Division of General Internal Medicine, Department of Family Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Beijing, 100730, China.
J Transl Med. 2023 Sep 5;21(1):594. doi: 10.1186/s12967-023-04477-w.
Fibromyalgia (FM) is a multifaceted disease. Along with the genetic, environmental and neuro-hormonal factors, inflammation has been assumed to have role in the pathogenesis of FM. The aim of the present study was to explore the differences in clinical features and pathophysiology of FM patients under different inflammatory status.
The peripheral blood gene expression profile of FM patients in the Gene Expression Omnibus database was downloaded. Differentially expressed inflammatory genes were identified, and two molecular subtypes were constructed according to these genes used unsupervised clustering analysis. The clinical characteristics, immune features and pathways activities were compared further between the two subtypes. Then machine learning was used to perform the feature selection and construct a classification model.
The patients with FM were divided into micro-inflammation and non-inflammation subtypes according to 54 differentially expressed inflammatory genes. The micro-inflammation group was characterized by more major depression (p = 0.049), higher BMI (p = 0.021), more active dendritic cells (p = 0.010) and neutrophils. Functional enrichment analysis showed that innate immune response and antibacterial response were significantly enriched in micro-inflammation subtype (p < 0.050). Then 5 hub genes (MMP8, ENPP3, MAP2K3, HGF, YES1) were screened thought three feature selection algorithms, an accurate classifier based on the 5 hub DEIGs and 2 clinical parameters were constructed using support vector machine model. Model scoring indicators such as AUC (0.945), accuracy (0.936), F1 score (0.941), Brier score (0.079) and Hosmer-Lemeshow goodness-of-fit test (χ = 4.274, p = 0.832) proved that this SVM-based classifier was highly reliable.
Micro-inflammation status in FM was significantly associated with the occurrence of depression and activated innate immune response. Our study calls attention to the pathogenesis of different subtypes of FM.
纤维肌痛（FM）是一种多方面的疾病。除了遗传、环境和神经-激素因素外，炎症也被认为在 FM 的发病机制中起作用。本研究旨在探讨不同炎症状态下 FM 患者的临床特征和病理生理学差异。
从基因表达综合数据库中下载 FM 患者的外周血基因表达谱。鉴定差异表达的炎症基因，并使用无监督聚类分析根据这些基因构建两个分子亚型。进一步比较两个亚型之间的临床特征、免疫特征和通路活性。然后使用机器学习进行特征选择并构建分类模型。
根据 54 个差异表达的炎症基因，FM 患者分为微炎症和非炎症亚型。微炎症组的主要抑郁症发生率更高（p=0.049），BMI 更高（p=0.021），活性树突状细胞（p=0.010）和中性粒细胞更多。功能富集分析显示，微炎症亚型中先天免疫反应和抗菌反应显著富集（p<0.050）。然后通过三种特征选择算法筛选出 5 个枢纽基因（MMP8、ENPP3、MAP2K3、HGF、YES1），使用支持向量机模型构建基于 5 个枢纽 DEIGs 和 2 个临床参数的精确分类器。模型评分指标，如 AUC（0.945）、准确性（0.936）、F1 评分（0.941）、Brier 评分（0.079）和 Hosmer-Lemeshow 拟合优度检验（χ=4.274，p=0.832）证明了该基于 SVM 的分类器具有很高的可靠性。
FM 中的微炎症状态与抑郁的发生和激活的先天免疫反应显著相关。我们的研究引起了对不同 FM 亚型发病机制的关注。
