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Frontiers in immunology
2024;15 1391848.

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition.

Min-Jing Chang, Qi-Fan Feng, Jia-Wei Hao, Ya-Jing Zhang, Rong Zhao, Nan Li, Yu-Hui Zhao, Zi-Yi Han, Pei-Feng He, Cai-Hong Wang
Author Information
  1. Min-Jing Chang: Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China.
  2. Qi-Fan Feng: Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China.
  3. Jia-Wei Hao: Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
  4. Ya-Jing Zhang: Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
  5. Rong Zhao: Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China.
  6. Nan Li: Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
  7. Yu-Hui Zhao: Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
  8. Zi-Yi Han: Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
  9. Pei-Feng He: Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
  10. Cai-Hong Wang: Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China.
PMID: 38983856 DOI: 10.3389/fimmu.2024.1391848

Abstract

Background: For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.
Methods: We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.
Results: Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.
Conclusions: The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

Keywords

gene expression profiles machine learning rheumatoid arthritis stratification unsupervised clustering

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MeSH Term

Arthritis, Rheumatoid
Humans
Transcriptome
Gene Expression Profiling
Antirheumatic Agents
Leukocytes, Mononuclear
Biomarkers
CD8-Positive T-Lymphocytes

Chemicals

Antirheumatic Agents
Biomarkers
Journal Article

Word Cloud

Created with Highcharts 10.0.0RAsubtypesmolecularperipheralbloodstratifiedSubtypeidentifynewsubtypingschemeprofilesgenescellsDEGsenrichmentanalysisgeneclusteringresponsesBexpressionCD8+machinelearninginsightsstratificationrheumatoidarthritisBackground:RheumatoidArthritislong-termchronicillnessessentialdescribepatientcomparablegoalstatusbiomarkersstudyaimsdevelopvalidateintegratesgenome-scaletranscriptomicprovidingfreshperspectivetreatmentsMethods:utilizedindependentmicroarraydatasetsmononuclearPBMCsUp-regulateddifferentiallyexpressedsubjectedfunctionalUnsupervisedclusteremployedexpression-drivendefinedthreedistinctbasedidentified404up-regulatedResults:namedNE-drivingenrichedpathwaysrelatedneutrophilactivationbacteriatermedinterferon-drivingIFN-drivingexhibitedabundantshowedincreasedtranscriptsinvolvedIFNsignalingdefensevirusesCT-cellsfoundultimatelydefiningT-cells-drivingvalidatedusingXGBoostalgorithmalsoevaluatedtherapeuticoutcomesbiologicaldisease-modifyinganti-rheumaticdrugsConclusions:findingsprovidevaluabledeepenablingdesigndiagnosisservingreferencetherapypatientsfutureDecipheringlandscapeofferstreatmentconditionunsupervised

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