基于生物信息学分析鉴定阿尔茨海默病中关键差异表达基因及其潜在治疗靶点

目的 利用生物信息学方法挖掘阿尔茨海默病(AD)的基因表达谱,筛选出与该病发生发展相关的关键基因。方法 从基因表达综合数据库(GEO)下载数据集GSE132903,利用R语言进行分析,鉴定出差异表达基因(DEGs),利用DAVID数据库对差异基因进行GO与KEGG功能富集分析,利用string数据库对差异表达基因进行蛋白互作网络分析,随后利用Cytoscape软件中的插件cytohubba进行关键基因筛选。结果 在AD的基因表达谱中鉴定了319个差异表达基因。其中上调基因118个,下调基因201个,与正常对照组样本相比,AD患者样本中神经活动配体-受体相互作用、GABA能突触、突触囊泡循环、长程增强效应等通路显著改变。基于多种算法得到关键基因分别是：SNAP25、SYP、SYT1、SYT4,其中SYT4在AD中的作用机制尚未有报道。结论 利用生物信息学方法成功构建了在AD中起关键作用的蛋白互作网络,发现了4个与疾病相关的关键基因,进一步证明了突触功能在AD中的重要作用,其中SYT4可能是AD治疗的新靶点。     

基于GEO数据库数据肝内胆管癌吉西他滨耐药基因筛选及生物学功能分析

目的 基于基因表达综合数据库(GEO)筛选肝内胆管癌吉西他滨耐药基因(简称吉西他滨耐药基因),分析吉西他滨耐药基因的生物学功能及关键耐药基因(MCC值排名前十)与肿瘤发生、预后的关系.方法 从GEO获得吉西他滨耐药肝内胆管癌基因集,通过GEO2R软件筛选吉西他滨敏感的肝内胆管癌细胞株和吉西他滨耐药的肝内胆管癌细胞株的差...     

锯齿状息肉病综合征的关键基因与潜在治疗药物的生物信息学筛选

目的 基于生物信息学方法利用GEO芯片数据库分析锯齿状息肉病综合征(serrated polyposis syndrome,SPS)的关键基因,探索SPS的分子调控机制及潜在的治疗药物.方法 从GEO数据库下载GSE19963数据集,利用GEO2R分析SPS组织样本和正常结肠黏膜组织样本的表达数据,利用DAVID数据库...     

基于生物信息学方法筛选结肠癌进展相关的枢纽基因

目的 利用生物信息学方法筛选结肠癌进展相关的差异表达基因(DEGs).方法 从GEO数据库下载GSE127069、GSE145626数据集,运用GEO在线分析工具GEO2R提取这两个数据集的原始数据,利用韦恩图在线分析工具筛选DEGs.利用基因功能注释在线工具DAVID对DEGs进行GO功能注释和KEGG通路富集分析;...     

基于生物信息学筛选研究衰老小鼠免疫B细胞的基因差异

目的 通过生物信息技术分析GSE72224基因芯片数据,比较衰老小鼠与青年小鼠B细胞基因差异,探索关键基因,为衰老机制研究及药物研发提供思路。方法 通过NCBI提供的GEO数据库下载GSE72224,利用生物信息方法筛选衰老与青年小鼠B细胞差异表达基因,然后对差异表达基因进行基因本体论(GO)功能富集分析和京都基因组百科全书(KEGG)信号通路分析,并且构建蛋白质相互作用网络获得关键基因和关键模块。结果 共筛选出87个表达基因,包括其中66个下调基因及21个上调基因。差异表达基因主要参与B细胞活化、内质网及内质网蛋白加工通路等。通过构建蛋白质相互作用网络从而筛选出H2A组蛋白家族成员(H2af)x、Zeste基因增强子人类同源物(Ezh)2、蛋白二硫化物异构酶(Pdi)A4、H2B组家族成员b(Hist2h2bb)、白细胞介素(IL)-10、Sel1l、Dnajb9、泛素样含PHD和环指域(Uhrf)1这8个关键基因。并且筛选出两个重要模块,模块中的基因主要也与内质网蛋白加工通路等有关。结论 H2afx、Ezh2、Pdia4、Hist2h2bb、IL-10、Sel1l、Dnajb9、U...     

基于GEO数据库对甲状腺乳头状癌的靶基因分析研究

目的初步探索分析甲状腺乳头状癌中的差异microRNA及其靶基因,为甲状腺乳头状癌的研究及治疗提供新的思路。方法从GEO数据库中查找适合的芯片,利用GEO2R在线网站及生物信息学分析软件进行差异的microRNA分析,并对符合筛选标准的microRNA及其靶基因进行GO和KEGG分析,筛选重要的microRNA及其靶基因。结果 (1)通过对芯片GSE73182及GSE113629数据挖掘发现,差异的microRNA有1535个,符合纳入研究标准的差异microRNA为12个,其中,上调的microRNA为8个,下调的microRNA为4个。(2)差异的microRNA进行预测靶基因,得出关键的靶基因有44个,主要参与生物学调控、代谢、免疫应答、蛋白质结合等方面。(3)文章发现:hsa-miR-21-5p与靶标JAG1及SOX5、hsa-miR-181a与靶标PROX1、hsa-miR-204-5p与其靶标BCL2可能存在作用。结论hsa-miR-21-5p与靶标JAG1及SOX5、hsa-miR-181a与靶标PROX1、hsa-miR-204-5p与其靶标BCL2有望成为新的研究方向。     

脑瘫差异表达基因生物信息学分析及脑瘫核心驱动基因筛选

目的 对文献报道的脑瘫差异表达基因进行生物信息学分析,并筛选脑瘫核心驱动基因.方法 通过检索Pubmed、中国知网数据库相关文献,获取脑瘫差异表达基因,并进行GO功能富集分析、KEGG信号通路分析和通路串话分析,应用STRING和Cytoscape软件构建蛋白质互作网络(PPI),并筛选排名前10的差异表达基因,即为脑...     

非特异性间质性肺炎相关基因筛选和生物信息学分析

目的通过生物信息学的方法筛选非特异性间质性肺炎(nonspecific interstitial pneumonia, NSIP)的致病基因,为进一步研究提供靶点。 方法从GEO数据库下载基因芯片数据集GSE110147、GSE21369、GSE40839,使用limma包分析工具筛选正常组织与NSIP的差异表达基因。通过clusterProfiler包对差异表达基因进行GO分析和KEGG通路富集分析,找到NSIP发病过程中差异表达基因主要参与的生物功能及其集中的信号通路。利用STRING数据库和CYTOSCAPE软件构建蛋白相互作用网络,使用MCODE软件提取蛋白相互作用网络中的子网络模块。 结果3个数据集的差异表达基因做韦恩图得到3个共同差异表达基因。GO富集分析表明NSIP中上调的差异表达基因主要影响RNA剪接、抗病毒感染、对肽的细胞反应等相关的生物过程,富集的分子主要参与细胞组分的囊腔合成分泌、剪接复合体,富集的分子功能主要参与ATP酶活性,受体配体活性及DNA结合转录激活因子活性。NSIP中下调的蛋白主要涉及转移酶活性调节的生物过程。KEGG通路分析表明NSIP中上调的差异表达基因主要参与PI3K-Akt、人类乳头瘤病毒感染及MAPK等信号通路。 结论利用生物信息学筛选出差异表达基因,可能是NSIP发病机制的新靶点,对诊断治疗NSIP具有临床意义。     

Screening of atrial fibrillation diagnostic markers based on a GEO database chip and bioinformatics analysis

BackgroundStudy have shown that atrial fibrillation (AF) is a disease with genetic risk, and its pathogenesis is still unclear. This study sought to screen the gene microarray data of AF patients and to perform a bioinformatics analysis to identify AF signature diagnostic genes.MethodsThe AF gene sets from the Gene Expression Omnibus (GEO) database were screened, and the differentially expressed genes (DEGs) were identified after the normalization of the data set by R software. We conducted a gene set enrichment analysis, a protein-protein interaction (PPI) network analysis, a gene-gene interaction (GGI) network analysis, and an immuno-infiltration analysis. The core genes were identified from the DEGs, and base on receiver operating characteristic, the top 5 core genes in the 2 data sets were selected as diagnostic factors and a nomogram was constructed. The miRNA of the core genes were predicted and an immune cell correlation analysis was performed.ResultsA total of 20 DEGs were identified. The functions of these DEGs were mainly related to muscle contraction, autophagosome, and bone morphogenetic protein (BMP) binding, and focused on the calcium signaling pathway, ferroptosis, the extracellular matrix-receptor interaction, and other pathways. A total of 5 core genes [i.e., GPR22 (G protein-coupled receptor 22), COG5 (component of oligomeric golgi complex 5), GALNT16 (polypeptide N-acetylgalactosaminyltransferase 16), OTOGL (otogelin-like), and MCOLN3 (mucolipin 3)] were identified, and a linear model for risk prediction was constructed, which has good prediction ability. Plasma cells and Macrophages M2 were significantly increased in AF, while T cells follicular helper and Dendritic cells activated were significantly decreased.ConclusionsIn our study, we identified 5 potential diagnostic key genes (i.e., GPR22, COG5, GALNT16, OTOGL, and MCOLN3). Our findings may provide a theoretical basis for susceptibility analyses and target drug development in AF.     

Exploration of biomarkers in osteoarthritis based on bioinformatics

Osteoarthritis (OA) seriously affects human health and brings a heavy social burden. This study aimed to identify new biomarkers involved in OA. Differential expression analysis and gene set enrichment analysis were performed on the microarray data set of OA. Identify key genes from immune-related DEGs and verify their expression in the validation set. CIBERSORT was used to analyze the infiltration of immune cells. The correlation between key genes and immune cells were conducted. A total of 1779 DEGs were identified in {"type":"entrez-geo","attrs":{"text":"GSE82107","term_id":"82107"}}GSE82107. Gene set enrichment analysis results of top 4 for hallmark revealed the enrichment of DEGs were associated with genes in “HALLMARK_TNFA_SIGNALING_VIA_NFKB”, “HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION”, “HALLMARK_INFLAMMATORY_RESPONSE” and “HALLMARK_HYPOXIA”. A total of 108 immune-related DEGs were identified from the overlap between 2498 immune-related genes and 1779 DEGs. The expression of top 6 immune-related DEGs including ADIPOQ, FABP4, FOS, IGLC1, IGLV1–44 and leptin were measured in the validation set, the results shown that IGLC1 and IGLV1–44 might play a key role in the synovial membrane of OA. A total of 8 kinds of cells including B cells memory, Plasma cells, T cells CD4 memory resting, T cells gamma delta, natural killer cells activated, macrophages M0, Mast cells resting and Mast cells activated have significant differences in infiltration between the OA group and the control group. Besides, the expressions of IGLC1 and IGLV1–44 are highly correlated. Our results indicated that IGLC1 and IGLV1–44 may play the role of immune-related biomarkers in OA.     

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、...     

exoRBase数据库外泌体基因在冠心病中的差异表达及意义

目的通过对exoRBase外泌体数据库中筛选出的冠心病(CHD)患者外周血外泌体差异表达基因的分析和CeRNA网络的构建,挖掘导致冠心病发病和疾病进展中的关键基因和调控机制,通过对差异表达mRNA关键基因的GO和KEGG富集分析,研究冠心病差异表达mRNA参与的分子功能和生物学过程。方法应用R语言筛选出在exoRBase外泌体数据库冠心病患者外周血中差异表达的外泌体基因,通过在线数据库和Cytoscape软件构建CeRNA网络,对关键基因进行可视化分析,并对差异表达的mRNA关键基因进行GO和KEGG富集分析。结果筛选出冠心病患者外周血中差异表达的外泌体mRNA 312个,lncRNA 43个,circRNA 85个;通过构建CeRNA网络,发现mRNA、lncRNA和circRNA竞争性地结合miRNA,且与miRNA相结合的lncRNA表达均显著上调,而mRNA和circRNA的表达则大多数呈现显著下调的趋势;差异表达mRNA关键基因的GO和KEGG富集分析结果显示,这些关键基因主要与"磷酸酶激活活动"和"磷酸酶调节活动"功能相关。结论由exoRBase数据库筛选出的冠心病患者外周...     

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

Background:Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.Methods:Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan–Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.Results:A total of 197 DEGs were identified. The protein–protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.Conclusion:Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.