AUTHOR=Zhao Fuyu , Zhao Jianan , Li Yang , Song Chenyang , Cheng Yaxin , Li Yunshen , Wu Shiya , He Bingheng , Jiao Juan , Chang Cen 

TITLE=Identification of diagnostic biomarkers for fibromyalgia using gene expression analysis and machine learning

JOURNAL=Frontiers in Genetics

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1535541

DOI=10.3389/fgene.2025.1535541

ISSN=1664-8021

ABSTRACT=ObjectiveFibromyalgia (FM) is a complex autoimmune disorder characterized by widespread pain and fatigue, with significant diagnostic challenges due to the absence of specific biomarkers. This study aims to identify and validate potential genetic markers for FM to facilitate earlier diagnosis and intervention.MethodsWe analyzed gene expression data from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) associated with FM. Comprehensive enrichment analyses, including Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathways, were performed to elucidate the biological functions and disease associations of the candidate genes. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop a diagnostic model, which was validated using independent datasets.ResultsThree genes, namely, dual-specificity tyrosine phosphorylation-regulated kinase 3 (DYRK3), regulator of G protein signaling 17 (RGS17), and Rho guanine nucleotide exchange factor 37 (ARHGEF37), were identified as key biomarkers for FM. These genes are implicated in critical processes such as ion homeostasis, cell signaling, and neurobiological functions, which are perturbed in FM. The diagnostic model demonstrated robust performance, with an area under the curve (AUC) of 0.8338 in the training set and 0.8178 in the validation set, indicating its potential utility in clinical settings.ConclusionThe study successfully identifies three diagnostic biomarkers for FM, supported by both bioinformatics analysis and machine learning models. These findings could significantly improve diagnostic accuracy for FM, leading to better patient management and treatment outcomes.