[1]陈远翔,林斌,陈东杰,等.基于WGCNA和机器学习方法筛选冠脉搭桥术后氧化应激相关的核心基因[J].福建医药杂志,2023,45(06):9-12.
　CHEN Yuanxiang,LIN Bin,CHEN Dongjie,et al.Identification of core genes associated with oxidative stress after coronary artery bypass grafting using WGCNA and machine learning methods[J].FUJIAN MEDICAL JOURNAL,2023,45(06):9-12.
点击复制

基于WGCNA和机器学习方法筛选冠脉搭桥术后氧化应激相关的核心基因()
分享到：

《福建医药杂志》[ISSN:1002-2600/CN:35-1071/R]

卷:
45
期数:
2023年06期
页码:
9-12
栏目:
临床研究
出版日期:
2023-12-15

文章信息/Info

Title:
Identification of core genes associated with oxidative stress after coronary artery bypass grafting using WGCNA and machine learning methods
文章编号:
1002-2600(2023)06-0009-04
作者:
陈远翔; 林斌; 陈东杰; 林先东; 翁国星1
福建医科大学省立临床医学院 福建省立医院心血管外科(福州 350001)
Author(s):
CHEN Yuanxiang;  LIN Bin;  CHEN Dongjie;  LIN Xiandong;  WENG Guoxing
Department of Cardiovascular Surgery, Fujian Provincial Hospital, Provincial Clinical Medical College of Fujian Medical University, Fuzhou, Fujian 350001, China
关键词:
冠脉搭桥;  机器学习;  氧化应激;  心肌缺血再灌注损伤
Keywords:
coronary artery bypass grafting;  machine learning;  oxidative stress;  myocardial ischemia/reperfusion injury
分类号:
R541.4
文献标志码:
A
摘要:
目的 利用加权基因共表达网络分析(WGCNA)和机器学习方法,筛选出冠脉搭桥(CABG)术后氧化应激(OS)相关的核心基因,并预测治疗心肌缺血再灌注损伤(MIRI)的靶向药物。方法 从GEO数据库CABG术前、术后心室组织表达谱,利用WGCNA得到术后最关联模块和基因,从而获得OS相关基因(OSRG),进一步富集分析。再通过最小绝对值收敛和选择算子(LASSO)回归和随机森林算法,筛选出冠脉搭桥术后核心OSRG,术前术后差异表达比较,预测靶向药物。结果 WGCNA结果得到32个CABG术后OSRG,富集分析结果显示这些基因主要参与细胞死亡调控、细胞程序性死亡、细胞凋亡、免疫过程以及多种信号通路的调节。机器学习筛选出3个核心OSRG:ATF3、JUN、VEGFA,其均在CABG术后上调表达(P<0.01)。并预测5种靶向药物。结论 本研究通过综合运用WGCNA和机器学习方法,成功筛选出CABG术后核心OSRG,这些核心基因术后均明显上调,通过诱导细胞死亡、免疫等方式,引起MIRI。
Abstract:
Objective This study aimed to use weighted gene co-expression network analysis(WGCNA)and machine learning methods to identify the core genes related to OS after CABG and predict targeted drugs for treating MIRI.Methods Gene expression profiles of pre- and post-operative ventricular tissues from the GEO database were analyzed using WGCNA to identify the most relevant modules and genes after surgery, resulting in OS-related genes(OSRG).Enrichment analysis was then performed.Subsequently, the least absolute shrinkage and selection operator(LASSO)regression and random forest algorithm were employed to screen for the core OSRG following CABG surgery.Differential expression analysis was conducted, and targeted drugs were predicted.Results WGCNA identified 32 OSRG after CABG surgery, and enrichment analysis revealed their involvement in cell death regulation, programmed cell death, apoptosis, immune processes, and regulation of various signaling pathways.Machine learning identified three core OSRG: ATF3, JUN, and VEGFA, all of which exhibited significant upregulation after CABG surgery(P<0.01).Additionally, five targeted drugs were predicted.Conclusion Through the integrated application of WGCNA and machine learning methods, this study successfully identified core OSRG after CABG surgery, which were significantly upregulated and involved in inducing cell death and immune responses, thereby contributing to MIRI.

参考文献/References:

[1] Yang C F.Clinical manifestations and basic mechanisms of myocardial ischemia/reperfusion injury[J].Tzu Chi Med J,2018,30(4):209-215.
[2] 李思源, 刘振兵.氧化应激在心肌缺血再灌注损伤中的研究进展[J].山东医药,2021,61(22):4.
[3] 肖倩,黄业保,刘春强,等.基于生物信息学与机器学习鉴定溃疡性结肠炎的诊断生物标志物[J].中国免疫学杂志, 2023, 39(8):1713-1718.
[4] Radulescu E, Jaffe A E, Straub R E, et al.Identification and prioritization of gene sets associated with schizophrenia risk by co-expression network analysis in human brain[J].Molecular Psychiatry,2020,25(4):791-804.
[5] 吕仪, 陈蓉, 郑雯婧, 等.基于氧化应激的心肌缺血再灌注损伤研究进展[J].中华中医药杂志,2020,35(2):5.
[6] Qiao S G, Sun Y, Sun B, et al.Sevoflurane postconditioning protects against myocardial ischemia/reperfusion injury by restoring autophagic flux via an NO-dependent mechanism[J].Acta Pharmacol Sin,2019,40(1):35-45.
[7] Yao Y, Li F, Zhang M, et al.Targeting CaMKII-δ9 Ameliorates Cardiac Ischemia/Reperfusion Injury by Inhibiting Myocardial Inflammation[J].Circ Res, 2022,130(6):887-903.
[8] Kolls J K, Lindén A.Interleukin-17 family members and inflammation[J].Immunity,2004,21(4):467-476.
[9] Hoetzenecker W, Echtenacher B, Guenova E, et al.ROS-induced ATF3 causes susceptibility to secondary infections during sepsis-associated immunosuppression[J].Nat Med,2011,18(1):128-134.
[10] Okamoto Y, Chaves A, Chen J, et al.Transgenic mice with cardiac-specific expression of activating transcription factor 3, a stress-inducible gene, have conduction abnormalities and contractile dysfunction[J].Am J Pathol,2001,159(2):639-650.
[11] Bei Y, Pan L L, Zhou Q, et al.Cathelicidin-related antimicrobial peptide protects against myocardial ischemia/reperfusion injury[J].BMC medicine,2019,17(1):42.
[12] Garcia R, Bouleti C, Sirol M, et al.VEGF-A plasma levels are associated with microvascular obstruction in patients with ST-segment elevation myocardial infarction[J].Int J Cardiol,2019,291:19-24.

相似文献/References:

[1]朱丽萍,王存泽,王,等.预测新冠病毒感染患者有无症状的机器学习模型的构建与验证[J].福建医药杂志,2023,45(04):107.
　ZHU Liping,WANG Cunze,WANG Ling.Development and validation of a machine learning model to predict the symptomatic status of COVID-19 patients[J].FUJIAN MEDICAL JOURNAL,2023,45(06):107.

备注/Memo

备注/Memo:
1 通信作者,Email:gxw001@139.com
更新日期/Last Update: 2023-12-15