysis were divided into two lists: genes overexpressed in cancer, and genes underexpressed in cancer. Each list was analyzed for over-represented functional categories based on molecular function, biological process, cellular component and chromosomal location using two different freeware programs: EASE http://david.niaid.nih.gov/david/ease.htmand OntoExpress http://vortex.cs.wayne.edu/ontoexpress/onto.htm. Given a list of genes, EASE forms subgroups of genes based on the functional categories assigned to each gene. EASE assigns a significance level to the functional category based on the probability of seeing the number of subgroup genes within a category given the frequency of genes from that category appearing on the microarray. The 'EASE score' is the upper bound of the distribution of Jacknife Fisher exact probabilities. Onto Express identifies overrepresented gene functional categories in a manner similar to EASE and was used to verify results obtained from EASE. All information on chromosomal location was obtained from Onto Express since EASE does provide information on chromosomal location.
This work was supported by the Ovarian Cancer Institute and awards from the Georgia Cancer Coallition and the Georgia Research Alliance.
- Jemal A, Tiwari RC, Murray T, Ghafoor A, Samuels A, Ward E, Feuer EJ, Thun MJ: Cancer statistics. CA Cancer J Clin. 2004, 54: 8-29.View ArticlePubMedGoogle Scholar
- Schwartz PE: Current diagnosis and treatment modalities for ovarian cancer. Cancer Treat Res. 2002, 107: 99-118.PubMedGoogle Scholar
- Ciro M, Bracken AP, Helin K: Profiling cancer. Curr Opin Cell Biol. 2003, 15: 213-220. 10.1016/S0955-0674(03)00007-3View ArticlePubMedGoogle Scholar
- Adib TR, Henderson S, Perrett C, Hewitt D, Bourmpoulia D, Ledermann J, Boshoff C: Predicting biomarkers for ovarian cancer using gene-expression microarrays. Br J Cancer. 2004, 90: 686-692. 10.1038/sj.bjc.6601603PubMed CentralView ArticlePubMedGoogle Scholar
- Jazaeri AA, Lu K, Schmandt R, Harris CP, Rao PH, Sotiriou C, Chandramouli GV, Gershenson DM, Liu ET: Molecular determinants of tumor differentiation in papillary serous ovarian carcinoma. Mol Carcinog. 2003, 36: 53-59. 10.1002/mc.10098View ArticlePubMedGoogle Scholar
- Lancaster JM, Dressman HK, Whitaker RS, Havrilesky L, Gray J, Marks JR, Nevins JR, Berchuck A: Gene expression patterns that characterize advanced stage serous ovarian cancers. J Soc Gynecol Investig. 2004, 11: 51-59. 10.1016/j.jsgi.2003.07.004View ArticlePubMedGoogle Scholar
- Matei D, Graeber TG, Baldwin RL, Karlan BY, Rao J, Chang DD: Gene expression in epithelial ovarian carcinoma. Oncogene. 2002, 21: 6289-6298. 10.1038/sj.onc.1205785View ArticlePubMedGoogle Scholar
- Schaner ME, Ross DT, Ciaravino G, Sorlie T, Troyanskaya O, Diehn M, Wang YC, Duran GE, Sikic TL, Caldeira S, Skomedal H, Tu IP, Hernandez-Boussard T, Johnson SW, O'Dwyer PJ, Fero MJ, Kristensen GB, Borresen-Dale AL, Hastie T, Tibshirani R, van de Rijn M, Teng NN, Longacre TA, Botstein D, Brown PO, Sikic BI: Gene expression patterns in ovarian carcinomas. Mol Biol Cell. 2003, 14: 4376-4386. 10.1091/mbc.E03-05-0279PubMed CentralView ArticlePubMedGoogle Scholar
- Schwartz DR, Kardia SL, Shedden KA, Kuick R, Michailidis G, Taylor JM, Misek DE, Wu R, Zhai Y, Darrah DM, Reed H, Ellenson LH, Giordano TJ, Fearon ER, Hanash SM, Cho KR: Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas. Cancer Res. 2002, 62: 4722-4729.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 U S A. 2001, 98: 1176-1181. 10.1073/pnas.98.3.1176PubMed CentralView ArticlePubMedGoogle Scholar
- Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A. 1999, 96: 2907-2912. 10.1073/pnas.96.6.2907PubMed CentralView ArticlePubMedGoogle Scholar
- Tammela J, Lele S: New modalities in detection of recurrent ovarian cancer. Curr Opin Obstet Gynecol. 2004, 16: 5-9. 10.1097/00001703-200402000-00003View ArticlePubMedGoogle Scholar
- Vaidya AP, Curtin JP: The follow-up of ovarian cancer. Semin Oncol. 2003, 30: 401-412. 10.1016/S0093-7754(03)00100-3View ArticlePubMedGoogle Scholar
- Ott Lyman, Longnecker Michael: An Introduction to statistical methods and data analysis. 2001, 1152-Thomson Learning, 5Google Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25: 402-408. 10.1006/meth.2001.1262View ArticlePubMedGoogle Scholar
- Hosack DA, Dennis G., Jr., Sherman BT, Lane HC, Lempicki RA: Identifying biological themes within lists of genes with EASE. Genome Biol. 2003, 4: R70- 10.1186/gb-2003-4-10-r70PubMed CentralView ArticlePubMedGoogle Scholar
- Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA: Global functional profiling of gene expression. Genomics. 2003, 81: 98-104. 10.1016/S0888-7543(02)00021-6View ArticlePubMedGoogle Scholar
- Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, Lapp H, Schultz PG, Powell SM, Moskaluk CA, Frierson H. F., Jr., Hampton GM: Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 2001, 61: 7388-7393.PubMedGoogle Scholar
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999, 286: 531-537. 10.1126/science.286.5439.531View ArticlePubMedGoogle Scholar
- Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A, Cheng C, Campana D, Wilkins D, Zhou X, Li J, Liu H, Pui CH, Evans WE, Naeve C, Wong L, Downing JR: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell. 2002, 1: 133-143. 10.1016/S1535-6108(02)00032-6View ArticlePubMedGoogle Scholar
- Hauptmann S, Dietel M: Serous tumors of low malignant potential of the ovary-molecular pathology: part 2. Virchows Arch. 2001, 438: 539-551. 10.1007/s004280100435View ArticlePubMedGoogle Scholar
- Seidman JD, Ronnett BM, Kurman RJ: Pathology of borderline (low malignant potential) ovarian tumours. Best Pract Res Clin Obstet Gynaecol. 2002, 16: 499-512. 10.1053/beog.2002.0300View ArticlePubMedGoogle Scholar
- Crispens MA: Borderline ovarian tumours: a review of the recent literature. Curr Opin Obstet Gynecol. 2003, 15: 39-43. 10.1097/00001703-200302000-00006View ArticlePubMedGoogle Scholar
- Dietel M, Hauptmann S: Serous tumors of low malignant potential of the ovary. 1. Diagnostic pathology. Virchows Arch. 2000, 436: 403-412. 10.1007/s004280050467View ArticlePubMedGoogle Scholar
- Chin JL, Reiter RE: Genetic alterations in prostate cancer. Curr Urol Rep. 2004, 5: 157-165.View ArticlePubMedGoogle Scholar
- Hogdall EV, Ryan A, Kjaer SK, Blaakaer J, Christensen L, Bock JE, Glud E, Jacobs IJ, Hogdall CK: Loss of heterozygosity on the X chromosome is an independent prognostic factor in ovarian carcinoma. Cancer. 2004, 100: 2387-2395. 10.1002/cncr.20213View ArticlePubMedGoogle Scholar
- Santarosa M, Ashworth A: Haploinsufficiency for tumour suppressor genes: when you don't need to go all the way. Biochim Biophys Acta. 2004, 1654: 105-122. 10.1016/j.bbcan.2004.01.001PubMedGoogle Scholar
- El-Osta A: The rise and fall of genomic methylation in cancer. Leukemia. 2004, 18: 233-237. 10.1038/sj.leu.2403218View ArticlePubMedGoogle Scholar
- Esteller M: Cancer epigenetics: DNA methylation and chromatin alterations in human cancer. Adv Exp Med Biol. 2003, 532: 39-49.View ArticlePubMedGoogle Scholar
- Herman JG, Baylin SB: Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med. 2003, 349: 2042-2054. 10.1056/NEJMra023075View ArticlePubMedGoogle Scholar
- Esteller M: CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene. 2002, 21: 5427-5440. 10.1038/sj.onc.1205600View ArticlePubMedGoogle Scholar
- Jain PK: Epigenetics: the role of methylation in the mechanism of action of tumor suppressor genes. Ann N Y Acad Sci. 2003, 983: 71-83.View ArticlePubMedGoogle Scholar
- Andersson A, Johansson B, Lassen C, Mitelman F, Billstrom R, Fioretos T: Clinical impact of internal tandem duplications and activating point mutations in FLT3 in acute myeloid leukemia in elderly patients. Eur J Haematol. 2004, 72: 307-313. 10.1111/j.1600-0609.2004.00225.xView ArticlePubMedGoogle Scholar
- Hogervorst FB, Nederlof PM, Gille JJ, McElgunn CJ, Grippeling M, Pruntel R, Regnerus R, van Welsem T, van Spaendonk R, Menko FH, Kluijt I, Dommering C, Verhoef S, Schouten JP, van't Veer LJ, Pals G: Large genomic deletions and duplications in the BRCA1 gene identified by a novel quantitative method. Cancer Res. 2003, 63: 1449-1453.PubMedGoogle Scholar
- Lucito R, Healy J, Alexander J, Reiner A, Esposito D, Chi M, Rodgers L, Brady A, Sebat J, Troge J, West JA, Rostan S, Nguyen KC, Powers S, Ye KQ, Olshen A, Venkatraman E, Norton L, Wigler M: Representational oligonucleotide microarray analysis: a high-resolution method to detect genome copy number variation. Genome Res. 2003, 13: 2291-2305. 10.1101/gr.1349003PubMed CentralView ArticlePubMedGoogle Scholar
- Menendez L, Benigno BB, McDonald JF: L1 and HERV-W retrotransposons are hypomethylated in human ovarian carcinomas. Mol Cancer. 2004, 3: 12- 10.1186/1476-4598-3-12PubMed CentralView ArticlePubMedGoogle Scholar
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9View ArticlePubMedGoogle Scholar
- LeRoith D, Roberts C. T., Jr.: The insulin-like growth factor system and cancer. Cancer Lett. 2003, 195: 127-137.View ArticlePubMedGoogle Scholar
- Mauro L, Salerno M, Morelli C, Boterberg T, Bracke ME, Surmacz E: Role of the IGF-I receptor in the regulation of cell-cell adhesion: implications in cancer development and progression. J Cell Physiol. 2003, 194: 108-116. 10.1002/jcp.10207View ArticlePubMedGoogle Scholar
- Moschos SJ, Mantzoros CS: The role of the IGF system in cancer: from basic to clinical studies and clinical applications. Oncology. 2002, 63: 317-332. 10.1159/000066230View ArticlePubMedGoogle Scholar
- Baron-Hay S, Boyle F, Ferrier A, Scott C: Elevated serum insulin-like growth factor binding protein-2 as a prognostic marker in patients with ovarian cancer. Clin Cancer Res. 2004, 10: 1796-1806.View ArticlePubMedGoogle Scholar
- Lukanova A, Lundin E, Toniolo P, Micheli A, Akhmedkhanov A, Rinaldi S, Muti P, Lenner P, Biessy C, Krogh V, Zeleniuch-Jacquotte A, Berrino F, Hallmans G, Riboli E, Kaaks R: Circulating levels of insulin-like growth factor-I and risk of ovarian cancer. Int J Cancer. 2002, 101: 549-554. 10.1002/ijc.10613View ArticlePubMedGoogle Scholar
- Surmacz E, Guvakova MA, Nolan MK, Nicosia RF, Sciacca L: Type I insulin-like growth factor receptor function in breast cancer. Breast Cancer Res Treat. 1998, 47: 255-267. 10.1023/A:1005907101686View ArticlePubMedGoogle Scholar
- Sundfeldt K: Cell-cell adhesion in the normal ovary and ovarian tumors of epithelial origin; an exception to the rule. Mol Cell Endocrinol. 2003, 202: 89-96.View ArticlePubMedGoogle Scholar
- Hazan RB, Qiao R, Keren R, Badano I, Suyama K: Cadherin switch in tumor progression. Ann N Y Acad Sci. 2004, 1014: 155-163. 10.1196/annals.1294.016View ArticlePubMedGoogle Scholar
- Patel IS, Madan P, Getsios S, Bertrand MA, MacCalman CD: Cadherin switching in ovarian cancer progression. Int J Cancer. 2003, 106: 172-177. 10.1002/ijc.11086View ArticlePubMedGoogle Scholar
- Goodman MT, Wu AH, Tung KH, McDuffie K, Kolonel LN, Nomura AM, Terada K, Wilkens LR, Murphy S, Hankin JH: Association of dairy products, lactose, and calcium with the risk of ovarian cancer. Am J Epidemiol. 2002, 156: 148-157. 10.1093/aje/kwf022View ArticlePubMedGoogle Scholar
- Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003, 31: e15- 10.1093/nar/gng015PubMed CentralView ArticlePubMedGoogle Scholar
- Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4: 249-264. 10.1093/biostatistics/4.2.249View ArticlePubMedGoogle Scholar
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.