MicroRNAs as Biomarker in Rheumatoid Arthritis: Pathogenesis to Clinical Relevance

Abstract

MicroRNAs (miRNAs) have emerged as intricate players in rheumatoid arthritis (RA), holding promise as discerning biomarkers for diagnostic and prognostic purposes. The lack of sensitivity and specificity in current diagnostic techniques, such as rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA), causes diagnosis delays in RA. The miR-146a and miR-155 act in inflammatory cascades and reduce joint deterioration, and miR-223 is paradoxical, acting differently in different illness scenarios. The microenvironment of RA is shaped by the complex modulation of gene expression and cytokine dynamics by miR-126 and miR-24. miRNAs serve as a promising candidate for precision medicine in the management of RA. There are obstacles encountered in validation, delivery optimization, and off-target effect mitigation before miRNA-based biomarkers may be applied in clinical settings. Machine learning (ML) and artificial intelligence (AI) have been used to integrate miRNA expression patterns with clinical data to greatly advance the treatment of RA. Because of the disease's inherent complexity and variability, these state-of-the-art models provide accurate predictions regarding the onset, development, and response to treatment of RA. By using clinical information and miRNA expression data, ML algorithms are revolutionizing the treatment of RA by predicting the onset and course of the disease with remarkably high accuracy. The development of therapeutic modalities and miRNA profiling has great potential to transform the diagnosis, prognosis, and treatment of RA, providing fresh hope for better patient outcomes.