Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA142791 | 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 (training set)
| PRJNA142789 | ENA
Project description:microRNA alterations associated to early relapse in advanced stage ovarian cancer patients (validation set)
| PRJNA280993 | ENA
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:Objectives: MicroRNAs (miRNAs) are a class of small non-coding RNAs that negatively regulate gene expression primarily through post-transcriptional modification. We tested the hypothesis that miRNA expression is associated with overall survival in advanced ovarian cancer. Methods: Cases included newly diagnosed patients with stage III or IV serous ovarian cancer. RNA from a training set of 62 cases was hybridized to an miRNA microarray containing 470 mature human transcripts. Cox regression was performed to identify miRNAs associated with overall survival. External validation was performed using quantitative RT-PCR miRNA assays in an independent test set of 123 samples. MiRNA targets and associated biologic pathways were predicted in silico. Results: Of all patients, 80% had high-grade, stage IIIC tumors and 64% underwent optimal cytoreduction. The median survival for the entire cohort was 49 ± 4 months. The training set identified 3 miRNAs associated with survival - miR-337, miR-410, and miR-645. An miRNA signature containing miR-410 and miR-645 was most strongly associated with overall survival in the training set (HR=2.96, 95% CI: 1.51-5.78). This miRNA survival signature (MiSS) was validated in the test set (HR=1.71, 95% CI: 1.05-2.78). The MiSS was independent of FIGO stage and surgical debulking. Conclusions: The data suggest that an MiSS that contains miR-410 and miR-645 is negatively associated with overall survival in advanced serous ovarian cancer. This signature, when further validated, may be useful in individualizing care for the ovarian cancer patient. Pathway analyses identify biologically plausible mechanisms. Cases included newly diagnosed patients with stage III or IV serous ovarian cancer. RNA from a training set of 62 cases was hybridized to an miRNA microarray containing 470 mature human transcripts. Cox regression was performed to identify miRNAs associated with overall survival.
2011-02-15 | E-GEOD-27290 | biostudies-arrayexpress
Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. Gene expression profiling was completed for an extensive set of 185 primary ovarian tumors and 10 normal ovarian surface epithelium using the Affymetrix human U133A microarray
2011-01-20 | E-GEOD-26712 | biostudies-arrayexpress
Project description:Objectives: MicroRNAs (miRNAs) are a class of small non-coding RNAs that negatively regulate gene expression primarily through post-transcriptional modification. We tested the hypothesis that miRNA expression is associated with overall survival in advanced ovarian cancer. Methods: Cases included newly diagnosed patients with stage III or IV serous ovarian cancer. RNA from a training set of 62 cases was hybridized to an miRNA microarray containing 470 mature human transcripts. Cox regression was performed to identify miRNAs associated with overall survival. External validation was performed using quantitative RT-PCR miRNA assays in an independent test set of 123 samples. MiRNA targets and associated biologic pathways were predicted in silico. Results: Of all patients, 80% had high-grade, stage IIIC tumors and 64% underwent optimal cytoreduction. The median survival for the entire cohort was 49 ± 4 months. The training set identified 3 miRNAs associated with survival - miR-337, miR-410, and miR-645. An miRNA signature containing miR-410 and miR-645 was most strongly associated with overall survival in the training set (HR=2.96, 95% CI: 1.51-5.78). This miRNA survival signature (MiSS) was validated in the test set (HR=1.71, 95% CI: 1.05-2.78). The MiSS was independent of FIGO stage and surgical debulking. Conclusions: The data suggest that an MiSS that contains miR-410 and miR-645 is negatively associated with overall survival in advanced serous ovarian cancer. This signature, when further validated, may be useful in individualizing care for the ovarian cancer patient. Pathway analyses identify biologically plausible mechanisms.
2011-02-15 | GSE27290 | GEO
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