Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA145643 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:<p>BRCA1 mutations are a hallmark of hereditary ovarian cancer, strongly linked to deficiencies in homologous recombination (HR) DNA repair and impaired DNA replication fork protection. However, its roles in cancer progression beyond maintaining genomic integrity remain poorly understood. Through metabolomics approaches, we found BRCA1-deficiency strikingly increased choline metabolism. Loss of BRCA1 promotes choline uptake through upregulating choline transporter-like protein 4 (CTL4). BRCA1 directly binds and recruits EZH2-mediated H3K27Me3 deposition to CTL4 promoter. CTL4 was therefore overexpressed in ovarian cancer tissues with BRCA1 mutations. Furthermore, BRCA1-deficiency significantly promotes ovarian cancer invasion, while inhibition of CTL4 reverses the high metastatic potential of BRCA1-deficient ovarian cancer cells, suggesting the functionality and specificity of CTL4 as a therapeutic target. Additionally, we discovered that phosphocholine, the choline metabolite increased by CTL4 overexpression, interacted with and stabilized the epithelial-to-mesenchymal transition inducer FAM3C in BRCA1-deficient ovarian cancer cells. Importantly, we identified a potent CTL4 inhibitor, DT-13, which significantly reduces choline metabolism and effectively suppresses metastasis in BRCA1-deficient ovarian cancers. Therefore, our study uncovers a mechanism underlying metastasis in BRCA1-deficient cancers and identifies CTL4 as a therapeutic target for metastatic ovarian cancer patients with BRCA1 mutations.</p>
2026-05-15 | MTBLS12245 | MetaboLights
Project description:Unique gene expression profile based upon pathologic response in epithelial ovarian cancer
| PRJNA146053 | ENA
Project description:Objective: The immune system is a key player in fighting cancer. Thus, we seeked to identify a molecular ‘immune response signature’ indicating the presence of epithelial ovarian cancer (EOC) and to combine this with a serum protein biomarker panel to increase the specificity and sensitivity for earlier detection of EOC. Methods: Comparing the expression of 32,000 genes in a leukocytes fraction from 48 EOC patients and 20 controls, three uncorrelated shrunken centroid models were selected, comprised of 7, 14, and 6 genes. A second selection step using RT-qPCR data and significance analysis of microarrays yielded 13 genes which were finally used in 343 samples (90 healthy, 6 cystadenoma, 8 low malignant potential tumor, and 239 EOC patients). Using new 65 controls and 224 EOC patients the abundances of six plasma proteins was determined and used in combination with the expression values from the 13 genes for diagnosis of EOC. Results: Combined diagnostic models using either each five gene expression and plasma protein abundance values or 13 gene expression and six plasma protein abundance values can discriminate controls from patients with EOC with Receiver Operator Characteristics Area Under the Curve values of 0.998 and bootstrap .632+ validated classification errors of 3.1% and 2.8%, respectively. The sensitivities were 97.8% and 95.6%, respectively, at a set specificity of 99.6%. Conclusion: The combination of expression based and plasma protein based biomarkers in one diagnostic model increases the sensitivity and the specificity significantly. Such a diagnostic test may allow earlier diagnosis of epithelial ovarian cancer. The expression of a blood cell fraction from 20 healthy controls and 48 patients with epithelial ovarian cancer was compared.
2011-09-01 | E-GEOD-31682 | biostudies-arrayexpress
Project description:A blood-based gene signature characterizing Idiopathic Parkinson's disease
| PRJNA387280 | ENA
Project description:Transcriptional profiling of Homo sapiens inflammatory skin diseases (whole skin biospies): Psoriasis (Pso), vs Atopic Dermatitis (AD) vs Lichen planus (Li), vs Contact Eczema (KE), vs Healthy control (KO) In recent years, different genes and proteins have been highlighted as potential biomarkers for psoriasis, one of the most common inflammatory skin diseases worldwide. However, most of these markers are not psoriasis-specific but also found in other inflammatory disorders. We performed an unsupervised cluster analysis of gene expression profiles in 150 psoriasis patients and other inflammatory skin diseases (atopic dermatitis, lichen planus, contact eczema, and healthy controls). We identified a cluster of IL-17/TNFα-associated genes specifically expressed in psoriasis, among which IL-36γ was the most outstanding marker. In subsequent immunohistological analyses IL-36γ was confirmed to be expressed in psoriasis lesions only. IL-36γ peripheral blood serum levels were found to be closely associated with disease activity, and they decreased after anti-TNFα-treatment. Furthermore, IL-36γ immunohistochemistry was found to be a helpful marker in the histological differential diagnosis between psoriasis and eczema in diagnostically challenging cases. These features highlight IL-36γ as a valuable biomarker in psoriasis patients, both for diagnostic purposes and measurement of disease activity during the clinical course. Furthermore, IL-36γ might also provide a future drug target, due to its potential amplifier role in TNFα- and IL-17 pathways in psoriatic skin inflammation. In recent years, different genes and proteins have been highlighted as potential biomarkers for psoriasis, one of the most common inflammatory skin diseases worldwide. However, most of these markers are not psoriasis-specific but also found in other inflammatory disorders. We performed an unsupervised cluster analysis of gene expression profiles in 150 psoriasis patients and other inflammatory skin diseases (atopic dermatitis, lichen planus, contact eczema, and healthy controls). We identified a cluster of IL-17/TNFα-associated genes specifically expressed in psoriasis, among which IL-36γ was the most outstanding marker. In subsequent immunohistological analyses IL-36γ was confirmed to be expressed in psoriasis lesions only. IL-36γ peripheral blood serum levels were found to be closely associated with disease activity, and they decreased after anti-TNFα-treatment. Furthermore, IL-36γ immunohistochemistry was found to be a helpful marker in the histological differential diagnosis between psoriasis and eczema in diagnostically challenging cases. These features highlight IL-36γ as a valuable biomarker in psoriasis patients, both for diagnostic purposes and measurement of disease activity during the clinical course. Furthermore, IL-36γ might also provide a future drug target, due to its potential amplifier role in TNFα- and IL-17 pathways in psoriatic skin inflammation.
2014-12-02 | GSE63741 | GEO
Project description:Asthma is a chronic inflammatory airway disease characterized by airway inflammation and remodeling. The role of 15-oxo-5Z,8Z,11Z,13E-eicosatetraenoic acid (15-oxoETE), a 15-HETE metabolite catalyzed by 15-prostaglandin dehydrogenase (15-PGDH), has been relatively unexplored in asthma. In this study, we used RNA-seq to explore the effect of 15-KETE on the transcriptome of airway epithelial cells, aiming to identify its potential downstream targets and mechanisms of action.
2026-05-21 | GSE325003 | GEO
Project description:Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by Selected Reaction Monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.
Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.
2019-08-14 | PXD014474 | panorama
Project description:Adaptation During Ovarian Cancer Metastasis Reveals a Predictive Gene Signature
| PRJNA144573 | ENA
Project description:Serum microRNA sequencing for diagnosis of invasive ovarian cancer
| PRJNA371423 | ENA
Project description:Gene expression profiling of immortalized human mesenchymal stem cells with hTERT/E6/E7 transfected MSCs. hTERT may change gene expression in MSCs. Goal was to determine the gene expressions of immortalized MSCs.
2010-09-25 | GSE24338 | GEO