The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches
| Autor: | Lu Li, James N. Jarvis, Carol A. Wallace, Hui Meng, Yijun Sun, Kerry E. Poppenberg, Kaiyu Jiang, Teresa Hennon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Male medicine.medical_specialty lcsh:Diseases of the musculoskeletal system Databases Factual Arthritis Cell morphology Machine learning computer.software_genre Peripheral blood mononuclear cell Transcriptome 03 medical and health sciences 0302 clinical medicine Internal medicine Medicine Juvenile Humans Gene Regulatory Networks Stage (cooking) Child 030203 arthritis & rheumatology Biological Products business.industry Sequence Analysis RNA Juvenile idiopathic arthritis medicine.disease Response to treatment Rheumatology Arthritis Juvenile 030104 developmental biology Methotrexate Peripheral blood mononuclear cells Leukocytes Mononuclear Feasibility Studies Female Artificial intelligence lcsh:RC925-935 business computer Biomarkers Research Article |
| Zdroj: | Arthritis Research & Therapy, Vol 21, Iss 1, Pp 1-10 (2019) Arthritis Research & Therapy |
| ISSN: | 1478-6362 |
| Popis: | Background The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population. Methods Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry. Results In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis. Conclusions This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal. |
| Databáze: | OpenAIRE |
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