The Correlation among Serum Proteomics and Clinical Profiles in Fibromyalgia Women
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ABSTRACT:
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: Prof. Chia-Li Han
PROVIDER: PXD013905 | JPOST Repository | Sun May 17 00:00:00 BST 2020
REPOSITORIES: jPOST
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2014-08-31 | E-MTAB-2883 | biostudies-arrayexpress
Project description:Objective: To evaluate the clinical impact of chromosomal microarray (CMA) on the management of paediatric patients in Hong Kong Methods: We performed NimbleGen 135k oligonucleotide array on 327 children with intellectual disability (ID)/ developmental delay (DD), autism spectrum disorders (ASD), and/or multiple congenital anomalies (MCAs) in a university-affiliated paediatric unit from January 2011 to May 2013. The medical records of patients were reviewed in September 2013, focusing on the pathogenic/likely pathogenic CMA findings and their “clinical actionability” based on established criteria. Results: Thirty-seven patients were reported to have pathogenic/likely pathogenic results, while 40 had findings of unknown significance. This gives a detection rate of 11% for clinically significant (pathogenic/likely pathogenic) findings. The significant findings have prompted clinical actions in 28 out of 37 patients (75.7%), while the findings with unknown significance have led to further management recommendation in only 1 patient (p<0.001). Nineteen out of the 28 management recommendations are “evidence-based” on either practice guidelines endorsed by a professional society (n=9, Level 1) or peer-reviewed publications making medical management recommendation (n=10, Level 2). CMA results impact medical management by precipitating referral to a specialist (n=24); diagnostic testing (n=24), surveillance of complications (n=19), interventional procedure (n=7), medication (n=15) or lifestyle modification (n=12).
2014-09-04 | E-MTAB-2884 | biostudies-arrayexpress
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Project description:Background: The clinical and pathologic diversity of systemic lupus erythematosus (SLE) has hindered diagnosis, management, and treatment development. This study clustered adult SLE patients through comprehensive molecular phenotyping to improve distinctions with prognostic and therapeutic relevance. Methods: Plasma, serum, and RNA were collected from 198 adult SLE patients. Disease activity was scored by modified SELENA-SLEDAI. Twenty-nine co-expression module scores were calculated from microarray gene-expression data. Plasma soluble mediators (n=23) and autoantibodies (n=13) were assessed by multiplex bead-based assays and ELISAs. Phenotypic patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators. Findings: SLEDAI scores correlated strongly with interferon module scores, more modestly with plasma cell and select cell cycle modules, and with circulating IFNα, IL21, IL1α, IL17A, IP10, and MIG levels. Co-expression modules and soluble mediators differentiated seven clusters of SLE patients with unique molecular phenotypes. Inflammation and interferon modules were elevated in Clusters 1 (moderately) and 4 (strongly), with decreased T cell modules in Cluster 4. The other clusters differed in monocyte, neutrophil, plasmablast, B cell, and T cell modules. Clusters 1 and 4 had higher SLEDAI scores, and more frequent anti-dsDNA, low complement, and renal activity. These features were also prominent in Cluster 3, which lacked the interferon and inflammation signatures. Arthritis and rashes were common in all clusters. Interpretation: Molecular profiles can distinguish SLE subsets. Prospective longitudinal studies of these profiles may help to improve prognostic evaluation, clinical trial design, and precision medicine approaches.
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Project description:Background Dengue virus (DENV) infection causes viral haemorrhagic fever that is characterized by extensive activation of the immune system. The aim of this study is to investigate the kinetics of the transcriptome signature changes during the course of disease and the association of genes in these signatures with clinical parameters. Methodology/principle findings Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays, which were analysed using principal component analysis, limma, gene set analysis, and weighted gene co-expression network analysis. We show that time since onset of disease, but not diagnosis, has a large impact on the blood transcriptome of patients with non-severe dengue. Clinical diagnosis (according to the WHO classification) does not associate with differential gene expression. Network analysis however indicated that the clinical markers platelet count, fibrinogen, albumin, IV fluid distributed per day and liver enzymes SGOT and SGPT strongly correlate with gene modules that are enriched for genes involved in the immune response and coagulation. Overall, we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of DENV infection. Conclusions/significance Time since onset of disease associates with the shift in transcriptome signatures from immunity and inflammation to cell cycle and repair mechanisms in patients with non-severe dengue. The strong association of time with blood transcriptome changes hampers both the discovery as well as the potential application of biomarkers in dengue. However, we identified gene expression modules that associate with key clinical parameters of dengue that reflect the systemic activity of disease during the course of infection. The expression level of these gene modules may support earlier detection of disease progression as well as clinical management of dengue.
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