基于加权基因共表达网络分析筛选阿尔茨海默病的血液关键基因

目的 基于加权基因共表达网络分析(WGCNA)筛选阿尔茨海默病(AD)的血液关键基因。方法 2021年5—7月,利用美国国家生物技术信息中心基因表达综合数据库收集AD相关数据,以AD患者为实验组,以年龄匹配的健康老年人为对照组。采用WGCNA构建差异基因的共表达网络,以进一步筛选与临床特征相关性较高的基因模块。利用注释、可视化和集成发现的数据库(DAVID)对基因模块进行GO富集分析和KEGG通路富集分析。以基因显著性(GS)>0.9和模块身份(MM)>0.9为临界标准筛选模块中的核心基因,再使用Cytoscape的cytoHubba插件筛选蛋白-蛋白相互作用(PPI)网络中的关键基因。结果 本研究数据集为GSE97760队列的19个血液样本,其中实验组10个血液样本、对照组9个血液样本。最终拆分出4个基因共表达模块,结果显示,黑色模块与AD呈正相关(r=0.89),绿色模块与AD呈负相关(r=-0.90)。GO富集分析结果显示,黑色模块和绿色模块基因的生物学功能主要富集于转录、参与泛素依赖性蛋白质分解代谢过程的蛋白质泛素化,细胞组成主要富集于核、核质,分子功能主要富集于蛋...     

肥厚型心肌病相关基因的生物信息学分析

目的筛查肥厚型心肌病相关的关键基因,为肥厚型心肌病的发病机制提供理论依据。方法从高通量基因表达(GEO)数据库中检索包含106例肥厚型心肌病样本及39例对照组样本的高通量测序数据集GSE36961。利用R软件筛选肥厚型心肌病组织和正常组织间差异表达的基因,通过WGCNA构建差异基因的加权重共表达网络,筛选出与肥厚型心肌病相关的模块,对模块中的基因行功能富集分析,并应用STRING数据库构建蛋白互作网络筛选出关键基因。结果从数据集中筛选出8002个差异表达基因(P<0.05),通过WGCNA构建出差异基因的加权重共表达网络,筛选出两个与肥厚型心肌病相关的模块:青色模块(Pearson cor=0.77,P=4e-29)和紫红色模块(Pearson cor=0.76,P=2e-28)。前者基因功能主要富集在能量代谢,后者基因功能主要富集在血管形成。通过蛋白质相互作用网络分析获得32个基因及157个互作关系,从中筛选出与肥厚型心肌病相关的关键基因,提示肥厚型心肌病可能与炎症反应相关。结论本研究通过系统性分析肥厚型心肌病患者的高通量测序数据集,筛选出可能与肥厚型心肌病有关的目标基因32个,再筛选出关键基因10个,其中甲酰肽受体2(formyl peptide receptor 2,FPR2)、毒蕈碱型胆碱受体M2(cholinergic receptor musca⁃rinic 2,CHRM2)与心肌炎症反应相关,余基因的作用仍需在未来的细胞及动物实验中得到进一步的验证。     

鉴定cAMP通路相关基因与胃癌的预后价值以及与免疫浸润的关系

目的 基于环磷酸腺苷(cAMP)通路基因构建与胃癌预后相关的风险模型,并研究预后特异性和免疫特征。方法选用TCGA数据库下载的胃腺癌和正常组织的转录组以及临床数据,采用加权基因共表达网络分析(WGCNA)、单因素Cox回归和迭代多因素Cox回归分析构建最佳的cAMP通路相关的胃癌预后模型,并在GEO数据中进行验证。用Kaplan-Meier生存分析、单因素和多因素研究模型的预后特征。采用基因集富集分析(GESA)分析探索高低危人群的差异表达信号通路。用免疫CIBERSORT算法探讨风险评分与肿瘤免疫微环境的关系。利用TIDE评分估计高低风险胃癌患者的免疫治疗反应。结果用WGCNA筛选出51个模块基因,最终构建了最佳的双基因(EDNRA和GNAI1)胃癌预后模型。多因素Cox回归分析表明,预后风险模型可作为一个独立预后因素(HR=2.641,P=6.33×10^-4)。GSEA发现高风险组主要在心肌病、细胞-基质黏附通路中富集,低风险组在DNA复制和氧化磷酸化通路中富集。免疫细胞浸润结果揭示了高危人群与静息记忆CD4+T细胞、单核细胞和静息肥大细胞等多种免疫细胞密切相...     

基于权重基因共表达分析的结肠癌肝转移相关基因挖掘与验证

目的挖掘结肠癌肝转移过程中的核心基因模块和分子靶点,并验证其对临床预后及结肠癌转移能力的影响。 方法基于GEO数据库结肠癌肝转移测序样本,利用权重基因共表达网络分析（WGCNA）技术筛选转移相关基因模块。利用MCODE软件进一步挖掘结肠癌肝转移相关核心子模块并分析其功能。基于TCGA数据库,进行子模块基因对结肠癌预后影响的大临床样本验证。分子生物学方法验证子模块基因对结肠癌细胞系HCT116迁移和侵袭能力的影响。 结果WGCNA分析筛选出5个基因模块,其中模块1与结肠癌肝转移关系密切。模块1共包含4个核心子模块,主要参与G蛋白偶联受体信号调节、表观遗传学调控、mRNA的剪接调节等功能。大临床样本验证发现子模块4中的FOXC1基因与结肠癌患者生存率密切相关。敲减FOXC1在HCT116细胞中的表达后,HCT116的迁移能力（t=3.123,P=0.035）和侵袭能力（t=2.936,P=0.043）受到显著抑制。 结论本研究筛选出的结肠癌肝转移相关子模块可能具有重要的促转移作用,子模块基因FOXC1与结肠癌患者较差的生存率相关并具有促结肠癌转移能力。     

动脉粥样硬化合并糖尿病的加权基因共表达网络构建与分析

目的 利用加权基因共表达网络分析(WGCNA)来识别动脉粥样硬化(As)合并糖尿病有关的功能基因模块。方法 从基因表达数据库(GEO)中下载GSE23304数据集,其中包含101例As外周斑块样本(其中25例患有糖尿病)的基因表达谱,使用WGCNA对数据集进行模块化分析并关联临床表型数据,根据相关系数大小,识别出与As最为相关的表型所在的模块,并对模块内基因进行功能注释(DAVID),最后使用STRING进行蛋白互作网络分析。结果 使用WGCNA分析,最终得到了33个模块,其中As中识别到的Darkturquoise模块与糖尿病最为相关,认为Darkturquoise为糖尿病合并As的关键基因模块。结论 WGCNA分析方法识别出的As关键基因模块Darkturquoise在糖尿病中可能起到重要作用。     

加权基因共表达网络分析和差异基因表达分析鉴定氯离子通道附件1基因对结肠癌预后保护作用

目的:结合应用加权基因共表达网络分析(WGCNA)和差异基因表达分析2种方法筛选结肠癌mRNA表达谱中的差异共表达基因,并分析差异共表达基因与预后的关系。方法:基于生物信息学方法从癌症基因组图谱(TCGA)和基因表达综合(GEO)数据库分别下载TCGA结肠腺癌数据集的转录组学数据和GSE68468数据集的芯片表达谱数据...     

基于GSEA和WGCNA分析幽门螺杆菌感染机制

背景幽门螺杆菌在世界范围内感染率高,参与胃溃疡、胃恶性肿瘤等多种疾病的发生发展,亟需对其感染及致癌机制进行研究。目的通过基因集富集分析(GSEA)、加权基因共表达网络分析(WGCNA)预测幽门螺杆菌感染的可能分子机制及枢纽基因。方法从基因表达综合数据库(GEO数据库)下载GSE27411数据集,通过GSEA分析与幽门螺杆菌感染相关的通路。利用WGCNA分析与幽门螺杆菌感染相关模块及枢纽基因,并对模块内的基因进行GO和KEGG富集分析。使用GEPIA对枢纽基因表达水平与胃癌的生存预后进行探索。结果通过GSEA分析挑选出10条hallmark通路和13条KEGG通路。通过构建WGCNA共表达网络,确定brown模块与幽门螺杆菌感染密切相关,该模块内的基因富集得到16个生物学过程及19条KEGG相关通路。GSEA和WGCNA交集得到4条KEGG通路。对模块基因筛选得到TNF、KIF2C、RRM2、CHEK1、PLK1、RAD51、CENPA、ASF1B共8个枢纽基因。利用GEPIA探索发现,KIF2C、RRM2、CHEK1、PLK1、RAD51、CENPA、ASF1B在胃癌组织中高表达,其中ASF1B与胃癌患者较差的总生存期及无疾病生存期相关。结论本研究通过GSEA和WGCNA分析方法,筛选出与幽门螺杆感染相关的4条核心KEGG通路,1个枢纽模块及8个枢纽基因。     

胃癌免疫相关LncRNA预后风险模型的建立与分析

目的 旨在分析免疫相关LncRNA在胃癌中的表达及预后的作用,建立免疫相关LncRNA预后风险模型.方法 从TCGA数据中下载443例胃癌样本的转录组数据和临床信息.从GSEA数据中获取免疫基因列表,采用共表达法筛选免疫相关LncRNA.采用单因素Cox回归分析筛选胃癌免疫预后相关LncRNA,纳入多因素Cox回归分析...     

孕中期羊水游离RNA中泌尿系统发育关键基因的筛选与生物学功能分析

目的 筛选并分析孕中期羊水游离RNA(AfcfRNA)中的泌尿系统发育关键基因.方法 从GEO数据库获取胎儿AfcfRNA的芯片检测数据.对56例AfcfRNA的芯片检测结果进行基因共表达网络(WGCNA)分析,建立共表达网络模块.从Human Protein Atlas数据库中筛选在泌尿组织中表达量高于平均表达量10...     

特应性皮炎外周血单个核细胞转录组特征及其在度普利尤单抗疗效预测中的应用

目的 探讨中国特应性皮炎(atopic dermatitis, AD)患者外周血单个核细胞(peripheral blood mononuclear cells, PBMCs)转录组特征及其在度普利尤单抗疗效预测中的应用。方法 本研究共纳入56例中重度成年AD患者接受度普利尤单抗治疗,在负荷剂量600 mg后,每隔一周自行皮下给予度普利尤单抗300 mg治疗,共随访16周,并在基线和治疗16周后收集PBMCs样本。其中35例AD患者样本及另外30名健康对照进行RNA测序。使用加权基因共表达网络分析(Weighted gene co-expression network analysis, WGCNA)寻找度普利尤单抗疗效预测的相关基因,并在其余21例AD患者样本中进行验证。结果 AD患者PBMCs中Th2/Th22通路、Th17抗菌肽相关基因以及天然调节性T细胞(nTreg)丰度上调,而TGF-β信号和NK细胞信号下调。度普利尤单抗治疗逆转了AD患者中Th2细胞因子受体表达的增加。WGCNA鉴定出两个与度普利尤单抗疗效显著相关的基因模块。通过Spearman相关性分析、ROC分析和回归...     

Potential impact of WTAP and YTHDF2 on tumor immunity in lung adenocarcinoma

WTAP and N6-methyladenosine (m6A) reader proteins (YTHDF2) are N6-methyladenosine (m6A) methyltransferase and m6A reading proteins, respectively. In recent years, the tumor immune environment has received more and more attention in the progress and treatment of cancer. The aim of this study was to investigate the relationship between N6-methyladenosine (m6A) methyltransferase (WTAP)/YTHDF2 and the immunological characteristics of lung adenocarcinoma (LUAD). Based on the expression of WTAP and YTHDF2 in the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database, LUAD patients were divided into 2 clusters by coherently clustering method, and performed gene set enrichment analysis (GSEA) to identify the functional differences. Immunoinvasion analysis was performed using ESTIMATE, CIBERSORT, and single-sample GSEA (ssGSEA), and expression of immune checkpoint inhibitors (ICIs) targets was assessed, while tumor mutation burden (TMB) was calculated in tumor samples. Weighted gene co-expression network analysis (WGCNA) was used to identify the genes related to both WTAP/YTHDF2 expression and immunity. The immunological characteristics between the 2 clusters were externally verified based on {"type":"entrez-geo","attrs":{"text":"GSE39582","term_id":"39582"}}GSE39582. The expression of WTAP was higher in cluster 1 and YTHDF2 was lower, but it was opposite in cluster 2. Cluster 1 had stronger immune infiltration, more ICIs target expression, more TMB. In addition, WGCNA identified 22 genes associated with WTAP/YTHDF2 expression and immune score, including TIM3 (HAVCR2) and CD86. WTAP and YTHDF2 influence immune contexture and may be novel prognostic and druggable targets associated with the immune system of LUAD.     

基于加权基因共表达网络分析运动性精子结构域包含蛋白2在类风湿关节炎中的表达及与疾病活动度的相关性

目的利用加权基因共表达网络分析(WGCNA)及实验验证寻找RA相关的关键基因。方法从GEO数据库下载了RA患者基因芯片数据,构建基因网络,利用WGCNA将基因划分为不同的模块,将与RA临床症状相关的模块中的关键基因进行了基因本体论分析。随后使用GEO不同的数据集用受试者工作特征曲线(ROC)评价关键基因对RA诊断的准确性。此外,还通过实时荧光定量反转录PCR(RT-PCR)及蛋白质印迹法验证关键基因在RA中的表达,分析其与DAS28的关系。采用配对样本t检验和Pearson相关性分析对结果进行分析。结果共筛选出5413个基因构建了加权基因共表达网络,将基因分为23个模块。其中,黑色模块与RA临床症状密切,包含346个基因。富集分析及京都基因与基因组百科全书(KEGG)信号通路分析显示其要富集于对IL-6的正调控、IL-1β分泌、破骨细胞分化、NOD样受体信号通路、辅助性T细胞(Th)17细胞分化等多个与RA密切相关的通路。其中运动性精子结构域包含蛋白2(MOSPD2)与临床症状具有明显相关性,在血液单核细胞、骨髓单核细胞中高表达(t=2.238,P=0.032;t=3.153,P=0.006),在RA关节滑膜液中与血液中表达呈正相关(r=0.683,P=0.03)。ROC曲线分析表明,MOSPD2能区分RA和对照组(曲线下面积分别为0.855和0.726)。RT-PCR及蛋白质印迹法结果显示,MOSPD2在RA患者中表达上调(t=-3.96,P=0.02)。MOSPD2在血液中的表达水平与RA患者的DAS28呈正相关(r=0.8846,P=0.0462)。结论MOSDP2与RA患者临床症状密切相关,可能是诊断及治疗RA的靶点之一。     

基于GEO数据库分析影响纳武单抗及派姆单抗治疗非小细胞肺癌疗效的差异基因

目的 筛选影响纳武单抗和派姆单抗治疗非小细胞肺癌(non-small cell lung cancer, NSCLC)疗效的差异基因,为免疫治疗药物的选择及治疗预后提供参考。方法 通过GEO数据库搜索“Nivolumab”、“Pembrolizumab”找到目的芯片,下载免疫治疗相关表达芯片“GSE93157”,筛选NSCLC相关样本共35个,利用R语言数据包对样本进行表达差异基因进行聚类分析。对差异基因进行基因功能注释GO分析和KEGG通路分析,构建蛋白相互作用网络,筛选枢纽基因进行生存分析,确定影响不同抗程序性细胞死亡蛋白1药物治疗的关键基因。结果 筛选出影响纳武单抗治疗疗效差异基因共58个,其中免疫相关基因25个;影响派姆单抗治疗疗效差异基因231个,免疫相关基因82个。基于两种药物免疫相关差异基因的蛋白互作网络提示纳武单抗共得到2个子网络,主要模块共11个节点,51个边;派姆单抗共得到4个子网络,主要模块共24个节点,231个边。影响两种药物治疗疗效的前10位主要免疫相关基因生存分析,显示生存差异具有统计学意义(P<0.05)的基因,与纳武单抗相关的免疫差异基因为CD5、...     

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of hepatocellular carcinoma (HCC)

Growing evidence supports that the tumor microenvironment plays a key role in the development and progression of tumors. But immune microenvironment of hepatocellular carcinoma (HCC) has not yet been fully explored. In the present investigation, the clinical value and prognostic significance of immune-related genes in HCC were investigated.The immune and stromal scores of HCC were calculated through the application of Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Algorithm based on the Cancer Genome Atlas database. Differentially expressed genes were identified using the “edgeR” package of the R software. Functional annotation and pathway enrichment were performed using “ggplots2” and “clusterProfiler” packages in R software. Protein-protein interaction network was constructed using STRING, and the hub genes were identified through the Cytoscape. Survival analysis was performed using Kaplan-Meier methods. Tumor Immune Estimation Resource algorithm was used to view the immune landscape of the microenvironment in HCC.Firstly, the immune and stromal scores of HCC were calculated and we found that the immune and stromal scores of HCC were closely related to the patients’ prognosis. Then the differentially expressed genes were identified respectively stratified by the median value of the immune and stromal scores, and the immune-related genes that related to the prognosis in HCC patients were further identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune-related biological process. In addition, dendritic cells were found to be the most abundant in the microenvironment of HCC through Tumor Immune Estimation Resource algorithm and were significantly associated with the patients’ prognosis. To robust the results, the immune-related genes were validated in an independent dataset from the Gene Expression Omnibus database.We arrived at a more comprehensive understanding of the microenvironment of HCC and extracted 7 immune-related genes that were significantly associated with the recurrence survival of HCC.     

转录组分析布鲁氏杆菌患者外周血免疫基因的表达情况

目的 通过生物信息学分析方法探讨布鲁氏杆菌病的关键失调免疫基因和分子机制。方法 从GSE69597中获取布鲁氏杆菌病患者全血转录组表达数据。通过差异分析筛选布鲁氏杆菌病患者和对照之间的差异表达基因(DEGs),并利用immport数据库调取DEGs中免疫相关的基因集(immune-DEGs)。通过Enirchr在线富集工具对immune-DEGs进行富集分析。构建immune-DEGs的蛋白质-蛋白质互作(PPI)网络,并鉴定网络中的高度互联的核心(hub)基因。利用qRT-PCR验证hub基因的表达,并绘制hub基因的ROC曲线。使用ssGSEA算法评估布鲁氏杆菌病患者中免疫细胞的评分,并通过流式细胞术检测血液样本中免疫细胞的水平。结果 共获得390个immune-DEGs,富集结果中发现了T细胞受体信号通路和Th17细胞分化等。在10个hub基因中IFNG和TNF在布鲁氏杆菌组中显著高表达。ROC曲线表明IFNG对布鲁氏杆菌病具有良好诊断意义。此外,活化型CD₄ T细胞、效应型CD₄ T细胞、效应记忆型CD₈ T细胞和2型T辅助细胞因子在布鲁氏杆菌患者中明显增多。流式细胞术检测发现与健康对照组相比,布鲁氏杆菌病患者外周血中Th2和Th17细胞比例增高,Th1和Treg细胞比例则降低。结论 本研究结果不仅提高了我们对布鲁氏杆菌感染后机体免疫反应的认识,还为诊断和治疗布鲁氏杆菌病提供了更多的方向。     

Identification of Critical Genes and Pathways for Influenza A Virus Infections via Bioinformatics Analysis

Influenza A virus (IAV) requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system but also provides further targets for antiviral drug development. To uncover critical pathways and potential targets of influenza infection, we assembled a large amount of data from 8 RNA sequencing studies of IAV infection for integrative network analysis. Weighted gene co-expression network analysis (WGCNA) was performed to investigate modules and genes correlated with the time course of infection and/or multiplicity of infection (MOI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and pathways of the genes in 5 significant modules. Top hub genes were identified using the cytoHubba plugin in the protein interaction network. The correlation between expression levels of 7 top hub genes and time course or MOI was displayed and validated, including BCL2L13, PLSCR1, ARID5A, LMO2, NDRG4, HAP1, and CARD10. Dysregulated expression of these genes potently impacted the development of IAV infection through modulating IAV-related biological processes and pathways. This study provides further insights into the underlying molecular mechanisms and potential targets in IAV infection.     

R语言下食管鳞状细胞癌关键驱动基因富集分析的生物信息学研究

目的本研究利用食管鳞状细胞癌(ESCC)相关微表达矩阵芯片数据,筛选出与ESCC发生、发展显著相关的关键通路及关键基因,并对关键基因所在的功能模块进行GO和KEGG富集分析、蛋白-蛋白相互作用分析以及关键基因在ESCC患者中的生存分析。方法从GEO数据库获得了GSE38129微表达矩阵芯片数据,利用R语言及其相关的软件包进行数据处理和差异表达分析,所有差异表达基因选取“FDR<0.05及logFC≥2或log2FC≤-2”为阈值。结果本研究筛选出51个上调基因和81个下调基因进行GO和KEGG富集分析,并将筛选出来的关键基因进行生存预后分析,表明PBK、VCAN、DLGAP5、ADAT2、TOP2A与ESCC生存期相关。结论利用生物信息学方法筛选出ESCC发生、发展过程中的关键基因和信号通路,为ESCC的诊疗提供潜在的候选靶点。     

Mining of gene modules and identification of key genes in head and neck squamous cell carcinoma based on gene co-expression network analysis

To explore the gene modules and key genes of head and neck squamous cell carcinoma (HNSCC), a bioinformatics algorithm based on the gene co-expression network analysis was proposed in this study.Firstly, differentially expressed genes (DEGs) were identified and a gene co-expression network (i-GCN) was constructed with Pearson correlation analysis. Then, the gene modules were identified with 5 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Gene Ontology (GO) analysis was used to annotate the biological pathways of the gene modules. Then, the key genes were identified with 2 methods, gene significance (GS) and PageRank algorithm. Moreover, we used the Disgenet database to search the related diseases of the key genes. Lastly, the online software onclnc was used to perform the survival analysis on the key genes and draw survival curves.There were 2600 up-regulated and 1547 down-regulated genes identified in HNSCC. An i-GCN was constructed with Pearson correlation analysis. Then, the i-GCN was divided into 9 gene modules. The result of association analysis showed that, sex was mainly related to mitosis and meiosis processes, event was mainly related to responding to interferons, viruses and T cell differentiation processes, T stage was mainly related to muscle development and contraction, regulation of protein transport activity processes, N stage was mainly related to mitosis and meiosis processes, while M stage was mainly related to responding to interferons and immune response processes. Lastly, 34 key genes were identified, such as CDKN2A, HOXA1, CDC7, PPL, EVPL, PXN, PDGFRB, CALD1, and NUSAP1. Among them, HOXA1, PXN, and NUSAP1 were negatively correlated with the survival prognosis.HOXA1, PXN, and NUSAP1 might play important roles in the progression of HNSCC and severed as potential biomarkers for future diagnosis.