斑马鱼急性无机砷暴露后肝脏差异表达基因的生物信息学分析

砷是一种致癌物,是心血管、外周血管疾病、神经疾病、糖尿病和各种癌症的致病因素。目的:利用GO数据库和KEGG数据库等生物信息学方法对GEO数据库数据中的差异表达基因进行评价。利用生物信息学分析软件对差异基因进行功能富集、功能注释分析和生存分析。利用Cytoscape上的蛋白-蛋白相互作用网络(Protein-protein interaction network, PPI)软件对179个差异基因进行筛选和分析。结果发现126个基因作用于蛋白靶点,其中有10个基因为关键基因分别为:PSMB3、HSP701、HSPE1、STIP1、HSPD1、HSP70、DNAJB1B、HSP90AA1.1、HSPA9H和TCP1。核心基因主要作用于内质网中的蛋白质加工通路。这可能会为砷对肝脏损伤的潜在生物标志物和生物学机制提供新的思路。     

肾透明细胞癌Caki-1细胞系差异表达基因的生物信息学分析

目的比较肾透明细胞癌Caki-1细胞系与正常肾上皮细胞系ASE-5063中的差异表达基因（DEGs）,寻找潜在的肾透明细胞癌特异性分子标志物。 方法利用GEO数据库自带的GEO2R在线分析工具分析基因芯片GSE78179,将筛选出的DEGs分别导入Metascape、STRING以及Cytoscape进行综合分析并筛选出核心基因。最后使用FunRich等软件对筛选出的核心基因进行GO和KEGG富集分析。 结果共筛选出562个DEGs,其中上调基因345个,下调基因217个。进一步使用MCODE筛选出36个关键基因,GO功能分析发现这些基因与细胞粘附分子活性、趋化因子活性、细胞通讯和信号转导等密切相关;KEGG通路富集结果则表明差异基因主要集中在趋化因子信号通路、TNF信号通路以及NF-κB信号通路等多种与肿瘤相关的通路上。 结论运用生物信息学方法筛选出肾透明细胞癌Caki-1细胞系中DEGs,其中数个核心基因广泛参与多种肿瘤的病理进程,但尚未在肾透明细胞癌有相关研究报道,提示其可能是治疗肾透明细胞癌的潜在靶点。     

基于生物信息学分析的肝癌核心基因的筛选及验证

本研究基于GEO数据库,选取由慢性乙型肝炎诱导的肝细胞癌芯片数据GSE121248为研究对象,利用GEO2R软件分析数据,筛选出差异表达基因,利用DAVID数据库进行GO分析和KEGG pathway富集分析.利用STRING数据库构建PPI网络,分析筛选核心基因.利用GEPIA对核心基因的表达进行验证,Kaplan ...     

生物信息学分析筛选结直肠癌靶基因及评估预后价值

为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路.从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因;通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析...     

丹参SmHB12基因生物信息学及表达分析

植物同源异型域-亮氨酸拉链Ⅰ(homeodomain-leucine zipperⅠ, HD-ZipⅠ)转录因子在响应非生物胁迫方面发挥了重要作用。本研究根据干旱胁迫下丹参(Salvia miltiorrhiza Bunge)幼苗根转录组数据,克隆获得丹参HB12(SmHB12)基因片段并进行测序验证,其开放阅读框长度603bp,编码200个氨基酸。蛋白理化性质分析表明蛋白质相对分子量为22.95 kDa,理论等电点为5.42,含有丝氨酸、苏氨酸和酪氨酸磷酸化位点。亚细胞定位结果表明SmHB12蛋白定位于细胞核。实时荧光定量PCR结果表明SmHB12基因在不同组织中均有表达,花中表达量最高,其次是茎和叶,根中最低,并受脱落酸(ABA)和聚乙二醇-6000 (PEG-6000)胁迫强烈诱导。上述结果为后续开展SmHB12基因的功能研究提供理论依据。     

拟南芥GHMP基因家族成员的组织表达及生物信息学分析

利用生物信息学方法获得拟南芥全基因组中12个GHMP基因家族成员。通过实时定量PCR技术研究这12个基因在不同组织中的表达,结果显示它们具有组织表达特异性。构建了拟南芥中GHMP基因家族成员的系统进化树。启动子区调控元件分析表明,大多数GHMP成员包含有光响应、生物钟及其它逆境胁迫响应的相关元件,预测这些GHMP基因家族成员可能参与了植物的光信号、生物钟及相关的逆境胁迫信号转导途径。     

寒冷适应差异表达基因的研究

为寻找寒冷适应机体表达上调的相关基因,以Balb／C小鼠建立寒冷适应模型,分别提取肌肉和肝脏RNA,运用代表性差异分析(RDA)方法,寻找寒冷适应表达上调的相关基因片段,进行序列分析和同源性比较。结果显示,在肌肉和肝脏中有部分表达上调的基因片段,经狭线杂交验证,其中3条基因表达存在显著差异。这些新发现的机体寒冷适应相关基因为进一步理解寒冷适应的分子机制提供了帮助。     

生物信息学基因表达差异分析

基因的差异化表达由多种因素共同导致,并且与许多疾病的发生和发展有密切联系,对差异化表达的基因进行生物信息学以及生物统计学的分析对于研究细胞调节机制和疾病机理有着重要意义。目前,对差异化表达的基因有以下几种主流的研究方法：DNA微阵列（DNA microarray）,抑制性消减杂交（SSH）,基因表达连续性分析（SAGE）,代表性差异分析（RDA）,以及mRNA差异显示PCR（mRNA DDRT-PCR）。目前许多基因差异化表达数据是建立在时段（time series）基础上,因此对基于时间变化的基因差异化表达分析变得尤为重要。本文将对差异化表达基因的几种主流方法进行详细阐述,并介绍一种基于傅里叶函数的时段基因差异化表达分析。     

金银花花形基因的发掘及生物信息学分析

金银花作为我国重要的中药材,具有消炎、抗菌、抗病毒、抗氧化、防癌等多种功效。随着金银花市场供需矛盾日益加剧,通过分子标记辅助选择育种方法来培育高产优质品种势在必行。通过NCBI的Blast工具扫描金银花蛋白组数据发掘花形候选基因,并执行候选基因的亲缘关系分析、结构域分析、表达模式分析、理化性质分析、蛋白质结构预测等一系列生物信息学分析。依据拟南芥调控花形的ABE类基因,通过NCBI-Blast工具扫描金银花氨基酸序列,筛选出包含MADS结构域的8个调控花形的金银花候选基因。经LjaFGD表达模式分析发现,金银花的花中GWHGAAZE016592和GWHGAAZE014905表达量显著高于其他部位,可能正向调控金银花花形。GWHGAAZE014905是一个包含MADS结构域的调控花器官发育的B类基因;GWHGAAZE016592是AP3同源基因。生物信息学分析发现,GWHGAAZE016592和GWHGAAZE014905均是稳定的亲水蛋白,属于非分泌蛋白,包括Motif1、Motif3、Motif4、Motif2、Motif6和Motif5,蛋白质三级结构模板为6byy.2.A和4ox0.2.C。GWHGAAZE014905被定位到细胞核上,而GWHGAAZE016592被定位到叶绿体上,且包含1个位于151~173 bp的跨膜螺旋区域,属于膜蛋白。研究结果为分子标记辅助选择方式培育道地高产优质金银花品种提供了基因资源和分子标记。     

Detecting differentially expressed genes by relative entropy

DNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types.     

家族性双侧大结节性肾上腺增生症生物标志物的生信分析

为寻找与家族性双侧大结节性肾上腺皮质增生症发展有关的潜在治疗靶点和生物标志物。从GEO数据库中下载GSE171558数据集,筛选受家族影响的肾上腺结节与正常的肾上腺组织之间的差异表达基因(Differentially expressed genes, DEGs),并进行基因功能富集分析和蛋白质-蛋白质相互作用网络分析。通过Cytoscape v3.9.1软件中的插件cytoHubba筛选出关键基因,进一步经NetworkAnalyst分析TF-miRNA共调控网络和蛋白质-化合物相互作用。共鉴定出336个DEGs,这些基因主要富集在细胞粘附过程、细胞增殖的正调节过程和RNA加工过程等生物过程,并涉及钙信号通路、PI3K-Akt信号通路和cAMP信号通路等。通过cytoHubba插件获得5个hub基因,经验证分析,多功能蛋白聚糖(Versican,VCAN)、双糖链蛋白聚糖(Biglycan,BGN)被认为是家族性双侧大结节性肾上腺皮质增生症的潜在生物标志物。进一步的GSEA分析结果显示,VCAN主要与丁酸代谢、ECM-受体相互作用和类固醇生物合成等有关。BGN主要涉及剪接体、皮质醇的合...     

肾透明细胞癌预后相关miRNAs的生物信息学分析

目的寻找可作为肾透明细胞癌（ccRCC）生物标志物的miRNA,以及ccRCC与正常组织间miRNA差异表达情况。 方法利用TCGA数据库下载ccRCC中miRNA表达数据,分析肿瘤与正常组织间差异表达miRNA。使用Kaplan-Meier曲线对患者进行生存分析,筛选出表达情况与临床预后相关的miRNA。通过生物信息学对miRNA的靶基因进行预测,然后运用FunRich软件和ClueGO对靶基因进行GO和KEGG富集分析。 结果通过TCGA数据库分析发现,ccRCC较正常组织差异表达miRNA共54个,其中上调33个,下调21个。通过生存分析发现hsa-miR-21和hsa-miR-155与患者预后相关,P≤0.05。进一步通过Perl软件在Targetscan、miRDB、miRTarBase、miRPath这四个数据库中预测miRNA靶基因并将结果取交集,共发现129个靶基因。GO和KEGG分析结果表明,这些靶基因主要与转录因子活性、信号转导以及FoxO、TNF等信号通路密切相关。 结论通过生物信息学分析发现了ccRCC与正常组织的差异表达miRNA;其中hsa-miR-21和hsa-miR-155与患者总体生存率相关,并通过调控靶基因参与相关的信号通路进而影响ccRCC的发生发展进程,提示hsa-miR-21和hsa-miR-155可能是ccRCC潜在的生物标志物。     

系统型硬化症尿液lncRNA-mRNA差异表达基因筛选及生物信息学分析

为探讨系统性硬化症(SSc)患者尿液样本中的长链非编码RNA(lncRNA)、信使RNA(mRNA)的表达谱和生物学功能。选取6名SSc患者和3名健康对照者(HC),采集样本为中段晨尿,应用mRNA和lncRNA微阵列检测总RNA表达变异,SSc组与HC组相比。检测尿液lncRNA和mRNA表达,Gene ontology (GO)分析Kyoto Encyclopedia of Genes and Genomes (KEGG)信号通路分析差异表达的lncRNA功能分布;STRING在线网站和Cytoscape软件网络应用分析构建蛋白质相互作用网络(PPI)并筛选出核心基因(Hub Gene)。结果发现：与HC相比,SSc患者尿液中共有645个(上调546,下调99)mRNA和1 888个(上调1 647,下调241)lncRNA差异表达(Fold Change绝对值≥2,且P≤0.05)。KEGG通路结果显示富集TGF-β信号通路、氧化磷酸化、磷酸戊糖通路。SSc的GO分析显示与转录调控、DNA去甲基化、白介素6反应等相关;PPI网络分析表明主要富集在氧化磷酸化、细胞凋亡、自噬途径通路...     

How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

Identifying differentially expressed genes is critical in microarray data analysis. Many methods have been developed by combining p-value, fold-change, and various statistical models to determine these genes. When using these methods, it is necessary to set up various pre-determined cutoff values. However, many of these cutoff values are somewhat arbitrary and may not have clear connections to biology. In this study, a genetic distance method based on gene expression level was developed to analyze eight sets of microarray data extracted from the GEO database. Since the genes used in distance calculation have been ranked by fold-change, the genetic distance becomes more stable when adding more genes in the calculation, indicating there is an optimal set of genes which are sufficient to characterize the stable difference between samples. This set of genes is differentially expressed genes representing both the genotypic and phenotypic differences between samples.     

Integrative analysis of miRNA–mRNA network in high altitude retinopathy by bioinformatics analysis

High-altitude retinopathy (HAR) is an ocular manifestation of acute oxygen deficiency at high altitudes. Although the pathophysiology of HAR has been revealed by many studies in recent years, the molecular mechanism is not yet clear. Our study aimed to systematically identify the genes and microRNA (miRNA) and explore the potential biomarkers associated with HAR by integrated bioinformatics analysis. The mRNA and miRNA expression profiles were obtained from the Gene Expression Omnibus database. We performed Gene Ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Potential target gene analysis and miRNA–mRNA network analysis were also conducted. Quantitative RT-PCR (qRT-PCR) was used to validate the results of the bioinformatics analysis. Through a series of bioinformatics analyses and experiments, we selected 16 differentially expressed miRNAs (DE-miRNAs) and 157 differentially expressed genes related to acute mountain sickness (AMS) and constructed a miRNA–mRNA network containing 240 relationship pairs. The hub genes were filtered from the protein-protein interaction network: IL7R, FOS, IL10, FCGR2A, DDX3X, CDK1, BCL11B and HNRNPH1, which were all down-regulated in the AMS group. Then, nine up-regulated DE-miRNAs and eight hub genes were verified by qRT-PCR in our hypoxia-induced HAR cell model. The expression of miR-3177-3p, miR-369-3p, miR-603, miR-495, miR-4791, miR-424-5p, FOS, IL10 and IL7R was consistent with our bioinformatics results. In conclusion, FOS, IL10, IL-7R and 7 DE-miRNAs may participate in the development of HAR. Our findings will contribute to the identification of biomarkers and promote the effective prevention and treatment of HAR in the future.