Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA125879 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Methylome sequencing identifies potential epigenetic prognostic biomarker signature that stratifies triple negative breast cancer
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Project description:This study identifies miR-138 as a prognostic biomarker for basal breast carcinoma. Identification of differentially regulated genes upon miR-138 knockdown (AntagomiR-138) in basal breast cancer cell lines MDA-MB-231
2020-04-13 | GSE110659 | GEO
Project description:Prognostic signature of epithelial-mesenchymal transition in breast cancer
| PRJNA110335 | ENA
Project description:A Basal Stem Cell Signature Identifies Aggressive Prostate Cancer Phenotypes
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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
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:Gene Expression Profiling of Primary Male Breast Cancers Reveals Two Unique Subgroups and Identifies N-acetyltransferase-1 (NAT1) as a Novel Prognostic Biomarker
| PRJNA144829 | ENA
Project description:The prognostic role of a gene signature from tumorigenic breast-cancer cells.
| PRJNA98557 | ENA
Project description:Interferons are crucial for adaptive immunity and play an important role in the immune landscape of breast cancer. Using microarray-based gene expression analysis, we examined the subtype specific prognostic significance of interferon-γ (IFN-γ) as a single gene as well as an IFN-γ signature covering the signaling pathway in 461 breast cancer patients. Prognostic significance of IFN-γ as well as the IFN-γ signature for metastasis-free survival (MFS) were examined using Kaplan Meier as well as univariate and multivariate Cox regression analyses in the whole cohort and in different molecular subtypes. Kaplan Meier curves and univariate Cox regression analyses showed that the prognostic significance of IFN-γ as a single gene was limited to basal-like breast cancer (P=0.033). In contrast, the IFN-γ associated gene signature was a significant prognostic factor in the whole cohort (HR 1.554; 95%CI 1.1099-2.199; P=0.013) as well as in the luminal B (P=0.007) and HER2-positive (P=0.033) molecular subtype with borderline significance in basal-like breast cancer(P=0.050). In multivariate analysis, the IFN-γ signature retained its independent prognostic significance (HR 2.287; 95% CI: 1.410-3.633;P<0.001) in the entire cohort. These results underline the subtype-dependent prognostic influence of the immune system in early breast cancer.
2020-09-22 | GSE158309 | GEO