一种挖掘关键基因的新方法及其应用(英文)

近来,人们发现从疾病相关基因中寻找关键基因对疾病的诊断和治疗很重要。癌相关基因的网络是根据正常和患病的胶质瘤组织的基因表达谱建立。根据建立的基因网络和CIPHER方法,不同阈值下的正常和患病的胶质瘤表型网络被建立。根据已知的疾病和表型间的关联,另一组正常和患病的胶质瘤表型网络被建立。将两种方法建立的相应的表型网络进行比较,匹配度最大时对应的阈值及基因和表型网络被确定。在此基础上,通过打分方法得到了7个关键基因:DMBT1,ERBB2,NF2,PDGFB,AR,ARAF和TP53。文献查询发现其中5个基因与胶质瘤的形成和发展密切相关。剩下两个基因中的ARAF也间接地参与胶质瘤形成。因此,这两个基因可能在胶质瘤的形成中起重要作用。这一预测仍需要实验验证。     

基于CNV区域网络识别神经胶质瘤的重要功能区域

目的：基于基因拷贝数变异（CNV）区域网络识别神经胶质瘤的重要功能区域。方法：运用独特的计算样本的共相关性值的方法,使CNV数据与基因数据产生联系;基于蛋白质互作关系,在CNV区域与基因之间搭建桥梁,构建CNV区域网络;分析网络拓扑性质,识别出神经胶质瘤的重要功能CNV区域。结果：本文共识别出了11个与神经胶质瘤相关的候选重要功能CNV区域,通过功能注释和通路分析,确认了识别到的区域与神经胶质瘤有重要联系。结论：通过基因与表型之间的联系,利用已知表型基因在同源、功能、互作、结构域上的特征将CNV区域与基因联系起来,通过基因的功能可以了解到CNV区域的功能,对于疾病的预测和诊断有重要的意义。     

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。     

基于基因组数据分析的细菌耐药基因识别与表型预测

利用基因组数据和生物信息学分析方法,快速鉴定耐药基因并预测耐药表型,为细菌耐药状况监测提供了有力辅助手段。目前,已有的数十个耐药数据库及其相关分析工具这些资源为细菌耐药基因的识别以及耐药表型的预测提供了数据信息和技术手段。随着细菌基因组数据的持续增加以及耐药表型数据的不断积累,大数据和机器学习能够更好地建立耐药表型与基因组信息之间的相关性,因此,构建高效的耐药表型预测模型成为研究热点。本文围绕细菌耐药基因的识别和耐药表型的预测,针对耐药相关数据库、耐药特征识别理论与方法、耐药数据的机器学习与表型预测等方面展开讨论,以期为细菌耐药的相关研究提供手段和思路。     

人类基因组17q11—q12：癌基因与抗癌基因富集区

由于人基因组１７ｑ１１－ｑ１２含有许多与癌发生有关的基因,因此成了人们研究的热点。本仅就该区域的基因克隆、基因组制图以及这些基因与癌发生的关系作一详细介绍。     

X射线损伤修复交叉互补基因1（XRCC1）单核苷酸多态性与神经胶质瘤易感性关系的meta分析

目的：采用meta 分析方法探讨X 射线损伤修复交叉互补基因1（XRCC1）单核苷酸多态性与神经胶质瘤易感性的关系。方法：研究检索了PubMed、EMBASE、ISI Web of sciences、ScienceDirect 及CNKI 数据库从建库至2012 年9 月关于XRCC1 基因多态性与神经胶质瘤相关性的相关文献。合并的OR 值及其95%CI用于评估不同基因模型与神经胶质瘤风险的关联强度。采用亚组分析和meta 回归分析来探索潜在的异质性来源。结果：研究最终纳入12 篇Arg399Gln、8 篇Arg194Trp 和5 篇Arg280HisXRCC1 位点多态性与神经胶质瘤关系文章用于meta 分析。Arg399Gln 位点多态性在所有基因模型下合并OR 值均有显著意义;Arg194Trp 位点多态性在纯合子基因模型和隐性基因模型下合并OR 值具有显著意义;未发现Arg280His 位点多态性与神经胶质瘤风险相关基因模型。亚组分析和meta 回归分析显示Arg399Gln 位点多态性的所有基因模型风险仅在亚洲人群当中具有显著意义,亚洲人群的风险显著高于白种人群。Arg194Trp对照组人群不符合Hardy-Weinberg平衡（HWE）可能高估了风险。结论：本研究结果显示XRCC1 Arg399Gln 基因多态性仅为亚洲人群的神经胶质瘤风险的候选基因,Arg194Trp 基因多态性的风险可能是由于对照组不符合HWE 的研究所导致的。     

X射线损伤修复交叉互补基因1(XRCC1)单核苷酸多态性与神经胶质瘤易感性关系的meta分析(英文)

目的:采用meta分析方法探讨X射线损伤修复交叉互补基因1(XRCC1)单核苷酸多态性与神经胶质瘤易感性的关系。方法:研究检索了PubMed、EMBASE、ISIWeb ofsciences、ScienceDirect及CNKI数据库从建库至2012年9月关于XRCC1基因多态性与神经胶质瘤相关性的相关文献。合并的OR值及其95%CI用于评估不同基因模型与神经胶质瘤风险的关联强度。采用亚组分析和meta回归分析来探索潜在的异质性来源。结果:研究最终纳入12篇Arg399Gln、8篇Arg194Trp和5篇Arg280His XRCC1位点多态性与神经胶质瘤关系文章用于meta分析。Arg399Gln位点多态性在所有基因模型下合并OR值均有显著意义;Arg194Trp位点多态性在纯合子基因模型和隐性基因模型下合并OR值具有显著意义;未发现Arg280His位点多态性与神经胶质瘤风险相关基因模型。亚组分析和meta回归分析显示Arg399Gln位点多态性的所有基因模型风险仅在亚洲人群当中具有显著意义,亚洲人群的风险显著高于白种人群。Arg194Trp对照组人群不符合Hardy-Weinberg平衡(HWE)可能高估了风险。结论:本研究结果显示XRCC1 Arg399Gln基因多态性仅为亚洲人群的神经胶质瘤风险的候选基因,Arg194Trp基因多态性的风险可能是由于对照组不符合HWE的研究所导致的。     

基于基因突变频率识别低级别脑胶质瘤驱动基因

癌症通常由基因变异的累积所驱动,有效地识别癌症的驱动突变是一个巨大的挑战。目前已有方法更多是通过将基因组区域中观察到的突变率与背景突变率(BMR)预期的突变率进行比较或功能影响测试来识别驱动基因,该驱动基因本质上是存在统计异常的基因。而且并未对已有明确分类的癌症的子类之间驱动基因进行研究。本文引入关联规则算法,探寻发生该基因突变诱使病人患该子类低级别脑胶质瘤的有效规则,将突变数据与患癌结果通过算法建立关系,再通过支持度、置信度和提升度这三个指标对产生的规则进行筛选和评估,来预测候选驱动基因以及类间驱动基因差异。最后利用491例低级别脑胶质瘤体细胞突变数据,得到22个与结果存在关联的驱动基因及其所属的子类,敏感性和假阳性结果优于目前已有的单一算法,且22个基因均具有重要的生物学功能。同时建立了基于22个基因的低级别脑胶质瘤子类识别方法,模型总体准确率达98.99%,方法可有效区分三子类。     

基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤（glioma）是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤（glioblastoma multiforme,GBM）和低级别胶质瘤（lower-grade gliomas,LGG）之间的差异表达基因（differentially expressed genes,DEGs）,以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3（delta like canonical Notch ligand 3,DLL3）这个基因。基于TCGA的基因表达谱交互分析（gene expression profiling interactive analysis,GEPIA）数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达谱交互式分析和肿瘤免疫学估计资源（tumor immune estimation resource,TIMER）数据库,研究关键基因在不同分级的脑胶质瘤中的表达,预测了与免疫治疗密切相关的生物标志物。cBioPortal数据库用于探索DLL3表达与25个免疫检查点之间的关系。基因集富集分析（gene set enrichment analysis,GSEA）进一步确定了与中心基因相关的途径。最后,在中国胶质瘤基因组图谱（Chinese glioma genome atlas,CGGA）中验证了生物标志物在预后和预测中的疗效。这些结果发现,预后基因与肿瘤增殖和进展有关,通过生物学信息和生存分析,表明这些基因可能作为一种有前途的预后生物标志物,并作为选择治疗策略的新靶点。     

脑胶质瘤组织中p16基因启动子区CpG岛甲基化检测及其临床意义研究

目的：研究脑胶质瘤中p16基因启动子区甲基化情况及其临床意义。方法：用甲基化特异性PCR技术检测42例脑胶质瘤组织和癌旁正常脑组织中p16基因启动子甲基化,并分析该基因启动子甲基化与临床病理特征之间的关系。结果：脑胶质瘤组织中p16基因异常甲基化率（38.27%）显著高于癌旁正常脑组织中p16基因的异常甲基化率（8.8%,P=0.000）。发生甲基化的肿瘤组织或者正常脑组织中p16基因mRNA和蛋白表达显著降低。此外,p16基因异常甲基化和肿瘤病理分级有相关性（P=0.007）,而与患者性别、年龄及肿瘤类型等临床特征无关（P=0.669,0.869和0.944）。结论：p16基因启动子区CpG岛高甲基化与p16表达下调相关,推测p16启动子区CpG岛高甲基化是导致p16基因在脑胶质瘤中表达下调的重要因素,有望成为脑胶质瘤早期辅助诊断的分子标志物之一。     

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.     

一种基于基因拓扑重要性的通路识别方法

癌症相关通路的识别是认识癌症发生发展过程机制的生物学基础。已有的通路识别方法很少考虑基因在通路中的拓扑重要性。重叠基因降权(PADOG)方法在基因集分析(GSA)方法的基础上融入了基因特异性的影响,提高了癌症相关通路的识别性能。为进一步提高癌症相关通路的识别性能,首先统计了KEGG通路数据集中基因出度的分布情况,根据基因出度的大小定义了基因的重要性。最后将基因的特异性和重要性融合在一起,提出了一种基于基因重要性和特异性的通路分析方法 PAGIS。在结肠癌、肺癌和胰腺癌3个数据集上的实验结果表明,PAGIS方法比PADOG能够提高很多癌症相关的排名,从而提高癌症相关通路的识别效果。     

整合分析基因表达与拷贝数变异识别癌症的驱动基因及调控子miRNAs

目的:通过整合分析基因的表达与拷贝数变异(CNV)识别癌症的驱动基因及调控子mi RNAs。方法:通过整合基因表达与CNV数据,分别计算了乳腺癌、结肠癌、肺癌、肾癌、膀胱癌、头颈癌六种癌症中mi RNAs的调控得分,提出了一个识别驱动基因和显著调控子mi RNAs的方法。结果:本文研究发现,CNV区域上编码的基因相比于非CNV区域上编码的基因更倾向于受mi RNAs调控。但是,癌相关CNV区域上的基因相比正常CNV区域上的基因更少受mi RNAs调控。本研究识别出了EXOSC4、ZNF7、BOP1等原癌基因,以及mi R-488、mi R-27a、mi R-454等在多种癌症中都起调控作用的调控子mi RNAs。结论:本文的方法为癌症研究带来了新的启发,这些具有调控扩增基因过表达作用的mi RNAs的发现,有助于我们更进一步了解癌基因表达的复杂调控机制,进而推动癌症的诊断、治疗和预后。     

癌症DNA甲基化调控位点的识别

DNA甲基化是一种重要的表观遗传学修饰,在基因的转录调控方面具有重要的作用。异常的DNA甲基化可以导致癌症等复杂疾病发生,癌基因相关的DNA甲基化调控位点的识别对于解析癌症的发生发展机制及识别新的癌症标记具有重要意义。本研究通过整合The Cancer Genome Atlas(TCGA)的泛癌症基因组的高通量甲基化谱和基因表达谱,识别癌基因相关的DNA甲基化调控位点。对于每种癌症分批次计算Cp G位点甲基化与相关基因表达之间的相关性,并筛选调控下游基因的Cp G位点(包括强调控位点、弱调控位点和不调控位点),结果表明仅有一半的Cp G位点对下游基因具有调控作用;对癌症间共享的调控位点的分析发现不同癌症间共享的调控位点不尽相同,表明癌症特异的甲基化调控位点的存在。进一步地,对差异甲基化和差异表达基因的功能富集分析揭示了受甲基化调控的基因确实参与了癌症发生发展相关的功能。本研究的结果是对当前甲基化调控位点集的重要补充,也是识别癌症新型分子标记特征的重要资源。     

Prioritizing human cancer microRNAs based on genes' functional consistency between microRNA and cancer

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers.     

Database and interaction network of genes involved in oral cancer: Version II

The oral cancer gene database has been compiled to enable fast retrieval of updated information and role of the genes implicated in oral cancer. The first version of the database with 242 genes was published in Online Journal of Bioinformatics 8(1), 41-44, 2007. In the second version, the database has been enlarged to include 374 genes by adding 132 gene entries. The architecture and format of the database is similar to the earlier version, and includes updated information and external hyperlinks for all the genes. The functional gene interaction network for important biological processes and molecular functions has been rebuilt based on 374 genes using 'String 8.3'. The database is freely available at http://www.actrec.gov.in/OCDB/index.htm and provides the scientist information and external links for the genes involved in oral cancer, interactions between them, and their role in the biology of oral cancer along with clinical relevance.     

Expression of Alpha-type Platelet-derived Growth Factor Receptor–influenced Genes Predicts Clinical Outcome in Glioma

BACKGROUND: Alpha-type platelet-derived growth factor receptor (PDGFRα) is a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor family. PDGFRα plays an important role in the regulation of several biological processes and contributes to the pathophysiology of a broad range of human cancers, including glioma. Here, we hypothesize that the genes directly or indirectly influenced by PDGFRα might be useful for prognosis in glioma. METHODS: By comparing the genome-wide gene expression pattern between PDGFRα⁺ and PDGFRα⁻ cells from human oligodendrocyte progenitor, we defined the genes potentially influenced by PDGFRα. RESULTS: The PDGFRα-influenced genes are strongly associated with cancer-related pathways. We subsequently developed a prognostic gene signature derived from the PDGFRα-influenced genes. This gene signature is able to predict clinical outcome of glioma. This signature is also independent of traditional prognostic factors of glioma. Resampling tests indicate that the prognostic power of this gene signature outperforms random gene sets selected from human genome. More importantly, this signature is superior to the random gene signatures selected from glioma related genes. CONCLUSIONS: Despite the absence of clear elucidation of molecular mechanisms, this study suggests the vital role of PDGFRα in carcinogenesis. Furthermore, the PDGFRα-based gene signature provides a promising prognostic tool for glioma and validates PDGFRα as a novel and effective therapeutic target in human cancers.     

Human adipose tissue macrophages display activation of cancer-related pathways

Obesity is associated with a significantly increased risk for cancer suggesting that adipose tissue dysfunctions might play a crucial role therein. Macrophages play important roles in adipose tissue as well as in cancers. Here, we studied whether human adipose tissue macrophages (ATM) modulate cancer cell function. Therefore, ATM were isolated and compared with monocyte-derived macrophages (MDM) from the same obese patients. ATM, but not MDM, were found to secrete factors inducing inflammation and lipid accumulation in human T47D and HT-29 cancer cells. Gene expression profile comparison of ATM and MDM revealed overexpression of functional clusters, such as cytokine-cytokine receptor interaction (especially CXC-chemokine) signaling as well as cancer-related pathways, in ATM. Comparison with gene expression profiles of human tumor-associated macrophages showed that ATM, but not MDM resemble tumor-associated macrophages. Indirect co-culture experiments demonstrated that factors secreted by preadipocytes, but not mature adipocytes, confer an ATM-like phenotype to MDM. Finally, the concentrations of ATM-secreted factors related to cancer are elevated in serum of obese subjects. In conclusion, ATM may thus modulate the cancer cell phenotype.