Ontology highlight
ABSTRACT:
PROVIDER: PRJNA134317 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE25202: microRNA alterations associated to clinical response in advanced stage ovarian cancer patients (training set). GSE25203: microRNA alterations associated to clinical response in advanced stage ovarian cancer patients (test set). Refer to individual Series
2012-03-09 | E-GEOD-25204 | biostudies-arrayexpress
Project description:microRNA alterations associated to clinical response in advanced stage ovarian cancer patients (test set)
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Project description:microRNA alterations associated to clinical response in advanced stage ovarian cancer patients (training set)
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Project description:microRNA alterations associated to early relapse in advanced stage ovarian cancer patients (validation set)
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Project description:A set of 45 surgical specimens has been profiled for miRNA expression to validate miRNA alterations associated to early relapse in advanced stage ovarian cancer patients. Fresh frozen samples were collected from a series of consecutive patients with high-grade advanced stage ovarian cancer who underwent primary surgery at INT-Milan. After surgery all patients received postoperative platinum-based chemotherapy. All patients signed an Institutional Review Board approved consent for bio-banking, clinical data collection and molecular analysis. Clinical codes: Histotype: according to WHO classification guidelines Stage: according to International Federation of Gynecological and Obstetrics (FIGO) guidelines Grading: according to WHO classification guidelines Debulking: NED: not evident disease; mRD: minimal residual disease; GRD: gross residual disease Therapy code: P: Platinum without taxanes; PT: Platinum/paclitaxel
2015-04-30 | E-GEOD-67819 | biostudies-arrayexpress
Project description:A set of 45 surgical specimens has been profiled for miRNA expression to validate miRNA alterations associated to early relapse in advanced stage ovarian cancer patients.
2015-04-30 | GSE67819 | GEO
Project description:BACKGROUND: Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events. METHODS: Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations. RESULTS: The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p?=?0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p?=?0.001), and that did differ significantly in survival (p?=?0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade. CONCLUSION: This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients. Sixteen early and sixteen advanced stage ovarian carcinomas
2012-08-22 | E-GEOD-24418 | biostudies-arrayexpress
Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung
cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC
2023-07-11 | BIOMD0000001074 | BioModels
Project description:BACKGROUND: Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events. METHODS: Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations. RESULTS: The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p = 0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p = 0.001), and that did differ significantly in survival (p = 0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade. CONCLUSION: This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients.
2012-08-22 | GSE24418 | GEO
Project description:Advanced ovarian cancer usually spreads to the omentum. However, the omental cell-derived molecular determinants modulating its progression have not been thoroughly characterized. Transcriptome profiling was performed on microdissected omental adipose tissue obtained from patients with benign gynecologic diseases and on cancer-associated omental tissue obtained from patients with advanced-stage, high grade serous ovarian cancer.
2019-09-20 | GSE120196 | GEO