摘要
目的通过对支气管肺发育不良(BPD)坏死性凋亡相关基因(NRGs)的分析,探讨BPD坏死性凋亡相关诊断标志物及其与免疫微环境的关系。方法从基因表达综合数据库(GEO)下载数据集GSE32472,并从京都基因与基因组百科全书(KEGG)通路和Gene Cards数据库下载NRGs。筛选差异表达的NRGs(DE-NRGs),通过功能富集分析探索DE-NRGs的生物学功能和通路。应用最小绝对收缩和选择算子(LASSO)和支持向量机递归特征消除(SVM-RFE)机器学习算法筛选特征基因。使用基于估计RNA转录物相对亚群进行细胞类型鉴定(CIBERSORT)算法和基于基因表达数据估算肿瘤组织中基质和免疫细胞(ESTIMATE)算法探索BPD免疫浸润特征;采用R语言中“corrplot”包对特征基因和免疫细胞等进行Spearman相关性分析。结果共筛选出19个DE-NRGs。DE-NRGs主要生物学功能和通路包括坏死性凋亡和炎症反应调节。进一步筛选出3种特征基因:脂筏特征蛋白-2(FLOT2)、Caspase-8和FADD样凋亡调节因子(CFLAR)和带电多泡体蛋白-7(CHMP7),构建列线图。在验证集GSE8586和GSE188944中,曲线下面积(AUC)值均>0.7。CIBERSORT分析结果显示,BPD组幼稚B细胞、中性粒细胞、嗜酸性粒细胞和静息肥大细胞比例高于对照组,差异有统计学意义(P<0.05)。同时,CD8^(+)T细胞、幼稚CD4^(+)T细胞、CD4^(+)静息记忆T细胞、调节性T细胞、静息自然杀伤(NK)细胞、M0巨噬细胞、M2巨噬细胞和活化树突状细胞比例低于对照组,差异有统计学意义(P<0.05)。ESTIMATE分析结果显示,BPD组基质评分高于对照组,免疫评分低于对照组,差异均有统计学意义(P<0.05)。3个特征基因与ESTIMATE的相关性分析表明,FLOT2和CFLAR与基质评分呈正相关,与免疫评分呈负相关,而CHMP7与免疫评分呈正相关,与基质评分呈负相关。结论3种坏死性凋亡特征基因可作为BPD坏死性凋亡相关诊断标志物,具有较高的诊断效能。三者可能通过免疫机制发挥重要作用,为BPD的早期诊断及免疫干预提供新思路及理论参考依据。
Objective To investigate necroptosis-related diagnostic biomarkers of bronchopulmonary dysplasia(BPD)and their relationships with the immune microenvironment through the analysis of necroptosis-related genes(NRGs)in BPD.Methods The dataset GSE32472 was downloaded from the Gene Expression Omnibus(GEO)database,and NRGs were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway and Gene Cards databases.Differentially expressed necroptosis-related genes(DE-NRGs)were screened,and their biological functions and pathways were explored through functional enrichment analysis.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE),were applied to screen feature genes.The Cell-type Identification By Estimating Relative Subsets of RNA Transcripts(CIBERSORT)algorithm and the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression Data(ESTIMATE)algorithm were used to explore the immune infiltration characteristics of BPD.Spearman correlation analysis between feature genes and immune cells was performed using the"corrplot"package in R language.Results A total of 19 DE-NRGs were identified.The main biological functions and pathways of DE-NRGs included the regulation of necroptosis and inflammatory responses.Three feature genes,namely flotillin-2(FLOT2),CASP8 and FADD-like apoptosis regulators(CFLAR),and charged multivesicular body protein 7(CHMP7),were further screened to construct a nomogram.In the validation sets GSE8586 and GSE188944,the area under the curve(AUC)values were all greater than 0.7.CIBERSORT analysis revealed that BPD group presented a higher proportion of naive B cells,neutrophils,eosinophils and resting mast cells compared to control group(P<0.05).Meanwhile,the proportion of CD8^(+)T cells,CD4^(+)naive T cells,CD4^(+)resting memory T cells,regulatory T cells,resting natural killer(NK)cells,M0 macrophages,M2 macrophages and activated dendritic cells was lower than that in the control group(P<0.05).ESTIMATE analysis showed that the stromal score in the BPD group was higher than that in the control group,while the immune score was lower,with statistically significant differences(P<0.05).Correlation analysis between the three feature genes and ESTIMATE scores indicated that FLOT2 and CFLAR were positively correlated with the stromal score and negatively correlated with the immune score,whereas CHMP7 was positively correlated with the immune score and negatively correlated with the stromal score.Conclusion The three necroptosis-related feature genes can serve as diagnostic biomarkers for BPD-related necroptosis,with high diagnostic efficacy.They may play an important roles through immune mechanisms,providing new insights and theoretical references for the early diagnosis and immune intervention of BPD.
作者
涂海霞
方长江
甘萍
彭娜娜
谷云云
姜红华
侯玮玮
舒桂华
TU Haixia;FANG Changjiang;GAN Ping;PENG Nana;GU Yunyun;JIANG Honghua;HOU Weiwei;SHU Guihua(Department of Neonatology,Northern Jiangsu People's Hospital Affiliated to Yangzhou University,Yangzhou,Jiangsu,225000;Department of Pediatrics,the First People's Hospital of Kunshan in Jiangsu Province,Suzhou,Jiangsu,215000;Yangzhou Maternal and Child Health Hospital Affiliated to Medical College of Yangzhou University,Yangzhou,Jiangsu,225000)
出处
《实用临床医药杂志》
2025年第14期80-87,共8页
Journal of Clinical Medicine in Practice
基金
江苏省医学会儿科医学第二期科研专项资金项目[SYH-32034-0110(2024014)]
江苏省妇幼保健协会科研项目(FYX202329)。
关键词
支气管肺发育不良
早产儿
诊断生物标志物
坏死性凋亡
功能富集
机器学习算法
免疫微环境
生物信息学
bronchopulmonary dysplasia
premature infant
diagnostic biomarker
necroptosis
functional enrichment
machine learning algorithm
immune microenvironment
bioinformatics
