基于CART算法的肺癌微阵列数据的分类

基因芯片技术是基因组学中的重要研究工具。而基因芯片数据( 微阵列数据) 往往是高维的,使得降维成为微阵列数据分析中的一个必要步骤。本文对美国哈佛医学院 G. J. Gordon 等人提供的肺癌微阵列数据进行分析。通过 t- test,Wilcoxon 秩和检测分别提取微阵列数据特征属性,后根据 CART( Classification and Regression Tree) 算法,以 Gini 差异性指标作为误差函数,用提取的特征属性广延的构造分类树; 再进行剪枝找到最优规模的树,目的是提高树的泛化性能使得能很好适应新的预测数据。实验证明: 该方法对肺癌微阵列数据分类识别率达到 96% 以上,且很稳定; 并可以得到人们容易理解的分类规则和分类关键基因。     

红花檵木LcFLS1基因的克隆及其表达与转化研究

该研究以红花檵木(Loropetalum chinense var.rubrum)为材料,根据转录组测序结果和PCR方法克隆到1个黄酮醇合成酶(FLS)同源基因,命名为LcFLS1。生物信息学分析显示,LcFLS1的开放阅读框为996bp,编码331个氨基酸。氨基酸序列分析显示,LcFLS1具有典型的2-酮戊二酸和铁依赖性双加氧酶结构域;蛋白结构预测表明,球形蛋白结构的核心区域存在10个与2-酮戊二酸配体互作的位点。进化树分析结果表明,LcFLS1与茶树(Camellia sinensis)等木本植物的亲缘关系较近,而与拟南芥(Arabidopsis thaliana)等草本植物的亲缘关系较远。荧光定量PCR检测显示,LcFLS1在红花檵木的花中相对表达量最高,而在茎中最少。成功构建了LcFLS1基因的过表达载体pLcFLS1-SUPER1300,经农杆菌侵染花序法将pLcFLS1-SUPER1300质粒转入拟南芥中获得转基因植株,PCR鉴定表明获得了转LcFLS1基因拟南芥阳性植株。该研究结果为红花檵木黄酮醇的生物合成机制研究,以及药用价值的开发利用奠定了基础。     

牦牛Eppin基因CDS的克隆及其在附睾不同区段中的表达研究

为了研究牦牛附睾组织中精子成熟的相关机理,并为探讨高原动物的生殖机制提供基本数据。本研究运用基因克隆技术对牦牛附睾Eppin基因CDS全长序列进行克隆,采用生物信息学方法进行分析,Eppin基因和编码序列特征进行了预测和分析。结果表明,牦牛Eppin基因的CDS含有一个405 bp长度的片段,由134个氨基酸编码;牦牛Eppin基因对应的蛋白分子量和理论等电点分别为15.09 ku和8.67 ku,其对应的氨基酸没有跨膜结构,归于近水性蛋白;25个α-螺旋、27个延伸链、2个β-折叠及80个无规则卷曲构成其蛋白质二级结构;牦牛Eppin基因编码氨基酸序列与黄牛、藏羚羊、绵羊等物种间同源性较高,系统进化情况与其亲缘关系远近一致。本研究应用实时荧光定量PCR技术分析Eppin基因在附睾组织3个不同区段(头部,颈部和尾部)中的表达情况,荧光定量PCR结果显示,Eppin基因在牦牛附睾组织3个不同区段中均有不同程度的表达,在附睾头部中表达最高,颈部和尾部表达较低。本研究将为牦牛附睾精子成熟的机制和Eppin基因在牦牛附睾上皮细胞中的功能提供一定的基础数据。     

基因转录表达数据的生物信息挖掘研究

基因表达是生物体中最重要和最基础的生物学过程和分子活动,生物体正是通过调控不同基因表达而实现生长发育和抵御刺激等生命活动.转录组测序是目前在生物医学研究中应用最为广泛的高通量检测基因表达的技术,也促进了大量针对转录组数据的生物信息挖掘方法和工具的发展.本文就基因表达中的转录组数据分析和挖掘方法进行了综述,从已有大规模转...     

Multi-class cancer classification through gene expression profiles:microRNA versus mRNA

Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.     

Pattern identification and classification in gene expression data using an autoassociative neural network model

The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.     

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.     

Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.     

Non-linear cancer classification using a modified radial basis function classification algorithm

Summary This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.     

MicroRNA‐guided gene expression in prostate cancer: Literature and database overview

Insight into the aberrant expression of microRNAs (miRNAs) and the genes that they regulate during the progression of cancer in general and prostate cancer (PCa) in particular is one of the most important issues in current molecular biomedicine and allows for the discovery of therapeutic or diagnostic miRNA targets. The present study aimed to analyze the available data regarding the direct or indirect effects of miRNAs on the expression of the mRNAs involved in carcinogenesis and to enable updating and optimizing the selection of the corresponding targets. The present review focuses on the data related to the genes with miRNA‐dependent expression during the development of PCa. The data used in this review have been extracted from research papers and the databases STRING, PANTHER and TargetScan, with a special focus on the genes directly associated with cell transformation and the maintenance of the transformed genotype, as well as tumor invasion and spread. The search for miRNA markers of PCa and therapeutically active molecules should rely on bioinformatics resources, such as data from recent experimental studies, as well as meta‐analysis and cross‐analysis of the data on the state of the tumor, patient status, histological/immunohistological data and data on mRNA–miRNA coexpression.     

基于基因表达谱的疾病亚型特征基因挖掘方法

在本研究中,提出了一种基于基因表达谱的疾病亚型特征基因挖掘方法,该方法基于过滤后基因表达谱,融合无监督聚类识别疾病亚型技术和提出的衡量特征基因对疾病亚型鉴别能力的模式质量测度,以嵌入的方式实现特征基因挖掘。最后将提出的方法应用于40例结肠癌组织与22例正常结肠组织中2000个基因的表达谱实验数据,结果显示:提出的方法是一种可行的疾病亚型特征基因挖掘方法,方法的优势在于可并行实现疾病亚型划分和特征基因识别。     

Novel markers of gene methylation and expression in breast cancer

An optimized methylation-sensitive restriction fingerprinting technique was used to search for differentially methylated CpG islands in the tumor genome and detected seven genes subject to abnormal epigenetic regulation in breast cancer: SEMA6B, BIN1, VCPIP1, LAMC3, KCNH2, CACNG4, and PSMF1. For each gene, the rate of promoter methylation and changes in expression were estimated in tumor and morphologically intact paired specimens of breast tissue (N = 100). Significant methylation rates of 38, 18, and 8% were found for SEMA6B, BIN1, and LAMC3, respectively. The genes were not methylated in morphologically intact breast tissue. The expression of SEMA6B, BIN1, VCPIP1, LAMC3, KCNH2, CACNG4, and PSMF1 was decreased in 44–94% of tumor specimens by the real-time RT-PCR assay. The most profound changes in SEMA6B and LAMC3 suggest that these genes can be included in biomarker panels for breast cancer diagnosis. Fine methylation mapping of the most frequently methylated CpG islands (SEMA6B, BIN1, and LAMC3) provides a fundamental basis for developing efficient methylation tests for these genes.