GWGS芯片整合分析识别外周血细胞中与骨质疏松症相关的枢纽基因

为了对骨质疏松症基因芯片数据集进行整合分析并识别出外周血细胞中与骨质疏松症相关的枢纽基因,通过检索GEO和ArrayExpress数据库获得骨质疏松症相关的表达谱芯片数据集;运用GWGS (genome-wide global significance)方法对纳入的数据集进行整合分析,筛选出差异表达基因(differentially expressed genes, DEGs);然后,运用GO (gene ontology)富集分析和KEGG (kyoto encyclopedia of genes and genomes)通路富集分析对差异表达基因进行功能注释,并建立蛋白质相互作用(protein-protein interaction, PPI)网络,筛选出骨质疏松症相关的枢纽基因。公共数据库检索得到3个符合纳入排除标准的研究集, GWGS整合分析筛选出排序前200的DEGs,这些基因主要富集的GO条目为脂多糖的细胞反应、凋亡过程和炎症反应,与骨质疏松症相关的KEGG富集通路为破骨细胞分化等。PPI分析进一步检测到与骨质疏松症相关的10个枢纽基因,其中9个基因已有研究报道和骨质疏松症的发生发展相关,而ELANE基因还未有研究报道与骨质疏松症有关。ELANE基因同时在人的骨髓组织、小鼠骨髓和骨组织中高表达,这个基因很可能与骨质疏松症有潜在的联系。本研究的结果将有助于进一步理解骨质疏松症的分子致病机理。     

基于拓扑分析方法鉴定与胃癌相关的生物标志物

胃癌(gastric cancer, GC)是最常见的恶性肿瘤之一。由于GC发病隐匿的特性,其早期检测困难。因此,研究与GC早期诊断和预后相关的生物标志物至关重要。从GEO数据库下载了3组基因表达数据集GSE79973、 GSE19826和GSE13911,通过Limma包筛选差异表达基因(differentially expressed genes, DEGs),并使用DEGs、 STRING V11数据库和Cytoscape构建了DEGs的蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,通过4种拓扑分析方法取交集筛选hub基因,并通过单变量Cox分析、多变量Cox分析、 Lasso回归分析、生存分析、通路分析以及文献法验证hub基因。从3个数据集中分别筛选了1 599个、 333个和662个DEGs。通过拓扑分析方法筛选了4个hub基因,即CDK1、AURKA、PTTG1和UBE2C。GO和KEGG富集分析结果表明4个hub基因参与了细胞外基质-受体相互作用、糖尿病并发症中的AGE-RAGE信号通路、小细胞肺癌和蛋白质消化吸收等通路。...     

慢性鼻-鼻窦炎伴鼻息肉基因表达谱的生物信息学分析

本研究从美国国立生物信息中心(NCBI)的子数据库基因表达数据库(GEO)中选择基因表达谱GSE36830数据集,采用GEO2R筛选正常钩突和鼻息肉组织之间的差异表达基因(DEGs),对关键通路和差异表达基因进行数据库挖掘和分析,经筛选后的差异表达基因采用戴维在线工具对其进行基因本体富集分析(GO)、京都基因和基因组百科全书(KEGG)分析,然后将DEGs导入String数据库进行蛋白质互作网络分析,绘制差异表达基因互作网络图,将其数据导入Cytoscape软件中,筛选网络中心节点和关键基因,分析关键子网络。共筛选出699个DEGs,其中475个基因为上调表达基因,224个基因为下调表达基因。在GO分析中,针对生物过程,上调的DEGs包括:炎症反应、免疫反应、细胞趋化性、炎症反应的正向调节和细胞的粘附等;下调的DEGs主要参与:唾液分泌、生物矿物组织发展、细胞氨基酸生物合成过程、视网膜内稳态及离子跨膜转运等。在KEGG分析中,上调的DEGs主要在参与造血细胞系、细胞因子-细胞因子受体相互作用、破骨细胞分化、趋化因子信号通路、癌症中的转录失调、哮喘、金黄色葡萄球菌感染等信号通路中富集,而下调的DEGs在唾液腺分泌及胆汁分泌信号通路中富集。差异表达基因互作网络图筛选出前10个关键基因:ITGAM、IL10、CD86、TLR8、ITGAX、CCL2、CCR7、SRC、EGF及ITGB2。本研究得到了一组鼻息肉差异表达基因的生物信息学分析结果,但仍需进一步用基础试验来验证。本文分析的结论为慢性鼻-鼻窦炎、鼻息肉的研究提供了新的研究方向,也为鼻息肉发病机制研究的思路提供了一定的建设性作用。     

COPD 差异表达基因的生物信息学分析及在LUSC样本中的表达

为确定慢性阻塞性肺病(COPD)的分子标记物及COPD与肺鳞状细胞癌(LUSC)共存的差异表达基因,探寻COPD合并肺癌的预测因子,发现新的治疗靶点。本研究采用生物信息学方法,从GEO数据库中筛选3套基因芯片数据集,挖掘COPD患者小气道上皮细胞(SAEC)的差异表达基因(DEG)以及潜在的生物标记物,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析预测DEGs的功能及参与的代谢途径。继而对DEGs构建PPI网络,使用Cytoscape软件筛选子模块和Hub基因,并将Hub基因通过TCGA数据库分析其在LUSC中的差异表达情况及差异基因间的相关性。结果共获得52个上调基因和24个下调基因,代谢通路主要集中在细胞色素P450对外源物质的代谢、化学致癌、花生四烯酸代谢及甲状腺激素合成四条途径上,通过Cytoscape软件从PPI网络中筛选得到2个功能模块和10个Hub基因,进一步验证发现其中5个基因在TCGA数据库中的LUSC样本中同样差异表达。由此推测SPP1、ALDH3A1、SPRR3、KRT6A和SPRR1B 可能为COPD 分子标记物及COPD与LUSC共存的DEGs,从而为研究COPD和LUSC的发病机制及二者潜在关系奠定良好的基础。     

基于生物信息学分析的非小细胞肺癌诊断预后相关基因的筛选

为了从基因层面探讨非小细胞肺癌(non-small cell lung cancer, NSCLC)发生发展的内在机制,筛选与NSCLC诊断、预后相关的基因,为NSCLC分子机制的进一步研究提供生物信息学依据,利用生物信息学方法对GEO数据库和TCGA数据库的数据集进行合并分析,筛选NSCLC组织与正常肺组织之间的差异表达基因(differentially expressed genes, DEGs),并对所取交集的DEGs进行基因集富集分析(gene set enrichment analysis, GSEA)、基因本体论(gene ontology, GO)分析、KEGG (kyoto encyclopedia of genes and genomes)通路富集分析、蛋白质相互作用(protein-protein interaction, PPI)分析、ROC曲线诊断效能分析及LASSO生存分析。文中共筛选出240个DEGs,主要涉及核分裂、染色体分离等生物学过程。GSEA分析结果显示,富集的通路主要涉及DNA修复和细胞周期。从PPI网络中筛选出20个hub基因, ROC结果显示, UBE2C (AUC=0.939)、TOP2A(AUC=0.927)、RRM2 (AUC=0.927)、CCNB1 (AUC=0.928)、MKI67 (AUC=0.930)、AURKA (AUC=0.931)、MELK(AUC=0.950)相对具有较高的诊断价值, LASSO COX回归结果则显示IL6、KIAA0101、MKI67、TPX2、AURKA、CDKN3及CDCA5与NSCLC患者的预后强相关。本研究结果表明, ZWINT、KIF2C、MELK、CDCA5可能在NSCLC中发挥着重要的作用,为阐明NSCLC的分子机制提供了新思路。     

家兔前体脂肪细胞分化不同时期基因表达谱分析

脂肪的过度积累严重危害人类健康。前体脂肪细胞分化是脂肪发育的关键过程,研究前体脂肪细胞分化相关基因的表达有助于认识脂肪沉积的机理。尽管家兔是一种理想的研究脂肪发育的动物模型,但是针对其前体脂肪细胞分化不同时期基因表达谱的研究鲜见报道。本研究通过诱导家兔前体脂肪细胞分化,在分化第0 d、3 d和9 d收集脂肪细胞,利用转录组测序(RNA-seq),在分化第3 d样本与第0 d样本的比较中筛选出1352个差异表达基因(differentially expressed genes, DEGs),在分化第9 d样本与第3 d样本的比较中筛选出888个DEGs。GO (gene ontology)功能富集和KEGG (kyoto encyclopedia of genes and genomes)通路分析发现,0~3 d分化期上调的DEGs显著富集在PPAR信号通路和PI3K-Akt信号通路上,3~9d分化期上调的DEGs显著富集到与细胞周期调控有关的GO条目和KEGG信号通路,0~3d和3~9d阶段特异上调的DEGs可能分别作用于细胞质和细胞核。通过DEGs的蛋白-蛋白互作(protein-protein interaction, PPI)网络分析发现,筛选出的核心节点(hub node)基因可能通过调控细胞周期而影响家兔前体脂肪细胞分化。     

胰腺癌中CDK1的表达与预后的生物信息学分析

为探讨胰腺癌的发病机制并为胰腺癌的防治提供生物信息学依据,用GEO2R在线工具分析GSE16515中胰腺癌患者肿瘤组织和相应正常组织的差异表达基因(differentially expressed genes, DEGs),通过DAVID数据库对DEGs进行GO分析和KEGG通路富集分析,然后通过STRING数据库构建蛋白质相互作用(protein-protein interaction, PPI)网络,用Cytoscape软件进行关键基因(hub基因)筛选和功能模块分析,并在GEPIA数据库对hub基因进行验证,用CCLE数据库检测靶基因在胰腺癌组织及细胞系中的表达水平。分析结果显示胰腺癌中筛选出的376个DEGs主要涉及细胞周期、p53信号通路、蛋白质消化吸收、ECM-受体相互作用、PI3K-Akt信号通路、血小板激活信号通路。GEPIA数据库验证结果显示10个hub基因均在胰腺癌组织中高表达,其中8个hub基因与胰腺癌患者的不良预后有关。CCLE数据库检测结果显示周期蛋白依赖性激酶1 (cyclin-dependent kinase 1, CDK1)在胰腺癌组织和细胞中均有较高的表达水平。本研究结果表明CDK1可能与胰腺癌的发生发展最为相关,为进一步探究胰腺癌的发病机制提供了生物信息学依据。     

生长激素对感染J亚群禽白血病病毒的鸡胚成纤维细胞系的影响

生长激素(growth hormone, GH)不仅能调节动物的代谢、生长、发育和生殖,也能调节动物的免疫系统。本研究旨在通过转录组测序及生物信息学技术分析GH影响J亚群禽白血病病毒(subgroup J avian leukosis viruses, ALV-J)感染的鸡胚成纤维细胞系(chicken fibroblast cells, DF-1)中的关键基因和信号通路。将空载和GH的过表达质粒分别转染至DF-1,接着均接种ALV-J毒株SCAU-HN06,采用转录组测序技术检测相关的基因表达谱变化。结果显示,与对照组相比,实验组中有上调差异表达基因(differentially expressed genes, DEGs)122个(49%),下调DEGs 127个(51%)。通过GO功能富集分析发现,DEGs主要富集于新陈代谢过程、生物调节、细胞过程、细胞部分、催化活性和结合等过程。经KEGG富集分析发现DEGs主要在免疫系统、信号转导、信号分子和相互作用、细胞生长和死亡等通路上富集。进一步构建蛋白质-蛋白质相互作用(protein-protein interaction, PPI...     

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。     

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...     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Construction of miRNA-mRNA network for the identification of key biological markers and their associated pathways in IgA nephropathy by employing the integrated bioinformatics analysis

BackgroundAbout half-century ago, Immunoglobulin A nephropathy (IgAN) was discovered as a complicated disease with frequent clinical symptoms. Until now, exact mechanism underlying the pathogenesis of IgAN is poorly known. Therefore, current study was aimed to understand the molecular mechanism of IgAN by identifying the key miRNAs and their targeted hub genes. The key miRNAs might contribute to the diagnosis and therapy of IgAN, and could turn out to be a new star in the field of IgAN.MethodsThe microarray datasets were downloaded from Gene Expresssion Omnibus (GEO) database and analyzed using R package (LIMMA) in order to obtain differential expressed genes (DEGs). Then, the hub genes were identified using cytoHubba plugin of cytoscpae tool and other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, and miRNA-hub gene network construction was also performed.ResultsA total of 348 DEGs were identified, of which 107 were upregulated genes and 241 were downregulated genes. Subsequently, the 12 overlapped genes were predicted from cytoHubba, and considered as hub genes. Moreover, a network among miRNA-hub genes was created to explore the correlation between the hub genes and their targeted miRNAs. Network construction ultimately lead to the identification of nine gene named FN1, EGR1, FOS, JUN, SERPINE1, MMP2, ATF3, MYC, and IL1B and one novel key miRNA namely, has-miR-144-3p as biomarker for diagnosis and therapy of IgAN.ConclusionThis study updates the information and yield a new perspective in context of understanding the pathogenesis and development of IgAN. In future, key miRNAs might be capable of improving the personalized detection and therapies for IgAN. In vivo and in vitro investigation of miRNAs and pathway interaction is essential to delineate the specific roles of the novel miRNAs, which may help to further reveal the mechanisms underlying IgAN.     

基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库(gene expression omnibus, GEO)中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因(differentially expressed genes,DEGs)后进行基因本体论(gene ontology,GO)、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3785个和3931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切...     

Identification of key genes and pathways in benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is one of the most common causes of lower urinary tract symptoms (LUTS) in elderly man. However, the underlying molecular mechanisms of BPH have not been completely elucidated. We identified the key genes and pathways by using analysis of Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using edgeR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the DEGs by Database for Annotation, Visualization and Integrated Discovery (DAVID) database and ConsensusPathDB, respectively. Then, protein–protein interaction (PPI) networks were established by the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Finally, we identified 660 DEGs ultimately including 268 upregulated genes and 392 downregulated genes. GO analysis revealed that DEGs were mainly enriched in extracellular exosome, identical protein binding, mitochondrial adenosine triphosphate (ATP) synthesis coupled proton transport, extracelluar matrix, focal adhesion, cytosol, Golgi apparatus, cytoplasm, protein binding, and Golgi membrane. Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway were selected. Ubiquitin-conjugating enzyme E2 C (UBE2C), serine/threonine kinase (AKT1), mitogen-activated protein kinase 1 (MAPK1), cyclin B1 (CCNB1), polo-like kinase 1 (PLK1) were filtrated as the hub genes according to the degree of connectivity from the PPI network. The five hub genes including UBE2C, AKT1, MAPK1, CCNB1, PLK1 may play key roles in the pathogenesis of benign prostatic hyperplasia (BPH). Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway may be crucial for the progression of BPH.     

Analysis of the potential pathways and target genes in spinal cord injury using bioinformatics methods

Spinal cord injury (SCI) remains to be the most devastating type of trauma for patients because of long lasting disability and limited response to the acute drug administration and efforts at rehabilitation. With the purpose to identify potential targets for SCI treatment and to gain more insights into the mechanisms of SCI, the microarray data of GSE2270, including 119 raphe magnus (RM) samples and 125 sensorimotor cortex (SMTC) samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in RM group and SMTC group compared with their corresponding controls, respectively. A protein–protein interaction (PPI) network was constructed based on the common DEGs identified in both RM group and SMTC group. Gene ontology (GO) and pathway enrichment analyses of the overlapping DEGs were performed. Furthermore, the common DEGs enriched in each pathway were analyzed to identify significant regulatory elements. Totally, 173 overlapping DEGs (130 up-regulated and 43 down-regulated) were identified in both RM and SMTC samples. These overlapping DEGs were enriched in different GO terms. Pathway enrichment analysis revealed that DEGs were mainly related to inflammation and immunity. CD68 molecule (CD68) was a hub protein in the PPI network. Moreover, the regulatory network showed that ras-related C3 botulinum toxin substrate 2 (RAC2), CD44 molecule (CD44), and actin related protein 2/3 complex (ARPC1B) were hub genes. RAC2, CD44, and ARPC1B may be significantly involved in the pathogenesis of SCI by participating significant pathways such as extracellular matrix-receptor signaling pathway and Toll-like receptor signaling pathway.     

Differences in potential key genes and pathways between primary and radiation-associated angiosarcoma of the breast

BackgroundAngiosarcoma of the breast is a high-grade malignant soft tissue tumor, it can be divided into primary and radiation-associated angiosarcoma(secondary). However, the differences between primary and secondary angiosarcomas in terms of pathogenesis, clinical behavior, early diagnosis biomarkers, genetic abnormalities, and therapeutic targets remain to be fully elucidated. At the same time, due to its rarity, most of current information relating to angiosarcoma is provided by case reports. Therefore, exploring the mechanisms of primary and secondary breast angiosarcoma have important value for the discovery of new biomarkers and research into potential therapeutic targets.MethodsThe differentially expressed genes (DEGs) between 36 cases of primary angiosarcoma and 54 cases of secondary angiosarcoma were screened. Then, the DEGs were used to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, a protein-protein interaction (PPI) network was constructed using the STRING database.ResultsA total of 18 DEGs were identified, of which 13 were upregulated and 5 were downregulated in secondary breast angiosarcoma. The GO enrichment analysis showed that the DEGs were most enriched in metabolism, energy pathways, and protein metabolism in biological processes. The enriched signaling pathways of DEGs were the transforming growth factor-β (TGF-β), Wnt, Hippo and PI3K-Akt signaling pathways. Then, the PPI network was conducted and hub genes were identified and they were involved in thyroid hormone, Hippo and other signaling pathways.ConclusionThis study lay the foundation for the discovery of effective and reliable molecular biomarkers and essential therapeutic targets for these malignancies.