264014e1-059c-49f4-af60-de91e5997093 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001004186 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The Estrogen Receptor beta (ERβ) has an essential role in endometriosis progression. However, the molecular mechanism of how ERβ drives endometriosis progression is not elucidated, yet. To define the role of genomic ERβ in endometriosis progression, we have employed whole-genome microarray expression profiling as a discovery platform to identify ERβ-regulated transcriptome in endometriotic tissues. To get this transcriptome, we applied endometrium specific ERβ overexpression (ERBOE) mouse model by crossing mouse having a pCAG promoter-loxPSTOPloxP-ERβ cassette with PRCre knockin mice that Cre recombinase cDNA was inserted into exon 1 of PR gene. Endometriosis was surgically induced in ERBOE mice and PRCre mice as the control by transplantation of uterine tissues. The ectopic lesions and eutopic endometrium were harvested at the estrus cycle in 4th weeks after endometriosis induction.
2019-09-12 | GSE114010 | GEO
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.
2014-10-01 | E-GEOD-51981 | biostudies-arrayexpress
Project description:Endometriosis is a common disease seen by gynecologists. Clinical features involve pelvic pain and unexplained infertility. Although endometriosis is pathologically characterized by endometrial tissue outside the normal uterine location, endometriosis is otherwise not easily explained. Endometriomas, endometriotic cysts of the ovary, typically cause pain and distortion of pelvic anatomy. To begin to understand the pathogenesis of endometriomas, we carried out transcriptome:microRNAome analysis of endometriomas and eutopic endometrium, using gene expression arrays and next generation small RNA sequencing technology. Keywords: two group comparison Patients undergoing surgery for endometriomas, suspected endometriosis, pelvic pain, abnormal uterine bleeding, pelvic organ prolapse, or uterine leiomyomas were approached for participation. After informed consent, the patients underwent scheduled surgical procedure. Tissues were collected either as cyst wall of endometrioma or endometrial curettage of hysterectomy specimen and placed directly into RNALater (Ambion, Austin, TX) and eventually frozen at -80?C. Samples were designated as endometriomas or non-endometriosis control endometrium based on surgical pathology reports. Total RNA was isolated from frozen tissue. High quality RNA was subjected to Illumina’s Human WG-6 version 2.0 BeadChips (Illumina). . *** This Series represents the transcriptome component of the study. ***
2011-05-09 | E-GEOD-23339 | biostudies-arrayexpress
Project description:Endometriosis is a common gynecological disease that affects 10-20% of women in child-bearing age. It is defined by the presence of endometrial tissues outside the uterus. The causes remain elusive, but the endometrium of these patients differs profoundly from that of disease-free subjects. During pregnancy, the maternal-fetal interface is built onto decidualized endometrium. We investigated whether this interface is normal when the subjects suffer of endometriosis and showed remarkly properties of eutopic endometrium from endometriosis-affected women to spontaneously develop endometriotic-like lesions within direct contact to non-uterine tissues.
2014-11-10 | GSE57831 | GEO
Project description:Endometriosis is a common gyncological disease that affects 10-20% of women in child-bearing age. It is defined by the presence of endometrial tissues outside the uterus. The causes remain elusive, but the endometrium of these patients differs profoundly from that of disease-free subjects. During pregnancy, the maternal-fetal interface is built onto decidualized endometrium. We investigated whether this interface is normal when the subjects suffer of endometriosis and showed remarkly properties of eutopic endometrium from endometriosis-affected women to spontaneously develop endometriotic-like lesions within direct contact to non-uterine tissues.
2014-11-10 | GSE57733 | GEO
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.
2014-10-01 | GSE51981 | GEO
Project description:Estrogen Receptor beta (ERβ) has an essential role in endometriosis progression. However, the role of genomic ERβ in the pathogenesis of endometriosis is not elucidated, yet. To get ERβ cistrome, we employed endometrium specific ERβ overexpression (ERBOE) mouse model by crossing mouse having pCAG promoter-loxPSTOPloxP-ERβ cassette with PRCre knockin mice that Cre recombinase cDNA was inserted into exon 1 of PR gene. Using ERBOE mouse model, we determine ERβ-cistrome in endometriotic lesions and eutopic endometrium of mice with endometriosis.
2019-09-12 | GSE114047 | GEO
Project description:RNA sequencing was conducted for RNA expression profiling from secretory eutopic endometrium of 6 patients with endometriosis who didn't receive surgery and 12 patients with endometriosis who received surgery in less than 24 months. The differential expression of RNAs was generated.
2023-12-31 | GSE202571 | GEO
Project description:Endometriosis is a common disease seen by gynecologists. Clinical features involve pelvic pain and unexplained infertility. Although endometriosis is pathologically characterized by endometrial tissue outside the normal uterine location, endometriosis is otherwise not easily explained. Endometriomas, endometriotic cysts of the ovary, typically cause pain and distortion of pelvic anatomy. To begin to understand the pathogenesis of endometriomas, we carried out transcriptome:microRNAome analysis of endometriomas and eutopic endometrium, using gene expression arrays and next generation small RNA sequencing technology. Keywords: two group comparison
2011-05-09 | GSE23339 | GEO
Project description:We performed gene expression analysis human peritoneal endometriosis lesions, eutopic endometrium from endometriosis patients and peritoneum form endometriosis patients.The goal of the study was to analyse gene expression differences between peritoneal endometriosis lesion and eutopic endometrium and peritoneal endometriosis lesion and peritoneum.
2012-12-06 | E-MTAB-694 | biostudies-arrayexpress