Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

ABSTRACT: Endometriosis is associated with increased risk of epithelial ovarian cancers (EOCs). Using data from large endometriosis and EOC genome-wide association meta-analyses we estimate the genetic correlation and evaluate the causal relationship between genetic liability to endometriosis and EOC histotypes, and identify shared susceptibility loci. We estimate a significant genetic correlation (rg) between endometriosis and clear cell (rg=0.71), endometrioid (rg=0.48) and high-grade serous (rg=0.19) ovarian cancer, associations supported by Mendelian randomization analyses. Bivariate meta-analysis identify 28 loci associated with both endometriosis and EOC, including 19 with evidence for a shared underlying association signal. Differences in the shared risk suggest different underlying pathways may contribute to the relationship between endometriosis and the different histotypes. Functional annotation using transcriptomic and epigenomic profiles of relevant tissues/cells highlights several target genes. This comprehensive analysis reveals profound genetic overlap between endometriosis and EOC histotypes with valuable genomic targets for understanding the biological mechanisms linking the diseases.

ORGANISM(S): Homo sapiens

PROVIDER: GSE197928 | GEO | 2022/03/07

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

Project description:A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

| PRJNA812889 | ENA

Transcriptome and DNA methylation analyses highlight the role of menstrual cycle phase in tumorigenesis of endometriosis-related ovarian carcinoma histotypes [methylation]

Project description:Epithelial ovarian cancer, or ovarian carcinoma (OC), is a diverse disease. Two of the rarer subtypes, clear cell OC (CCOC) and endometrioid OC (ENOC), both arise from ovarian endometriotic cysts, particularly atypical endometriosis. CCOC and ENOC have similar mutation profiles and share a common cell of origin, but their cellular phenotypes and clinical outcomes are distinct; in particular, CCOC has poor clinical survival. The most well-known difference between the histotypes is the universal overexpression of HNF1B (Hepatocyte nuclear factor-1β) in clear cell tumors and overexpression of ESR1 in ENOC. It is not well understood how these discrete histotypes arise from the same cell of origin. Via transcriptional (n=57) and DNA methylation analysis (N=127) of these rare tumors, we show that CCOC closely resembles secretory endometrium in transcriptional profile; whereas ENOC transcriptomes resemble proliferative endometrium.

2023-10-13 | GSE226823 | GEO

Transcriptome and DNA methylation analyses highlight the role of menstrual cycle phase in tumorigenesis of endometriosis-related ovarian carcinoma histotypes [RNA-Seq]

2023-10-13 | GSE226870 | GEO

Cross-cancer genome-wide association study of endometrial cancer and epithelial ovarian cancer identifies genetic risk regions associated with risk of both cancers

Project description:Accumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10-9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10-7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.

2020-07-30 | GSE155328 | GEO

Exploring the tumor micro-environment of ovarian cancer histotypes and tumor sites

Project description:Ovarian cancer is a common, malignant cancer in the female reproductive system. Despite the commonly affected tissue ovary, ovarian cancer is a heterogeneous disease consisting of at least five different histological subtypes and varying clinical features, cells of origin, molecular composition, risk factors, and treatments. With cumulative studies on the tumor microenvironment, a comprehensive landscape of the constituent cell types, and their interactions are yet to be established in ovarian cancer and its histotypes. Further characterization of tumor progression, metastasis, and various histotypes is needed to connect molecular signatures to pathological grading for tailored diagnosis and treatment. In this study, we leveraged high-resolution single-cell RNA sequencing technology to elucidate the cellular compositions on 21 solid tumor samples collected from 12 patients with six ovarian cancer histotypes and both primary (ovaries) and metastatic (omentum, rectum) sites. The diverse collection allows us to zoom in on histotype and tumor site-specific expression patterns of cells in the tumor and identify key marker genes and ligand-receptor pairs that are active in the ovarian tumor microenvironment. Our findings can be used in improving disease stratification and design of customized treatment.

2023-12-15 | GSE235931 | GEO

Transcriptional profiling of ovarian cancer samples with different grades

Project description:We have previously demonstrated (Calura E et al., 2013) that different epithelial ovarian cancer (EOC) histotypes have different, defined characteristics at the microRNA level, which might highlight a different originating tissue for the disease. From these results we have invesigated whole-genome gene expression on a cohort of samples with different FIGO grades, in order to investigate the link between the tumor grade and gene expression.

2014-12-12 | E-MTAB-1814 | biostudies-arrayexpress

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

Project description:To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five new histotype-specific EOC risk regions (P-value < 5 x 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (P-value < 10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue data sets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (FDR < 0.05). Finally, by integrating genome-wide HiChIP interactome analysis with TWAS, variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8 and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by GWAS.

2024-03-27 | GSE255142 | GEO

Global Endometrial DNA Multi-omics Analysis Reveals Insights into mQTL Regulation and Associated Endometriosis Disease Risk and Endometrial Function

Project description:Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Biological and genetic effects on DNA methylation (DNAm) can influence gene regulation and contribute to complex disease. Herein, we characterize variation in endometrial DNAm and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genome-wide genotype data and methylation at 759,345 DNAm sites in endometrial samples from 984 deeply-phenotyped participants. We estimate that 15.4% of the variation in endometriosis is captured by DNAm and identify significant differences in DNAm profiles and DNAm networks, associated with endometriosis, endometriosis sub-phenotypes and menstrual cycle phase, including opening of the window for embryo implantation. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis highlighting candidate genes contributing to disease risk. Our work provides functional evidence for epigenetic targets contributing to endometriosis risk and pathogenesis. Data generated serve as a valuable resource for understanding tissue-specific effects of methylation on endometrial biology in health and disease.

2023-09-12 | GSE223817 | GEO

Gene expression patterns of chemoresistant and chemosensitive serous epithelial ovarian tumors with possible predictive value in response to initial chemotherapy

Project description:Chemotherapy (CT) resistance in ovarian cancer is broad and encompasses diverse, unrelated drugs, suggesting more than one mechanism of resistance. We aimed to analyze the gene expression patterns in primary serous epithelial ovarian cancer (EOC) samples displaying different responses to first-line CT in an attempt to identify specific molecular signatures associated with response to CT. Initially, the expression profiles of 15 chemoresistant serous EOC tumors [time to recurrence (TTR) ≤6 months] and 10 chemosensitive serous EOC tumors (TTR ≥30 months) were independently analyzed which allowed the identification of specific sets of differentially expressed genes that might be functionally implicated in the evolution of the chemoresistant or the chemosensitive phenotype. Our data suggest that the intrinsic chemoresistance in serous EOC cells may be attributed to the combined action of different molecular mechanisms and factors linked with drug influx and efflux and cell proliferation, as possible implications of other molecular events including altered metabolism, apoptosis and inflammation cannot be excluded. Next, gene expression comparison using hierarchical clustering clearly distinguished chemosensitive and chemo- resistant tumors from the 25 serous EOC samples (training set), and consecutive class prediction analysis was used to develop a 43-gene classifier that was further validated in an independent cohort of 15 serous EOC patients and 2 patients with other ovarian cancer histotypes (test set). The 43-gene predictor set properly classified serous EOC patients at high risk for early (≤22 months) versus late (>22 months) relapse after initial CT. Thus, gene expression array technology can effectively classify serous EOC tumors according to CT response. The proposed 43-gene model needs further validation. 2 condition experiment: chemoresistant clinical samples versus chemosensitive samples

2011-07-23 | E-GEOD-28739 | biostudies-arrayexpress

Identity and Nature of Neural Stem Cells in the Adult Human Subventricular Zone

Project description:The existence of neural stem cells (NSCs) in adult human brain neurogenic regions remains unresolved. To address this, we created a cell atlas of the adult human subventricular zone (SVZ) derived from fresh neurosurgical samples using single-cell transcriptomics. We discovered 2 adult radial glia (RG)-like populations, aRG1 and aRG2. aRG1 shared features with fetal early RG (eRG) and aRG2 were transcriptomically similar to fetal outer RG (oRG). We also captured early neuronal and oligodendrocytic NSC states. We found that the biological programs driven by their transcriptomes support their roles as early-lineage NSCs. Finally, we show that these NSCs have the potential to transition between states and along lineage trajectories. These data reveal that multipotent NSCs reside in the adult human SVZ.

2023-12-27 | GSE248995 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data