基于RNA-seq的db/db小鼠大脑皮质的转录组学分析

目的通过对db/db和野生型(WT)小鼠大脑皮质组织全转录组学分析,探索参与调节2型糖尿病诱导的脑功能障碍的差异表达基因(DEGs)及相关通路和网络。方法取雄性野生型WT和db/db小鼠各9只,在第8和24周检测小鼠的体质量和血糖,之后收集动物大脑皮质进行全转录组测序(RNA-seq),并进行DEGs,GO、KEGG及蛋白互作网络分析。结果与WT组相比,db/db组大脑皮质发生变化的306个转录本中有178个表达上调,128个表达下调。DEGs中,43个上调(如Clcnka和Trim17),59个下调(如Arih1和Nectin-3)。蛋白互作网络图中的13个枢纽基因均下调,且大多属于线粒体编码家族。同时,db/db小鼠在多项GO富集类别中具有显著差异,如细胞过程、细胞部分等。此外,KEGG功能富集结果显示DEGs在代谢、帕金森病(PD)、阿尔茨海默病(AD)等相关通路中高度富集,且这些富集通路中的DEGs主要影响了线粒体氧化磷酸化过程。结论揭示了2型糖尿病与中枢神经系统损伤之间的关系及潜在的相关基因、通路及网络。     

基于转录组测序技术研究高原低氧对小鼠脾脏组织的影响北大核心CSCD

目的:利用转录组学测序技术探究免疫系统在高原低氧胁迫适应过程中相关基因的表达及响应的分子机制。方法:本研究在高、低海拔环境分别饲养C57BL/6小鼠30 d,取脾脏组织利用RNA-Seq进行转录组测序,将得到的差异基因(DEGs)进行GO和KEGG富集分析,并通过荧光定量PCR验证测序数据的准确性。结果:与平原常氧组相比,共富集到4218个DEGs(P<0.05)。其中,ANXA1、S100A8、S100A9和HSPB1等基因显著富集;GO结果表明DEGs主要富集于B细胞激活、免疫球蛋白复合体和抗原结合分类,且JAK-STAT及NOD样受体信号通路为显著富集通路。结论:免疫系统响应高原低氧胁迫的转录调控分子可能致使机体内部免疫调节和炎症反应失衡,为相关高原病的病因学探究提供了新的理论依据。     

IDH1调控干扰素通路相关基因IFIT3的表达

目的 探究在人胶质瘤细胞系LN229中异柠檬酸脱氢酶1基因(IDH1)对RNA结合蛋白(RBPs)表达的影响.方法 首先通过慢病毒感染构建IDH1稳定敲低和对照组LN229细胞株,分别对两组细胞株进行转录组测序(RNA-seq),利用limma包分析差异表达基因(DEGs)及其富集通路;然后通过string比对RBP数...     

糖尿病肾病肾小管病变关键基因的预测北大核心CSCD

目的:基于生物信息学探索糖尿病肾病(DKD)肾小管差异表达基因(DEGs)与相关信号通路,结合蛋白互作(PPI)网络分析与比较毒理基因组学数据库(CTD)筛选在DKD肾小管病变中发挥关键作用的基因。方法:选取基因表达公共数据库(GEO)中芯片数据集GSE30529与Karolinska肾脏研究中心RNA-seq数据集,采用R4.03软件的“limma”和“DESeq2”包分析两个数据集共有的DEGs,设定筛选阈值为差异倍数≥2,P<0.05。应用clusterProfiler包进行GO分析和KEGG信号通路富集分析,STRING数据库建立PPI网络,Cytoscape和CTD筛选DKD核心基因。结果:共得到277个DEGs,DEGs的GO分析主要表现于细胞外基质组织,涉及免疫反应、中性粒细胞激活、免疫效应调节等生物学过程。KEGG信号通路分析结果表明,吞噬小体、补体及凝血级联反应、趋化因子信号通路、糖尿病并发症AGE-RAGE信号通路及NF-κB信号通路参与DKD肾小管病变的发生、发展。通过PPI网络及CTD数据库联合分析筛选出CXCL1、CXCL8、CCL5、FN1及EGF共5个关键基因。结论:本研究从转录组水平对两个不同来源数据集进行联合分析,有利于了解DKD肾小管病变发生的潜在分子机制,为进一步研究提供了有意义的线索。     

探讨哮喘和SARS-CoV-2的相互作用关系的生物信息学分析

目的 采用生物信息学方法探讨哮喘和SARS-CoV-2感染的相互作用关系及发生的潜在机制,为哮喘和新冠肺炎(COVID-19)进一步治疗提供新线索。方法 本文使用的研究数据来源于GEO数据库。利用R语言和Perl语言对数据进行预处理,筛选差异表达基因(DEGs),并获得GO功能富集分析、KEGG、Reactome、WikiPathways和BioCarta通路富集分析。通过Cytoscape软件获得蛋白质相互作用(PPI)网络分析可视化结果。使用RegNetwork数据库筛选与DEGs相互作用的转录因子(TF),再利用NetworkAnalyst构建miRNA-TF-mRNA共调控网络。最后,从DSigDB数据库筛选治疗药物。结果获得哮喘和SARS-CoV-2感染的数据集并且筛选得到25个受两者影响的重叠DEGs。GO功能富集分析和通路富集分析显示,DEGs参与病毒蛋白与细胞因子和细胞因子受体、补体和凝血级联反应通路。miRNA-TF-mRNA共调控网络表明关键基因与相关miRNA和TF之间复杂的调控关系。筛选到作用于DEGs的可能药物分子,包括雷洛昔芬、他莫昔芬和孕酮等。结论 在数据...     

类风湿关节炎基因差异谱的生物信息学分析

目的通过生物信息学方法探究类风湿关节炎患者的滑膜成纤维细胞差异表达基因及相关信号通路,寻找潜在的类风湿关节炎特异性分子标志物。方法利用R语言limma包等程序方法分析基因芯片GSE21959并筛选差异基因(differentially expressed genes,DEGs),利用DAVID数据库分析DEGs获得其GO富集分析和KEGG信号通路分析的结果。利用STRING数据库构建蛋白互作网络,再将结果导入Cytoscape软件中模块化核心基因并绘制蛋白互作网络图。结果筛选获得了123个差异基因,其中表达上调的基因38个,表达下调的基因85个。GO富集分析表明DEGs主要参与了趋化因子调节、CXCR趋化因子受体结合和血管生成正向调控等生物学过程,KEGG信号通路富集分析主要包括了趋化因子信号通路、Rap1信号通路和血管平滑肌收缩等信号通路。模块化分析获得了7个核心基因分别为:CXCL1、CXCL8、CXCL6、ADRA2A、ADCY8、S1PR1和SAA1。结论通过生物信息学分析获得类风湿关节炎的DEGs、核心基因、生物学过程和信号通路等信息,为探究类风湿关节炎的发病机制、发现诊断标志物和探索新治疗靶点提供理论依据与新的方向。     

人疱疹病毒6型感染HSB-2细胞的lncRNA表达谱变化分析

目的运用高通量测序技术检测人疱疹病毒6型(human herpesvirus 6, HHV-6)感染人淋巴细胞系HSB-2后长链非编码RNA(long non-coding RNA, lncRNA)表达谱的改变, 探究lncRNA在HHV-6感染复制中的作用。方法 HHV-6感染HSB-2细胞72 h后, 提取对照细胞和病毒感染细胞的RNA进行测序, 筛选差异表达的lncRNA;通过生物信息学方法对预测的lncRNA靶基因进行GO注释和KEGG信号通路分析, 构建共表达网络图。qRT-PCR检测差异表达lncRNA的变化倍数。结果共筛选到612个显著差异表达lncRNA, 其中420个为表达上调, 192个表达下调。通过对lncRNA靶基因进行GO和KEGG富集分析显示, 差异表达lncRNA的靶基因与表观染色体调控、免疫应答及细胞代谢等生物学过程密切相关。qRT-PCR确定10条上调IncRNAs表达变化趋势与高通量测序数据一致。结论本研究对HHV-6感染HSB-2细胞的lncRNA表达谱进行分析, 为深入探索lncRNA在HHV-6复制增殖及相关疾病中的作用奠定基础。     

肝癌患者外周血单个核细胞circRNAs诊断价值分析

目的探讨肝细胞癌(HCC)患者外周血单个核细胞(PBMCs)中环状RNA(circRNAs)作为HCC分子标志物的临床诊断价值。方法利用高通量测序(RNA-seq)获取4例HCC患者和3例健康对照者(NC组)PBMCs中circRNAs的差异表达谱,GO/KEGG分析差异表达的circRNAs显著富集的潜在功能和信号通路;qRT-PCR对其中6种显著差异的circRNAs在72例HCC患者和30例健康对照者PBMCs样本中再次验证;最后结合ROC曲线(ROC)评估circ_0000798作为HCC分子诊断标志物的潜力。结果 RNA-seq鉴定出HCC组较NC组共有58种显著差异的circRNAs(21种上调,37种下调);GO/KEGG分析发现差异表达的circRNAs多涉及免疫和免疫相关的信号通路;qRT-PCR结果表明circ_0000798在HCC患者PBMCs中表达显著高于NC组(P0.05),ROC曲线分析提示circ_0000798有潜力作为HCC分子诊断标志物。结论 HCC患者PBMCs中显著差异表达的circ_0000798有潜力作为HCC患者无创性诊断分子标志物。     

胃相关性疾病伴肠上皮化生差异表达基因及中药预测的生物信息学分析

目的：通过生物信息学方法筛选胃相关性疾病伴肠上皮化生（IM）的关键基因与通路,探讨其发病机制及潜在治疗靶点,进而预测治疗IM的中药。方法：从公共基因芯片数据库（GEO）数据库中下载包含IM患者的胃黏膜基因表达谱数据,利用Rstudio3.5.2筛选出IM组织与正常胃黏膜组织的差异表达基因（DEGs）;使用DAVID 6.8数据库对DEGs进行GO和KEGG富集分析;基于STRING数据库和Cytoscape 3.6.1软件构建蛋白相互作用（PPI）网络,明确关键基因及核心功能模块;通过将关键基因与医学本体信息检索平台（Coremine Medical）相对应,筛选治疗IM的中药。结果：纳入2个包含IM的基因芯片数据集GSE78523和GSE60427,将2个数据集中IM相关的DEGs取交集获得135个基因,其中上调基因90个、下调基因45个。GO分析结果显示,DEGs主要涉及消化、细胞增殖的调控、细胞间黏附、钠离子跨膜转运、钾离子转运、胆囊收缩素信号通路、单核细胞趋化性、白细胞迁移、细胞外泌体等功能。KEGG通路富集结果显示DEGs显著富集于胃酸分泌、氮代谢、肾素-血管紧张素系统、蛋白...     

儿童新诊断与慢性免疫性血小板减少症差异基因分析

目的筛选儿童新诊断与慢性免疫性血小板减少症(ITP)之间的差异表达基因(DEGs)并进行生物信息学分析。方法从基因表达数据库中下载芯片表达谱GSE46922数据集,利用BRB-ArrayTools软件鉴定DEGs,然后分别对差异基因进行基因本体(GO)功能富集分析、Pathway富集分析和互作网络分析。结果共筛选出1225个DEGs,其中上调基因665个,下调基因560个。GO富集分析发现DEGs主要参与转录调控、小分子代谢、蛋白泛素化、凋亡调控、固有免疫反应、病毒复制等生物学过程。Pathway富集分析发现DEGs显著富集于代谢通路、内质网蛋白加工、破骨细胞分化、MAPK信号通路、病毒感染、凋亡等。网络分析鉴定出的核心基因有CHD4、UQCR10、AP2M1、SIRPγ和GPR180,核心Pathways包括MAPK信号通路、细胞周期和细胞凋亡。结论明确了儿童新诊断与慢性ITP的基因表达谱不同,为进一步阐明儿童ITP发生发展的分子机制和指导早期治疗干预提供了基础。     

利什曼原虫感染树突状细胞早期基因表达差异分析及关键基因筛选

目的:分析利什曼原虫感染树突状细胞(DCs)早期的基因表达与信号通路变化,探究DCs感染后应答,寻找利什曼原虫感染后基于DCs的免疫治疗方法。方法:GEO数据库下载利什曼原虫感染前后DCs基因芯片数据,RStudio软件筛选差异表达基因(DEGs),STRING构建DEGs蛋白质相互作用网络(PPI),Cytoscape筛选差异表达蛋白质的核心模块,RStudio软件对DEGs进行GO和KEGG富集分析。结果:共筛选出DEGs 129个,其中IL12B与CXCL10差异最为显著,GO分析共富集23个过程,主要涉及病毒感染过程相关细胞反应及Ⅰ-IFN相关免疫反应;KEGG分析共富集3条信号通路,分别为甲型流感、麻疹及DNA复制信号通路。结论:利什曼原虫感染DCs前后Ⅰ-IFN信号通路和TLR4/NF-κB信号通路激活,影响IL12表达,提示Ⅰ-IFN/IL12信号通路与TLR4/NF-κB/IL12信号通路可作为利什曼原虫感染治疗的靶点,CXCL10也有望成为潜在的治疗靶点;利什曼原虫感染后,出现类似病毒感染现象,推测抗病毒免疫疗法可能在对抗利什曼原虫感染中具有一定疗效。     

Comparison of the gene expression profiles between gallstones and gallbladder polyps

Background: Gallstones and gallbladder polyps (GPs) are two major types of gallbladder diseases that share multiple common symptoms. However, their pathological mechanism remains largely unknown. The aim of our study is to identify gallstones and GPs related-genes and gain an insight into the underlying genetic basis of these diseases. Methods: We enrolled 7 patients with gallstones and 2 patients with GP for RNA-Seq and we conducted functional enrichment analysis and protein-protein interaction (PPI) networks analysis for identified differentially expressed genes (DEGs). Results: RNA-Seq produced 41.7 million in gallstones and 32.1 million pairs in GPs. A total of 147 DEGs was identified between gallstones and GPs. We found GO terms for molecular functions significantly enriched in antigen binding (GO:0003823, P=5.9E-11), while for biological processes, the enriched GO terms were immune response (GO:0006955, P=2.6E-15), and for cellular component, the enriched GO terms were extracellular region (GO:0005576, P=2.7E-15). To further evaluate the biological significance for the DEGs, we also performed the KEGG pathway enrichment analysis. The most significant pathway in our KEGG analysis was Cytokine-cytokine receptor interaction (P=7.5E-06). PPI network analysis indicated that the significant hub proteins containing S100A9 (S100 calcium binding protein A9, Degree=94) and CR2 (complement component receptor 2, Degree=8). Conclusion: This present study suggests some promising genes and may provide a clue to the role of these genes playing in the development of gallstones and GPs.     

Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis

ObjectiveTo identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).MethodsThe gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.ResultsA total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.ConclusionsThese newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.     

基于 TCGA 数据库筛选、 分析并验证宫颈癌生物标志物

目的 基于生物信息学筛选分析宫颈癌差异表达基因 ( differentially expressed gene, DEGs) 及差 异表达 miRNA, 并进一步对差异基因和蛋白进行验证, 以期寻找潜在的生物标志物和治疗靶点。 方法 从 肿瘤基因组图谱 (the cancer genome atlas, TCGA) 数据库获取宫颈癌相关数据, edgeR 算法筛选 DEGs 和差 异 miRNAs。 利用 Cytoscape3. 8. 2 软件构建 mRNA-miRNA 共表达网络。 利用 DAVID 软件对 DEGs 和通过 miRWalk 网站预测的差异 miRNA 的目标基因进行 GO 富集分析和 KEGG 富集分析。 利用 qPCR 和 Western 印 迹技术对 DEGs 进行进一步验证。 结果 筛选出 149 个上调的 DEGs 和 171 个下调的 DEGs, 以及 46 个上调 的差异 miRNAs 和 64 个下调的差异 miRNAs。 DEGs 和 miRNA 目标基因在细胞组成上的富集具有一致性, 都富集在胞质、 核和核质中。 但共表达网络发现 DEGs 和差异 miRNAs 之间不存在明显的调控关系。 因此, 后续实验重点放在了对 DEGs 的验证上, 对差异表达性较为显著的 TCEAL6、 CLEC3B、 LMOD1、 CNN1 进行 了验证。 qPCR 显示它们在宫颈癌中表达量均显著降低, 符合预期, 对 CNN1 进行的 Western 印迹也显示其 在宫颈癌中的低表达。 结论 TCEAL6、 CLEC3B、 LMOD1、 CNN1 在宫颈癌中均显著低表达, 有望成为宫颈 癌生物标志物。     

Identification of the key pathways and genes involved in HER2-positive breast cancer with brain metastasis

BackgroundThe risk of brain metastasis (BM) in HER2-positive (+) breast cancer (BC) patients is significantly higher than that in HER2-negative (-) BC patients. The high incidence and mortality rate makes it urgent to elucidate the key pathways and genes involved and identify patients who are more at risk of developing BM.Materials and methodsTo identify the target genes in HER2+BC patients with BM, we analyzed the microarray datasets (GSE43837) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to extract the differentially expressed genes (DEGs) involved in HER2+ primary BC and BC with BM. Bioinformatics methods including Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the screened DEGs. The protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Finally, GSEA analysis was performed to identify the hub genes and the important pathways.ResultsA total of 751 upregulated and 285 downregulated DEGs were identified. The GO function and KEGG pathway enrichment analyses indicated that the DEGs were all enriched in the protein binding molecular function. The top five hub nodes were screened out, included PHLPP1, UBC, ACACB, TGFB1, and ACTB. The GSEA results demonstrated that the five hub genes are mainly enriched in the ribosomal pathway.ConclusionOur study suggests that the five hub genes (PHLPP1, UBC, ACACB, TGFB1, and ACTB) are associated with HER2+BC with BM. The GSEA analysis revealed that the ribosomal pathway seems to play a very important role in the pathogenesis of HER2+BC with BM.     

阿尔茨海默病小鼠脑组织长链非编码RNA与信使RNA表达谱及调控网络的分析

目的 分析阿尔茨海默病（AD）小鼠脑组织长链非编码RNA（LncRNA）和信使RNA（mRNA）表达谱,构建竞争内源性RNA（ceRNA）调控网络,探讨差异表达LncRNA在AD发病机制中的潜在作用。方法 选取3只10月龄雄性APP/PS1转基因小鼠作为AD组,3只年龄及体质量相匹配的普通C57小鼠作为对照组。使用基因芯片技术检测2组小鼠脑组织LncRNA和mRNA的表达,筛选出差异表达的LncRNA和mRNA。对部分差异表达的LncRNA进行实时定量聚合酶链反应（qRT-PCR）。对差异表达的mRNA进行基因本体论（GO）和京都基因、基因组百科全书（KEGG）通路分析。随机挑选6个差异表达LncRNA构建ceRNA网络,进行AD的靶基因功能预测分析。结果 与对照组相比,AD组小鼠脑组织差异表达1.5倍以上的LncRNA有933个,其中上调222个,下调711个;差异表达1.5倍以上的mRNA有529个,其中上调189个,下调340个。qRT-PCR检测结果显示,AD组与对照组比较,7个差异表达的LncRNA上调或下调趋势与基因芯片结果一致,差异均有统计学意义（P值均<0.05）。GO和KEGG通路分析结果显示,差异表达基因主要参与氨基酸代谢、炎症反应和免疫反应。ceRNA调控网络靶基因的功能富集分析显示,LncRNA在胰岛素抵抗以及糖尿病并发症中的AGE-RAGE信号通路中显著富集。结论 AD小鼠脑组织LncRNA表达谱发生显著变化,由LncRNA Dgkb、Svip等构建的ceRNA调控网络有助于增进对AD发病分子机制的研究,差异表达的LncRNA或通路有可能成为潜在的治疗靶点。     

Bioinformatics analysis of gene expression profiles of Inclusion body myositis

Inclusion body myositis (IBM) is a disease with a poor prognosis and limited treatment options. This study aimed at exploring gene expression profile alterations, investigating the underlying mechanisms and identifying novel targets for IBM. We analysed two microarray datasets (GSE39454 and GSE128470) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between IBM and normal samples. Gene Ontology（GO）function and Kyoto Encyclopedia of Genes and Genomes（KEGG）pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Finally, protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, in order to identify hub genes. A total of 144 upregulated DEGs and one downregulated DEG were identified. The GO enrichment analysis revealed that the immune response was the most significantly enriched term within the DEGs. The KEGG pathway analysis identified 22 significant pathways, the majority of which could be divided into the immune and infectious diseases. Following the construction of PPI networks, ten hub genes with high degrees of connectivity were picked out, namely PTPRC, IRF8, CCR5, VCAM1, HLA-DRA, TYROBP, C1QB, HLA-DRB1, CD74 and CXCL9. Our research hypothesizes that autoimmunity plays an irreplaceable role in the pathogenesis of IBM. The novel DEGs and pathways identified in this study may provide new insight into the underlying mechanisms of IBM at the molecular level.     

Identification of DNA methylation-regulated differentially-expressed genes and related pathways using Illumina 450K BeadChip and bioinformatic analysis in gastric cancer

In the current research, we aimed to identify and analyze methylation-regulated differentially-expressed genes (MeDEGs) and related pathways using bioinformatic methods. We downloaded RNA-seq, Illumina Human Methylation 450 K BeadChip and clinical information of gastric cancer (GC) from The Cancer Genome Atlas (TCGA) project. Differentially-expressed genes (DEGs) were identified using the edgeR package. Then, we performed Spearman’s correlation analysis between DEG expression levels and methylation levels. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database. We then conducted Kaplan–Meier survival analysis to explore the relationship between methylation, expression and prognosis. The protein–protein interaction networks were further analyzed using the STRING database. A total of 204 down-regulated DEGs and 164 up-regulated DEGs were identified as MeDEGs. GO and KEGG pathway analyses showed that MeDEGs were enriched in multiple cancer-related terms. Kaplan–Meier survival analysis showed that eight up-regulated MeDEGs (CAMKV, COMP, FGF3, FGF19, FOXL2, IGF2BP1, IGFBP1 and NPPB) and five down-regulated MeDEGs (ALDH3B2, CALML3, FLRT1, G6PC and HRASLS2) were associated with prognosis of GC patients. In addition, PPI networks and KEGG pathway analyses further confirmed the critical role of prognosis-related MeDEGs. In conclusion, methylation plays a critical role in GC progression. Multiple MeDEGs are related to prognosis, suggesting that they may be potential targets in tumor treatment.     

Brief Exercises Affect Gene Expression in Circulating Monocytes

We aimed to give a systematic hypothesis on the functions of exercise on circulating monocytes by identifying a discrete set of genes in circulating monocytes that were altered by exercise. The microarray expression profile of GSE51835 was downloaded from gene expression omnibus (GEO) database for the identification of differentially expressed genes (DEGs) using limma and affy packages in R language. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for DEGs, followed by the construction of co‐expression network and protein–protein interaction (PPI) network. The top 10 nodes in PPI network were screened, and subnetwork was constructed for the key genes identification. Totally, 35 DEGs, including 2 upregulated genes and 33 downregulated genes, were identified. The enriched GO terms were mainly linked to immune response and defence response, and the enriched KEGG pathways were mainly associated with natural killer cell‐mediated cytotoxicity and graft‐versus‐host disease. Dual‐specificity phosphatase 2 (DUSP2) was identified as a key node in the co‐expression network. In the PPI network, CD247 module (CD247), chemokine (C‐X‐C motif) receptor 4 (CXCR4), granzyme B (GZMB) and perforin 1 (PRF1) were identified as key nodes. An important interaction, GZMB/PRF1, was detected. Five key genes, including DUSP2, CD247, CXCR4, GZMB and PRF1, and an interaction of GZMB/PRF1, were significant factors in the immune processes of circulating monocytes, which might be regulated by brief exercises, leading to the enhancement of immune function.