Zhang Yue, Kong Xiangyong, Zhang Jie, Wang Xu
Department of Neonatal Intensive Care Unit, Beijing Aiyuhua Maternal and Children Hospital, Beijing, China.
BaYi Children's Hospital, Seventh Medical Center of Chinese PLA General Hospital, China.
Comput Math Methods Med. 2022 Apr 25;2022:5682599. doi: 10.1155/2022/5682599. eCollection 2022.
Bronchopulmonary dysplasia (BPD) has a high mortality rate. This study was aimed at identifying and analysing the risk factors associated with BPD using bioinformatic and mechanical analyses and establishing a predictive model to assess the risk of BPD in preterm infants.
We identified differentially expressed RNAs via the intersection of miRNAs between datasets. Online analysis tools were used to predict genes targeted by differentially expressed miRNAs (DEmiRNAs) and to generate and visualise competing endogenous RNA (ceRNA) coexpression networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed on the DEmiRNAs. In addition, an intersection analysis was performed on mRNA and neuropeptide-related genes in the ceRNA network. DEmiRNAs associated with BPD and those involved in ceRNA networks were used to establish a diagnostic prediction model. The GSE108604 dataset was used as a validation set to verify the model.
A total of 26 DEmiRNAs were identified from the tracheal aspirates (TAs) of patients with BPD and healthy controls. In addition, a total of 1076 DEmRNAs were obtained from the GSE8586 dataset. Functional enrichment analysis of DEmRNAs revealed an abnormal reduction in mitochondrial-related activity and cellular responses to oxidative stress in patients with BPD. The neuropeptide-related genes and were found to be upregulated in BPD samples. Eventually, hsa-miR-1258, hsa-miR-298, hsa-miR-483-3p, and hsa-miR-769-5p were screened out and used to establish the prediction model. Calibration curves and detrended correspondence analysis (DCA) revealed that the model had good clinical applicability.
The prediction model provided a simple method for individualised assessment, early diagnosis, and prevention of BPD risk in preterm infants.
支气管肺发育不良（BPD）死亡率较高。本研究旨在通过生物信息学和力学分析识别并分析与BPD相关的危险因素，并建立一个预测模型来评估早产儿患BPD的风险。
我们通过数据集之间miRNA的交集鉴定差异表达的RNA。使用在线分析工具预测差异表达的miRNA（DEmiRNA）靶向的基因，并生成和可视化竞争性内源性RNA（ceRNA）共表达网络。随后对DEmiRNA进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。此外，对ceRNA网络中的mRNA和神经肽相关基因进行交集分析。将与BPD相关且参与ceRNA网络的DEmiRNA用于建立诊断预测模型。使用GSE108604数据集作为验证集来验证该模型。
从BPD患者和健康对照的气管吸出物（TA）中总共鉴定出26种DEmiRNA。此外，从GSE8586数据集中总共获得了1076种DEmRNA。对DEmRNA的功能富集分析显示，BPD患者中线粒体相关活性和细胞对氧化应激的反应异常降低。发现神经肽相关基因在BPD样本中上调。最终，筛选出hsa-miR-1258、hsa-miR-298、hsa-miR-483-3p和hsa-miR-769-5p并用于建立预测模型。校准曲线和去趋势对应分析（DCA）显示该模型具有良好的临床适用性。
该预测模型为个体化评估、早期诊断和预防早产儿BPD风险提供了一种简单方法。
