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[1]陳遠(yuǎn)翔,林斌,陳東杰,等.基于WGCNA和機(jī)器學(xué)習(xí)方法篩選冠脈搭橋術(shù)后氧化應(yīng)激相關(guān)的核心基因[J].福建醫(yī)藥雜志,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.
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基于WGCNA和機(jī)器學(xué)習(xí)方法篩選冠脈搭橋術(shù)后氧化應(yīng)激相關(guān)的核心基因()
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《福建醫(yī)藥雜志》[ISSN:1002-2600/CN:35-1071/R]

卷:
45
期數(shù):
2023年06期
頁碼:
9-12
欄目:
臨床研究
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
文章編號(hào):
1002-2600(2023)06-0009-04
作者:
陳遠(yuǎn)翔; 林斌; 陳東杰; 林先東; 翁國星1
福建醫(yī)科大學(xué)省立臨床醫(yī)學(xué)院 福建省立醫(yī)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
關(guān)鍵詞:
冠脈搭橋;  機(jī)器學(xué)習(xí);  氧化應(yīng)激;  心肌缺血再灌注損傷
Keywords:
coronary artery bypass grafting;  machine learning;  oxidative stress;  myocardial ischemia/reperfusion injury
分類號(hào):
R541.4
文獻(xiàn)標(biāo)志碼:
A
摘要:
目的 利用加權(quán)基因共表達(dá)網(wǎng)絡(luò)分析(WGCNA)和機(jī)器學(xué)習(xí)方法,篩選出冠脈搭橋(CABG)術(shù)后氧化應(yīng)激(OS)相關(guān)的核心基因,并預(yù)測治療心肌缺血再灌注損傷(MIRI)的靶向藥物。方法 從GEO數(shù)據(jù)庫CABG術(shù)前、術(shù)后心室組織表達(dá)譜,利用WGCNA得到術(shù)后最關(guān)聯(lián)模塊和基因,從而獲得OS相關(guān)基因(OSRG),進(jìn)一步富集分析。再通過最小絕對(duì)值收斂和選擇算子(LASSO)回歸和隨機(jī)森林算法,篩選出冠脈搭橋術(shù)后核心OSRG,術(shù)前術(shù)后差異表達(dá)比較,預(yù)測靶向藥物。結(jié)果 WGCNA結(jié)果得到32個(gè)CABG術(shù)后OSRG,富集分析結(jié)果顯示這些基因主要參與細(xì)胞死亡調(diào)控、細(xì)胞程序性死亡、細(xì)胞凋亡、免疫過程以及多種信號(hào)通路的調(diào)節(jié)。機(jī)器學(xué)習(xí)篩選出3個(gè)核心OSRG:ATF3、JUN、VEGFA,其均在CABG術(shù)后上調(diào)表達(dá)(P<0.01)。并預(yù)測5種靶向藥物。結(jié)論 本研究通過綜合運(yùn)用WGCNA和機(jī)器學(xué)習(xí)方法,成功篩選出CABG術(shù)后核心OSRG,這些核心基因術(shù)后均明顯上調(diào),通過誘導(dǎo)細(xì)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.

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更新日期/Last Update: 2023-12-15