Large-scale analysis of gene expression profiles

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.     

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.     

Integrated analysis of gait parameters and gene expression profiles in a murine model of subarachnoid hemorrhage

Gait analysis has been widely used to examine the behavioral presentation of numerous neurological disorders. Thorough murine model evaluation of the subarachnoid hemorrhage (SAH)-associated gait deficits is missing. This study measures gait deficits using a clinically relevant murine model of SAH to examine associations between gait variability and SAH-associated gene expressions. A total of 159 dynamic and static gait parameters from the endovascular perforation murine model for simulating clinical human SAH were determined using the CatWalk system. Eighty gait parameters and the mRNA expression levels of 35 of the 88 SAH-associated genes were differentially regulated in the diseased models. Totals of 42 and 38 gait parameters correlated with the 35 SAH-associated genes positively and negatively with Pearson's correlation coefficients of >0.7 and <−0.7, respectively. p-SP1⁴⁵³ expression in the motor cortex in SAH animal models displays a significant correlation with a subset of gait parameters associated with muscular strength and coordination of limb movements. Our data highlights a strong correlation between gait variability and SAH-associated gene expression. p-SP1⁴⁵³ expression could act as a biomarker to monitor SAH pathological development and a therapeutic target for SAH.     

通过miRNA基因表达谱的基因共表达网络构建对星形细胞瘤的基因靶标进行预测

星形细胞瘤为浸润性生长肿瘤,生长缓慢,多为隐形症状,难以早期发现。多数肿瘤切除后有复发可能,且复发后肿瘤可演变成间变性星形细胞瘤或多形性胶质母细胞瘤。因此寻找其生物标志物早期诊断,并研究新的治疗方法就十分重要。方法:通过选用GEO数据库的星形细胞瘤miRNA表达谱数据,进行差异分析以及靶基因预测,通过2者共表达网络的构建对筛选出的miRNA的研究价值进行探讨。结果:得出hsa-miR-29b-2-5p;hsa-miR-339-5p与hsa-miR-362-3p3个较为关键的miRNA及与3者关系密切的8个mRNA。结论:通过对8个mRNA在癌症中的作用的讨论,肯定了这3个关键miRNA作为潜在靶点的研究价值。     

Towards precise classification of cancers based on robust gene functional expression profiles

Background

Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which there is not a large collection of known gene sequences, such as the recently published chicken genome. We used the chicken sequence to test comparative and homology-based gene-finding methods followed by experimental validation as an effective genome annotation method.

Results

We performed experimental evaluation by RT-PCR of three different computational gene finders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram was computed and each component of it was evaluated. The results showed that de novo comparative methods can identify up to about 700 chicken genes with no previous evidence of expression, and can correctly extend about 40% of homology-based predictions at the 5' end.

Conclusions

De novo comparative gene prediction followed by experimental verification is effective at enhancing the annotation of the newly sequenced genomes provided by standard homology-based methods.     

Statistical framework for phylogenomic analysis of gene family expression profiles

Microarray technology has produced massive expression data that are invaluable for investigating the genome-wide evolutionary pattern of gene expression. To this end, phylogenetic expression analysis is highly desirable. On the basis of the Brownian process, we developed a statistical framework (called the E(0) model), assuming the independent expression of evolution between lineages. Several evolutionary mechanisms are integrated to characterize the pattern of expression diversity after gene duplications, including gradual drift and dramatic shift (punctuated equilibrium). When the phylogeny of a gene family is given, we show that the likelihood function follows a multivariate normal distribution; the variance-covariance matrix is determined by the phylogenetic topology and evolutionary parameters. Maximum-likelihood methods for multiple microarray experiments are developed, and likelihood-ratio tests are designed for testing the evolutionary pattern of gene expression. To reconstruct the evolutionary trace of expression diversity after gene (or genome) duplications, we developed a Bayesian-based method and use the posterior mean as predictors. Potential applications in evolutionary genomics are discussed.     

Integrated analysis of gene expression and methylation profiles of novel pancreatic cancer cell lines with highly metastatic activity

Pancreatic cancer is one of the most lethal human malignancies, partly because of its propensity for metastasis. However, highly metastatic human pancreatic cancer cell lines suitable for studies of metastasis are currently lacking. Here we established two highly metastatic human pancreatic cancer cell lines, MIA PaCa-2 In8 and Panc-1 In8, by Matrigel induction assay. The cell lines were further characterized both in vitro and in vivo. MIA PaCa-2 In8 and Panc-1 In8 cells demonstrated increased migration and invasion compared with their respective parental cells. Following injection into nude mice, MIA PaCa-2 In8 and Panc-1 In8 cells resulted in more pulmonary metastases compared with the parental cells. Furthermore, analyses of m RNA, long non-coding RNA, micro RNA, and methylation profiling revealed that these factors were aberrantly regulated in the highly metastatic cells,indicating that they probably affected metastasis. We thus established and characterized two highly metastatic human pancreatic cell lines that could be used as valuable tools for future investigations into the pathogenesis, metastasis, and potential treatment of human pancreatic cancer.     

Prediction and uncertainty in the analysis of gene expression profiles

We have developed a complete statistical model for the analysis of tumor specific gene expression profiles. The approach provides investigators with a global overview on large scale gene expression data, indicating aspects of the data that relate to tumor phenotype, but also summarizing the uncertainties inherent in classification of tumor types. We demonstrate the use of this method in the context of a gene expression profiling study of 27 human breast cancers. The study is aimed at defining molecular characteristics of tumors that reflect estrogen receptor tatus. In addition to good predictive performance with respect to pure classification of the expression profiles, the model also uncovers conflicts in the data with respect to the classification of some of the tumors, highlighting them as critical cases for which additional investigations are appropriate.     

Use of gene networks for identifying and validating drug targets

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.     

基因鉴定集成法:全基因组基因表达研究的新策略

人类基因组包含的核苷酸数目庞大,基因鉴定（识别）的技术策略是基因克隆研究至为重要的基础。在全基因组基因表达分析策略方面,已相继建立了ｍＲＮＡ差异显示、代表性差异分析、抑制性消减杂交、基因表达系列分析和ｃＤＮＡ微阵列等技术。基因鉴定集成法是新近在综合上述技术的优缺点的基础上建立的全基因组分析新策略,具有充分利用生物基因信息数据库进行基因鉴定（识别）,并能提高稀有拷贝基因鉴定效率的优点。本文简要介绍其     

Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis

Background

Pandemic and seasonal respiratory viruses are a major global health concern. Given the genetic diversity of respiratory viruses and the emergence of drug resistant strains, the targeted disruption of human host-virus interactions is a potential therapeutic strategy for treating multi-viral infections. The availability of large-scale genomic datasets focused on host-pathogen interactions can be used to discover novel drug targets as well as potential opportunities for drug repositioning.

Methods/Results

In this study, we performed a large-scale analysis of microarray datasets involving host response to infections by influenza A virus, respiratory syncytial virus, rhinovirus, SARS-coronavirus, metapneumonia virus, coxsackievirus and cytomegalovirus. Common genes and pathways were found through a rigorous, iterative analysis pipeline where relevant host mRNA expression datasets were identified, analyzed for quality and gene differential expression, then mapped to pathways for enrichment analysis. Possible repurposed drugs targets were found through database and literature searches. A total of 67 common biological pathways were identified among the seven different respiratory viruses analyzed, representing fifteen laboratories, nine different cell types, and seven different array platforms. A large overlap in the general immune response was observed among the top twenty of these 67 pathways, adding validation to our analysis strategy. Of the top five pathways, we found 53 differentially expressed genes affected by at least five of the seven viruses. We suggest five new therapeutic indications for existing small molecules or biological agents targeting proteins encoded by the genes F3, IL1B, TNF, CASP1 and MMP9. Pathway enrichment analysis also identified a potential novel host response, the Parkin-Ubiquitin Proteasomal System (Parkin-UPS) pathway, which is known to be involved in the progression of neurodegenerative Parkinson''s disease.

Conclusions

Our study suggests that multiple and diverse respiratory viruses invoke several common host response pathways. Further analysis of these pathways suggests potential opportunities for therapeutic intervention.