可识别多种癌症的基因功能模块

在应用基因芯片技术研究包括癌症在内的多因素疾病时, 筛选差异表达基因是最重要的问题之一. 然而, 目前基因芯片研究的样本量较小, 不能完全反映在癌症中广泛的基因表达改变的特点, 导致基于统计学方法筛选的差异表达基因在不同的研究中的重复性很低. 本文通过分析7套癌症数据, 发现每种癌症中都有大范围的功能模块发生了基因表达的改变, 因此这些模块有很强的疾病识别能力. 其中7个功能模块能够有效识别多种癌症的疾病样本, 提示在不同癌症的发生与发展过程中存在着共同的基因表达改变机制. 因此, 功能模块可以作为疾病的功能性的标记来取代在基因芯片应用中难以重复发现的单个基因, 用于研究癌症的核心机制和建立更稳定的诊断分类器.     

肾肿瘤相关基因的共表达网络构建与分析

作为泌尿系统常见的肿瘤之一,肾肿瘤发病率在逐年上升。针对Affymetrix hgu133b的基因芯片数据进行差异表达基因筛选,应用加权基因共表达网络分析算法构建肾肿瘤差异表达基因的共表达网络。分析肾部正常组织和肿瘤组织差异表达基因之间的关联模式;选取与肿瘤发生关联程度最高的模块,筛选枢纽基因。最后,针对枢纽基因进行基因本体富集分析。细胞衰老是抑制肿瘤发生的主要机制之一,分析结果显示枢纽基因PLA2R1和TBX3与细胞衰老有关,可能对肾肿瘤的形成具有重要影响。该结果与基因PLA2R1通过促进细胞衰老抑制肾部肿瘤发生的研究结论一致。     

基于双聚类挖掘癌症共享的基因功能模块

基因多效性是癌症遗传机制中的普遍现象, 但罕见系统性的分析。文章提出采用双聚类挖掘基因功能模块的新思路探索癌症的共享分子机制和不同癌症间的关系。获取20种癌症的基因表达数据, 应用改良t检验和倍数法筛选出至少在两种癌症中差异表达的基因, 得到10417×20的数据矩阵; 采用双聚类方法获得22个癌症共享的基因簇; 进一步富集分析得到17个基因功能模块(Bonferroni校正后P<0.05), 主要参与有丝分裂染色单体分离的调控、细胞分化、免疫和炎症反应、胶原纤维组织等生物过程; 主要执行ATP结合和微管活动、MHCⅡ类受体活性、肽链内切酶抑制活性等分子功能; 活动区域主要在细胞骨架、染色体、MHCⅡ蛋白质复合体、中间丝蛋白、胶原纤维等。基于模块构建癌症相关网络, 显示胃癌、卵巢腺癌、宫颈鳞癌和间皮瘤等之间相关程度较高, 而两种血液系统癌症(急性髓细胞性白血病与多发性骨髓瘤)分子机制与其他癌症存在较大差异。可见癌症共享的基因功能模块与多种生物机制有关, 癌症之间相似性可能与组织起源、共同的致癌机制等有关。文章提出的基因多效性分析方法有助于解释人类复杂性疾病的共享分子机制。     

基于单细胞转录物组测序和加权基因共表达网络分析鉴定食蟹猴原始生殖细胞分化过程中的基因功能模块和分子网络

在哺乳动物中,作为能发育成精子或卵子的始祖细胞,原始生殖细胞(primordial germ cells,PGCs)在生命繁衍、遗传信息传递中发挥着关键作用.然而,目前人们对于灵长类PGCs分化过程中的基因功能模块和分子网络知之甚少.在本研究中,首先诱导食蟹猴胚胎干细胞向PGCs分化,在该过程的不同阶段(第0d、第2d...     

基于不同分类模型的基因芯片癌症诊断方法研究

基因芯片技术的发展为生物信息学带来了机遇,使在基因表达水平上进行癌症诊断成为可能。但基因芯片数据高维小样本的特征也使传统机器学习方法面临挑战。本文利用真实的基因表达数据,测试了目前主要的分类方法和降维方法在癌症诊断方面的效果,通过实验对比发现：基于线性核函数的支持向量机可以有效地分类肿瘤与非肿瘤的基因表达,从而为癌症诊断提供借鉴。     

一种简单快速克隆IL-6信号转导相关基因的方法

细胞因子作用于受体时的一个重要结果是诱导基因表达。为了克隆与IL-6诱导相关的基因,我们利用一个快速的改良DD-PCR方法,分离并检测了IL-6诱导和未诱导的U937细胞的差异表达基因。用三个完全变性的6—mer引物进行反转录,用2或3个较长的随机引物进行PCR扩增,扩增产物很在2%琼脂糖凝胶电泳上分离,之后回收差异片段并直接用于克隆和测序。在研究中,获得了7个不同的EST,序列分析表明其中2个EST可能是与细胞信号转导相关的新基因片段;反向Northern杂交证实它们是与IL-6作用相关的差异表达基因。     

基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。     

一种基于本体集成的生物医学数据库中语义映射维护方法研究

生物医学数据库到生物医学本体的语义映射是基于本体集成生物医学数据库系统的一个重要环节。而生物医学本体随着学科的发展不断演化,造成了集成系统不稳定。针对这个问题,本文在本体演化条件下,分析并发现了语义映射的变化规律,设计了对应的维护流程和维护方法,并通过计算维护收益率证明了该方法对映射的维护是有效性的,从而增强了集成系统在本体演化条件下的稳定性。     

基因表达系列分析的原理、进展及在肿瘤研究中的应用

基因表达系列分析(SAGE)技术不仅能够全面地分析特定组织或细胞内表达的基因,还可以比较不同组织在不同时间、空间条件下基因表达的差异,从而发现新基因.SAGE技术在肿瘤研究中的应用发展很快.本文综述了SAGE技术的原理、应用及其近年来的发近几年展与演变.     

一种基于本体集成的生物医学数据库中语义映射维护方法研究

生物医学数据库到生物医学本体的语义映射是基于本体集成生物医学数据库系统的一个重要环节.而生物医学本体随着学科的发展不断演化,造成了集成系统不稳定.针对这个问题,本文在本体演化条件下,分析并发现了语义映射的变化规律,设计了对应的维护流程和维护方法,并通过计算维护收益率证明了该方法对映射的维护是有效性的,从而增强了集成系统在本体演化条件下的稳定性.     

Linking co-expression modules with phenotypes

The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status.     

Systematic prediction of key genes for ovarian cancer by co-expression network analysis

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R² = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.     

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.     

权重基因共表达网络分析在生物医学中的应用

高通量生物监测方法可以同时检测同一样本的上千个参数,其在生物医学中的应用越来越广泛,但如何系统地分析并从高通量数据中挖掘有用信息,仍是一项重要的课题。网络生物学的出现使人们对复杂生物系统有了更深刻的理解,组织/细胞功能执行具有模块化特点。目前,相关网络(Correlation network)被越来越多地应用于生物信息学,权重基因共表达网络分析(Weighted gene co-expression network analysis,WGCNA)是描述样品基因表达相关模式的一种系统生物学工具。在此,对WGCNA在疾病分型及预后、发病机制和其他相关领域研究进展作一个较为系统的综述。首先,对WGCNA的原理、分析流程和优势缺点进行总结。其次,介绍如何用WGCNA研究疾病、正常组织、药物、进化和基因组注释。最后,结合新高通量技术展望WGCNA应用新空间。以期科研工作者能够对WGCNA的应用有所了解。     

刺盘孢菌的蛋白互作预测及侵染早期共表达模块分析

刺盘孢菌是一类重要的植物病原真菌,在全球范围内危害众多单双子叶植物。有许多研究对病菌的侵染模式进行了探索,但仍未阐明其确切的分子机制。本研究在预测病菌蛋白互作的基础上,结合表达谱数据对病菌在活体生存环境下的共表达模块进行挖掘和分析,以期为分子机制的研究提供新的线索。通过同源映射法和结构域法,预测得到刺盘孢菌的4 288个蛋白之间存在41 700个潜在互作,其中39 776个互作发生于异源蛋白之间,1 924个互作发生于同一蛋白内。将蛋白互作数据分别与4个表达谱数据进行整合,构建得到离体I、离体II、活体I和活体II 4个共表达互作组。对离体和活体互作组的共有基因的表达水平进行比较分析,结果表明,与离体互作组相比,活体互作组中与翻译、蛋白代谢等有关的基因表达水平下降,与离子转运、糖物质转运等有关的基因表达水平上升,暗示了物质转运在刺盘孢菌侵染早期的重要作用。进一步对活体互作组进行模块化分析,结果表明,活体I和活体II的特异模块分别与胁迫响应、肌动蛋白纤维长度调控有关,其中胁迫响应子网是以热激蛋白Hsp70为核心的互作簇,可能参与病菌对寄主的识别与对抗;肌动蛋白纤维长度调控子网则可能与病菌菌丝在寄主细胞间的延伸有关。     

Recovering context-specific gene network modules from expression data: A brief review

With the popularization of microarray experi-ments in biomedical laboratories, how to make context-specific knowledge discovery from expression data becomes a hot topic. While the static "reference networks"for key model organisms are nearly at hand, the endeavors to recover context-specific network modules are still at the beginning. Currently, this is achieved through filtering existing edges of the ensemble reference network or constructing gene networks ab initio. In this paper, we briefly review recent progress in the field and point out some research directions awaiting improved work, includ-ing expression-data-guided revision of reference networks.     

A network partition algorithm for mining gene functional modules of colon cancer from DNA microarray data

Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free. Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules＇ functions changed with their structures.