炎症相关基因在支气管肺发育不良中的诊断价值分析  

The diagnostic value of inflammation-related genes in bronchopulmonary dysplasia

作　　者：温雅 张俊峰 尚世强[1] Wen Ya;Zhang Junfeng;Shang Shiqiang(Department of Clinical Laboratory,Children′s Hospital,Zhejiang University School of Medicine/National Clinical Research Center for Child Health,Hangzhou 310052,China)

机构地区：[1]浙江大学医学院附属儿童医院实验检验中心,国家儿童健康与疾病临床医学研究中心,杭州310052

出　　处：《中华预防医学杂志》2024年第6期898-904,共7页Chinese Journal of Preventive Medicine

摘　　要：探讨外周血单个核细胞中的炎症相关基因在支气管肺发育不良(BPD)中的诊断价值。采用生物信息学分析手段,纳入GSE32472、GSE125873和GSE220135三个数据集,共包括251例新生儿的全基因组表达谱数据。其中GSE32472数据集作为训练数据集,检测非BPD与BPD新生儿的外周血单个核细胞之间的差异表达基因,并使用基因富集分析(GSEA)检测BPD新生儿中上调基因的通路富集情况,使用主调控因子分析(MRA)算法筛选炎症相关通路中(GO:0006954)的主调控基因。得到主调控基因后,检测主调控基因在GSE32472、GSE125873和GSE220135数据集中的表达,并通过logistic回归模型分析,据此对新生儿进行风险赋分。对比非BPD与BPD新生儿的危险分数,最后使用曲线下面积(AUC)评估模型的分类性能。结果显示,相较于非BPD新生儿,BPD新生儿外周血单个核细胞中出现486个上调基因和433个下调基因,上调基因中炎症相关通路高度富集,最终确定磷脂酰肌醇酶Cβ1(PLCB1)、网状蛋白1(NID1)、血清反应因子结合蛋白1(SRFBP1)、中心体蛋白72(CEP72)、切除修复交叉互补组6样(ERCC6L)与肽基脯氨酰异构酶1(PPIL1)共6个基因为主调控基因。预测模型预测危险分数公式为PLCB1×0.26+NID1×0.97+SRFBP1×1.58+CEP72×(-0.36)+ERCC6L×2.14+PPIL1×0.67,在GSE32472数据集、GSE125873数据集和GSE220135数据集中的AUC分别为0.88、0.86与0.89,能够区分非BPD与BPD新生儿。综上,基于炎症相关通路基因的预测模型,对于BPD具有一定的诊断价值。This study aims to explore the diagnostic value of inflammation-related genes in peripheral blood mononuclear cells in bronchopulmonary dysplasia(BPD).By using bioinformatics analysis,three datasets including GSE32472,GSE125873,and GSE220135,which contain whole-genome expression profile data of 251 neonates,were included.The GSE32472 dataset was used as a training dataset to detect differentially expressed genes between non-BPD and BPD neonates in peripheral blood mononuclear cells.The gene enrichment analysis(GSEA)was used to detect the pathway enrichment of up-regulated genes in BPD newborns.The main regulatory factors analysis(MRA)algorithm was used to filter the main regulatory genes in the inflammation-related pathway(GO:0006954).After obtaining the main regulatory genes,the expression of the main regulatory genes in the GSE32472,GSE125873,and GSE220135 datasets was detected.Through the logistic regression model,risk scoring was conducted for neonates,and the risk scores of non-BPD and BPD neonates were compared.Lastly,the classification performance of the model was evaluated using the area under the curve(AUC).The results showed that compared with non-BPD neonates,there were 486 up-regulated genes and 433 down-regulated genes in the peripheral blood mononuclear cells of BPD neonates.The inflammation-related pathway was highly enriched in the up-regulated genes.Ultimately,phospholipase C beta 1(PLCB1),nidogen 1(NID1),serum response factor binding protein 1(SRFBP1),centrosomal protein 72(CEP72),excision repair cross complementation group 6 like(ERCC6L),and peptidylprolyl isomerase like 1(PPIL1)were identified as the main regulatory genes.The prediction model′s calculation formula for risk score was PLCB1×0.26+NID1×0.97+SRFBP1×1.58+CEP72×(-0.36)+ERCC6L×2.14+PPIL1×0.67.The AUCs in the GSE32472 test dataset,GSE125873 dataset,and GSE220135 dataset were 0.88,0.86,and 0.89,respectively.This prediction model could distinguish between non-BPD and BPD neonates.In conclusion,the prediction model based on inflam

关 键 词：支气管肺发育不良 生物信息学 炎症 外周血单个核细胞 

分 类 号：R722.1[医药卫生—儿科]