利用eQTL构建基因-基因网络挖掘类风湿性关节炎风险基因

目的：类风湿性关节炎是一种全身的慢性炎症型疾病,可能影响许多组织和器官,主要发作于灵活的关节。全世界人群中大约有1％会患有类风湿性关节炎。目前已经证实了一些基因与类风湿性关节炎相关,但是这些基因只能解释一小部分遗传风险,因此我们需要新的策略和方法来解决这个问题。方法：表达数量性状位点（eQTL）是指能够调控基因或蛋白质表达的基因组位点,本文采用了eQTL数据构建基因一基因网络并挖掘候选类风湿性关节炎风险基因。结果：首先,利用eQTL数据,基于基因之间的共调控系数,建立基因-基因网络,我们建立了5个不同阈值（0、O．2、0．4、0．6和0．8）的基因-基因网络;然后,在OMIM和GAD数据库中搜索已经证实的与类风湿性关节炎相关的186个基因;最后我们将已证实与类风湿性关节炎相关的186个基因分别投入到这5个网络中,利用基因与基因之间的相关性来挖掘到一些可能与类风湿性关节炎相关的候选风险基因。结论：本文基于eQTL构建了基因．基因网络,结合已知类风湿性关节炎风险基因,挖掘未知风险基因,得到了较好的结果,证明了本方法的有效性,且对于类风湿性关节炎的发病机制研究具有重要价值。除了类风湿性关节炎外,本方法还可推广到其它复杂疾病中,因此本方法对人类复杂疾病的研究具有很强的学术理论价值和应用价值。     

HER2参与的基因表达调控

HER2与许多恶性肿瘤的发生、发展密切相关。HER2可通过信号转导途径间接调控许多肿瘤相关基因的表达,亦可作为转录因子直接调控某些基因的表达,而一些基因表达产物又进而增强HER2或其他基因的表达,这就构成了以HER2为中心的基因表达调控网络,这些基因表达产物和HER2可能共同成为肿瘤诊断和预后的标志物。阐明这个网络中各个分子间的相互作用关系,将为HER2过表达肿瘤的治疗提供新的药物设计靶标。     

利用eQTL构建基因-基因网络挖掘类风湿性关节炎风险基因*

摘要目的：类风湿性关节炎是一种全身的慢性炎症型疾病，可能影响许多组织和器官，主要发作于灵活的关节。全世界人群中大 约有1%会患有类风湿性关节炎。目前已经证实了一些基因与类风湿性关节炎相关，但是这些基因只能解释一小部分遗传风险， 因此我们需要新的策略和方法来解决这个问题。方法：表达数量性状位点（eQTL）是指能够调控基因或蛋白质表达的基因组位点， 本文采用了eQTL数据构建基因- 基因网络并挖掘候选类风湿性关节炎风险基因。结果：首先，利用eQTL 数据,基于基因之间的 共调控系数，建立基因- 基因网络，我们建立了5 个不同阈值(0、0.2、0.4、0.6和0.8)的基因-基因网络；然后，在OMIM 和GAD数 据库中搜索已经证实的与类风湿性关节炎相关的186 个基因；最后我们将已证实与类风湿性关节炎相关的186 个基因分别投入 到这5 个网络中，利用基因与基因之间的相关性来挖掘到一些可能与类风湿性关节炎相关的候选风险基因。结论：本文基于 eQTL构建了基因-基因网络，结合已知类风湿性关节炎风险基因，挖掘未知风险基因，得到了较好的结果，证明了本方法的有效 性，且对于类风湿性关节炎的发病机制研究具有重要价值。除了类风湿性关节炎外，本方法还可推广到其它复杂疾病中，因此本 方法对人类复杂疾病的研究具有很强的学术理论价值和应用价值。     

脑胶质瘤患者组织和血清MGMT、hMLH1和hMSH2基因启动子甲基化状态检测

目的探讨脑胶质瘤患者组织和血清中MGMT、hMLH1和hMSH2基因启动子CpG岛甲基化发生率及相关性。方法甲基化特异性PCR（MSP）检测39例脑胶质瘤组织样本及32例预处理的脑胶质瘤血清样本中MGMT、hMLH1和hMSH2基因启动子区的甲基化状态。结果脑胶质瘤组织MGMT、hMLH1和hMSH2基因启动子区甲基化发生率分别为46．2％、10．3％和20．5％,肿瘤组织中至少有一种基因甲基化的发生率为64．1％（25／39）;在脑胶质瘤患者外周循环血液中检测到了相关基因甲基化系列,并且与组织中基因甲基化发生率明显相关。结论MGMT、hMLH1和hMSH2基因启动子甲基化是脑胶质瘤发生过程中常见的分子事件,血清中相关基因DNA甲基化检测有可能为脑胶质瘤诊断和个体化化疗提供一种稳定的无创性检测指标。     

肿瘤基因疫苗的研究进展

基因疫苗技术自从20世纪90年代问世以来被迅速应用到传染病、免疫缺陷、肿瘤等重大疾病的预防和治疗的研究中,有一部分已经进入临床试验阶段.肿瘤基因疫苗可以打破免疫耐受,增强免疫原性,诱导机体产生针对肿瘤的体液和细胞反应,既有预防又有治疗肿瘤的作用.能够防治肿瘤的基因疫苗发展迅猛,主要包括与肿瘤相关抗原（TAAs）有关的全长、表位、独特型（Id）和融合DNA疫苗,能够自主复制的RNA疫苗,与树突细胞（DCs）相关的肿瘤基因疫苗等.肿瘤基因疫苗的分子作用机制及其存在的弊端也日益成为关注的问题.     

hsa-miR-126的靶基因预测及其生物信息学分析

目的:采用生物信息学的方法预测hsa-miR-126的靶基因及相关功能,为后续研究提供理论基础。方法:采用PubMed搜索hsa-miR-126相关文章,总结目前研究进展,使用mi RBase获得hsa-miR-126的基本信息,并用NCBI BLAST分析其序列保守性,以Targetscan、mi Randa及PicTar预测结果的两两交集为预测靶基因,并结合mi RTarBase上已证实的靶基因作为基因集合做GO分析和Pathway分析。结果:搜索PubMed获得相关文献24篇,多数研究集中于hsa-miR-126与肿瘤的关系,hsa-miR-126在多种物种间具有高度保守性,其5个预测靶基因和46个已证实靶基因于白细胞迁移、对糖皮质激素反应、促进内皮细胞增殖、蛋白信号转导等有功能富集(P0.01),于多种肿瘤如肾细胞癌、前列腺癌、非小细胞肺癌、胰腺癌有信号通路富集(P0.01)。结论:hsa-miR-126可能参与多种肿瘤的发生发展过程。     

当前肿瘤基因治疗中存在的主要问题及其解决途径

本文概述了当前肿瘤基因治疗研究中存在的一些主要问题,如绝大多数治疗方案中目的基因只有一个,肿瘤基因治疗缺乏靶向性,基因转移载体的效率、安全性及容量等问题。讨论了解决这些问题的主要途径,即肿瘤多基因联合治疗、直接体内途径基因治疗与靶向基因治疗、基因转移载体的改造。     

糙皮侧耳不同单核体基因序列的保守性分析

从已公布的糙皮侧耳基因组信息入手,用全局比对法计算两个不同单核之间基因序列的相似性,这种相似性与基因序列的保守性有关。通过对保守和不保守的基因集合进行功能富集分析研究,分析与序列保守性相关的Gene Ontology功能。保守基因集合中显著富集的主要是一些代谢过程、催化酶活性、输送等功能。不保守基因集合中显著富集的多为激酶活性、绑定、调控等功能。     

高速泳动族蛋白与肿瘤

近年来,在肿瘤的研究中高速泳动族蛋白(high mobility group proteins,HMG)的作用受到广泛关注。HMG蛋白质的主要功能有：参与基因转录调控,与细胞转化和一些肿瘤的发生有关。根据HMG蛋白质的特性建立了一种全新的基因转移系统,这种基因转移系统在肿瘤分子生物学的研究中有很好的应用前景。     

家养动物肉质性状相关基因研究进展

动物的肉质综合了许多特性,诸多因素共同作用于其生长发育,特别是与肉质发育相关的基因,在这个过程中起到了至关重要的作用。动物的市场经济效益直接由肉质品质的优劣决定,所以研究与其相关的基因就十分重要。本综述主要从与牛、羊、猪和鸡这四类家养动物的肉质相关的基因,如与肌肉纤维形成有关的生肌调节因子家族基因,与调节脂肪沉积和代谢相关的Leptin及其受体基因,与肌内脂肪相关的脂蛋白酶脂酶基因(LPL)、脂肪酸结合蛋白(FABPs)基因、PPARγ基因,与背膘厚相关的黑素皮质素受体基因(MCR) 4个方面,就其近十年来的研究进展进行了综述,并对其未来的研究和应用等进行展望。     

Identifying the Genetic Variation of Gene Expression Using Gene Sets: Application of Novel Gene Set eQTL Approach to PharmGKB and KEGG

Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set - expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.     

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.     

A computational approach to measuring coherence of gene expression in pathways

This study uses a computational approach to analyze coherence of expression of genes in pathways. Microarray data were analyzed with respect to coherent gene expression in a group of genes defined as a pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Our hypothesis is that genes in the same pathway are more likely to be coordinately regulated than a randomly selected gene set. A correlation coefficient for each pair of genes in a pathway was estimated based on gene expression in normal or tumor samples, and statistically significant correlation coefficients were identified. The coherence indicator was defined as the ratio of the number of gene pairs in the pathway whose correlation coefficients are significant, divided by the total number of gene pairs in the pathway. We defined all genes that appeared in the KEGG pathways as a reference gene set. Our analysis indicated that the mean coherence indicator of pathways is significantly larger than the mean coherence indicator of random gene sets drawn from the reference gene set. Thus, the result supports our hypothesis. The significance of each individual pathway of n genes was evaluated by comparing its coherence indicator with coherence indicators of 1000 random permutation sets of n genes chosen from the reference gene set. We analyzed three data sets: two Affymetrix microarrays and one cDNA microarray. For each of the three data sets, statistically significant pathways were identified among all KEGG pathways. Seven of 96 pathways had a significant coherence indicator in normal tissue and 14 of 96 pathways had a significant coherence indicator in tumor tissue in all three data sets. The increase in the number of pathways with significant coherence indicators may reflect the fact that tumor cells have a higher rate of metabolism than normal cells. Five pathways involved in oxidative phosphorylation, ATP synthesis, protein synthesis, or RNA synthesis were coherent in both normal and tumor tissue, demonstrating that these are essential genes, a high level of expression of which is required regardless of cell type.     

Identifying consensus disease pathways in Parkinson's disease using an integrative systems biology approach

Parkinson's disease (PD) has had six genome-wide association studies (GWAS) conducted as well as several gene expression studies. However, only variants in MAPT and SNCA have been consistently replicated. To improve the utility of these approaches, we applied pathway analyses integrating both GWAS and gene expression. The top 5000 SNPs (p<0.01) from a joint analysis of three existing PD GWAS were identified and each assigned to a gene. For gene expression, rather than the traditional comparison of one anatomical region between sets of patients and controls, we identified differentially expressed genes between adjacent Braak regions in each individual and adjusted using average control expression profiles. Over-represented pathways were calculated using a hyper-geometric statistical comparison. An integrated, systems meta-analysis of the over-represented pathways combined the expression and GWAS results using a Fisher's combined probability test. Four of the top seven pathways from each approach were identical. The top three pathways in the meta-analysis, with their corrected p-values, were axonal guidance (p = 2.8E-07), focal adhesion (p = 7.7E-06) and calcium signaling (p = 2.9E-05). These results support that a systems biology (pathway) approach will provide additional insight into the genetic etiology of PD and that these pathways have both biological and statistical support to be important in PD.     

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.     

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. Availability: GCAT is freely available at http://binf1.memphis.edu/gcat.