Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA221194 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:High-grade serous carcinoma has a poor prognosis, owing primarily to its early dissemination throughout the abdominal cavity. Genomic and proteomic approaches have provided snapshots of the proteogenomics of ovarian cancer, but a systematic examination of both the tumour and stromal compartments is critical in understanding ovarian cancer metastasis. We developed a label- free proteomic workflow to analyse as few as 5,000 formalin-fixed, paraffin-embedded cells microdissected from each compartment. The tumour proteome was stable during progression from in situ lesions to metastatic disease; however, the metastasis-associated stroma was characterized by a highly conserved proteomic signature, prominently including the methyltransferase nicotinamide N-methyltransferase (NNMT) and several of the proteins that it regulates. Stromal NNMT expression was necessary and sufficient for functional aspects of the cancer-associated fibroblast (CAF) phenotype, including the expression of CAF markers and the secretion of cytokines and oncogenic extracellular matrix. Stromal NNMT expression supported ovarian cancer migration, proliferation and in vivo growth and metastasis. Expression of NNMT in CAFs led to depletion of S-adenosyl methionine and reduction in histone methylation associated with widespread gene- expression changes in the tumour stroma. This work supports the use of ultra-low-input proteomics to identify candidate drivers of disease phenotypes. NNMT is a central, metabolic regulator of CAF differentiation and cancer progression in the stroma that may be therapeutically targeted.
2019-04-18 | PXD006396 | Pride
Project description:Tissue-resident CXCR4+ macrophage as a poor prognosis signature promotes pancreatic ductal adenocarcinoma progression
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Project description:Identifying ovarian cancer populations associated with poor prognosis
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Project description:Gastric cancer (GC) is associated with high mortality rates and an unfavorable prognosis at advanced stages. In addition, there are no effective methods for diagnosing gastric cancer at an early stage or for predicting the outcome for the purpose of selecting patient-specific treatment options. Therefore, it is important to investigate new methods for GC diagnosis. We designed a custom microarray of gastric cancer. The customized microarray contained 1042 canceration and prognosis related genes identical to the probes on the Agilent microarray. DNA microarray profilling analysis was performed on gastric cancer tissues and premalignant tissues (20 samples per group).
2014-03-01 | E-MTAB-1881 | biostudies-arrayexpress
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)
2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress
Project description:Ovarian metastatic tumors that contain component of signet-ring cells are known as Krukenberg tumors and originate mainly from the stomach (76%), intestines (11%), breast (4%) and other organs(1). KTs chiefly affect premenopausal women. Although a few studies have suggested that patients might benefit from metastasectomy with systemic chemotherapy, optimal treatment options are limited, and the prognosis is poor. Affymetrix Oncoscan arrays were performed according to the manufacturer's directions on DNA extracted from FFPE-Krukenberg tumor tissues and primary gastric cancer tissues.
2018-12-31 | E-MTAB-6277 | biostudies-arrayexpress
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis.
2011-07-19 | GSE13861 | GEO
Project description:The standardized uptake value (SUV), an indicator of the glucose uptake degree in 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), has been used as a prognostic factor in malignant tumors. We aimed to identify a signature reflecting prognostic SUV characteristics in triple negative breast cancer (TNBC). Transcriptome profiling was performed to identify a signature associated with the SUV in TNBC patients who underwent preoperative FDG-PET. We defined a signature significantly associated with the SUV (|r| > .35; P < .01). The SUV signature showed independent clinical utility for predicting BRC prognosis. Integrative analysis demonstrated a significance of the signature in predicting the response to immunotherapy and revealed that a signaling axis defined by TP53-FOXM1 and its downstream effectors in glycolysis-gluconeogenesis, including LDHA, might be important mediators in the FDG-PET process. Our results reveal characteristics of glucose uptake captured by FDG-PET, supporting an understanding of glucose metabolism as well as poor prognosis in TNBC patients with a high SUV.
2020-03-18 | GSE135565 | GEO
Project description:Clinical heterogeneity of gastric cancer reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of gastric cancer. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis.
2012-04-29 | E-GEOD-28541 | biostudies-arrayexpress
Project description:Advanced-stage ovarian cancer is one of the most lethal gynecologic malignancies. To improve prognosis of patients with ovarian cancers, a predictive biomarkers leading to personalized treatments are required. In this large-scale cross-platform study of six microarray datasets consisting of 1054 ovarian cancer patients, we developed a novel risk classification system based on a 126-gene expression signature for predicting overall survival by applying elastic net7 and 10-fold cross validation to a Japanese dataset A (n = 260). We further validated its predictive ability with the five other datasets using multivariate analysis. Also, through gene ontology and pathway analyses of 1109 high-risk ovarian cancer specific transcripts, we identified a significant reduction of expression of immune-response related genes, especially on the antigen presentation pathway. Furthermore, an immunohistochemical analysis demonstrated that the number of CD8 T lymphocytes infiltrating into tumor tissue was significantly decreased in high-risk ovarian cancers. These predictive biomarkers based on the 126-gene expression signature will identify high-risk ovarian cancer patients who need novel immune-activating therapeutic approaches, leading to improved outcomes for such patients. 40 patients who were diagnosed as advanced-stage high-grade serous ovarian cancer were recruited in this study.
2012-03-01 | E-GEOD-32063 | biostudies-arrayexpress