Dataset Information

Distinct DNA methylation profiles in ovarian serous neoplasms and their implications in ovarian carcinogenesis.

ABSTRACT:

Objective

The purpose of this study was to analyze DNA methylation profiles among different types of ovarian serous neoplasm, which is a task that has not been performed.

Study design

The Illumina beads array (Illumina Inc, San Diego, CA) was used to profile DNA methylation in enriched tumor cells that had been isolated from 75 benign and malignant serous tumor tissues and 6 tumor-associated stromal cell cultures.

Results

We found significantly fewer hypermethylated genes in high-grade serous carcinomas than in low-grade serous carcinoma and borderline tumors, which in turn had fewer hypermethylated genes than serous cystadenoma. Unsupervised analysis identified that serous cystadenoma, serous borderline tumor, and low-grade serous carcinomas tightly clustered together and were clearly different from high-grade serous carcinomas. We also performed supervised analysis to identify differentially methylated genes that may contribute to group separation.

Conclusion

The findings support the view that low-grade and high-grade serous carcinomas are distinctly different with low-grade, but not high-grade, serous carcinomas that are related to serous borderline tumor and cystadenoma.

SUBMITTER: Shih IeM

PROVIDER: S-EPMC2993872 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Publications

Distinct DNA methylation profiles in ovarian serous neoplasms and their implications in ovarian carcinogenesis.

Shih Ie-Ming IeM Chen Li L Wang Chen C CC Gu Jinghua J Davidson Ben B Cope Leslie L Kurman Robert J RJ Xuan Jianhua J Wang Tian-Li TL

American journal of obstetrics and gynecology 20101020 6

<h4>Objective</h4>The purpose of this study was to analyze DNA methylation profiles among different types of ovarian serous neoplasm, which is a task that has not been performed.<h4>Study design</h4>The Illumina beads array (Illumina Inc, San Diego, CA) was used to profile DNA methylation in enriched tumor cells that had been isolated from 75 benign and malignant serous tumor tissues and 6 tumor-associated stromal cell cultures.<h4>Results</h4>We found significantly fewer hypermethylated genes i ...[more]

PMID: 20965493

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Project description:PurposeOvarian cancer is typically diagnosed at late stages, and thus, patients' prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.MethodsAn unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.ResultsWe found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them-DSPG3 and LOX-were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival.ConclusionsWe distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Project description:BackgroundEpithelial ovarian carcinoma is a significant cause of cancer mortality in women worldwide and in the United States. Epithelial ovarian cancer comprises several histological subtypes, each with distinct clinical and molecular characteristics. The natural history of this heterogeneous disease, including the cell types of origin, is poorly understood. This study applied recently developed methods for high-throughput DNA methylation profiling to characterize ovarian cancer cell lines and tumors, including representatives of three major histologies.Methodology/principal findingsWe obtained DNA methylation profiles of 1,505 CpG sites (808 genes) in 27 primary epithelial ovarian tumors and 15 ovarian cancer cell lines. We found that the DNA methylation profiles of ovarian cancer cell lines were markedly different from those of primary ovarian tumors. Aggregate DNA methylation levels of the assayed CpG sites tended to be higher in ovarian cancer cell lines relative to ovarian tumors. Within the primary tumors, those of the same histological type were more alike in their methylation profiles than those of different subtypes. Supervised analyses identified 90 CpG sites (68 genes) that exhibited 'subtype-specific' DNA methylation patterns (FDR<1%) among the tumors. In ovarian cancer cell lines, we estimated that for at least 27% of analyzed autosomal CpG sites, increases in methylation were accompanied by decreases in transcription of the associated gene.SignificanceThe significant difference in DNA methylation profiles between ovarian cancer cell lines and tumors underscores the need to be cautious in using cell lines as tumor models for molecular studies of ovarian cancer and other cancers. Similarly, the distinct methylation profiles of the different histological types of ovarian tumors reinforces the need to treat the different histologies of ovarian cancer as different diseases, both clinically and in biomarker studies. These data provide a useful resource for future studies, including those of potential tumor progenitor cells, which may help illuminate the etiology and natural history of these cancers.

Dataset Information

Distinct DNA methylation profiles in ovarian serous neoplasms and their implications in ovarian carcinogenesis.

Objective

Study design

Results

Conclusion

Publications

Distinct DNA methylation profiles in ovarian serous neoplasms and their implications in ovarian carcinogenesis.

Similar Datasets

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