Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene expression profiling of epithelial ovarian cancer by bulk RNA-seq

ABSTRACT: Ovarian cancer has one of the worst prognoses among gynecologic malignancies. About 60% of ovarian cancer cases are diagnosed at Stages III and IV, and their five-year survival rate is less than 30%. Ovarian cancer is classified into four major histological subtypes; serous, clear cell, endometrioid and mucinous. Ovarian clear cell carcinoma (OCCC) is less sensitive to conventional platinum-based chemotherapy and has worse prognosis than other subtypes. In this study, we attempted to identify novel molecular targets critical for the proliferation and tumorigenicity of OCCC using the CRISPR/Cas9 system, and identified some genes as important candidates. Some of these candedates were upregulated in ovarian cancer tissue, especially OCCC. Our results suggest that not only may be these candedates a promising diagnostic maker for OCCC, drugs against these candedates would be useful for the therapeutic treatment of OCCC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE189553 | GEO | 2022/06/08

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Transcription profiling of human ovarian tumors and normal ovaries

Project description:99 individual ovarian tumors (37 endometrioid, 41 serous, 13 mucinous, and 8 clear cell carcinomas) and 4 individual normal ovary samples, each assayed on an Affymetrix HG_U133A array Experiment Overall Design: 99 individual ovarian tumors (37 endometrioid, 41 serous, 13 mucinous, and 8 clear cell carcinomas) and 4 individual normal ovary samples. RNA expression was analyzed using one Affymetrix HG_U133A array per sample.

2008-06-14 | E-GEOD-6008 | biostudies-arrayexpress

DNA methylation analysis of ovarian cancers and normal peripheral blood lymphocyte DNA by Illumina Infinium platform

2012-01-01 | GSE26989 | GEO

DNA methylation analysis of ovarian cancers and normal peripheral blood lymphocyte DNA by Illumina Infinium platform

Project description:We performed a genome-scale DNA methylation analysis of 41 ovarian tumors and 10 normal lymphocytic samples. We used the Illumina Infinium HumanMethylation27 Beadchip platform that interrogates the DNA methylation profiles of approximately 27,000 CpGs to identify sensitive markers for ovarian cancer detection. The 41 tumor samples included one mixed (clear cell and endometrioid) tumor, three clear cell carcinomas, four mucinous, four endometrioid, and 32 serous epithelial ovarian carcinomas. The lymphocytic DNA samples were obtained from 10 healthy older women. DNA obtained from 41 ovarian cancer tissue samples and 10 peripheral blood leukocyte (PBL) samples was bisulfite converted and then hybridized to the Illumina Infinium 27k Human Methylation Beadchip v.1.2 biological replicate: sample WBC6, WBC11 biological replicate: sample WBC8, WBC12

2012-01-01 | E-GEOD-26989 | biostudies-arrayexpress

Expression data from ovarian cancer cell lines

Project description:Ovarian clear cell carcinoma (OCCC) shows unique clinical features including an association with endometriosis and poor prognosis. We previously reported that the contents of endometriotic cysts, especially high concentrations of free iron, are a possible cause of OCCC carcinogenesis through iron-induced persistent oxidative stress. In this study, we conducted gene expression microarray analysis using 38 ovarian cancer cell lines and identified genes commonly expressed in both OCCC cell lines and clinical samples, which comprise an OCCC gene signature. The OCCC signature reproducibly predicts OCCC specimens in other microarray data sets, suggesting that this gene profile reflects the inherent biological characteristics of OCCC. The OCCC signature contains known markers of OCCC, such as hepatocyte nuclear factor-1b (HNF-1b) and versican (VCAN), and other genes that reflect oxidative stress. Expression of OCCC signature genes was induced by treatment of immortalized ovarian surface epithelial cells with the contents of endometriotic cysts, indicating that the OCCC signature is largely dependent on the tumor microenvironment. Induction of OCCC signature genes is at least in part epigenetically regulated, as we found hypomethylation of HNF-1b and VCAN in OCCC cell lines. This genomewide study indicates that the tumor microenvironment induces specific gene expression profiles that contribute to the development of distinct cancer subtypes. Affymetrix Human Genome U133A 2.0 Array was conducted for 38 ovarian cancer cell lines (13 OCCC cell lines and 25 non-OCCC cell lines). All specimens were arrayed in parallel and used for RMA normalization.

2011-05-16 | E-GEOD-29175 | biostudies-arrayexpress

Ovarian Cancer: 19 ovarian tumors of diverse histologic type vs. pool of all 19 tumors.

2014-01-17 | GSE49888 | GEO

Human ovarian tumors and normal ovaries

2007-04-09 | GSE6008 | GEO

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions [H3K4me3]

Project description:In the current study we performed characterization of 18 different in vitro models of high-grade serous (HGSOC), low-grade serous (LGSOC), mucinous (MOC), endometrioid (ENOC) and clear cell (CCOC) carcinoma. This study seeks to fill in this gap by generating an integrative Ovarian Cancer Regulatory Atlas (OCRA) of 18 ovarian normal and cancer cells profiled for epigenomic, transcriptomic, chromatin state and chromatin looping signatures.

2024-03-18 | GSE261637 | GEO

Ovarian Cancer: 19 ovarian tumors of diverse histologic type vs. pool of all 19 tumors.

Project description:Transcriptional profiling of 19 ovarian tumors of 5 distinct histologic types (serous, endometrioid, clear cell, mucinous and small cell), to investigate similar and distinct biology associated with each histologic type. RNA from fresh-frozen ovarian tumor samples were individually hybridized to a pooled RNA from all 19 tumor cases

2014-01-17 | E-GEOD-49888 | biostudies-arrayexpress

Gene expression data of ovarian clear cell carcinoma from Singapore

Project description:Ovarian clear cell carcinoma (OCCC) is an epithelial ovarian cancer (EOC) histology having distinct pathology, biology, and molecular footprints. OCCC is chemo-resistant and has the worst stage-adjusted prognosis amongst EOC. Yet, treatment for OCCC patients is no different than other EOC. As OCCC incidence rate has significantly increase in recent decades, it is critical to find OCCC-tailored therapeutic. However, majority of EOC gene expression molecular subtypes (GEMS) studies for personalized medicine focused on the high grade serous histology, and studies in OCCC GEMS were of small sample size. Using more than 200 OCCC gene expression profiles, we identified two OCCC subtypes: EpiCC—epithelial-like, early stage, good prognosis, relative higher rate of gene mutations in SWI/SNF complex; and MesCC—mesenchymal-like, late stage, and poor prognosis. The differential prognosis between EpiCC and MesCC could be observed as early as stage IC. Genetic, copy number and transcriptome profiling showed that both EpiCC and MesCC carried OCCC-associated aberrations. The EpiCC and MesCC are reproducible in validation cohorts and OCCC cell lines. Cell lines assays showed that MesCC is more proliferative and more anoikis resistant than EpiCC. Both EpiCC and MesCC are resistant to cisplatin. Applying the OCCC subtypes to TCGA 534 renal clear cell carcinoma indicated interoperability of the subtyping scheme, and revealed preferential response of EpiCC to sorafenib, and MesCC to bevacizumab. The EpiCC and MesCC classification shows promise in prognostication utility especially for stage IC OCCC patients, and warrants further investigation for subtype-tailored treatment regimen.

2020-01-08 | GSE129617 | GEO

Expression data from ovarian cancer cell lines

2011-05-16 | GSE29175 | 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