Microarrays最新文献_第4页

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods. 基因组尺度DNA甲基化的癌症生物标志物:进化和语义分析方法的比较。

Microarrays Pub Date : 2015-11-27 DOI: 10.3390/microarrays4040647

Ioannis Valavanis, Eleftherios Pilalis, Panagiotis Georgiadis, Soterios Kyrtopoulos, Aristotelis Chatziioannou

{"title":"Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods.","authors":"Ioannis Valavanis, Eleftherios Pilalis, Panagiotis Georgiadis, Soterios Kyrtopoulos, Aristotelis Chatziioannou","doi":"10.3390/microarrays4040647","DOIUrl":"https://doi.org/10.3390/microarrays4040647","url":null,"abstract":"DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina's Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"647-70"},"PeriodicalIF":0.0,"publicationDate":"2015-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34712579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications. 整合结肠癌微阵列数据:将基因座特异性甲基化组与基于基因表达的分类相关联。

Microarrays Pub Date : 2015-11-23 DOI: 10.3390/microarrays4040630

Ana Barat, Heather J Ruskin, Annette T Byrne, Jochen H M Prehn

{"title":"Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications.","authors":"Ana Barat, Heather J Ruskin, Annette T Byrne, Jochen H M Prehn","doi":"10.3390/microarrays4040630","DOIUrl":"https://doi.org/10.3390/microarrays4040630","url":null,"abstract":"Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"630-46"},"PeriodicalIF":0.0,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34712578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Liposome-Based Approach to the Integrated Multi-Component Antigen Microarrays. 基于脂质体的集成多组分抗原微阵列方法。

Microarrays Pub Date : 2015-11-20 DOI: 10.3390/microarrays4040618

Denong Wang

引用次数: 0

An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference. 基于nca的转录调控网络推断算法综述。

Microarrays Pub Date : 2015-11-16 DOI: 10.3390/microarrays4040596

Xu Wang, Mustafa Alshawaqfeh, Xuan Dang, Bilal Wajid, Amina Noor, Marwa Qaraqe, Erchin Serpedin

{"title":"An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference.","authors":"Xu Wang, Mustafa Alshawaqfeh, Xuan Dang, Bilal Wajid, Amina Noor, Marwa Qaraqe, Erchin Serpedin","doi":"10.3390/microarrays4040596","DOIUrl":"https://doi.org/10.3390/microarrays4040596","url":null,"abstract":"In systems biology, the regulation of gene expressions involves a complex network of regulators. Transcription factors (TFs) represent an important component of this network: they are proteins that control which genes are turned on or off in the genome by binding to specific DNA sequences. Transcription regulatory networks (TRNs) describe gene expressions as a function of regulatory inputs specified by interactions between proteins and DNA. A complete understanding of TRNs helps to predict a variety of biological processes and to diagnose, characterize and eventually develop more efficient therapies. Recent advances in biological high-throughput technologies, such as DNA microarray data and next-generation sequence (NGS) data, have made the inference of transcription factor activities (TFAs) and TF-gene regulations possible. Network component analysis (NCA) represents an efficient computational framework for TRN inference from the information provided by microarrays, ChIP-on-chip and the prior information about TF-gene regulation. However, NCA suffers from several shortcomings. Recently, several algorithms based on the NCA framework have been proposed to overcome these shortcomings. This paper first overviews the computational principles behind NCA, and then, it surveys the state-of-the-art NCA-based algorithms proposed in the literature for TRN reconstruction. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"596-617"},"PeriodicalIF":0.0,"publicationDate":"2015-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection. 结合微阵列和芯片上的实验室数据进行动物育种和选择的高效SNP发现。

Microarrays Pub Date : 2015-11-16 DOI: 10.3390/microarrays4040570

Chao-Wei Huang, Yu-Tsung Lin, Shih-Torng Ding, Ling-Ling Lo, Pei-Hwa Wang, En-Chung Lin, Fang-Wei Liu, Yen-Wen Lu

{"title":"Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection.","authors":"Chao-Wei Huang, Yu-Tsung Lin, Shih-Torng Ding, Ling-Ling Lo, Pei-Hwa Wang, En-Chung Lin, Fang-Wei Liu, Yen-Wen Lu","doi":"10.3390/microarrays4040570","DOIUrl":"https://doi.org/10.3390/microarrays4040570","url":null,"abstract":"The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"570-95"},"PeriodicalIF":0.0,"publicationDate":"2015-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34712576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

SNPs Array Karyotyping in Non-Hodgkin Lymphoma. 非霍奇金淋巴瘤的snp阵列核型分析。

Microarrays Pub Date : 2015-11-12 DOI: 10.3390/microarrays4040551

Maryam Etebari, Mohsen Navari, Pier Paolo Piccaluga

引用次数: 7

Evaluating the Effect of Cell Culture on Gene Expression in Primary Tissue Samples Using Microfluidic-Based Single Cell Transcriptional Analysis. 利用微流体单细胞转录分析评估细胞培养对原代组织样本基因表达的影响。

Microarrays Pub Date : 2015-11-04 DOI: 10.3390/microarrays4040540

Michael Januszyk, Robert C Rennert, Michael Sorkin, Zeshaan N Maan, Lisa K Wong, Alexander J Whittam, Arnetha Whitmore, Dominik Duscher, Geoffrey C Gurtner

{"title":"Evaluating the Effect of Cell Culture on Gene Expression in Primary Tissue Samples Using Microfluidic-Based Single Cell Transcriptional Analysis.","authors":"Michael Januszyk, Robert C Rennert, Michael Sorkin, Zeshaan N Maan, Lisa K Wong, Alexander J Whittam, Arnetha Whitmore, Dominik Duscher, Geoffrey C Gurtner","doi":"10.3390/microarrays4040540","DOIUrl":"https://doi.org/10.3390/microarrays4040540","url":null,"abstract":"Significant transcriptional heterogeneity is an inherent property of complex tissues such as tumors and healing wounds. Traditional methods of high-throughput analysis rely on pooling gene expression data from hundreds of thousands of cells and reporting a population-wide average that is unable to capture differences within distinct cell subsets. Recent advances in microfluidic technology have permitted the development of large-scale single cell analytic methods that overcome this limitation. The increased granularity afforded by such approaches allows us to answer the critical question of whether expansion in cell culture significantly alters the transcriptional characteristics of cells isolated from primary tissue. Here we examine an established population of human adipose-derived stem cells (ASCs) using a novel, microfluidic-based method for high-throughput transcriptional interrogation, coupled with advanced bioinformatic analysis, to evaluate the dynamics of single cell gene expression among primary, passage 0, and passage 1 stem cells. We find significant differences in the transcriptional profiles of cells from each group, as well as a considerable shift in subpopulation dynamics as those subgroups better able to adhere and proliferate under these culture conditions gradually emerge as dominant. Taken together, these findings reinforce the importance of using primary or very early passage cells in future studies. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"540-50"},"PeriodicalIF":0.0,"publicationDate":"2015-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery. 反相蛋白质阵列数据分析:从实验设计到靶向生物标志物发现。

Microarrays Pub Date : 2015-11-03 DOI: 10.3390/microarrays4040520

Astrid Wachter, Stephan Bernhardt, Tim Beissbarth, Ulrike Korf

{"title":"Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery.","authors":"Astrid Wachter, Stephan Bernhardt, Tim Beissbarth, Ulrike Korf","doi":"10.3390/microarrays4040520","DOIUrl":"https://doi.org/10.3390/microarrays4040520","url":null,"abstract":"Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"520-39"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Cancer-Osteoblast Interaction Reduces Sost Expression in Osteoblasts and Up-Regulates lncRNA MALAT1 in Prostate Cancer. 前列腺癌与成骨细胞相互作用降低成骨细胞中Sost的表达并上调lncRNA MALAT1。

Microarrays Pub Date : 2015-10-29 DOI: 10.3390/microarrays4040503

Aimy Sebastian, Nicholas R Hum, Bryan D Hudson, Gabriela G Loots

{"title":"Cancer-Osteoblast Interaction Reduces Sost Expression in Osteoblasts and Up-Regulates lncRNA MALAT1 in Prostate Cancer.","authors":"Aimy Sebastian, Nicholas R Hum, Bryan D Hudson, Gabriela G Loots","doi":"10.3390/microarrays4040503","DOIUrl":"https://doi.org/10.3390/microarrays4040503","url":null,"abstract":"Dynamic interaction between prostate cancer and the bone microenvironment is a major contributor to metastasis of prostate cancer to bone. In this study, we utilized an in vitro co-culture model of PC3 prostate cancer cells and osteoblasts followed by microarray based gene expression profiling to identify previously unrecognized prostate cancer-bone microenvironment interactions. Factors secreted by PC3 cells resulted in the up-regulation of many genes in osteoblasts associated with bone metabolism and cancer metastasis, including Mmp13, Il-6 and Tgfb2, and down-regulation of Wnt inhibitor Sost. To determine whether altered Sost expression in the bone microenvironment has an effect on prostate cancer metastasis, we co-cultured PC3 cells with Sost knockout (Sost(KO)) osteoblasts and wildtype (WT) osteoblasts and identified several genes differentially regulated between PC3-Sost(KO) osteoblast co-cultures and PC3-WT osteoblast co-cultures. Co-culturing PC3 cells with WT osteoblasts up-regulated cancer-associated long noncoding RNA (lncRNA) MALAT1 in PC3 cells. MALAT1 expression was further enhanced when PC3 cells were co-cultured with Sost(KO) osteoblasts and treatment with recombinant Sost down-regulated MALAT1 expression in these cells. Our results suggest that reduced Sost expression in the tumor microenvironment may promote bone metastasis by up-regulating MALAT1 in prostate cancer. ","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"503-19"},"PeriodicalIF":0.0,"publicationDate":"2015-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

SNP Analysis and Whole Exome Sequencing: Their Application in the Analysis of a Consanguineous Pedigree Segregating Ataxia. SNP分析和全外显子组测序:它们在分离共济失调亲缘家系分析中的应用。

Microarrays Pub Date : 2015-10-23 DOI: 10.3390/microarrays4040490

Sarah L Nickerson, Renate Marquis-Nicholson, Karen Claxton, Fern Ashton, Ivone U S Leong, Debra O Prosser, Jennifer M Love, Alice M George, Graham Taylor, Callum Wilson, R J McKinlay Gardner, Donald R Love

引用次数: 5