Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis.
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ABSTRACT:
SUBMITTER: Rychkov D
PROVIDER: S-EPMC8223752 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundBone metabolism is disrupted in rheumatoid arthritis (RA); however, the bone metabolic signature of RA is poorly known. The objective of the study is to further characterize the bone metabolic profile of RA and compare it to psoriatic arthritis (PsA), systemic sclerosis (SSc) and healthy controls.MethodsWe did a cross-sectional case-control study on consecutively enrolled patients and age-matched controls. We collected clinical characteristics, serum biomarkers related to bone metabolism and Bone Mineral Density (BMD). A multiple correlation analysis using Spearman's rank correlation coefficient was conducted within the RA patient group to investigate associations between biomarker levels and clinical variables. Machine learning (ML) models and Principal Component Analysis (PCA) was performed to evaluate the ability of bone biomarker profiles to differentiate RA patients from controls.ResultsWe found significantly lower BMD in RA patients compared to PsA, and Systemic Sclerosis SSc groups. RA patients exhibited higher Dkk1, sclerostin and lower P1nP and B-ALP levels compared to controls. No significant differences in CTX levels were noted. Correlation analysis revealed associations between bone biomarkers and clinical variables. PCA and ML highlighted distinct biomarker patterns in RA which can effectively discriminated bone biomarkers profile in RA from controls.ConclusionOur study helped uncover the distinct bone profile in RA, including changes in bone density and unique biomarker patterns. These findings enhance our comprehension of the intricate links between inflammation, bone dynamics, and RA activity, offering potential insights for diagnostic and therapeutic advancements in managing bone involvement in this challenging condition.
| S-EPMC10665911 | biostudies-literature
Project description:ObjectivesAround 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers.MethodsModels were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). The model's predictions were explained using Shapley values to extract a biomarker of inadequate response.ResultsWe included 493 therapeutic sequences from ESPOIR, 239 from EAC and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count and corticosteroid co-treatment as predictors. The model reached an AUROC of 0.72 [95% CI (0.63, 0.80)] on the external validation set, where 70% of patients were responders to MTX. Patients predicted as inadequate responders had only 38% [95% CI (20%, 58%)] chance to respond and using the algorithm to decide to initiate MTX would decrease inadequate-response rate from 30% to 23% [95% CI: (17%, 29%)]. A biomarker was identified in patients with moderate or high activity (DAS28 > 3.2): patients with a lymphocyte count superior to 2000 cells/mm3 are significantly less likely to respond.ConclusionOur study highlights the usefulness of machine learning in unveiling subgroups of inadequate responders to MTX to guide new therapeutic strategies. Further work is needed to validate this approach.
| S-EPMC10321123 | biostudies-literature
Project description:BackgroundRheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study's purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE.MethodsA total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package.ResultsCompared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA.ConclusionsWe established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.
| S-EPMC8666017 | biostudies-literature
Project description:Investigating genetic interactions (epistasis) has proven difficult despite the recent advances of both laboratory methods and statistical developments. With no 'best' statistical approach available, combining several analytical methods may be optimal for detecting epistatic interactions. Using a multi-stage analysis that incorporated supervised machine learning and methods of association testing, we investigated epistatic interactions with a well-established genetic factor (PTPN22 1858T) in a complex autoimmune disease (rheumatoid arthritis (RA)). Our analysis consisted of four principal stages: Stage I (data reduction)-identifying candidate chromosomal regions in 292 affected sibling pairs, by predicting PTPN22 concordance using multipoint identity-by-descent probabilities and a supervised machine learning algorithm (Random Forests); Stage II (extension analysis)-testing detailed genetic data within candidate chromosomal regions for epistasis with PTPN22 1858T in 677 cases and 750 controls using logistic regression; Stage III (replication analysis)-confirmation of epistatic interactions in 947 cases and 1756 controls; Stage IV (combined analysis)-a pooled analysis including all 1624 RA cases and 2506 control subjects for final estimates of effect size. A total of seven replicating epistatic interactions were identified. SNP variants within CDH13, MYO3A, CEP72 and near WFDC1 showed significant evidence for interaction with PTPN22, affecting susceptibility to RA.
| S-EPMC3118040 | biostudies-literature
Project description:BackgroundThis study aimed to search for blood biomarkers among the profiles of patients with RA-ILD by using machine learning classifiers and probe correlations between the markers and the characteristics of RA-ILD.MethodsA total of 153 RA patients were enrolled, including 75 RA-ILD and 78 RA-non-ILD. Routine laboratory data, the levels of tumor markers and autoantibodies, and clinical manifestations were recorded. Univariate analysis, least absolute shrinkage and selection operator (LASSO), random forest (RF), and partial least square (PLS) were performed, and the receiver operating characteristic (ROC) curves were plotted.ResultsUnivariate analysis showed that, compared to RA-non-ILD, patients with RA-ILD were older (p < 0.001), had higher white blood cell (p = 0.003) and neutrophil counts (p = 0.017), had higher erythrocyte sedimentation rate (p = 0.003) and C-reactive protein (p = 0.003), had higher levels of KL-6 (p < 0.001), D-dimer (p < 0.001), fibrinogen (p < 0.001), fibrinogen degradation products (p < 0.001), lactate dehydrogenase (p < 0.001), hydroxybutyrate dehydrogenase (p < 0.001), carbohydrate antigen (CA) 19-9 (p < 0.001), carcinoembryonic antigen (p = 0.001), and CA242 (p < 0.001), but a significantly lower albumin level (p = 0.003). The areas under the curves (AUCs) of the LASSO, RF, and PLS models attained 0.95 in terms of differentiating patients with RA-ILD from those without. When data from the univariate analysis and the top 10 indicators of the three machine learning models were combined, the most discriminatory markers were age and the KL-6, D-dimer, and CA19-9, with AUCs of 0.814 [95% confidence interval (CI) 0.731-0.880], 0.749 (95% CI 0.660-0.824), 0.749 (95% CI 0.660-0.824), and 0.727 (95% CI 0.637-0.805), respectively. When all four markers were combined, the AUC reached 0.928 (95% CI 0.865-0.968). Notably, neither the KL-6 nor the CA19-9 level correlated with disease activity in RA-ILD group.ConclusionsThe levels of KL-6, D-dimer, and tumor markers greatly aided RA-ILD identification. Machine learning algorithms combined with traditional biostatistical analysis can diagnose patients with RA-ILD and identify biomarkers potentially associated with the disease.
| S-EPMC9118651 | biostudies-literature
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.
2022-04-19 | PXD032912 | Pride
Project description:BackgroundMajor challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients.MethodsExome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set.FindingsSignificantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients.InterpretationMajority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways.FundingSingapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.
| S-EPMC8808170 | biostudies-literature
Project description:BackgroundDifferential diagnosis among psoriatic arthritis (PsA) and seronegative rheumatoid arthritis (Abneg RA) can be challenging particularly in the clinical setting of peripheral phenotype and autoantibodies seronegativity. The aim of the study was to identify synovial tissue (ST) biomarkers differentially expressed in PsA and Abneg RA and test their predictive value of therapeutic response.MethodsThirty-four PsA patients [12 DMARD naive and 22 non-responder to methotrexate (MTX-IR)] with peripheral joint involvement and 55 Abneg RA (27 DMARD naive and 28 MTX-IR) underwent US-guided ST biopsy and immunohistochemistry (IHC) for CD68+, CD3+, CD20+, CD21+, CD117+, and CD138+ cells. After study entry, each DMARD-naive patient started MTX therapy and was followed in an outpatient setting for at least 6 months to define the achievement of Minimal Disease Activity (PsA) and DAS remission (Abneg RA) status respectively. Each IR-MTX patient was treated according to EULAR recommendations.ResultsAt study entry, IHC analysis revealed that PsA patients had comparable levels of lining and sublining CD68+ and sublining CD21+, CD20+, and CD3+ cells than Abneg RA, despite the therapeutic regimen. Moreover, regardless of the therapeutic scheme, PsA patients showed higher IHC score of CD117+ cells (p = 0.0004 and p = 0.0005 for naive and MTX-IR patients respectively) compared to Abneg RA patients. Conversely, Abneg RA patients showed higher IHC score of CD138+ cells, irrespective to the therapeutic scheme (p = 0.04 and p = 0.002 for naive and MTX-IR patients respectively). Analyzing the response rate to the therapeutic scheme, naive PsA patients reaching MDA status at 6 months follow-up, showed, at the study entry, lower IHC score of CD3+ cells compared to PsA patients not reaching this outcome (p = 0.02); conversely, naive Abneg RA patients reaching DAS remission status at 6 months follow-up, showed, at the study entry, lower IHC score of sublining CD68+ cells compared to Abneg RA patients not reaching this outcome (p < 0.001).ConclusionsCD117+ and CD138+ cells are differentially distributed among PsA and Abneg RA. Histological analysis of ST may help to solve the clinical overlap between the two diseases and provides prognostic data about the therapy success.
| S-EPMC6509792 | biostudies-literature
Project description:BackgroundFatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline.MethodsFifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel.ResultsThe highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus.ConclusionsThe results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.
| S-EPMC9236264 | biostudies-literature
Project description:ObjectiveThe objectives of this study were to assess the 1-year persistence to methotrexate (MTX) initiated as the first ever conventional synthetic disease-modifying antirheumatic drug in new-onset rheumatoid arthritis (RA) and to investigate the marginal gains and robustness of the results by increasing the number and nature of covariates and by using data-driven, instead of hypothesis-based, methods to predict this persistence.MethodsThrough the Swedish Rheumatology Quality Register, linked to other data sources, we identified a cohort of 5475 patients with new-onset RA in 2006-2016 who were starting MTX monotherapy as their first disease-modifying antirheumatic drug. Data on phenotype at diagnosis and demographics were combined with increasingly detailed data on medical disease history and medication use in four increasingly complex data sets (48-4162 covariates). We performed manual model building using logistic regression. We also performed five different machine learning (ML) methods and combined the ML results into an ensemble model. We calculated the area under the receiver operating characteristic curve (AUROC) and made calibration plots. We trained on 90% of the data, and tested the models on a holdout data set.ResultsOf the 5475 patients, 3834 (70%) remained on MTX monotherapy 1 year after treatment start. Clinical RA disease activity and baseline characteristics were most strongly associated with the outcome. The best manual model had an AUROC of 0.66 (95% confidence interval [CI] 0.60-0.71). For the ML methods, Lasso regression performed best (AUROC = 0.67; 95% CI 0.62-0.71).ConclusionApproximately two thirds of patients with early RA who start MTX remain on this therapy 1 year later. Predicting this persistence remains a challenge, whether using hypothesis-based or ML models, and may yet require additional types of data.
| S-EPMC8280803 | biostudies-literature