Similar Datasets
Project description:The JGOG3025 study was conducted by the Japanese Gynecologic Oncology Group (JGOG) on 710 patients with epithelial ovarian cancer (NCT03159572). In the JGOG3025-TR2 study, fresh frozen tumor tissues from 274 and 15 cases diagnosed as stage II or higher high-grade serous carcinoma (HGSC) or high-grade endometrioid carcinoma (HGEC) in the central pathological review were submitted to SNP array, total RNA-sequencing, and DNA methylation array analyses.
2024-08-31 | GSE263437 | GEO
Project description:The JGOG3025 study was conducted by the Japanese Gynecologic Oncology Group (JGOG) on 710 patients with epithelial ovarian cancer (NCT03159572). In the JGOG3025-TR2 study, fresh frozen tumor tissues from 274 and 15 cases diagnosed as stage II or higher high-grade serous carcinoma (HGSC) or high-grade endometrioid carcinoma (HGEC) in the central pathological review were submitted to SNP array, total RNA-sequencing, and DNA methylation array analyses.
2024-08-31 | GSE263434 | GEO
Project description:The JGOG3025 study was conducted by the Japanese Gynecologic Oncology Group (JGOG) on 710 patients with epithelial ovarian cancer (NCT03159572). In the JGOG3025-TR2 study, fresh frozen tumor tissues from 274 and 15 cases diagnosed as stage II or higher high-grade serous carcinoma (HGSC) or high-grade endometrioid carcinoma (HGEC) in the central pathological review were submitted to SNP array, total RNA-sequencing, and DNA methylation array analyses.
2024-08-31 | GSE263083 | GEO
Project description:The JGOG3025 study was conducted by the Japanese Gynecologic Oncology Group (JGOG) on 710 patients with epithelial ovarian cancer (NCT03159572). In the JGOG3025-TR1 study, fresh frozen tumor tissues from 189 cases diagnosed as clear cell carcinoma (CCC) in the central pathological review were submitted to total RNA-sequencing.
2026-02-25 | GSE307292 | GEO
Project description:BackgroundThis study aimed to evaluate the homologous recombination repair pathway deficiency (HRD) in ovarian high-grade serous carcinoma (HGSC).MethodsIn the ovarian cancer data from The Cancer Genome Atlas, we identified genes differentially expressed between tumours with and without HRD genomic scars and named these genes "HRDness signature". We performed SNP array, RNA sequencing, and methylation array analyses on 274 HGSC tumours for which targeted sequencing of 51 genes and clinical data were available to generate JGOG3025-TR2 dataset. The HRDness signature was tested on external datasets, including the JGOG3025-TR2 cohort, by computational scoring and machine-learning prediction.ResultsHigh scores and positive predictions of the HRDness signature were significantly associated with BRCA alterations, genomic scar scores, and better survival. On the other hand, among cases with high scores and/or positive predictions, those with BRCA1 methylation showed poorer survival. In the JGOG3025-TR2 cohort, HRD status was significantly associated with the use of olaparib after relapse and progression-free survival after its initiation.ConclusionsThe HRDness gene expression signature is associated with a good prognosis, while BRCA1 methylation is associated with a poor prognosis. The newly generated JGOG3025-TR2 dataset will be useful in future HGSC studies.
| S-EPMC10006095 | biostudies-literature
Project description:Multi-omics data analysis for the JGOG3025-TR1 ovarian clear cell carcinoma cohort.
| PRJNA1315155 | ENA
Project description:Classification of ovarian cancer by morphologic features has a limited effect on serous ovarian cancer (SOC) treatment and prognosis. Here, we proposed a new system for SOC subtyping based on the molecular categories from the Cancer Genome Atlas project. We analyzed the DNA methylation, protein, microRNA, and gene expression of 1203 samples from 599 serous ovarian cancer patients. These samples were divided into nine subtypes based on RNA-seq data, and each subtype was found to be associated with the activation and/or suppression of the following four biological processes: immunoactivity, hormone metabolic, mesenchymal development and the MAPK signaling pathway. We also identified four DNA methylation, two protein expression, six microRNA sequencing and four pathway subtypes. By integrating the subtyping results across different omics platforms, we found that most RNA-seq subtypes overlapped with one or two subtypes from other omics data. Our study sheds light on the molecular mechanisms of SOC and provides a new perspective for the more accurate stratification of its subtypes.
| S-EPMC4868982 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-07-01 | GSE171958 | GEO
Project description:BackgroundLarge-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and protein expression, offer tantalizing possibilities for realizing the promise and potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including heterogeneity, and high-dimensionality of omics data.MethodsWe propose a novel framework for multi-omics data integration using multi-view feature selection. We introduce a novel multi-view feature selection algorithm, MRMR-mv, an adaptation of the well-known Min-Redundancy and Maximum-Relevance (MRMR) single-view feature selection algorithm to the multi-view setting.ResultsWe report results of experiments using an ovarian cancer multi-omics dataset derived from the TCGA database on the task of predicting ovarian cancer survival. Our results suggest that multi-view models outperform both view-specific models (i.e., models trained and tested using a single type of omics data) and models based on two baseline data fusion methods.ConclusionsOur results demonstrate the potential of multi-view feature selection in integrative analyses and predictive modeling from multi-omics data.
| S-EPMC6157248 | biostudies-literature
Project description:Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein-protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the "Human Protein Atlas" database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.
| S-EPMC8480361 | biostudies-literature