179110b8-0867-4607-a0ea-19a69d8679bb - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001009703 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:RNAseq for Patients of NIBIT-M4 clinical trial
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Project description:GOYA DLBCL clinical trial - RNASeq dataset
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Project description:This dataset includes a total of 18 prostate cancer patients undergoing cryoimmunotherapy (cryoIT). The clinical trial is registered at ClinicalTrials.gov under the identifier NCT02423928. PBMC specimens obtained before and after cryoIT and prostate biopsies at the time of cryoablation are included in this dataset. The specific time points of sampling can be found in the table provided with the dataset. Both visit number and study week are provided for this purpose, with study week 0 corresponding to the time of cryoablation (cryoIT).
2022-10-30 | E-MTAB-10998 | biostudies-arrayexpress
Project description:We have analyzed gene expression microarray datasets from four different clinical trials to assess accuracy of gene expression based signature in predicting treatment complete response in patients with multiple myeloma. Two of four datasets were made available via The Intergroupe Francophone du Myélome (IFM) group, and remaining two datasets were downloaded from NCBI GEO portal with accession IDs: GSE19784 (HOVON65/GMMG-HD4 trial) and GSE9782 (APEX/SUMMIT trial). Analysis UUID: datasets_archive--2afcd42a-7e12-11e3-9145-5fcc1e060548--15-Jan-2014-12-23-44-CST. Following dataset provides gene expression microarray dataset for IFM-2005 trial involving 67 newly diagnosed patients with MM. A second and larger dataset involving 136 newly diagnosed patients with MM from IFM-2005 trial can be accessed via NCBI GSE ID: GSE39754.
2014-04-21 | GSE55145 | GEO
Project description:We have analyzed gene expression microarray datasets from four different clinical trials to assess accuracy of gene expression based signature in predicting treatment complete response in patients with multiple myeloma. Two of four datasets were made available via The Intergroupe Francophone du Myélome (IFM) group, and remaining two datasets were downloaded from NCBI GEO portal with accession IDs: GSE19784 (HOVON65/GMMG-HD4 trial) and GSE9782 (APEX/SUMMIT trial). Analysis UUID: datasets_archive--2afcd42a-7e12-11e3-9145-5fcc1e060548--15-Jan-2014-12-23-44-CST. Following dataset provides gene expression microarray dataset for IFM-2005 trial involving 67 newly diagnosed patients with MM. A second and larger dataset involving 136 newly diagnosed patients with MM from IFM-2005 trial can be accessed via NCBI GSE ID: GSE39754. CD-138 purified plasma cells from 67 newly diagnosed patients with MM were profiled using Affymetrix Exon-1.0 ST microarray platform. Pre-processing and normalization of dataset was carried out using dChip (http://www.hsph.harvard.edu/cli/complab/dchip/exon.htm#expressio) and R package - aroma.affymetrx (http://www.aroma-project.org/vignettes/FIRMA-HumanExonArrayAnalsis). Patients subsequently received bortezomib based induction therapy, followed by autologus stem cell transplant. Post-induction treatment response is attached in metadata file.
2014-04-21 | E-GEOD-55145 | biostudies-arrayexpress
Project description:This gene expression set contains data from patients included in the HOVON95 clinical trial. Using this data the relation between a signature identifying patients with aggressive biology and clinical parameters was studied in newly diagnosed multiple myeloma patients. This dataset was used to identify the relationship between a signature for aggressive disease and clinical parameters.
2022-06-01 | GSE164706 | GEO
Project description:This gene expression set contains data from patients included in the HOVON129 clinical trial. Using this data the relation between a signature identifying patients with aggressive biology and clinical parameters was studied in newly diagnosed plasma cell leukemia patients. This dataset was used to identify the relationship between a signature for aggressive disease and clinical parameters in plasma cell leukemia.
2022-06-01 | GSE164703 | GEO
Project description:In this study, we employed a hypothesis-free and data-driven analytical approach to investigate the CSF proteome in 29 patients with SLE. Herein, our aim was to explore the CSF proteome with data-independent acquisition mass spectrometry (DIA-MS) and cluster SLE patients based on their CSF proteomic patterns. In addition, we aimed to investigate the relationship between protein patterns and a comprehensive clinical dataset including demographic variables, medical history, clinical rheumatologic and neurologic disease manifestations, neurocognitive functionality, cerebral MRI and laboratory measurements.The proteomic data was used for sample clustering and clusters were analyzed for clinical dataset variance. Proteins were clustered in modules using Weighted Gene Co-expression Correlation Network Analysis (WGCNA) and modules were biologically characterized and analyzed for correlation to the clinical dataset. Three patient clusters were identified. Cluster 1 was characterized by the highest frequency of nephritis, depression, and cognitive dysfunction. Cluster 2 showed the highest frequency of alopecia and SSA-antibodies, and a low frequency of cognitive impairment. Cluster 3 had a higher frequency of autonomic neuropathy and lupus headache. Six protein modules were identified (M1-M6). The modules were characterized by nervous tissue proteins (M1), CNS lipoproteins (M2), macrophage proteins (M3), plasma proteins (M4), immunoglobulins (M5), and intracellular metabolic proteins (M6). Module 1 and M2 proteins were most abundant in patient cluster 1 and correlated with nephritis, depression and cognitive impairment. Increased abundance of M4 and M5 proteins were most distinct in patient cluster 2 and inversely correlated to cognitive impairment and brain atrophy. We conclude that patients clustered by their CSF proteomic pattern had different disease phenotypes. Nephritis and neuronal damage defined the group with higher levels of neuronal proteins in CSF, which may suggest shared pathogenetic pathways in SLE affecting the kidney and CNS.
2026-02-09 | PXD053422 | Pride