共查询到20条相似文献,搜索用时 15 毫秒
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Ivan P. Gorlov Gary E. Gallick Olga Y. Gorlova Christopher Amos Christopher J. Logothetis 《PloS one》2009,4(8)
Background
Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes).Methodology/Principal Findings
We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes.Conclusions/Significance
We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development. 相似文献2.
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Rebeca Sanz-Pamplona Antoni Berenguer David Cordero Samantha Riccadonna Xavier Solé Marta Crous-Bou Elisabet Guinó Xavier Sanjuan Sebastiano Biondo Antonio Soriano Giuseppe Jurman Gabriel Capella Cesare Furlanello Victor Moreno 《PloS one》2012,7(11)
Introduction
The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.Methods
A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.Results
Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.Conclusions
The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic. 相似文献4.
Background
Gene expression profiling using microarrays is a powerful technology widely used to study regulatory networks. Profiling of mRNA levels in mutant organisms has the potential to identify genes regulated by the mutated protein.Methodology/Principle Findings
Using tissues from multiple lines of knockout mice we have examined genome-wide changes in gene expression. We report that a significant proportion of changed genes were found near the targeted gene.Conclusions/Significance
The apparent clustering of these genes was explained by the presence of flanking DNA from the parental ES cell. We provide recommendations for the analysis and reporting of microarray data from knockout mice 相似文献5.
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Purpose
This study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC).Patients and Methods
Gene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.Results
A 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.Conclusion
We have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested. 相似文献7.
Benjamin D Korman Chiang-Ching Huang Carly Skamra Peggy Wu Renee Koessler David Yao Qi Quan Huang William Pearce Kim Sutton-Tyrrell George Kondos Daniel Edmundowicz Richard Pope Rosalind Ramsey-Goldman 《Arthritis research & therapy》2014,16(4):R147
Introduction
Our objectives were to examine mononuclear cell gene expression profiles in patients with systemic lupus erythematosus (SLE) and healthy controls and to compare subsets with and without atherosclerosis to determine which genes’ expression is related to atherosclerosis in SLE.Methods
Monocytes were obtained from 20 patients with SLE and 16 healthy controls and were in vitro-differentiated into macrophages. Subjects also underwent laboratory and imaging studies to evaluate for subclinical atherosclerosis. Whole-genome RNA expression microarray was performed, and gene expression was examined.Results
Gene expression profiling was used to identify gene signatures that differentiated patients from controls and individuals with and without atherosclerosis. In monocytes, 9 out of 20 patients with SLE had an interferon-inducible signature compared with 2 out of 16 controls. By looking at gene expression during monocyte-to-macrophage differentiation, we identified pathways which were differentially regulated between SLE and controls and identified signatures based on relevant intracellular signaling molecules which could differentiate SLE patients with atherosclerosis from controls. Among patients with SLE, we used a previously defined 344-gene atherosclerosis signature in monocyte-to-macrophage differentiation to identify patient subgroups with and without atherosclerosis. Interestingly, this signature further classified patients on the basis of the presence of SLE disease activity and cardiovascular risk factors.Conclusions
Many genes were differentially regulated during monocyte-to-macrophage differentiation in SLE patients compared with controls. The expression of these genes in mononuclear cells is important in the pathogenesis of SLE, and molecular profiling using gene expression can help stratify SLE patients who may be at risk for development of atherosclerosis. 相似文献8.
Mark Pinese Christopher J. Scarlett James G. Kench Emily K. Colvin Davendra Segara Susan M. Henshall Robert L. Sutherland Andrew V. Biankin 《PloS one》2009,4(4)
Background
Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression profiling is conventionally employed to identify disease-associated genes, however, traditional approaches for the analysis of profiling experiments may miss molecular aberrations which define biologically relevant subtypes.Methodology/Principal Findings
Here we present Messina, a method that can identify those genes that only sometimes show aberrant expression in cancer. We demonstrate with simulated data that Messina is highly sensitive and specific when used to identify genes which are aberrantly expressed in only a proportion of cancers, and compare Messina to contemporary analysis techniques. We illustrate Messina by using it to detect the aberrant expression of a gene that may play an important role in pancreatic cancer.Conclusions/Significance
Messina allows the detection of genes with profiles typical of markers of molecular subtype, and complements existing methods to assist the identification of such markers. Messina is applicable to any global expression profiling data, and to allow its easy application has been packaged into a freely-available stand-alone software package. 相似文献9.
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Zhenxing Yang Yu Xu Hongrong Luo Xiaohong Ma Qiang Wang Yingcheng Wang Wei Deng Tao Jiang Guangqing Sun Tingting He Jingchu Hu Yingrui Li Jun Wang Tao Li Xun Hu 《PloS one》2014,9(4)
Objective
To identify and investigate the susceptibility genes of Kashin–Beck disease (KBD) in Chinese population.Methods
Whole-exome capturing and sequencing technology was used for the detection of genetic variations in 19 individuals from six families with high incidence of KBD. A total of 44 polymorphisms from 41 genes were genotyped from a total of 144 cases and 144 controls by using MassARRAY under the standard protocol from Sequenom. Association was applied on the data by using PLINK1.07.Results
In the sequencing stage, each sample showed approximately 70-fold coverage, thus covering more than 99% of the target regions. Among the single nucleotide polymorphisms (SNPs) used in the transmission disequilibrium test, 108 had a p-value of <0.01, whereas 1056 had a p-value of <0.05. Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis indicates that these SNPs focus on three major pathways: regulation of actin cytoskeleton, focal adhesion, and metabolic pathways. In the validation stage, single locus effects revealed that two of these polymorphisms (rs7745040 and rs9275295) in the human leukocyte antigen (HLA)-DRB1 gene and one polymorphism (rs9473132) in CD2-associated protein (CD2AP) gene have a significant statistical association with KBD.Conclusions
HLA-DRB1 and CD2AP gene were identified to be among the susceptibility genes of KBD, thus supporting the role of the autoimmune response in KBD and the possibility of shared etiology between osteoarthritis, rheumatoid arthritis, and KBD. 相似文献13.
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Background
Acute myeloid leukemia (AML) is a heterogeneous disease with an overall poor prognosis. Gene expression profiling studies of patients with AML has provided key insights into disease pathogenesis while exposing potential diagnostic and prognostic markers and therapeutic targets. A systematic comparison of the large body of gene expression profiling studies in AML has the potential to test the extensibility of conclusions based on single studies and provide further insights into AML.Methodology/Principal Findings
In this study, we systematically compared 25 published reports of gene expression profiling in AML. There were a total of 4,918 reported genes of which one third were reported in more than one study. We found that only a minority of reported prognostically-associated genes (9.6%) were replicated in at least one other study. In a combined analysis, we comprehensively identified both gene sets and functional gene categories and pathways that exhibited significant differential regulation in distinct prognostic categories, including many previously unreported associations.Conclusions/Significance
We developed a novel approach for granular, cross-study analysis of gene-by-gene data and their relationships with established prognostic features and patient outcome. We identified many robust novel prognostic molecular features in AML that were undetected in prior studies, and which provide insights into AML pathogenesis with potential diagnostic, prognostic, and therapeutic implications. Our database and integrative analysis are available online (http://gat.stamlab.org). 相似文献17.
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Jun Hou Joachim Aerts Bianca den Hamer Wilfred van IJcken Michael den Bakker Peter Riegman Cor van der Leest Peter van der Spek John A. Foekens Henk C. Hoogsteden Frank Grosveld Sjaak Philipsen 《PloS one》2010,5(4)