Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human endometrium from patients with a history of endometriosis vs. normal controls sampled at a range of menstrual cycle timepoints reveals progesterone resistance and candidate susceptibility genes in women with endometriosis

ABSTRACT: The transition of regularly cycling endometrium from the proliferative or Estrogen-dominant phase of the menstrual cycle to the Progesterone-dominant Early and Mid Secretory phases requires wide-spread changes in gene expression that shift the endometrium from a proliferative capacity to a differentiated 'decidual' phenotype in preparation for implantation. This process appears delayed in women with severe endometriosis, suggestive of a progesterone resistant endometrium in this disease. Experiment Overall Design: Endometrial biopsies were obtained from women both with normal endometrial pathologies and no history of endometriosis and from women with laporoscopy proven moderate-severe stage endometriosis. Samples were collected from the Proliferative(PE), Early Secretory (ESE) and Midsecretory (MSE) phases. Samples were then processed for Total RNA isolation and Affymetrix chip hybridization.

ORGANISM(S): Homo sapiens

SUBMITTER: Linda Giudice

PROVIDER: E-GEOD-6364 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression analysis of endometrium reveals progesterone resistance and candidate susceptibility genes in women with endometriosis.

Burney Richard O RO Talbi Said S Hamilton Amy E AE Vo Kim Chi KC Nyegaard Mette M Nezhat Camran R CR Lessey Bruce A BA Giudice Linda C LC

Endocrinology 20070517 8

The identification of molecular differences in the endometrium of women with endometriosis is an important step toward understanding the pathogenesis of this condition and toward developing novel strategies for the treatment of associated infertility and pain. In this study, we conducted global gene expression analysis of endometrium from women with and without moderate/severe stage endometriosis and compared the gene expression signatures across various phases of the menstrual cycle. The transc ...[more]

PMID: 17510236

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Project description:Endometriosis, an estrogen-dependent, progesterone-resistant, inflammatory disorder affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Since the uterine lining (endometrium) in women with endometriosis has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n=148 archived endometrial samples from women with endometriosis or without endometriosis (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with endometriosis or no endometriosis involved two binary decisions each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no endometriosis from endometriosis, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified endometriosis with 90-100% accuracy, were cycle phase-specific or independent, and utilized relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism and growth factor signaling in endometrium of women with endometriosis. Similar findings were observed with other disorders versus controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic endometriosis with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate-genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of endometriosis and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue. We analyzed endometrial samples from n=148 women without or with endometriosis and/or other uterine/pelvic pathologies, using whole genome microarrays.

Dataset Information

Transcription profiling of human endometrium from patients with a history of endometriosis vs. normal controls sampled at a range of menstrual cycle timepoints reveals progesterone resistance and candidate susceptibility genes in women with endometriosis

Publications

Gene expression analysis of endometrium reveals progesterone resistance and candidate susceptibility genes in women with endometriosis.

Similar Datasets

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