共查询到20条相似文献,搜索用时 109 毫秒
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Pingzhao Hu Xinchen Wang Jack J. Haitsma Suleiman Furmli Hussain Masoom Mingyao Liu Yumiko Imai Arthur S. Slutsky Joseph Beyene Celia M. T. Greenwood Claudia dos Santos 《PloS one》2012,7(10)
Objectives
To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.Methods
We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.Results
Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.Conclusion
Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications. 相似文献4.
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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6.
Ann Cashion Ansley Stanfill Fridtjof Thomas Lijing Xu Thomas Sutter James Eason Mang Ensell Ramin Homayouni 《PloS one》2013,8(3)
Background
The aim of this study was to investigate the association of gene expression profiles in subcutaneous adipose tissue with weight change in kidney transplant recipients and to gain insights into the underlying mechanisms of weight gain.Methodology/Principal Findings
A secondary data analysis was done on a subgroup (n = 26) of existing clinical and gene expression data from a larger prospective longitudinal study examining factors contributing to weight gain in transplant recipients. Measurements taken included adipose tissue gene expression profiles at time of transplant, baseline and six-month weight, and demographic data. Using multivariate linear regression analysis controlled for race and gender, expression levels of 1553 genes were significantly (p<0.05) associated with weight change. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes classifications identified metabolic pathways that were enriched in this dataset. Furthermore, GeneIndexer literature mining analysis identified a subset of genes that are highly associated with obesity in the literature and Ingenuity pathway analysis revealed several significant gene networks associated with metabolism and endocrine function. Polymorphisms in several of these genes have previously been linked to obesity.Conclusions/Significance
We have successfully identified a set of molecular pathways that taken together may provide insights into the mechanisms of weight gain in kidney transplant recipients. Future work will be done to determine how these pathways may contribute to weight gain. 相似文献7.
Mev Dominguez-Valentin Christina Therkildsen Srinivas Veerla Mats J?nsson Inge Bernstein ?ke Borg Mef Nilbert 《PloS one》2013,8(8)
Introduction
Heredity is estimated to cause at least 20% of colorectal cancer. The hereditary nonpolyposis colorectal cancer subset is divided into Lynch syndrome and familial colorectal cancer type X (FCCTX) based on presence of mismatch repair (MMR) gene defects.Purpose
We addressed the gene expression signatures in colorectal cancer linked to Lynch syndrome and FCCTX with the aim to identify candidate genes and to map signaling pathways relevant in hereditary colorectal carcinogenesis.Experimental design
The 18 k whole-genome c-DNA-mediated annealing, selection, extension, and ligation (WG-DASL) assay was applied to 123 colorectal cancers, including 39 Lynch syndrome tumors and 37 FCCTX tumors. Target genes were technically validated using real-time quantitative RT-PCR (qRT-PCR) and the expression signature was validated in independent datasets.Results
Colorectal cancers linked to Lynch syndrome and FCCTX showed distinct gene expression profiles, which by significance analysis of microarrays (SAM) differed by 2188 genes. Functional pathways involved were related to G-protein coupled receptor signaling, oxidative phosphorylation, and cell cycle function and mitosis. qRT-PCR verified altered expression of the selected genes NDUFA9, AXIN2, MYC, DNA2 and H2AFZ. Application of the 2188-gene signature to independent datasets showed strong correlation to MMR status.Conclusion
Distinct genetic profiles and deregulation of different canonical pathways apply to Lynch syndrome and FCCTX and key targets herein may be relevant to pursue for refined diagnostic and therapeutic strategies in hereditary colorectal cancer. 相似文献8.
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Shengjun Fan Zhenyu Pan Qiang Geng Xin Li Yefan Wang Yu An Yan Xu Lu Tie Yan Pan Xuejun Li 《PloS one》2013,8(6)
Background
Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.Methodology/Principal Findings
In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.Results
Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.Conclusions
The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions. 相似文献14.
Background
MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.Results
We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.Conclusions
Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users. 相似文献15.
Xiaopei Shen Shan Li Lin Zhang Hongdong Li Guini Hong XianXiao Zhou Tingting Zheng Wenjing Zhang Chunxiang Hao Tongwei Shi Chunyang Liu Zheng Guo 《PloS one》2013,8(4)
Background
Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.Results
Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.Conclusion
The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy. 相似文献16.
Background
Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as ‘a form of biological systems’, consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic.Methodology/Principal Findings
In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways.Conclusions/Significance
Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases. 相似文献17.
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18.
Yolande F. M. Ramos Wouter den Hollander Judith V. M. G. Bovée Nils Bomer Ruud van der Breggen Nico Lakenberg J. Christiaan Keurentjes Jelle J. Goeman P. Eline Slagboom Rob G. H. H. Nelissen Steffan D. Bos Ingrid Meulenbelt 《PloS one》2014,9(7)
Objective
Identify gene expression profiles associated with OA processes in articular cartilage and determine pathways changing during the disease process.Methods
Genome wide gene expression was determined in paired samples of OA affected and preserved cartilage of the same joint using microarray analysis for 33 patients of the RAAK study. Results were replicated in independent samples by RT-qPCR and immunohistochemistry. Profiles were analyzed with the online analysis tools DAVID and STRING to identify enrichment for specific pathways and protein-protein interactions.Results
Among the 1717 genes that were significantly differently expressed between OA affected and preserved cartilage we found significant enrichment for genes involved in skeletal development (e.g. TNFRSF11B and FRZB). Also several inflammatory genes such as CD55, PTGES and TNFAIP6, previously identified in within-joint analyses as well as in analyses comparing preserved cartilage from OA affected joints versus healthy cartilage were among the top genes. Of note was the high up-regulation of NGF in OA cartilage. RT-qPCR confirmed differential expression for 18 out of 19 genes with expression changes of 2-fold or higher, and immunohistochemistry of selected genes showed a concordant change in protein expression. Most of these changes associated with OA severity (Mankin score) but were independent of joint-site or sex.Conclusion
We provide further insights into the ongoing OA pathophysiological processes in cartilage, in particular into differences in macroscopically intact cartilage compared to OA affected cartilage, which seem relatively consistent and independent of sex or joint. We advocate that development of treatment could benefit by focusing on these similarities in gene expression changes and/or pathways. 相似文献19.
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