基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性相关基因的研究

目的：应用生物信息学技术筛选影响胶质母细胞瘤（GBM）化疗敏感性的相关基因。方法：对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料,筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因,明确差异基因参与的功能和通路,并构建与烷化剂相关基因的信号传导网络,结合芯片数据、患者预后和信号传导网络,筛选GBM化疗敏感性的相关基因。结果：两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能,主要参与31条信号传导通路。通过对差异基因功能、通路,烷化剂信号转导网络的分析,得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论：通过严谨的实验设计和科学的统计学判别,结合患者完整的生存资料,本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析,并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因,为进一步功能实验和患者个体化治疗奠定了基础。     

小儿脓毒症休克相关基因的筛选及网络构建

为探究脓毒症休克与SIRS的差异表达基因及网络的构建,筛选潜在的核心基因,从GEO数据库下载相关基因表达谱GSE26378,数据分为脓毒症休克与SIRS各29个样本,通过在线软件GCBI对其进行标准化及差异基因筛选;对差异基因进行GO分析;基于KEGG进行功能通路分析以及基因信号网络分析;差异基因共表达网络分析。结果表明:两组中总共有1 456个基因被识别为差异基因(P0.05),与SIRS组相比,脓毒症休克组中有条859条下调基因,597条上调基因。GO功能富集分析显示差异基因主要参与了细胞周期、细胞免疫、细胞代谢。KEGG功能通路分析显示差异基因主要参与了MAPK信号通路、P53信号通路、wnt信号通路、细胞凋亡信号通路,细胞周期受体信号通路等。共表达分析发现基因CCNB1、NUSAP1、OIP5、SHCBP1、ZWINT、TOP2A、DLGAP5等位于网络中央部位,而基因信号网络分析发现基因PLCB1、PIK3CA、STAT3、CAMK2D、PRKCB、CREB1位于网络核心。基因芯片分析有助于发现脓毒症休克与SIRS患儿外周血单核细胞在转录组学上的改变,而生物信息学网络分析有助于发现潜在的靶点。     

生物信息学方法筛选胶质母细胞瘤的核心基因

多形性胶质母细胞瘤(GBM)是成人最常见的恶性神经上皮肿瘤,关于其诊断和治疗的靶点研究一直是困扰研究者的难题。采用生物信息学的方法对GBM的基因表达信息进行分析,从TCGA中共获取169例GBM样本,正常脑组织样本5例,共17 847个基因。筛选出差异基因3 184个,利用Gene Ontology (GO)富集分析和Kyoto Encyclopedia of Genes and Genomes (KEGG)对差异基因的功能进行富集分析。利用STRING工具和cytoscape构建蛋白互作网络,连通性得分最高的被筛选为核心基因。联合ONCOMINE平台对核心基因进行表达分析。采用Kaplan-Meier法绘制核心基因生存曲线,了解核心基因对GBM患者生存时间和生存几率的相关性。共筛选出上调差异基因1 582个,下调差异基因1 601个。BottleNeck算法中连通性得分前十被选为核心基因,联合ONCOMINE分析,KCNAB2在GBM中低表达。KCNAB2的过表达预测了GBM患者更短的生存时间。相较于正常脑组织,KCNAB2在GBM中低表达。而当KCNAB2过表达时,GBM患者的生存时间明显缩短。但是,核心基因KCNAB2在GBM患者生存时间中的影响机制和存在价值仍需要进一步的研究去验证。     

基于生物信息学方法识别非小细胞肺癌(NSCLC)潜在治疗靶基因

本研究运用生物信息学方法识别非吸烟女性非小细胞肺癌(NSCLC)潜在的靶基因,并从分子水平探索其潜在的发病机制。从GEO数据库下载非吸烟女性非小细胞肺癌相关基因芯片数据集,经癌症组和癌旁对照组差异表达基因识别,并利用R软件对差异基因进行层次聚类分析,DAVID进行基因本体(gene ontology)和KEGG通路富集分析,STRING和Cytoscape软件构建蛋白-蛋白交互(PPI)网络,以及运用PASTAA分析,识别NSCLC相关转录因子,构建转录因子-基因共表达网络。结果表明,185个基因在NSCLC中差异表达,其中40个上调,145个下调;通过PASTAA分析识别出5个NSCLC基因相关转录因子。差异基因与胶原分解代谢过程、炎症反应的正调控等生物过程密切相关,基因的产物主要参与蛋白质细胞外基质、胶原三聚体等细胞组分,且主要发挥调节金属内肽酶活性、肝素结合和调节受体活性等分子功能;KEGG通路富集分析表明差异基因显著富集到胞外基质-受体信号通路、粘着斑信号通路、PPAR信号通和PI3K-Akt信号通路等,与非小细胞肺癌的发生发展密切相关。通过生物信息学方法,最终筛选到4个NSCLC关键基因:IL6、MMP1、COL1A1、CD36,其可能是非吸烟女性NSCLC潜在的治疗靶点。     

髓母细胞瘤基因表达谱芯片关键基因的生物信息学分析

筛选髓母细胞瘤(medulloblastoma, MB)发生发展的关键基因,可为MB分子机制的进一步研究提供生物信息学依据。本文通过下载GEO (Gene Expression Omnibus)数据库GSE50161原始数据,利用R语言对正常脑组织与髓母细胞瘤组织中差异表达的基因进行分析;通过生物信息学分析工具(DAVID、STRING和Cytoscape)对差异基因进行生物学功能和蛋白质相互作用(protein-protein interaction, PPI)分析,并通过PPI筛选互作调控的关键基因。结果显示,总共筛选出999个差异表达的基因,鉴定了CCNB1、AURKB、MAD2L1、CENPE、KIF2C、BUB1、BUB1B、NDC80、CENPF、CDC20十个关键基因。差异基因生物学功能主要富集于有丝分裂的核分裂、染色体分离、微管蛋白结合、RAGE受体结合等生物过程。KEGG信号通路分析结果显示差异基因主要富集于细胞周期、NF-κB、IL-17和T细胞受体等信号通路。10个关键基因的生物学功能和信号通路主要富集于细胞有丝分裂和细胞周期通路。因此,细胞周期通路对MB的增殖和分裂起着关键性的作用,相关的分子机制值得进一步深入研究。     

脊髓损伤机制研究的生物信息学分析

用生物信息学方法筛选参与脊髓损伤(spinal cord injury, SCI)发展过程的关键分子和通路,可为脊髓损伤发展机制的研究提供指导。从GEO数据库下载基因芯片数据,并将数据集中的样本分为脊髓损伤组(SCI组)和正常组(normal组)。应用R语言处理来自不同数据集样本间的批次效应,同时对基因芯片的表达数据进行标准化处理,并通过PCA分析监测标准化处理后数据的质量。应用R语言中的limma包分析标准化后的基因表达矩阵,以得到差异基因。将差异基因导入DAVID数据库进行GO (gene ontology)分析,并通过KEGG数据库进行通路分析。然后应用STRING数据库构建PPI网络,并通过Cytoscape中的cytoHubba插件分析得到10个hub基因。最后应用箱式图监测hub基因在不同样本中的表达,并用GeneCards数据库查询hub基因的功能。此外,为了补充差异基因筛选的不足,通过R语言对基因表达矩阵进行了GSEA (gene set enrichment analysis)分析。结果显示:TYROBP、ITGB2、PTPRC和FCER1G等基因在脊髓损伤发展过程中发挥重要的作用;细胞外基质的炎症反应、葡糖醛酸基转移酶活性的变化和星形胶质细胞的迁移等与脊髓损伤的发展机制关系密切; TNF信号通路、NF-κB信号通路和p53信号通路在脊髓损伤的发展机制中发挥重要的作用。这些关键的分子和通路在脊髓损伤中的作用值得我们进行更深入的探讨。     

基于RNA-seq研究胶质母细胞瘤中差异表达基因及蛋白相互作用网络

本研究基于RNA-seq数据分析了胶质母细胞瘤中的差异表达基因,并对差异表达基因进行了功能(GO term)和通路(KEGG)富集分析。进一步通过蛋白相互作用网络挖掘了胶质母细胞瘤的调控机理。结果表明,405个基因在肿瘤组织中差异表达(p-value≤0.05,|log FC|≥1.5),其中216个(53.3%)基因上调,189个(46.6%)基因下调。基因本体(gene ontology,GO)富集结果表明,这些差异表达基因参与了离子转运,神经冲动传递,细胞信号转导和细胞粘附等。此外,KEGG通路富集结果表明,差异基因参与了许多重要的生物学通路,包括ECM受体相互作用、黏着和钙信号等通路。进一步的蛋白相互作用网络分析鉴定了5个关键的hub基因,包括PTK2B、CDK1、FN1、CCND1和FOS。这5个关键基因对于胶质母细胞瘤的发生和发展可能起到了关键作用,可以作为潜在的调控位点和筛选的标志物。     

龋病感染牙髓相关基因的筛选与生物信息学分析

目的:综合应用生物信息学技术,从分子水平对龋坏牙髓与正常牙髓的差异基因进行筛选分析,初步探讨其作用机制。方法:从GEO基因表达数据库中下载龋坏牙髓相关芯片数据集,采用MORPHEUS在线筛选差异表达基因,结合DAIVID、STRING等在线分析工具对差异表达基因进行GO功能富集分析及KEGG通路分析,后用Cytoscape软件构建蛋白质相互作用网络。结果:共筛选出375个差异表达基因,其中表达上调253个、下调122个,主要涉及免疫应答、炎症反应、细胞因子应答和生物矿化组织发育等生物过程,以及抗原加工提呈和NF-κB信号等生物通路。通过蛋白质互作网络构建分析发现,MMP9、IL-8、PTPRC、CXCR4等10个基因处于核心节点位置。结论:借助生物信息学方法能得到可靠的相关差异基因信息,能够有效指导进一步的研究。得到的差异基因可以作为龋病诊断的指示因子和机制研究的候选基因。     

应用基因芯片筛选伊马替尼和尼洛替尼处理K562 细胞表达差异基因

目的:探讨两种酪氨酸激酶抑制剂(TKIs)处理K562细胞的基因表达谱变化,为认识慢性粒细胞白血病(CML)的发病机制、耐药机制提供新的思路,同时也为治疗寻找潜在重要靶标分子。方法:利用GEO数据库下载慢性粒细胞细胞系(K562)基因表达芯片数据,通过差异基因的筛选,趋势分析、GO分析,信号通路分析,最后获得的重要差异表达核心基因,并建立相互作用网络。结果:获得显著表达差异基因1000,通过趋势分析、功能富集分析及信号通路分析提示主要涉及到代谢途径、细胞凋亡、癌症的途径、细胞周期、p53信号通路。相互作用网络分析及同表达相互作用网络分析,得到重要的核心节点基因:AK2、ADSL、PKLR、PKM、SHMT2、ATIC、LDHA、ENO1、CTH、GOT1;YARS、CYP3A5、CTH、GYPA、ALAS2、MTHFD2、BLVRB、GUK1、CTSH、LMO2。结论:利用生物信息学的方法,分析慢性粒细胞白血病细胞基因芯片得到参与不同TKIs处理K562细胞的重要的细胞功能及信号通路主要涉及代谢通路及增殖凋亡信号途径,并且寻找到核心节点基因,为认识K562的耐药机制及治疗靶点提供新的思路。     

自噬基因CTSL表达与胶质母细胞瘤患者预后相关

本研究旨在探讨自噬基因CTSL对胶质母细胞瘤(GBM)患者的预后影响。利用癌症基因组图谱(TCGA)、人类自噬数据库(HADB)、中国脑胶质瘤基因组图谱(CGGA)数据库、基因表达谱分析(GEPIA)获取数据信息,通过筛选差异表达基因及单因素和多因素COX分析确定GBM的独立预后危险因素,同时通过基因本体论(GO)、基因组百科全书途径(KEGG)、临床病理相关性、基因集富集分析(GSEA)、自噬基因网络分析CTSL的相关作用机制。结果显示:(1)富集分析显示胶质母细胞瘤中差异自噬基因(ARG)与自噬体的形成、细胞凋亡、血管生成、细胞化疗等相关;(2)GBM中CTSL的mRNA水平明显高于正常组织样本;(3)多因素COX回归分析显示自噬基因CTSL的高表达为GBM预后的独立危险因素,STUPP治疗(术后替莫唑胺[Tmz]同步放化疗+Tmz辅助化疗)为独立保护因素;(4)自噬基因CTSL在非GCIMP(CpG岛甲基化)型、间质型、IDH野生型、1p/19q无缺失型胶质母细胞瘤及化疗后表达量更高。综上所述,本研究分析了自噬基因在GBM中的作用,并表明自噬基因CTSL的过表达预示胶质母细胞瘤患者不良预后,显示自噬基因CTSL有作为有效靶标的潜质。     

SAMSN1 Is Highly Expressed and Associated with a Poor Survival in Glioblastoma Multiforme

Objectives

To study the expression pattern and prognostic significance of SAMSN1 in glioma.

Methods

Affymetrix and Arrystar gene microarray data in the setting of glioma was analyzed to preliminarily study the expression pattern of SAMSN1 in glioma tissues, and Hieratical clustering of gene microarray data was performed to filter out genes that have prognostic value in malignant glioma. Survival analysis by Kaplan-Meier estimates stratified by SAMSN1 expression was then made based on the data of more than 500 GBM cases provided by The Cancer Genome Atlas (TCGA) project. At last, we detected the expression of SAMSN1 in large numbers of glioma and normal brain tissue samples using Tissue Microarray (TMA). Survival analysis by Kaplan-Meier estimates in each grade of glioma was stratified by SAMSN1 expression. Multivariate survival analysis was made by Cox proportional hazards regression models in corresponding groups of glioma.

Results

With the expression data of SAMSN1 and 68 other genes, high-grade glioma could be classified into two groups with clearly different prognoses. Gene and large sample tissue microarrays showed high expression of SAMSN1 in glioma particularly in GBM. Survival analysis based on the TCGA GBM data matrix and TMA multi-grade glioma dataset found that SAMSN1 expression was closely related to the prognosis of GBM, either PFS or OS (P<0.05). Multivariate survival analysis with Cox proportional hazards regression models confirmed that high expression of SAMSN1 was a strong risk factor for PFS and OS of GBM patients.

Conclusion

SAMSN1 is over-expressed in glioma as compared with that found in normal brains, especially in GBM. High expression of SAMSN1 is a significant risk factor for the progression free and overall survival of GBM.     

Identification of UGP2 as a progression marker that promotes cell growth and motility in human glioma

Glioblastoma (GBM) is one of the most common highly malignant primary brain tumor with poor prognosis. This study aimed to explore the possible mechanism by bioinformatics method and detect potential function of UGP2 of GBM. Gene expression microarray data of GSE4412 and messenger RNA-sequencing data of GBM with samples clinical information were downloaded from the Gene Expression Omnibus database and The Cancer Genome Atlas database, respectively. Differentially expressed genes (DEGs) analysis using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology based on R language. A total of 1000 common DEGs were identified in GBM samples, including 353 upregulated and 647 downregulated genes. Based on the random survival forest model, we identified UDP-glucose pyrophosphorylase 2 (UGP2) (upregulated gene) had a significant effect on GBM prognosis. Functional enrichment showed that UGP2 was enriched in the biological progresses of cell proliferation, migration, and invasion. Furthermore, UGP2 expression is aberrantly overexpressed in human glioma and positively correlated with pathologic grade. A loss-of-function study showed that knockdown of UGP2 decreases U251 cell growth, migration, and invasion in vivo and vitro. We proposed the development and progression of human glioma were associated with survival based on bioinformatics analysis. We also found that UGP2 might function as prognostic markers in the pathogenesis of GBM.     

Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high- and low-risk group. WGCNA was applied to the high- and low-risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone-mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87-MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single-gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8⁺ T cells while CDCA8 with CD4⁺ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.     

应用寡核苷酸芯片筛选维甲酸诱导神经母细胞瘤细胞系SH-SY5Y分化成神经元过程中的差异表达基因

目的:应用寡核苷酸芯片筛选维甲酸(RA)诱导神经母细胞瘤细胞系SH-SY5Y分化成神经元过程中的差异表达基因。方法:从人胎脑及不同类型神经系统肿瘤组织中获取目的基因,查询相应基因mRNA序列,设计并合成探针,制备了含218种基因的神经功能相关的寡核苷酸芯片。应用RA诱导SH-SY5Y8d分化成成熟神经元,提取对照组和实验组每天的总RNA,通过逆转录荧光标记cDNA探针并与芯片杂交,洗片后扫描获取图像,数据分析获得差异表达基因,并通过RT-PCR进行验证。结果:发现13种基因表达上调,没有得到下调基因。RT-PCR验证结果基本与芯片结果一致。结论:SH-SY5Y经RA诱导分化成神经元存在一些差异表达的基因,寡核苷酸芯片技术可为研究SH-SY5Y诱导分化成神经元的分子作用机理提供技术平台。     

Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer with limited therapeutic options. While efforts to identify genes responsible for GBM have revealed mutations and aberrant gene expression associated with distinct types of GBM, patients with GBM are often diagnosed and classified based on MRI features. Therefore, we seek to identify molecular representatives in parallel with MRI classification for group I and group II primary GBM associated with the subventricular zone (SVZ). As group I and II GBM contain stem-like signature, we compared gene expression profiles between these 2 groups of primary GBM and endogenous neural stem progenitor cells to reveal dysregulation of cell cycle, chromatin status, cellular morphogenesis, and signaling pathways in these 2 types of MRI-classified GBM. In the absence of IDH mutation, several genes associated with metabolism are differentially expressed in these subtypes of primary GBM, implicating metabolic reprogramming occurs in tumor microenvironment. Furthermore, histone lysine methyltransferase EZH2 was upregulated while histone lysine demethylases KDM2 and KDM4 were downregulated in both group I and II primary GBM. Lastly, we identified 9 common genes across large data sets of gene expression profiles among MRI-classified group I/II GBM, a large cohort of GBM subtypes from TCGA, and glioma stem cells by unsupervised clustering comparison. These commonly upregulated genes have known functions in cell cycle, centromere assembly, chromosome segregation, and mitotic progression. Our findings highlight altered expression of genes important in chromosome integrity across all GBM, suggesting a common mechanism of disrupted fidelity of chromosome structure in GBM.     

Identification of novel SNPs in glioblastoma using targeted resequencing

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other "omics" approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.