纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析  

Screening of biomarkers for fibromyalgia syndrome and analysis of immune infiltration

作　　者：刘雅妮[1] 杨静欢[2] 陆慧慧[1] 易玉芳[1] 李智翔 欧阳福 吴璟莉[3] 魏兵 Liu Yani;Yang Jinghuan;Lu Huihui;Yi Yufang;Li Zhixiang;Ou Yangfu;Wu Jingli;Wei Bing(Department of General Practice,Affiliated Hospital of Guilin Medical University,Guilin 541001,Guangxi Zhuang Autonomous Region,China;Department of Respiratory and Critical Care Medicine,Affiliated Hospital of Guilin Medical University,Guilin 541001,Guangxi Zhuang Autonomous Region,China;College of Computer Science and Engineering,Guangxi Normal University,Guilin 541000,Guangxi Zhuang Autonomous Region,China;Department of Geriatrics,Affiliated Hospital of Guilin Medical University,Guilin 541001,Guangxi Zhuang Autonomous Region,China)

机构地区：[1]桂林医学院附属医院全科医疗科,广西壮族自治区桂林市541001 [2]桂林医学院附属医院呼吸与危重症医学科,广西壮族自治区桂林市541001 [3]广西师范大学计算机科学与工程学院,广西壮族自治区桂林市541000 [4]桂林医学院附属医院老年病科,广西壮族自治区桂林市541001

出　　处：《中国组织工程研究》2025年第5期1091-1100,共10页Chinese Journal of Tissue Engineering Research

基　　金：国家自然科学基金地区科学基金项目(62366007),项目负责人:吴璟莉;广西自然科学基金面上项目(2022GXNSFAA035625),项目负责人:吴璟莉;广西医疗卫生重点培育学科建设项目,项目负责人:魏兵;广西壮族自治区卫生健康委员会自筹经费科研课题(Z20190719),项目负责人:刘雅妮;桂林医学院教育教学研究与改革项目(重点项目,JG202030),项目负责人:刘雅妮;桂林医学院全科医学院教育教学研究与改革项目(一般项目,GYQKYB214),项目负责人:刘雅妮。

摘　　要：背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(PBACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance.OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning.METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome.RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of

关 键 词：纤维肌痛综合征 生物信息学 机器学习 免疫浸润 加权基因共表达网络分析 套索回归 支持向量机递归特征消除算法 单样本基因集富集分析 基因集富集分析 

分 类 号：R452[医药卫生—治疗学] R363[医药卫生—临床医学] R364