使用线性微分方程构建初步的乳腺癌转移相关基因网络模型

随着基因芯片的技术的推广,越来越多的表达数据需要被处理和分析．利用这些表达数据提取基因调控矩阵从而构建基因网络是一个重要的问题．通过线性微分方程模型可以初步构建基因网络,了解网络结构,提取最显著的信息．然而由于分子生物学的条件限制或者数据来源的限制,导致实验数据不充分,使方程组无解．本文使用三次样条方法,对26例临床、病理资料完备的具有淋巴结转移的乳腺癌基因表达数据进行插值处理,使表达数据满秩,从而使用最小二乘法解出加权矩阵,构建初步的表达基因调控网络．通过对构建的基因网络的初步分析表明:乳腺癌转移的形成是由多基因异常引起多条传导通路异常,致使细胞恶性转化的结果,这与生物学上公认的看法是相一致的．因此,利用此线性模型方法对基因表达谱进行分析兵有一定可行性,在认识乳腺癌转移机制,乳腺癌诊断和治疗方面具有一定的理论和应用价值．     

整合冠心病风险SNPs功能及分子网络特征筛选疾病易感基因

目的:冠心病(Coronary Heart Disease,CHD)是一种由多因素(遗传因素、环境因素以及它们之间的相互作用)引起的复杂疾病。本文从遗传因素和分子互作模式识别新的冠心病易感基因。方法:结合冠心病群体遗传SNPs数据和PPI数据,通过群体遗传数据的风险评估、功能SNPs的判定和PPI网络基因的分类,以功能SNPs属性、网络拓扑属性和基因功能属性为特征,利用两步分类的方法筛选新的冠心病易感基因。结果:获得了69个新的冠心病易感基因,其中43个被文献证实与冠心病的发生发展密切相关,且识别的新的易感基因注释的KEGG通路中有很多是已知的易感基因所没有注释到的,如MAPK signaling pathway,Calcium signaling pathway,Focal adhesion和Chemokine signaling pathway等,其中Chemokine signaling pathway被证实是CHD发展的关键通路。结论:应用本文提出的整合筛选策略,能识别与冠心病相关的新的易感基因,可为冠心病的预防、诊断和治疗提供新的研究方向。     

融合基因关系网和功能预测恶性胶质瘤基因方法研究

目的:建立挖掘恶性胶质瘤候选基因的方法并进行系统分析。方法:结合恶性胶质瘤已知通路内基因和发生点突变和拷贝数改变的基因构建扩展基因关系网络,计算并分别寻找在网络中度和中心性得分高,脆弱性为正数的节点(基因),将满足一种或多种测度并与已知恶性胶质瘤基因共功能的基因作为恶性胶质瘤候选基因。最后,通过文献验证方法评价多种测度预测恶性胶质瘤基因的效能。结果:融合基因功能后,利用基因在网络中的度和脆弱性可识别大部分恶性胶质瘤基因,但利用中心性预测的结果较差;当将三个测度融合后,效能并没比单独使用脆弱性高。结论:融合基因功能关系和网络脆弱性是预测恶性胶质瘤基因的最佳测度。     

乳腺癌转移相关基因的筛选与生物信息学分析

乳腺癌是女性最常见的恶性肿瘤,转移与复发是乳腺癌患者死亡的主要原因. 研究与乳腺癌细胞转移相关的分子靶点对预防乳腺癌术后复发、提高疗效有重要意义. 本研究以3组乳腺癌转移相关的基因表达谱数据(GSE2034, GSE2603, GSE12276)为分析材料,采用GeneSpring软件筛选乳腺癌原发瘤与转移瘤芯片数据的差异表达基因,结合生物信息学工具PATHER、STRING、pSTIING和文献挖掘工具iHOP对差异基因及其相互作用关系进行分析. 结果显示,共筛选出乳腺癌转移共同差异基因147个,其中表达上调93个,表达下调54个. 这些差异基因主要涉及细胞周期与增殖、细胞粘附、细胞迁移、血管形成及信号转导等生物通路和生物过程. 差异基因编码蛋白间的相互作用主要集中在14个蛋白,且在更为复杂的网络图谱中仍可见其中9个基因（CXCR4、MMP1、MMP2、MMP3、CTGF、COL1A1、MEF2C、PTGS2及SPARC）在重要的节点位置. 文献挖掘发现,COL1A1基因可能为新发现的乳腺癌转移候选基因,为乳腺癌转移的发病机制提供新的思路,也为转移性乳腺癌的分子诊断和个体化治疗奠定基础.     

基因的单核苷酸多态性与乳腺癌关系的研究进展

乳腺癌是女性发病率最高的恶性肿瘤,具有家族聚集性,其发生发展是一个复杂的过程,涉及多种不同基因的相互作用和相互调控。随着基因技术和分子生物技术的飞速发展,许多基因被揭示在乳腺癌的发生发展中起关键作用,如BRCA、FGFR、ATM、ZNF365、Pokemon基因等。基于流行病学研究,这些易感基因可被分为高外显率易感基因和低外显率易感基因,其多态性对乳腺癌具有重要的影响。对乳腺癌易感基因的研究有助于阐明乳腺癌的发生机制、发展过程,对其早期诊断、预后判断和良恶性鉴别也有着重要的意义,可为乳腺癌的临床生物基因靶向治疗提供新的靶点和理论基础。本文主要对这些基因的单核苷酸多态性与乳腺癌关系的研究进展进行了综述。     

拟南芥TCH4基因启动区转录调控元件的计算识别

TCH4基因在植物次生生长、疾病抵抗和逆境适应方面具有重要作用, 能被多种激素、环境和机械信号诱导表达。利用拟南芥TCH4的直系同源基因和芯片数据进行了启动子序列分析, 结果共识别出9个转录调控元件。它们均包含有已知元件序列, 并且在部分共表达基因和对应的直系同源基因启动子中排列顺序一致。根据已有TCH4基因启动子研究, 其中4个已被报道, 另5个为本研究新发现。根据预测结果进行知识整合, 构建了TCH4基因转录调控机制模型。     

如何在基因库中查打基因资料

基因库是所有已知核酸和蛋白质序列及其文献和生物学注释的公共数据库,可以通过INTERNET获得其中的数据,本文介绍了利用Entrez查询软件检索基因的一些基本方法。     

最短路径法在水稻ABA 和环境胁迫条件下基因应答网络研究中的应用

目前微阵列数据分析方法都基于具有相似表达模式的基因可能具有相近的生物学功能这一假设, 而实际上参与同一生物学功能的基因, 在表达时间和空间上是有关联的, 而并非表现为相似模式。利用水稻cDNA微阵列, 对水稻在ABA及干旱、寒冷和高盐胁迫条件下的基因表达进行了研究。选取环境胁迫和ABA应答的相关基因, 采用最短路径法(shortest path), 利用自行编制的计算软件, 在表达模式不直接相关的基因之间构建最短路径。研究表明, 通过分析这些基因的表达数据, 可以发现它们在功能上的关联性, 并对未知基因的功能预测进行了探索, 为构建水稻在ABA和环境胁迫条件下的分子应答网络奠定了基础。     

最短路径法在水稻ABA和环境胁迫条件下基因应答网络研究中的应用

目前微阵列数据分析方法都基于具有相似表达模式的基因可能具有相近的生物学功能这一假设,而实际上参与同一生物学功能的基因,在表达时间和空间上是有关联的,而并非表现为相似模式。利用水稻cDNA微阵列,对水稻在ABA及干旱、寒冷和高盐胁迫条件下的基因表达进行了研究。选取环境胁迫和ABA应答的相关基因,采用最短路径法(shortest path),利用自行编制的计算软件,在表达模式不直接相关的基因之间构建最短路径。研究表明,通过分析这些基因的表达数据,可以发现它们在功能上的关联性,并对未知基因的功能预测进行了探索,为构建水稻在ABA和环境胁迫条件下的分子应答网络奠定了基础。     

时间延迟基因调控网络重构的决策树方法研究

基因调控网络的重构是功能基因组中最具挑战性的课题之一. 针对基因间转录调控的时间延迟性, 提出了一种寻找时间延迟调控关系的方法: 多点延迟调控网络算法, 简称TdGRN (time-delayed gene regulatory networking). 该方法根据时间序列基因表达谱数据, 构建时间延迟基因表达矩阵, 利用有监督决策树分类器方法和随机重排技术挖掘基因之间的时间延迟调控关系, 从而构建时间延迟的基因调控网络. 该方法是一种不依赖模型的基因网络重建方法, 相对于目前采用的基于模型的网络重建方法有显著优势, 可直接利用连续的基因表达谱数据发现延迟任一时间单位差的基因表达调控关系, 并避免了目前一些研究方法中需要人为设定基因的最大调控子数目(k)的问题. 将该方法应用于酿酒酵母细胞周期的基因表达谱数据, 并构建时间延迟的基因调控网络, 结果发现多数时间延迟调控关系获得了已有知识的支持.     

GroupRank: Rank Candidate Genes in PPI Network by Differentially Expressed Gene Groups

Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-exprssed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.     

NOP56在乳腺癌组织中的表达情况及对临床预后的意义

NOP56是一种与癌基因表达密切相关的核仁蛋白。本文通过对在线数据进行差异表达基因分析,发现NOP56在乳腺癌组织中高表达。再以NOP56的表达高低为表型,分析不同表型与临床预后的差异,结果表明NOP56高表达与乳腺癌不良临床病理参数和预后密切相关。通过富集分析获得NOP56的蛋白互作网络、计算共表达基因语义相似性。最后通过在线数据库获得NOP56及其共表达基因的的临床靶向药物放线菌素D(更生霉素)。这些结果为乳腺癌防治提供了潜在的新的预测指标,完善了临床靶向药物使用的分子机制,为靶向药物的临床使用提供依据和线索。     

circRNA-associated ceRNA network construction reveals the circRNAs involved in the progression and prognosis of breast cancer

Recently, increasing evidences show that circular RNAs (circRNAs) are important regulators of various diseases, especially cancer. However, the regulatory role and the potential mechanism of action of circRNAs in breast cancer remain largely unknown. In this study, weighted gene co-expression network analysis was conducted with the differentially expressed miRNAs and mRNAs in breast cancer from The Cancer Genome Atlas database to identify the key modules associated with the carcinogenesis of breast cancer. In the significant turquoise and brown modules, 22 miRNAs and 1877 mRNAs were identified, respectively. Then, We compared and predicted the target genes and performed survival analysis to identify the miRNAs and mRNAs related to the prognosis of breast cancer. A circRNA-related competitive endogenous RNA network was identified by database co-screening, and deleted in liver cancer 1 (DLC1) was identified as a key gene. Finally, to assess how genes in key modules and key genes contribute to the development of breast cancer, relevant pathway information was obtained through DAVID and Gene Set Enrichment Analysis. These data demonstrated that three circRNAs (hsa-circ-0083373, hsa-circ-0083374, and hsa-circ-0083375) that regulate DLC1 expression via hsa-mir-511 and are involved in the pathogenesis and development of breast cancer.     

Gene expression and DNA methylation analyses suggest that immune process-related ADCY6 is a prognostic factor of luminal-like breast cancer

Breast cancer is a malignant tumor that seriously threatens women's health, and luminal-like cancer subtypes account for the majority of the cases. The purpose of this study was to investigate the relationships among DNA methylation, gene expression profile, and the tumor-immune microenvironment of luminal-like breast cancer, and to identify the potential key genes that regulate immune cell infiltration in luminal-like breast cancer. The ESTIMATE algorithm was applied to calculate immune scores and stromal scores of patients with breast cancer. Kaplan-Meier curves were generated for survival analysis. The clusterProfile package was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Correlations between ADCY6 expression and immune cell infiltration-related pathways were analyzed by gene set variation analysis. R software was used for the statistical analysis and figure generation. Disease-free survival was higher in the immune score-high group than it was in the immune score-low group, while the stromal score had no correlation with prognosis. There were 515 genes that differed in both gene expression and DNA methylation levels, and these genes were mainly enriched in immune process-related pathways. ADCY6 was enriched in module A of the PPI network. Patients with downregulation and hypermethylation of ADCY6 associated with a better prognosis. ADCY6 expression was negatively correlated with the activation of immune process-related signaling pathways, immune checkpoint receptors, and ligands, except for CLEC4G. DNA methylation was found to be involved in the regulation of the key cellular pathways of luminal-like breast cancer immune cell infiltration. Additionally, ADCY6 was identified as a prognostic factor involved in the DNA methylation-regulated immune processes in luminal-like breast cancer.     

乳腺癌转移相关基因表达调控网络的线性微分方程模型的初步研究

初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,并分析模型的可靠性和生物学意义. 采用基因芯片技术,分别对30例伴有淋巴结转移的乳腺癌组织及其相应淋巴结转移癌组织进行基因表达谱的比较,选择差异基因通过线性微分数学方法构建表达调控网络模型. 差异表达基因共27个,其中Ratio > 3的明显上调基因14个,而Ratio < 0.33的明显下调基因13个. 比较伴有淋巴结转移的乳腺癌组织和其相应淋巴结转移癌组织,分析筛选了27个表达差异基因,应用数学线性微分方程方法初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,通过分析模型中重要节点、通路的生物学意义,判定网络的数学特性,初步表明,调控网络的可靠性和乳腺癌转移的形成是与多基因、多通路异常引起的细胞恶性转化相关.     

基于生物信息学的胃癌早期诊断预测模型研究

利用The Cancer Genome Atlas和Genotype-Tissue Expression公共数据检索收集胃癌(Gastric cancer,GC)基因表达数据集,筛选与早期胃癌密切相关的基因并构建胃癌早期诊断预测模型。运用Deseq2软件包筛选早期胃癌差异基因,并对差异基因进行GO和KEGG富集分析。通过STRING数据库建立其蛋白质相互作用网络并利用Cytoscape软件提取关键子网得到候选关键基因,进一步利用MedCalc软件确认胃癌早期诊断关键基因。根据筛选得到的10个关键基因构建基于支持向量机、随机森林、朴素贝叶斯、K-近邻、极限梯度提升和自适应提升等六种算法的胃癌早期诊断预测模型,依据ROC曲线和准确率等评价指标对各个分类器模型进行评估,通过独立测试集验证得到极致梯度提升诊断预测模型为最优模型。本研究成果为提高结胃癌早期诊断的研究提供了新的思路和方法。     

A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers

Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.     

乳腺癌发生的特征基因筛选及模式识别

本文选取癌症基因组图谱数据库的乳腺癌样本作为数据集,在全基因组的水平上研究乳腺癌病人从正常到发病Ⅰ期基因表达的变化,寻找与乳腺癌发病密切相关的特征基因,建立乳腺癌发生的模式识别分类方法,为乳腺癌预防及早期诊断提供理论支持.研究中,综合利用相关性、t检验、置信区间等统计学方法,建立乳腺癌发生特征基因筛选方法,获得与乳腺癌发生具有显著性差异的特征基因336个.通过机器学习方法建模,得到的分类准确率能达到98%以上,与之前乳腺癌相关的研究相比,准确率更高.同时采用KEGG(kyoto encyclopedia of genes and genomes)通路分析得到与基因显著相关(P0.05)的通路有8个,GO(gene ontology)基因功能富集分析显示与基因显著相关(P0.05)的功能有18个.最后对映射在8个通路中的一部分基因进行简要功能分析,说明了其在调控水平上的密切关系,表明识别的特征基因在乳腺癌的发生过程中有重要的作用,这对了解乳腺癌发病机理以及乳腺癌的早期诊断非常重要.