Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA248377 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The prognostic and predictive value of a three-microRNA-based classifier in NSCLC
| PRJNA234769 | ENA
Project description:The prognostic and predictive value of a six-microRNA-based classifier in stage II colon cancer
| PRJNA213381 | ENA
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative.
2010-05-05 | GSE21653 | GEO
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative. The IPC series contained frozen tumor samples obtained from 266 early breast cancer patients who underwent initial surgery in our institution between 1992 and 2004. They included 227 cases previously reported {Finetti, 2008 #1758} and 39 additional cases, all similarly profiled using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays as previously described {Finetti, 2008 #1758}. The study was approved by the IPC review board, and informed consent was available for each case. Gene expression data of 266 BCs were quantified by using whole-genome DNA microarrays (HG-U133 plus 2.0, Affymetrix).
2010-05-05 | E-GEOD-21653 | biostudies-arrayexpress
Project description:Background MicroRNA expression is frequently dysregulated in cancer and it could be used potentially as a disease classifier and a prognostic tool in cancer. It has been reported that the cancer associated specific microRNAs were stably detected in blood. The objective of this study was to discover a panel of circulating microRNAs as potential ER+/HER2- breast cancer biomarkers. Methods We compared levels of circulating microRNAs in blood samples from 11 ER+/HER2- advanced breast cancer patients with age-matched 5 control subjects by using microarray-based expression profiling. We validated the level of microRNAs by real-time quantitative polymerase cycle reaction (RT-qPCR) in 40 control subjects, 180 early breast cancer patients (EBC), and 52 metastatic breast cancer patients (MBC). Then, we assessed the association between the levels of microRNA and clinical outcomes of ER+/HER2- metastatic breast cancer. Background MicroRNA expression is frequently dysregulated in cancer and it could be used potentially as a disease classifier and a prognostic tool in cancer. It has been reported that the cancer associated specific microRNAs were stably detected in blood. The objective of this study was to discover a panel of circulating microRNAs as potential ER+/HER2- breast cancer biomarkers. Methods We compared levels of circulating microRNAs in blood samples from 11 ER+/HER2- advanced breast cancer patients with age-matched 5 control subjects by using microarray-based expression profiling. We validated the level of microRNAs by real-time quantitative polymerase cycle reaction (RT-qPCR) in 40 control subjects, 180 early breast cancer patients (EBC), and 52 metastatic breast cancer patients (MBC). Then, we assessed the association between the levels of microRNA and clinical outcomes of ER+/HER2- metastatic breast cancer. Controls: 5 cases; ER +/HER2- breast cancer patients : 11 cases
2013-12-12 | E-GEOD-53179 | biostudies-arrayexpress
Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification
2008-08-29 | GSE12071 | GEO
Project description:Background MicroRNA expression is frequently dysregulated in cancer and it could be used potentially as a disease classifier and a prognostic tool in cancer. It has been reported that the cancer associated specific microRNAs were stably detected in blood. The objective of this study was to discover a panel of circulating microRNAs as potential ER+/HER2- breast cancer biomarkers. Methods We compared levels of circulating microRNAs in blood samples from 11 ER+/HER2- advanced breast cancer patients with age-matched 5 control subjects by using microarray-based expression profiling. We validated the level of microRNAs by real-time quantitative polymerase cycle reaction (RT-qPCR) in 40 control subjects, 180 early breast cancer patients (EBC), and 52 metastatic breast cancer patients (MBC). Then, we assessed the association between the levels of microRNA and clinical outcomes of ER+/HER2- metastatic breast cancer. Background MicroRNA expression is frequently dysregulated in cancer and it could be used potentially as a disease classifier and a prognostic tool in cancer. It has been reported that the cancer associated specific microRNAs were stably detected in blood. The objective of this study was to discover a panel of circulating microRNAs as potential ER+/HER2- breast cancer biomarkers. Methods We compared levels of circulating microRNAs in blood samples from 11 ER+/HER2- advanced breast cancer patients with age-matched 5 control subjects by using microarray-based expression profiling. We validated the level of microRNAs by real-time quantitative polymerase cycle reaction (RT-qPCR) in 40 control subjects, 180 early breast cancer patients (EBC), and 52 metastatic breast cancer patients (MBC). Then, we assessed the association between the levels of microRNA and clinical outcomes of ER+/HER2- metastatic breast cancer.
2013-12-12 | GSE53179 | GEO
Project description:MicroRNA Expression Analysis of Advanced Colorectal Cancer Reveals a microRNA Signature with Prognostic and Predictive Value
| PRJNA422598 | ENA
Project description:Pre-clinical studies reported the immunogenic or immunomodulatory effects of traditional cancer therapies. However, the publicly available well-curated and harmonized breast cancer datasets, such as TCGA, METABRIC, and MetaGxBreast, lack careful curation of treatment regimens. Hence, limited exploration of the impact of therapies on the prognostic/predictive value of breast cancer biomarkers. Herein, we describe a pooled and treatment-curated gene-expression dataset to investigate the impact of treatments on the prognostic/predictive value of biomarkers. We searched the gene expression omnibus database to identify potential human breast cancer gene-expression datasets with anthracycline/taxane treatment. Published datasets with the detailed treatment regimen, clinical endpoint, clinical-pathological, and gene-expression data were extracted and harmonized. The dataset described herein would help researchers explore the interaction between gene-expression biomarkers and immunogenic/immunomodulatory treatments in breast cancer.
2024-03-09 | GSE205568 | GEO
Project description:Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.
2022-02-14 | PXD021491 | Pride