Precision medicine in rheumatoid arthritis: advances and clinical applications of multi-omics biomarkers
TL;DR
This paper analyzes the challenges faced by multi-omics biomarkers during clinical translation, including technical barriers, cost-effectiveness, and standardization, and critically evaluates the nascent integration of spatialomics and liquid biopsy technologies within the “multi-omics + AI” framework.
Abstract
Rheumatoid arthritis (RA) is a highly heterogeneous autoimmune disease characterized by the presence of synovitis, joint destruction, and systemic inflammation. Despite the substantial improvement in patient outcomes brought about by the advent of targeted therapies, the traditional “one-size-fits-all” treatment model still faces numerous challenges. These include individual variability in treatment response, a “trial-and-error” process in drug selection, and the emergence of refractory RA. Precision medicine is the development of optimal prevention and treatment strategies based on patients’ individual characteristics (including genetic, environmental, and lifestyle factors). This new approach offers a way to address the therapeutic challenges of RA. Multi-omics biomarkers, which integrate data from multiple dimensions including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, are central tools for achieving precision medicine in RA. This article reviews the current status and challenges of precision medicine for RA, systematically summarizing the latest research advances in multi-omics biomarkers regarding RA pathogenesis, early diagnosis, treatment response prediction, prognosis assessment, and risk stratification of complications. The primary focus of this research is the identification of novel biomarkers [including TAOK3 and S-adenosylmethionine (SAM)] and the subsequent assessment of their clinical significance. Additionally, the study explores the application of artificial intelligence (AI) technology in the integration of multi-omics data and the development of predictive models. This paper analyzes the challenges faced by multi-omics biomarkers during clinical translation. These challenges include technical barriers, cost-effectiveness, and standardization. Distinct from previous reviews, which have focused broadly on the pathogenesis of RA, this review synthesizes cutting-edge evidence on TAOK3 and SAM as emerging translational biomarkers. It also critically evaluates the nascent integration of spatialomics and liquid biopsy technologies within the “multi-omics + AI” framework. Furthermore, it outlines the prospects for the “clinically integrated multi-omics AI model” in propelling RA treatment into a new era of individualization and precision.