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Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods. BIOMED RESEARCH INTERNATIONAL 2023; 2023:5333361. [PMID: 36644165 PMCID: PMC9833906 DOI: 10.1155/2023/5333361] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023]
Abstract
Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.
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Roodenrijs NMT, Welsing PMJ, van Roon J, Schoneveld JLM, van der Goes MC, Nagy G, Townsend MJ, van Laar JM. Mechanisms underlying DMARD inefficacy in difficult-to-treat rheumatoid arthritis: a narrative review with systematic literature search. Rheumatology (Oxford) 2022; 61:3552-3566. [PMID: 35238332 PMCID: PMC9434144 DOI: 10.1093/rheumatology/keac114] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/07/2022] [Accepted: 02/14/2022] [Indexed: 12/03/2022] Open
Abstract
Management of RA patients has significantly improved over the past decades. However, a substantial proportion of patients is difficult-to-treat (D2T), remaining symptomatic after failing biological and/or targeted synthetic DMARDs. Multiple factors can contribute to D2T RA, including treatment non-adherence, comorbidities and co-existing mimicking diseases (e.g. fibromyalgia). Additionally, currently available biological and/or targeted synthetic DMARDs may be truly ineffective (‘true’ refractory RA) and/or lead to unacceptable side effects. In this narrative review based on a systematic literature search, an overview of underlying (immune) mechanisms is presented. Potential scenarios are discussed including the influence of different levels of gene expression and clinical characteristics. Although the exact underlying mechanisms remain largely unknown, the heterogeneity between individual patients supports the assumption that D2T RA is a syndrome involving different pathogenic mechanisms.
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Affiliation(s)
- Nadia M T Roodenrijs
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Paco M J Welsing
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Joel van Roon
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Jan L M Schoneveld
- Department of Rheumatology, Bravis Hospital, Roosendaal, the Netherlands
| | - Marlies C van der Goes
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands.,Department of Rheumatology, Meander Medical Center, Amersfoort, the Netherlands
| | - György Nagy
- Department of Rheumatology & Clinical Immunology, Semmelweis University, Budapest, Hungary.,Department of Genetics, Cell and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Michael J Townsend
- Biomarker Discovery OMNI, Genentech Research & Early Development, South San Francisco, USA
| | - Jacob M van Laar
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
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Wang Z, Huang J, Xie D, He D, Lu A, Liang C. Toward Overcoming Treatment Failure in Rheumatoid Arthritis. Front Immunol 2022; 12:755844. [PMID: 35003068 PMCID: PMC8732378 DOI: 10.3389/fimmu.2021.755844] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/06/2021] [Indexed: 12/29/2022] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.
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Affiliation(s)
- Zhuqian Wang
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China.,Law Sau Fai Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Jie Huang
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Duoli Xie
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China.,Law Sau Fai Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Dongyi He
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai, China
| | - Aiping Lu
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China.,Law Sau Fai Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.,Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, China
| | - Chao Liang
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China.,Law Sau Fai Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
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Tao W, Concepcion AN, Vianen M, Marijnissen ACA, Lafeber FPGJ, Radstake TRDJ, Pandit A. Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis. Arthritis Rheumatol 2021; 73:212-222. [PMID: 32909363 PMCID: PMC7898388 DOI: 10.1002/art.41516] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/01/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment. METHODS Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study. RESULTS Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. CONCLUSION Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.
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Affiliation(s)
- Weiyang Tao
- University Medical Center Utrecht and Utrecht UniversityThe Netherlands
| | | | - Marieke Vianen
- University Medical Center Utrecht and Utrecht UniversityThe Netherlands
| | | | | | | | - Aridaman Pandit
- University Medical Center Utrecht and Utrecht UniversityThe Netherlands
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Donlin LT, Park SH, Giannopoulou E, Ivovic A, Park-Min KH, Siegel RM, Ivashkiv LB. Insights into rheumatic diseases from next-generation sequencing. Nat Rev Rheumatol 2019; 15:327-339. [PMID: 31000790 PMCID: PMC6673602 DOI: 10.1038/s41584-019-0217-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Rheumatic diseases have complex aetiologies that are not fully understood, which makes the study of pathogenic mechanisms in these diseases a challenge for researchers. Next-generation sequencing (NGS) and related omics technologies, such as transcriptomics, epigenomics and genomics, provide an unprecedented genome-wide view of gene expression, environmentally responsive epigenetic changes and genetic variation. The integrated application of NGS technologies to samples from carefully phenotyped clinical cohorts of patients has the potential to solve remaining mysteries in the pathogenesis of several rheumatic diseases, to identify new therapeutic targets and to underpin a precision medicine approach to the diagnosis and treatment of rheumatic diseases. This Review provides an overview of the NGS technologies available, showcases important advances in rheumatic disease research already powered by these technologies and highlights NGS approaches that hold particular promise for generating new insights and advancing the field.
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Affiliation(s)
- Laura T Donlin
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sung-Ho Park
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
| | - Eugenia Giannopoulou
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
- Biological Sciences Department, New York City College of Technology, City University of New York, New York, NY, USA
| | - Aleksandra Ivovic
- Immunoregulation Section, Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Kyung-Hyun Park-Min
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Richard M Siegel
- Immunoregulation Section, Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Lionel B Ivashkiv
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA.
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA.
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