Transcription profiling of human stromal and epithelial cells derived from ovarian tumors
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Prof David Bowtell PhD
PROVIDER: E-GEOD-9890 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:This SuperSeries is composed of the following subset Series:; GSE9890: Expression profile of 5 ovarian tumour samples (two different cell types from each sample profiles); GSE9891: Expression profile of 285 ovarian tumour samples Experiment Overall Design: Refer to individual Series
2008-04-06 | E-GEOD-9899 | biostudies-arrayexpress
Project description:We used microarrays to profile the expression levels of 285 ovarian samples in order to identify molecular subtypes of the tumour Experiment Overall Design: Randomly selected samples from the AOCS ( Australian Ovarian Cancer Study) was expression profiled on the affymetrix U133_plus2 platform to identify novel subtypes of ovarian tumour
2008-04-06 | E-GEOD-9891 | biostudies-arrayexpress
Project description:Analysis of gene expression changes in tumour epithelium (DCIS and invasive breast cancer) and stroma both immediately surrounding the lesions and more distantly. Total RNA obtained from Formalin Fixed Paraffin Embedded archival material and the individual compartments (stroma and epithelium) compared independently across the samples. Sample abbreviation key: BC = breast cancer DCIS = ductal carcinoma in situ IDC = invasive ductal carcinoma RM = remote metastasis S = stroma NS = near stroma.
2012-05-10 | E-GEOD-35019 | biostudies-arrayexpress
Project description:Analysis of gene expression changes in tumour epithelium (DCIS and invasive breast cancer) and stroma both immediately surrounding the lesions and more distantly.
2012-05-11 | GSE35019 | GEO
Project description:ObjectiveTo evaluate the diagnostic ability of magnetic resonance imaging (MRI) based radiomics and traditional characteristics to differentiate between Ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs).MethodsWe consecutively included a total of 148 patients with 173 tumors (81 SCSTs in 73 patients and 92 EOCs in 75 patients), who were randomly divided into development and testing cohorts at a ratio of 8:2. Radiomics features were extracted from each tumor, 5-fold cross-validation was conducted for the selection of stable features based on development cohort, and we built radiomics model based on these selected features. Univariate and multivariate analyses were used to identify the independent predictors in clinical features and conventional MR parameters for differentiating SCSTs and EOCs. And nomogram was used to visualized the ultimately predictive models. All models were constructed based on the logistic regression (LR) classifier. The performance of each model was evaluated by the receiver operating characteristic (ROC) curve. Calibration and decision curves analysis (DCA) were used to evaluate the performance of models.ResultsThe final radiomics model was constructed by nine radiomics features, which exhibited superior predictive ability with AUCs of 0.915 (95%CI: 0.869-0.962) and 0.867 (95%CI: 0.732-1.000) in the development and testing cohorts, respectively. The mixed model which combining the radiomics signatures and traditional parameters achieved the best performance, with AUCs of 0.934 (95%CI: 0.892-0.976) and 0.875 (95%CI: 0.743-1.000) in the development and testing cohorts, respectively.ConclusionWe believe that the radiomics approach could be a more objective and accurate way to distinguish between SCSTs and EOCs, and the mixed model developed in our study could provide a comprehensive, effective method for clinicians to develop an appropriate management strategy.
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Project description:Ovarian cancer is the most lethal malignancy in the United States. While studies on ovarian cancer pathogenesis were mainly focused on the epithelial component of the tumor, understanding in the role of cancer associated fibroblasts (CAFs) in ovarian cancer progression is limited. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer–stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer.
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Project description:Ovarian cancer is the most lethal malignancy in the United States. In the year 2012, there will be an estimated 22,280 new cases and 15,500 deaths from ovarian cancer in the country (Siegel et al., 2012). While studies on ovarian cancer pathogenesis were mainly focused on the epithelial component of the tumor, understanding in the role of cancer associated fibroblasts (CAFs) in ovarian cancer progression is limited. We hypothesized that comparing the gene expression profiles of different components from laser capture microdissected ovarian tissue will allow us to identify an ovarian CAFs specific gene signature which accounts for the supportive role of CAFs in ovarian cancer progression. In this study, gene expression profiling was completed for 31 cancer stroma samples and 32 samples of epithelial component from high grade serous ovarian cancer patients. 8 microdissected normal ovarian stroma and 6 normal human ovarian surface epithelium (HOSE) samples were also included in the study. By comparing the expression data from cancer stroma against that from normal stroma, cancer cells and HOSE, we identified a set of differential expressed genes in ovarian CAFs which showed correlation with cancer patient survival. Further study on these genes can reveal their roles in ovarian cancer progression and pathogenesis. Ultimately, ovarian CAFs specified genes identified in this study may aid in the classification and enhancement of patient outcome. Transcriptome profiling analyses were performed on 31 laser microdissected cancer associated stroma samples, 32 epithelial tumor samples from high grade serous ovarian cancer patients, 8 microdissected normal ovarian stroma samples and 6 ovarian surface epthelium (HOSE) samples using the Affymetrix human genome U133 Plus 2.0 microarray.
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Project description:The cell of origin of serious ovarian cancer is unknown. To create a mouse model for this lethal cancer and identify early cancer biomarkers, we conditionally deleted both Dicer (essential for microRNA biosynthesis) and Pten (a negative regulator of the PI3K pathway) in the female reproductive tract. Beginning at ~3-5 months, these Dicer/Pten mutant mice develop high-grade serious carcinomas that initiate in the stroma of the fallopian tube through a mesenchymal-to-epithelial transition (MET), subsequently envelop the ovary, and then metastasize throughout the peritoneum, resulting in ascites and 100% lethality by 13 months. The fallopian tube cancers demonstrate upregulation of genes encoding known and novel secreted proteins that are potential biomarkers. This study uncovers a new paradigm for the initiation of high-grade serous ovarian cancer. We generated gene expression profiles of 8 human primary serious tumors, and 2 independent samples of human normal fimbria. We defined genes that were high or low in tumors relative to fimbria, and compared these results with those of the correponding mouse model.
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