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基于WGCNA和机器学习算法识别圆锥角膜基底膜相关基因标志物

Identification of Basement Membrane-Related Gene Signatures for Diagnosis in Keratoconus Through WGCNA and Machine Learning
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摘要 目的:运用生物信息学方法探讨基底膜(BM)在圆锥角膜(KC)进展中的潜在作用,并提供新的治疗靶点。方法:从GEO数据库中的公共数据集GSE112155和GSE151631以及Genecard数据库,提取KC相关差异表达基因(DEGs)和BM相关基因,并进行功能富集分析。通过应用加权基因共表达网络分析(WGCNA)以及随机森林-递归特征消除和支持向量机-递归特征消除算法,筛选BM相关基因作为KC诊断的潜在生物标志物,并通过一致性聚类、列线图和受试者工作特征(ROC)曲线分析进行验证。此外,采用CIBERSORT算法进行免疫浸润分析以评估炎症因子在KC中的作用。利用Wilcoxon秩和检验进行正常对照组和KC组之间基因表达水平和免疫细胞分布差异比较。采用Pearson相关分析计算相关系数。基于Logistic回归模型构建列线图。结果:从227个KC差异基因中筛选出195个BM相关DEGs。通过WGCNA和2种机器学习算法,筛选出4个关键基因:CRY2、RNF19B、PPP1R18和PFKFB3,并且在正常对照组和KC组中的表达量差异均有统计学意义(均P<0.05)。ROC曲线分析显示,4个基因在内部数据验证中均表现出优异的诊断效能;在外部数据验证中,CRY2、RNF19B和PPP1R18展现了良好的诊断效能。一致性聚类和列线图也证实了这些基因的诊断效能。此外,一致性聚类表明,这4个基因主要分布在KC的A亚型中。免疫浸润分析和功能富集分析结果显示,免疫炎症、代谢和凋亡也参与了KC的发生。结论:本研究筛选出3个KC相关BM关键基因,为KC的发病机制、诊断和治疗提供了新的视角。 Objective:This study aimed to investigate the correlation between choroidal thickness,choroidal blood flow parameters,and obesity in children and adolescents using optical coherence tomography angiography(OCTA).Methods:A cross-sectional study was conducted from October to December 2023 among 514 children(514 eyes)aged 6 to 18 years from three schools in Jinzhou,Liaoning Province.Based on gender,age,and body mass index(BMI),participants were divided into three groups:non-overweight(n=293),overweight(n=102),and obese(n=119).Ocular parameters included,axial length,and choroidal thickness in nine macular subregions(foveal center and superior,inferior,nasal,and temporal areas of both inner and outer macular rings).Blood flow parameters included vascular volume,stromal volume,and vascular index of large choroidal vessels.Systemic parameters included age,height,weight,BMI,waist-to-hip ratio,body fat percentage,systolic blood pressure and diastolic blood pressure.One-way analysis of variance and Pearson correlation analysis were used for statistical evaluation.Results:The mean ages of the non overweight,overweight and obese group were 14.0±3.3,13.4±3.7,13.5±3.3 years,respectively,with male to-female ratios of 124:169,46:56,and 63:56.Significant intergroup differences were found in choroidal thickness across all nine macular subregions(F=9.56-13.14,all P<0.001),with the obese group exhibiting significantly thinner choroid compared to the other two groups(all P<0.05),while no significant difference was observed between the non-overweight and overweight groups.Similar trends were noted in choroidal vascular volume and stromal volume(F=6.70-12.34,all P<0.05).Pearson correlation analysis revealed that choroidal thickness,vascular volume,and stromal volume were negatively correlated with body weight,BMI,waist-to-hip ratio and body fat percentage(r=-0.23 to-0.08,all P<0.05).Conclusions:Choroidal thickness and blood flow parameters were inversely associated with obesity-related parameters,suggesting a potential impact of obesity on the choroidal vasculature in children and adolescents.
作者 谢佩纭 袁博伟 顾展豪 李蓉 陈鼎 Peiyun Xie;Bowei Yuan;Zhanhao Gu;Rong Li;Ding Chen(National Clinical Research Center for Ocular Diseases,Eye Hospital,Wenzhou Medical University,Wenzhou325027,China;Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital,Qingyuan 511518,China)
出处 《中华眼视光学与视觉科学杂志(中英文)》 北大核心 2025年第5期328-337,共10页 Chinese Journal Of Optometry Ophthalmology And Visual Science
基金 浙江省自然科学基金(LWY20H120001) 浙江省卫生健康科技计划项目(2022PY073) 温州市重大科技创新攻关医疗卫生项目(ZY2019012)。
关键词 圆锥角膜 基底膜 生物信息学分析 加权基因共表达网络分析 机器学习 生物标志物 keratoconus basement membrane bioinformatic analysis weighted gene co-expression network analysis machine learning biomarker
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