Article(id=1246459846653006342, tenantId=1146029695717560320, journalId=1246415837536497731, issueId=1246459843930903036, articleNumber=null, orderNo=null, doi=10.12307/2025.523, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1713283200000, receivedDateStr=2024-04-17, revisedDate=1720800000000, revisedDateStr=2024-07-13, acceptedDate=1717171200000, acceptedDateStr=2024-06-01, onlineDate=1775108785541, onlineDateStr=2026-04-02, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1775108785541, onlineIssueDateStr=2026-04-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1775108785541, creator=13701087609, updateTime=1775108785541, updator=13701087609, issue=Issue{id=1246459843930903036, tenantId=1146029695717560320, journalId=1246415837536497731, year='2025', volume='29', issue='36', pageStart='7701', pageEnd='7920', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1775108784853, creator=13701087609, updateTime=1775108852483, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1246460127511991018, tenantId=1146029695717560320, journalId=1246415837536497731, issueId=1246459843930903036, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1246460127511991019, tenantId=1146029695717560320, journalId=1246415837536497731, issueId=1246459843930903036, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=7909, endPage=7920, ext={EN=ArticleExt(id=1246459846946607629, articleId=1246459846653006342, tenantId=1146029695717560320, journalId=1246415837536497731, language=EN, title=Machine learning-based analysis of neutrophil-associated potential biomarkers for acute myocardial infarction, columnId=1246459846866915850, journalTitle=Chinese Journal of Tissue Engineering Research, columnName=Big data analysis, runingTitle=null, highlight=null, articleAbstract=
BACKGROUND: Accurate early diagnosis and timely reperfusion therapy are important prerequisites for saving the lives and improving the prognosis of patients with acute myocardial infarction. Therefore, it is important to find ideal biomarkers for early diagnosis of acute myocardial infarction.
OBJECTIVE: To analyze key genes associated with neutrophils by acute myocardial infarction through bioinformatics and machine learning to explore new biomarkers.
METHODS: Differentially expressed genes were identified based on the Gene Expression Omnibus (GEO) database and Limma R package. Deconvolution algorithm was used to explore the immune cells infiltration level. Then, acute myocardial infarction and neutrophils-related biomarkers were screened by weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks, machine learning, and functional enrichment analysis. Receiver operating characteristic curve analysis was conducted to assess the diagnostic efficacy of biomarkers for acute myocardial infarction. Targeted drugs for biomarkers were screened through the STITCH and Herb database. Finally, the hospitalized patients who were first diagnosed with acute myocardial infarction in the Department of Cardiology of Affiliated Hospital of Guizhou Medical University from March to June 2023 were used as the experimental group, and the hospitalized patients who had no ischemic changes on electrocardiograms and no stenosis on coronary angiograms during the same period were used as the control group. Peripheral blood of the patients in the two groups was collected. The relative expressions of the genes were verified in the human peripheral blood samples by RT-qPCR.
RESULTS AND CONCLUSION: (1) A total of 2 349 differentially expressed genes were obtained, and immune infiltration analysis revealed differences in immune cell scores such as B cells memory, NK cells resting, and Neutrophils between the disease and normal groups. (2) Using WGCNA, two gene modules, ME green and ME turquoise, were found to exhibit the highest correlation with neutrophil fine with acute myocardial infarction. (3) Twenty-four differential module genes were obtained after intersecting with differentially expressed genes. Functional enrichment analysis revealed that they were associated with a variety of processes such as innate immune response and defense response to bacteria. KEGG results showed that they were mainly associated with the tumor necrosis factor signaling pathway. (4) The genes mined by the machine learning algorithm took the intersection to obtain three genes, namely, S100A12, PTCH1, and LOC400499, all of which were greater than 0.7 by the area under the receiver operating characteristic curve in both the GSE48060 and GSE66360 datasets. They were considered as potential biomarkers. (5) Based on the STITCH and Herb databases, 11 target drugs were found for S100A12 and a total of 6 target drugs were found for PTCH1. (6) RT-qPCR results showed that S100A12, PTCH1, and LOC400499 were significantly differentially expressed in acute myocardial infarction patients compared with controls (P < 0.05). (7) S100A12, PTCH1, and LOC400499 may be potential diagnostic biomarkers for acute myocardial infarction, but their specificity in relation to acute myocardial infarction needs to be further investigated, in which S100A12 may be a potential target for regulating acute myocardial infarction.
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Yu Zhenqiu, Chief physician, Department of Hypertension, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou Province, China
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背景： 精确的早期诊断和及时的再灌注治疗是挽救急性心肌梗死患者的生命并改善预后的重要前提条件。因此，寻找能够早期诊断急性心肌梗死的理想生物标志物尤为重要。
目的： 拟通过生物信息学和机器学习分析急性心肌梗死与中性粒细胞相关的关键基因，以探寻新的生物标志物。
方法： 基于GEO数据库和limma包鉴定急性心肌梗死的差异表达基因。使用反卷积算法探究免疫细胞浸润情况，然后结合加权基因共表达网络分析（Weighted gene co-expression network analysis，WGCNA）、蛋白互作网络和机器学习筛选急性心肌梗死与中性粒细胞相关的特征基因，并进行功能富集分析。用ROC曲线评估特征基因对急性心肌梗死的诊断价值。通过STITCH和Herb数据库筛查生物标志物的靶向药物。最后将在2023年3-6月于贵州医科大学附属医院心内科首次诊断为急性心肌梗死的住院患者作为实验组，同期心电图无缺血性改变、冠状动脉造影无狭窄的住院患者作为对照组，收集两组患者外周血，通过RT-qPCR验证基因在人外周血样本中的相对表达量。
结果与结论： ①共获得差异表达基因2 349个，免疫浸润分析发现B cells memory，NK cells resting和Neutrophils等免疫细胞评分在疾病和正常组之间存在差异；②使用WGCNA发现ME green和ME turquoise这两个基因模块与中性粒细胞与急性心肌梗死表现出最高的相关性；③与差异表达基因相交后获得24个差异模块基因，功能富集分析发现其与先天免疫反应、细菌的防御反应等多种过程相关；KEGG结果显示其主要与肿瘤坏死因子信号通路有关。④机器学习算法挖掘到的基因取交集后得到的特征基因为S100A12，PTCH1和LOC400499，在GSE48060和GSE66360数据集中的ROC曲线下面积均大于0.7，将其视为潜在的生物标志物。⑤基于STITCH和Herb数据库发现S100A12有11种靶向药物，PTCH1共发现6种靶向药物。⑥RT-qPCR结果显示，与对照组相比，急性心肌梗死患者中S100A12，PTCH1和LOC400499表达具有显著差异性（P < 0.05）。⑦S100A12，PTCH1和LOC400499可能是急性心肌梗死潜在的诊断生物标志物，但是其与急性心肌梗死相关的特异性尚需进一步研究，其中S100A12可能是调控急性心肌梗死的潜在靶点。
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余振球，主任医师，贵州医科大学附属医院高血压科，贵州省贵阳市 550004
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文章出版前全体作者与编辑部签署了文章版权转让协议。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=MJ8FkXZM3Pr1uzX8Oxfw9g==, magXml=IJt5xt7Ban7CT0FPdNZcfA==, pdfUrl=null, pdf=xEF8reNxbtoG+LOIeEX+Yw==, pdfFileSize=8634523, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=STMiJDre/fJcTjMYgb9rSA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=jEfDxiP3a8xxqIkXmkkSUw==, mapNumber=null, authorCompany=null, fund=null, authors=
作者贡献：
文章设计者为余振球和杨定燕。资料收集和数据分析者为杨定燕和杨中愉。杨定燕撰写论文。余振球审校。
Yang Dingyan, Master candidate, Department of Hypertension, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou Province, China
杨定燕，女，1997年生，贵州省人，苗族，贵州医科大学在读硕士，主要从事心肌梗死相关研究。
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杨定燕，女，1997年生，贵州省人，苗族，贵州医科大学在读硕士，主要从事心肌梗死相关研究。
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2川北医学院，四川省南充市 637100)])], figs=[ArticleFig(id=1246459860594873295, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=EN, label=null, caption=null, figureFileSmall=AINibSZPgGvZ9PNF0Xa5YQ==, figureFileBig=STMiJDre/fJcTjMYgb9rSA==, tableContent=null), ArticleFig(id=1246459860674565080, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=CN, label=图1, caption=
急性心肌梗死和健康样本中差异表达基因（GSE48060数据集）图注：图A为GSE48060数据集中差异表达基因的火山图，横坐标表示差异倍数，纵坐标表示差异的显著性；红色的点表示上调基因，蓝色的点表示下调基因；B为急性心肌梗死和健康样本中86个差异表达基因的热图，每行表示一个基因在样品中的表达量，每列表示一个样品中基因的表达量。每个小方格表示每个样本的不同基因标准化之后的表达量，其颜色表示含量多少，表达量越高颜色越红，越少越蓝。
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急性心肌梗死和健康样本中差异表达基因（GSE66030数据集）图注：图A为GSE66030数据集中差异表达基因的火山图，横坐标表示差异倍数，纵坐标表示差异的显著性；红色的点表示上调基因，蓝色的点表示下调基因；B为急性心肌梗死和健康样本中2 349个差异表达基因的热图，每行表示一个基因在样品中的表达量，每列表示一个样品中基因的表达量。每个小方格表示每个样本的不同基因标准化之后的表达量，其颜色表示含量多少，表达量越高颜色越红，越少越蓝。
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免疫浸润分析结果图注：图A为反卷积算法得到的免疫细胞柱状堆叠图，每一列表示每一例患者中不同免疫细胞评分高低情况，不同颜色表示不同类型的免疫细胞；图B为免疫细胞在正常疾病组中的免疫评分小提琴图展示，*P < 0.05，**P < 0.01，nsP > 0.05。MI为急性心肌梗死组，control为对照组。B cell naive为B幼稚细胞，B cell memory为B记忆细胞，Piasma cells为浆细胞，T cells CD8为CD8 T细胞，T cells CD4 naive为CD4 T幼稚细胞，T cells CD4 memory resting为静息的CD4 T记忆细胞；T cells follicular helper为T细胞滤泡辅助细胞；T cells regulatory (Tregs）为调节性T细胞；T cells gramma delta为γδ T细胞；NK cells resting为静息的NK细胞；NK cells activated为活化的NK细胞；Monocytes为单核细胞；Macrophages M0为M0型巨噬细胞；Macrophages M2为M2型巨噬细胞；Dendritic cells resting为静息的树突状细胞；Dendritic cells activated为活化的树突状细胞；Mast cells resting为静息的肥大细胞；Eosinophils为嗜酸性粒细胞；Neutrophils为中性粒细胞。
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样本和形状树形图图注：此图分为两部分，上半部分表示样本聚类树形图，纵坐标为高度，高度越高基因表达越不相似；下半部分为表型性状热图，颜色越深表示表型与样本的基因表达量关系越密切。Sample dendrogram and trait heatmap为样本树状图和表型性状热图；MI为心肌梗死；Neutrophils为中性粒细胞。
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无尺度软阈值的筛选结果图注：两张图横轴均代表权重参数power值，左图纵轴表示signed R2，相关系数的平方越高，说明该网络越逼近无尺度分布；右图纵轴表示对应的基因模块中所有基因邻接函数的均值。
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聚类模块树形图图注：纵坐标为高度，高度越高，数字越大，基因表达越不相似，树状图不同的分支代表不同的模块基因。Clustering of module eigengenes为模块基因聚类；Height为高度；MEgreen为绿色模块基因；MEpurple为紫色模块基因；MEturquoise为蓝绿色模块基因；MEyellow为黄色模块基因；MEblack为黑色模块基因；MEred为红色模块基因；MEmagenta为品红色模块基因；MEpink为粉色模块基因；MEgrey为粉色模块基因；MEblue为蓝色模块基因；MEbrown为棕色模块基因。
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模块识别与合并分析结果图注：图的上半部分是一个层次聚类图，每个叶子结点都是一条竖线，代表一个基因；纵坐标为高度，高度越高基因表达越不相似；下半部分为基因模块，不同的颜色代表不同的模块，灰色的模块为未归类于任何共表达模块的基因组成。Height为高度；Dynamic Tree Cut为动态树剪切；Merged dynamic为动态合并相似模块后的颜色；Gene dendrogram and module color为基因树状图和模块颜色。
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模块基因和临床性状相关性分析结果图图注：纵坐标为不同模块，横坐标为表型性状，颜色条带表示相关性范围；每一个方块表示模块基因和性状的相关性，红色表示正相关，蓝色表示负相关，颜色越深相关性越强。方块内的数字表示相关性和显著性。MI为心肌梗死；Neutrophils为中性粒细胞；Module-trait relationships为模块与表型特征关联图谱；MEgreen为绿色模块基因；MEturquoise为蓝绿色模块基因；MEblack为黑色模块基因；MEpurple为紫色模块基因；MEmagenta为品红色模块基因；MEblue为蓝色模块基因；MEpink为粉色模块基因；MEgrey为粉色模块基因。
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模块基因和差异表达基因的交集图图注：图A为差异表达的上调基因的交集图；B为差异表达的下调基因的交集图。Module-gene为模块基因，up上调基因，down为下调基因。模块基因以粉色表示，GSE48060数据集中的差异表达基因以蓝色表示，GSE66360数据集中的差异表达基因以橘色表示，三者交集部分基因代表在差异表达的模块基因、GSE48060和GSE66360数据集中均存在。
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对24个差异模块基因进行GO富集分析结果图图注：纵坐标表示GO富集前30条分析结果，横坐标表示基因数目；BP代表生物过程；CC代表细胞成分；MF代表分子功能。颜色表征差异的显著性。defense response to bacterium为对细菌的防御反应；response to corticosteroid为对皮质类固醇的反应；response to glucocorticoid为对糖皮质激素的反应；myeloid leukocyte mediated immunity为髓系白细胞介导免疫；animal organ regeneration为动物器官再生；negative regulation of oxidoreductase activity为氧化还原酶活性的负调控；neutrophil mediated immunity为中性粒细胞介导免疫；embryonic placenta morphogenesis为胚胎胎盘形态发生；labyrinthine layer morphogenesis为迷路层形态发生；branching involved in labyrinthine layer morphogenesis为分支参与迷路层的形态发生；specific granule为特定颗粒；endocytic vesicle为内吞囊泡；vesicle lumen为囊泡腔；cytoplasmic vesicle lumen为细胞质泡腔；secretory granule lumen为分泌颗粒腔；tertiary granule为三级颗粒；endocytic vesicle membrane为胞吞囊泡膜；specific granule lumen为特定颗粒内腔；tertiary granule membrane为三级颗粒膜；tertiary granule lumen为三级颗粒管腔；serine hydrolase activity为丝氨酸水解酶活性；serine-type peptidase activity为丝氨酸型肽酶活性；serine-type endopeptidase activity丝氨酸型内肽酶活性；hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds为水解酶活性，作用于碳氮键（而不是肽键）；calcium-dependent protein binding为钙依赖性蛋白结合；IgG binding为IgG结合；hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in linear amidines为水解酶活性，作用于线性脒的碳氮键（而不是肽键）；prostanoid receptor activity为前列腺素受体活性；RAGE receptor binding为RAGE受体结合；prostaglandin receptor activity为前列腺素受体活性。
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对24个差异模块基因进行KEGG富集分析结果图图注：横坐标表示基因数目，纵坐标表示显著富集的通路，颜色表征差异的显著性。TNF signaling pathway为肿瘤坏死因子信号通路，Proteoglycans in cancer为蛋白聚糖，Pantothenate and CoA biosynthesis为泛酸盐和辅酶A生物合成；Neuroactive ligand-receptor nteraction为神经活性配体-受体相互作用，Glycosphingolipid biosynthesis-lacto and neolacto series为鞘糖脂生物合成-乳酸和新乳酸系列，Estrogen signaling pathway为雌激素信号通道，Arginine biosynthesis为精氨酸生物合成。
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蛋白互作网络（PPI）分析结果图图注：LogFC为对数差异倍数。S100A12为钙结合蛋白A12；PTCH1为修补1；LOC382210为THO复合体2；MGAM为麦芽糖酶；FCGR1B为FC γ受体1B；GZMA为颗粒酶A；PTGDR为前列腺素D2受体；AQP9为水通道蛋白9；MCEMP1为肥大细胞表达的膜蛋白；HP为肝素酶；MMP9为基质金属蛋白酶9；ASGR2为去唾液酸糖蛋白受体2；ARG1为精氨酸酶1；VNN1为血管非炎性分子1；KRT23为角蛋白23；SLPI为分泌性白细胞肽酶抑制因子；SOCS3为细胞因子信号传导抑制因子3；DSC2为桥粒糖蛋白2；ANXA3为膜联蛋白A3；SULT1B1为磺基转移酶家族；ADM为肾上腺髓质素。
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核心基因的筛选结果图图注：图A为S100A12的蛋白互作网络（PPI）分析；B为Cytohubba筛选的24个模块基因中的核心基因。S100A12为钙结合蛋白A12；MGAM为麦芽糖酶；GZMA为颗粒酶A；PTGDR为前列腺素D2受体；AQP9为水通道蛋白9；MCEMP1为肥大细胞表达的膜蛋白；HP为肝素酶；MMP9为基质金属蛋白酶9；ASGR2为去唾液酸糖蛋白受体2；ARG1为精氨酸酶1；VNN1为血管非炎性分子1；SLPI为分泌性白细胞肽酶抑制因子；SOCS3为细胞因子信号传导抑制因子3；SULT1B1为磺基转移酶家族；ADM为肾上腺髓质素；ANXN3为膜联蛋白A3；PTGDR为前列腺素D2受体。
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通过机器学习方法识别候选生物标志物图注：图A为支持向量模型算法筛选特征基因；B为广义线性模型算法筛选特征基因；C为随机森林模型算法筛选特征基因。图A-C横坐标均表示特征基因的个数，纵坐标均表示泛化误差；折线图线的趋势代表特征基因个数与泛化误差的关系。图D为支持向量模型、广义线性模型和随机森林模型算法的特征基因取交集确定候选生物标志物。rf代表随机森林模型；svm代表支持向量模型；glm代表广义线性模型。
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ROC曲线分析结果图注：图A，B分别为GSE48060数据集、GSE66360数据集中S100A12，PTCH1，LOC400499，SULT1B1 4个基因的ROC曲线分析。横坐标表示1-特异性，纵坐标表示敏感度。
, figureFileSmall=qXhPPtygjDwJAKttBTZgzw==, figureFileBig=FvovmC21dYrIUhAbvpzG/g==, tableContent=null), ArticleFig(id=1246459867297370364, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=EN, label=null, caption=null, figureFileSmall=ypSsPYB2dEPh1XIhjEdwog==, figureFileBig=cCJdRlWXTIdBsSkfwAVi5A==, tableContent=null), ArticleFig(id=1246459868845068548, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=CN, label=图16, caption=
靶向药物预测结果图注：图A为可能靶向调控S100A12的药物：amlexanox为氨来占诺；calcium ions为钙离子；sodium为钠；copper为铜；paquinimod为帕奎尼莫；crtric acid为柠檬酸；AGER为晚期糖基化终产物；NOX1为氧化酶1；seenomethicni。图B为可能靶向调控PTCH1的药物：quercitin为槲皮苷；17-bate-estradiol为17β雌二醇；trans-resverao为白藜芦醇；cyclopamine为氯丙咪嗪；meletin为槲皮素。
, figureFileSmall=ypSsPYB2dEPh1XIhjEdwog==, figureFileBig=cCJdRlWXTIdBsSkfwAVi5A==, tableContent=null), ArticleFig(id=1246459868962509067, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=EN, label=null, caption=null, figureFileSmall=+erEtNEC3mcL3Ahwgg4fHg==, figureFileBig=mTdgnwztuLHqci9p4XdALw==, tableContent=null), ArticleFig(id=1246459870917054737, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=CN, label=图17, caption=
关键基因表达的RT-qPCR分析结果图注：与对照组相比，aP < 0.05。
, figureFileSmall=+erEtNEC3mcL3Ahwgg4fHg==, figureFileBig=mTdgnwztuLHqci9p4XdALw==, tableContent=null), ArticleFig(id=1246459871021912347, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
| 基因名称 | 引物序列（5’-3’） | 片段长度（bp） |
|---|
| S100A12 | 正向：CAC ATT CCT GTG CAT TGA GG | 122 |
| | 反向：GGT GTC AAA ATG CCC CTT C | |
| PTCH1 | 正向：ACA AAC TCC TGG TGC AAA CC | 207 |
| | 反向：CTT TGT CGT GGA CCC ATT C | |
| LOC400499 | 正向：CTG CTT CCG GGT GGT AGA C | 291 |
| | 反向：TGA GTT TCC CGG CAT TCC AT | |
| β-actin | 正向：CTC GCT TCG GCA GCA CA | 96 |
| | 反向：AAC GCT TCA CGA ATT TGC GT | |
), ArticleFig(id=1246459871118381339, tenantId=1146029695717560320, journalId=1246415837536497731, articleId=1246459846653006342, language=CN, label=表1, caption=
内参基因和生物标志物的引物序列
, figureFileSmall=null, figureFileBig=null, tableContent=
| 基因名称 | 引物序列（5’-3’） | 片段长度（bp） |
|---|
| S100A12 | 正向：CAC ATT CCT GTG CAT TGA GG | 122 |
| | 反向：GGT GTC AAA ATG CCC CTT C | |
| PTCH1 | 正向：ACA AAC TCC TGG TGC AAA CC | 207 |
| | 反向：CTT TGT CGT GGA CCC ATT C | |
| LOC400499 | 正向：CTG CTT CCG GGT GGT AGA C | 291 |
| | 反向：TGA GTT TCC CGG CAT TCC AT | |
| β-actin | 正向：CTC GCT TCG GCA GCA CA | 96 |
| | 反向：AAC GCT TCA CGA ATT TGC GT | |
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