Dataset Information

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer.

ABSTRACT: In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selected 18 genes with expression profiles that enable the distinction of NOSE from these three groups of EOC with 92% accuracy. Validation using an independent published data set derived from tissues or primary cultures confirmed a high accuracy (87-96%). The distinctive expression pattern of a subset of genes was validated by quantitative reverse transcription-PCR. An ovarian-specific tissue array representing tissues from NOSE and EOC samples of various subtypes and grades was used to further assess the protein expression patterns of two differentially expressed genes (Msln and BMP-2) by immunohistochemistry. This study highlights the relevance of using primary cultures of epithelial ovarian cells as a model system for gene profiling studies and demonstrates that the statistical analysis of gene expression profiling is a useful approach for selecting novel molecular tumour markers.

SUBMITTER: Le Page C

PROVIDER: S-EPMC2361148 | biostudies-literature | 2006 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer.

Le Page C C Ouellet V V Madore J J Ren F F Hudson T J TJ Tonin P N PN Provencher D M DM Mes-Masson A-M AM

British journal of cancer 20060201 3

In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selec ...[more]

PMID: 16421595

Similar Datasets

Project description:PurposeClinical models to identify patients at high risk of primary graft dysfunction (PGD) after heart transplantation (HT) are limited, and the underlying pathophysiology of this common post-transplant complication remains poorly understood. We sought to identify whether pre-transplant levels of circulating proteins reporting on immune activation and inflammation are associated with incident PGD.MethodsThe study population consisted of 219 adult heart transplant recipients identified between 2016 and 2020 at Duke University Medical Center, randomly divided into derivation (n = 131) and validation (n = 88) sets. PGD was defined using modified ISHLT criteria. Proteomic profiling was performed using Olink panels (n = 354 proteins) with serum samples collected immediately prior to transplantation. Association between normalized relative protein expression and PGD was tested using univariate and multivariable (recipient age, creatinine, mechanical circulatory support, and sex; donor age; ischemic time) models. Significant proteins identified in the derivation set (p < 0.05 in univariate models), were then tested in the validation set. Pathway enrichment analysis was used to test candidate biological processes. The predictive performance of proteins was compared to that of the RADIAL score.ResultsNine proteins were associated with PGD in univariate models in the derivation set. Of these, only CLEC4C remained associated with PGD in the validation set after Bonferroni correction (OR [95% CI] = 3.04 [1.74,5.82], p = 2.8 × 10-4). Patterns of association were consistent for CLEC4C in analyses stratified by biventricular/left ventricular and isolated right ventricular PGD. Pathway analysis identified interferon-alpha response and C-type lectin signaling as significantly enriched biologic processes. The RADIAL score was a poor predictor of PGD (AUC = 0.55). CLEC4C alone (AUC = 0.66, p = 0.048) and in combination with the clinical covariates from the multivariable model (AUC = 0.69, p = 0.018) improved discrimination for the primary outcome.ConclusionsPre-transplantation circulating levels of CLEC4C, a protein marker of plasmacytoid dendritic cells (pDCs), may identify HT recipients at risk for PGD. Further studies are needed to better understand the potential role pDCs and the innate immune response in PGD.

Project description:BackgroundEpithelial 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.ResultsGene 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.ConclusionOur 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.

Dataset Information

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer.

Publications

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets