Zhao Fuyu, Zhao Jianan, Li Yang, Song Chenyang, Cheng Yaxin, Li Yunshen, Wu Shiya, He Bingheng, Jiao Juan, Chang Cen
Department of Rheumatology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Genet. 2025 Apr 17;16:1535541. doi: 10.3389/fgene.2025.1535541. eCollection 2025.
Fibromyalgia (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.
We 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.
Three genes, namely, dual-specificity tyrosine phosphorylation-regulated kinase 3 , regulator of G protein signaling 17 and Rho guanine nucleotide exchange factor 37 , 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.
The 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.
纤维肌痛（FM）是一种复杂的自身免疫性疾病，其特征为广泛疼痛和疲劳，由于缺乏特异性生物标志物，诊断面临重大挑战。本研究旨在识别和验证FM的潜在遗传标志物，以促进早期诊断和干预。
我们分析了来自基因表达综合数据库（GEO）的基因表达数据，以识别与FM相关的差异表达基因（DEG）。进行了全面的富集分析，包括基因本体论（GO）、京都基因与基因组百科全书（KEGG）和Reactome通路分析，以阐明候选基因的生物学功能和疾病关联。我们使用极端梯度提升（XGBoost）算法开发了一个诊断模型，并使用独立数据集进行验证。
三个基因，即双特异性酪氨酸磷酸化调节激酶3、G蛋白信号调节因子17和Rho鸟嘌呤核苷酸交换因子37，被确定为FM的关键生物标志物。这些基因参与离子稳态、细胞信号传导和神经生物学功能等关键过程，而这些过程在FM中受到干扰。诊断模型表现出强大的性能，训练集的曲线下面积（AUC）为0.8338，验证集的AUC为0.8178，表明其在临床环境中的潜在应用价值。
本研究通过生物信息学分析和机器学习模型成功识别出FM的三种诊断生物标志物。这些发现可显著提高FM的诊断准确性，从而实现更好的患者管理和治疗效果。
