Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA260713 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The study entails novel bio-marker discovery of Tumor Aggressive Grade signature (TAGs) genes and their role in recurrence free survival of breast cancer (BC) patients. Current BC dataset was used for co-expression analysis of TAGs genes and their role in BC progression. Additionally, recent findings have suggested an importance of structural organization of sense-antisense gene pairs (SAGPs) for transcription, post-transcriptional and post-translational events and their associations with cancer and disease. We studied SAGPs in which both gene partners are protein encoding genes (coding-coding SAGPs), their role in human BC development and demonstrated their potential for BC stratification and prognosis. Based on gene expression and correlation analyses we identified the robust set of breast cancer-relevant SAGPs (BCR-SAGPs). We isolated and characterized the sense-antisense gene signature (SAGS) and evaluated its prognostic potential in various gene expression datasets comprising 1161 BC patients. The methods used included the Cox proportional survival analysis, statistical analysis of clinicopathologic parameters and differential gene expression. The SAGS was effective in identification of BC patients with the most aggressive disease. Independently, we validated the SAGS using 58 RNA samples of breast cancer tumors purchased from OriGene Technologies (Rockville, MD).
2015-05-10 | GSE61304 | GEO
Project description:The study entails novel bio-marker discovery of Tumor Aggressive Grade signature (TAGs) genes and their role in recurrence free survival of breast cancer (BC) patients. Current BC dataset was used for co-expression analysis of TAGs genes and their role in BC progression. Additionally, recent findings have suggested an importance of structural organization of sense-antisense gene pairs (SAGPs) for transcription, post-transcriptional and post-translational events and their associations with cancer and disease. We studied SAGPs in which both gene partners are protein encoding genes (coding-coding SAGPs, ccSAGPs), their role in human BC development and demonstrated their potential for BC stratification and prognosis. Based on gene expression and correlation analyses we identified the robust set of breast cancer-relevant ccSAGPs (BCR-ccSAGPs). We isolated and characterized the sense-antisense gene classifier (SAGC) and evaluated its prognostic potential in various gene expression datasets comprising 1161 BC patients. The methods used included the Cox proportional survival analysis, statistical analysis of clinicopathologic parameters and differential gene expression.The SAGC was effective in identification of BC patients with the most aggressive disease. Independently, we validated the SAGC using 58 RNA samples of breast cancer tumors purchased from OriGene Technologies (Rockville, MD). Sixty two total RNA samples from breast tumors and normal breast epithelium have been purchased from OriGene Technologies in March, 2011. Four RNA samples were obtained from normal individuals. Among 58 breast tumors (58 patients) 56 were diagnosed as ductal breast adenocarcinoma, 1 - as lobular breast adenocarcinoma and 1 - as squamous cell carcinoma. Gene expression data for all the samples were quantified by using whole-genome RNA microarrays (HG-U133 plus 2.0, Affymetrix).
2015-05-10 | E-GEOD-61304 | biostudies-arrayexpress
Project description:Our findings demonstrate that CDCP1 is a novel modulator of HER2 signalling, and a biomarker for the stratification of breast cancer patients with poor prognosis GEP analysis of human breast cancer cell lines SKBR3 overexpressing CDCP1 and control.
2016-01-08 | E-GEOD-67019 | biostudies-arrayexpress
Project description:Our findings demonstrate that CDCP1 is a novel modulator of HER2 signalling, and a biomarker for the stratification of breast cancer patients with poor prognosis
2016-01-08 | GSE67019 | GEO
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:Genome-Wide Discovery of Novel Breast Cancer Predisposing Mutations
| PRJNA264559 | ENA
Project description:Genome-Wide Discovery of Novel Breast Cancer Predisposing Mutations
| PRJNA264560 | ENA
Project description:Triple-negative breast cancer (TNBC) is the most heterogeneous and aggressive subtype of breast carcinoma, defined by the absence of clinical biomarkers and the lack of targeted therapies. Despite numerous clinical trials, patient stratification remains suboptimal, limiting the identification of effective treatment strategies. In this study, we aimed to identify biomarkers exclusively expressed in the basal mammary epithelial compartment to refine TNBC subclassification. Through computational analysis of single-cell RNA sequencing data, we defined a set of basal identity genes, which were subsequently validated by immunohistochemistry in two independent TNBC cohorts. This approach enabled the identification of a novel TNBC subgroup, termed true basal TNBC (tB-TNBC), associated with poorer prognosis and distinct molecular features. To uncover therapeutic vulnerabilities in this subgroup, we conducted a high-throughput screen of 3,200 FDA-approved compounds in breast cancer cell lines classified by basal marker expression. This analysis identified dasatinib as a promising candidate with selective activity against tB-TNBC models. Furthermore, TAGLN emerged as a strong predictive biomarker of dasatinib response, with functional studies confirming its role in modulating drug sensitivity. Altogether, these findings support the clinical utility of basal markers for TNBC stratification and highlight a targeted treatment opportunity for tB-TNBC patients.
2026-02-04 | GSE300385 | GEO
Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis 44 samples
2009-11-30 | E-GEOD-11901 | biostudies-arrayexpress