Project description:Classifying Patients with Psoriatic Arthritis According to their Disease Activity Status using Serum Metabolites and Machine Learning
Project description:Background: We and others have previously demonstrated the potential for circulating exosome microRNAs to aid in disease diagnosis. In this study, we sought the possible utility of serum exosome microRNAs as biomarkers for disease activity in multiple sclerosis patients in response to fingolimod therapy. We studied patients with relapsing-remitting multiple sclerosis prior to and 6 months after treatment with fingolimod. Methods: Disease activity was determined using gadolinium-enhanced magnetic resonance imaging. Serum exosome microRNAs were profiled using next-generation sequencing. Data were analysed using univariate/multivariate modelling and machine learning to determine microRNA signatures with predictive utility. Results: we identified 15 individual miRNAs that were differentially expressed in serum exosomes from post-treatment patients with active versus quiescent disease. The targets of these microRNAs clustered in ontologies related to the immune and nervous systems, and signal transduction. While the power of individual microRNAs to predict disease status post-fingolimod was modest (average 77%, range 65 to 91%), several combinations of 2 or 3 miRNAs were able to distinguish active from quiescent disease with greater than 90% accuracy. Further stratification of patients identified additional microRNAs associated with stable remission, and a positive response to fingolimod in patients with active disease prior to treatment. Conclusions: Overall, these data underscore the value of serum exosome microRNA signatures as non-invasive biomarkers of disease in multiple sclerosis and suggest they may be used to predict response to fingolimod in future clinical practice. Additionally, these data suggest that fingolimod may have mechanisms of action beyond its known functions.
Project description:This dataset contains .RAW files acquired for the paper: Identifying Serum Metabolomic Markers Associated with Skin Disease Activity in patients with Psoriatic Arthritis.
Project description:This dataset contains .RAW files acquired for the paper: Identifying Serum Metabolomic Markers Associated with Skin Disease Activity in patients with Psoriatic Arthritis.
Project description:Rheumatoid arthritis (RA) causes serious disability and productivity loss, and there is an urgent need for appropriate biomarkers for diagnosis, treatment assessment, and prognosis evaluation. To identify serum markers of RA, we performed mass spectrometry (MS)-based proteomics, and we obtained 24 important markers in normal and RA patient samples using a random forest machine learning model and 11 protein-protein interaction (PPI) network topological analysis methods. Re-analyze markers using additional proteomics datasets, immune infiltration status, tissue specificity, subcellular localization, correlation analysis with disease activity-based diagnostic indications, diagnostic receiver-operating characteristic analysis We discovered that ORM1 in serum is significantly differentially expressed in normal and RA patient samples, which is positively correlated with disease activity, and is closely related to CD56dim natural killer cell, effector memory CD8+ T cell and natural killer cell in the pathological mechanism, which can be better utilized for diagnosis, monitoring disease progression and efficacy assessment. This study supplies a comprehensive strategy for discovering potential serum biomarkers of RA, and provides a different perspective for comprehending the pathological mechanism of RA, identifying potential therapeutic targets, and disease management.
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
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.
Project description:Large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system.
Project description:A total of 762 proteins were identified from 35 pooled rheumatoid arthritis (RA) patients with moderate to high disease activity and 60 pooled healthy serum samples. Among them, 142 proteins including 68 up-regulation and 74 down-regulation were identified in RA compared to healthy subjects.