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Identification of genes associated with methotrexate resistance in methotrexate-resistant osteosarcoma cell lines

JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH 2015年第1期10卷 1-9页

作者： Yang, Xiao-rong Xiong, Yan Duan, Hong Gong, Ren-rong Sichuan Univ West China Hosp Dept Operat Room Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Dept Orthoped Chengdu 610041 Sichuan Peoples R China

Background: This study aimed to better understand the mechanisms underlying methotrexate (MTX)-resistance in osteosarcoma. Methods: The raw transcription microarray data GSE16089 collected from three MTX-sensitive osteosarcoma (Saos-2) cell samples and three MTX-resistant osteosarcoma (Saos-2) cell samples were downloaded from Gene Expression Omnibus. After data processing, the differentially expressed genes (DEGs) were identified. Next, DEGs were submitted to DAVID for functional annotation based on the GO (Gene Ontology) database, as well as pathway enrichment analysis based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Transcription factors (TFs) and tumor-associated genes (TAGs) were identified with reference to TRANSFAC and TAG, and TSGene databases, respectively. The protein-protein interaction (PPI) network of the gene-encoded products was constructed, and the subnetwork with the highest score was also detected using Search Tool for the Retrieval of Interacting Genes and BioNet package. Results: A total of 690 up-regulated genes and down-regulated 626 genes were identified. Up-regulated DEGs (including AARS and PARS2) were associated to transfer RNA (tRNA) aminoacylation while down-regulated DEGs (including AURKA, CCNB1, CCNE2, CDK1, and CENPA) were correlated with mitotic cell cycle. Totally, 13 TFs (including HMGB2), 13 oncogenes (including CCNA2 and AURKA), and 19 tumor suppressor genes (TSGs) (including CDKN2C) were identified from the down-regulated DEGs. Ten DEGs, including nine down-regulated genes (such as AURKA, CDK1, CCNE2, and CENPA) and one up-regulated gene (GADD45A), were involved in the highest score subnetwork. Conclusion: AARS, AURKA, AURKB, CENPA, CCNB1, CCNE2, and CDK may contribute to MTX resistance via aminoacyl-tRNA biosynthesis pathway, cell cycle pathway, or p53 signaling pathway.

关键词： Osteosarcoma Methotrexate resistance microarray data

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Analysis of the real EADGENE data set:: Comparison of methods and guidelines for data normalisation and selection of diffrentially expressed genes (Open Access publication)

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GENETICS SELECTION EVOLUTION 2007年第6期39卷 633-650页

作者： Jaffrezic, Florence De Koning, Dirk-Jan Boettcher, Paul J. Bonnet, Agnes Buitenhuis, Bart Closset, Rodrigue Dejean, Sebastien Delmas, Celine Detilleux, Johanne C. Dovc, Peter Duval, Mylene Foulley, Jean-Louis Hedegaard, Jakob Hornshoj, Henrik Hulsegge, Ina Janss, Luc Jensen, Kirsty Jiang, Li Lavric, Miha Le Cao, Kim-Anh Lund, Mogens Sando Malinverni, Roberto Marot, Guillemette Nie, Haisheng Petzl, Wolfram Pool, Marco H. Granie, Christele Robert Cristobal, Magali San Van Schothorst, Evert M. Schuberth, Hans-Joachim Sorensen, Peter Stella, Alessandra Tosser-Klopp, Gwenola Waddington, David Watson, Michael Yang, Wei Zerbe, Holm Seyfert, Hans-Martin INRA UR337 Jouy En Josas France Roslin Inst Roslin Midlothian Scotland Parco Tecnol Padano Lodi Italy INRA UMR444 Castanet Tolosan France Univ Aarhus Tjele Denmark Univ Liege Liege Belgium Univ Toulouse 3 Toulouse France INRA UR631 Castanet Tolosan France Univ Liege Fac Vet Med Liege Belgium Univ Ljubljana Ljubljana Slovenia Anim Sci Grp Wageningen Lelystad Netherlands Univ Wageningen & Res Ctr Wageningen Netherlands Univ Munich Munich Germany RIKILT Inst Food Safety Wageningen Netherlands Univ Vet Med Hannover Germany Inst Anim Hlth Compton England Res Inst Biol Farm Anim Dummerstorf Germany

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE ( European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

关键词： quality control differentially expressed genes mastitis resistance microarray data normalisation

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Computational cluster validation in post-genomic data analysis

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BIOINFORMATICS 2005年第15期21卷 3201-3212页

作者： Handl, J Knowles, J Kell, DB Univ Manchester Sch Chem Manchester M60 1QD Lancs England

Motivation: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge-whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. Results: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. Availability: The software used in the experiments is available at http://***/handl/clustervalidation/ Contact: ***@*** Supplementary information: Enlarged colour plots are provided in the Supplementary Material, which is available at http://***/handl/clustervalidation/

关键词： GENE-EXPRESSION data microarray data MOLECULAR CLASSIFICATION PATTERNS VALIDITY PREDICTION CRITERIA NETWORK INDEXES NUMBER

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MMG: a probabilistic tool to identify submodules of metabolic pathways

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BIOINFORMATICS 2008年第8期24卷 1078-1084页

作者： Sanguinetti, Guido Noirel, Josselin Wright, Phillip C. Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Sheffield Dept Chem Proc Engn Biol & Environm Syst Grp Sheffield S1 3JD S Yorkshire England

Motivation: A fundamental task in systems biology is the identification of groups of genes that are involved in the cellular response to particular signals. At its simplest level, this often reduces to identifying biological quantities (mRNA abundance, enzyme concentrations, etc.) which are differentially expressed in two different conditions. Popular approaches involve using t-test statistics, based on modelling the data as arising from a mixture distribution. A common assumption of these approaches is that the data are independent and identically distributed;however, biological quantities are usually related through a complex (weighted) network of interactions, and often the more pertinent question is which subnetworks are differentially expressed, rather than which genes. Furthermore, in many interesting cases (such as high-throughput proteomics and metabolomics), only very partial observations are available, resulting in the need for efficient imputation techniques. Results: We introduce Mixture Model on Graphs (MMG), a novel probabilistic model to identify differentially expressed submodules of biological networks and pathways. The method can easily incorporate information about weights in the network, is robust against missing data and can be easily generalized to directed networks. We propose an efficient sampling strategy to infer posterior probabilities of differential expression, as well as posterior probabilities over the model parameters. We assess our method on artificial data demonstrating significant improvements over standard mixture model clustering. Analysis of our model results on quantitative high-throughput proteomic data leads to the identification of biologically significant subnetworks, as well as the prediction of the expression level of a number of enzymes, some of which are then verified experimentally.

关键词： microarray data GENOMIC data NETWORKS EXPRESSION MODEL

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Developing an effective biclustering technique using an enhanced proximity measure

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2020年第1期9卷 6-6页

作者： Patowary, Pallabi Sarmah, Rosy Bhattacharyya, Dhruba K. Tezpur Univ Dept Comp Sci & Engn Tezpur Assam India

This paper introduces an enhanced version of Pearson's correlation coefficient (PCC) to achieve better biclustering-enabled co-expression analysis. The modified measure called local pearson correlation measure (LPCM) helps detect shifting, scaling, and shifting-and-scaling correlation patterns effectively over gene expression data in the presence of outlier. An LPCM-based biclustering technique called local correlation-based biclustering technique (LCBT) has also been proposed to identify biclusters of high biological significance. The biclustering results have been established both statistically and biologically using benchmarked gene expression data.

关键词： microarray data Clustering Biclustering P value GO annotation Proximity measure

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Building an intelligent integrated method of gene selection for facioscapulohumeral muscular dystrophy diagnosis

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2017年第3期24卷 285-296页

作者： Anand, Divya Pandey, Babita Pandey, Devendra K. Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India Lovely Profess Univ Sch Comp Applicat Phagwara Punjab India Lovely Profess Univ Sch Biosci Phagwara Punjab India

These days, the genetic testing of neuromuscular diseases is an active area of research. The microarray technology is playing a vital role in analysing the whole genome simultaneously. In microarrays, due to the presence of large number of genes and a small number of samples, it is very difficult to find the disease specific gene subset. So, here gene selection for classification plays a major role in the genetic testing of the disease. In this paper, the gene selection is performed by deploying genetic algorithm with different number of genes wherein the fitness function is evaluated using three different classifiers namely linear discriminant analysis, quadratic discriminant analysis and k-nearest neighbour one-at-a-time for classification of facioscapulohumeral muscular dystrophy. The comparative analysis of the same is also shown in this paper. A facioscapulohumeral muscular dystrophy gene dataset consisting of 50 samples and 33,297 genes is used to evaluate the performance of integrated algorithms. The result shows that the integration of genetic algorithm with k-nearest neighbour is found to be the best for gene selection and diagnosis of facioscapulohumeral muscular dystrophy.

关键词： facioscapulohumeral muscular dystrophy genetic diagnosis genetic algorithm k-nearest neighbour linear discriminant analysis quadratic discriminant analysis muscular dystrophy microarray data neuromuscular dystrophy

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Spatial analysis of cDNA microarray experiments

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CROP SCIENCE 2005年第2期45卷 748-757页

作者： Burgueño, J Crossa, J Grimanelli, D Leblanc, O Autran, D CIMMYT Biometr & Stat Unit Int Maize & Wheat Improvement Ctr Mexico City 06600 DF Mexico IRD Mexico City 06501 DF Mexico

microarray experiments allow RNA level measurements for many genes in multiple samples. However, mining the biological information from the large sets of data generated by microarrays requires the use of appropriate statistical methods to adjust the observed values for experimentally introduced variability (normalization process) before testing differences among samples. Normalization of microarray experiments is a critical step for reducing false positives and false negatives. This paper explores the normalization of cDNA microarray experiments by a method that uses the blank spot intensity values to make spatial adjustment (SA) of both foreground and background DNA spot intensity values, by fitting an autoregressive mixed linear model through the residual maximum likelihood (REML) methodology in the direction of the rows and the columns of the microarray. Application of this spatial normalization to three cDNA array experiments serves as a case study to validate the SA. Results show that the spatial analysis allows selection of candidate genes with lesser numbers of false positive and false negative genes.

关键词： .microarrays spatial analysis normalization candidate genes REML autoregressive Mixed linear microarray data positives foreground false positive false negative intensity values

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Genome-wide co-expression based prediction of differential expressions

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BIOINFORMATICS 2008年第5期24卷 666-673页

作者： Lai, Yinglei George Washington Univ Dept Stat Washington DC 20052 USA George Washington Univ Ctr Biostat Washington DC 20052 USA

Motivation: microarrays have been widely used for medical studies to detect novel disease-related genes. They enable us to study differential gene expressions at a genomic level. They also provide us with informative genome-wide co-expressions. Although many statistical methods have been proposed for identifying differentially expressed genes, genome-wide co-expressions have not been well considered for this issue. Incorporating genome-wide co-expression information in the differential expression analysis may improve the detection of disease-related genes. Results: In this study, we proposed a statistical method for predicting differential expressions through the local regression between differential expression and co-expression measures. The smoother span parameter was determined by optimizing the rank correlation between the observed and predicted differential expression measures. A mixture normal quantile-based method was used to transform data. We used the gene-specific permutation procedure to evaluate the significance of a prediction. Two published microarray data sets were analyzed for applications. For the data set collected for a prostate cancer study, the proposed method identified many genes with weak differential expressions. Several of these genes have been shown in literature to be associated with the disease. For the data set collected for a type 2 diabetes study, no significant genes could be identified by the traditional methods. However, the proposed method identified many genes with significantly low false discovery rates.

关键词： LINEAR-REGRESSION MODELS GENE-EXPRESSION microarray data STATISTICAL-METHOD PROSTATE-CANCER DNA microarray CELL-CYCLE DISCOVERY CLASSIFICATION IDENTIFICATION

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Analysis of the Arabidopsis histidine kinase ATHK1 reveals a connection between vegetative osmotic stress sensing and seed maturation

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PLANT CELL 2008年第4期20卷 1101-1117页

作者： Wohlbach, Dana J. Quirino, Betania F. Sussman, Michael R. Univ Wisconsin Dept Biochem Madison WI 53706 USA Univ Wisconsin Dept Genet Madison WI 53706 USA Univ Cattolica Brasilia Genom Sci & Biotechnol Program BR-70790 Brasilia DF Brazil Embrapa Agroenergia BR-70770 Brasilia DF Brazil

To cope with water stress, plants must be able to effectively sense, respond to, and adapt to changes in water availability. The Arabidopsis thaliana plasma membrane His kinase ATHK1 has been suggested to act as an osmosensor that detects water stress and initiates downstream responses. Here, we provide direct genetic evidence that ATHK1 not only is involved in the water stress response during early vegetative stages of plant growth but also plays a unique role in the regulation of desiccation processes during seed formation. To more comprehensively identify genes involved in the downstream pathways affected by the ATHK1-mediated response to water stress, we created a large-scale summary of expression data, termed the AtMegaCluster. In the AtMegaCluster, hierarchical clustering techniques were used to compare whole-genome expression levels in athk1 mutants with the expression levels reported in publicly available data sets of Arabidopsis tissues grown under a wide variety of conditions. These experiments revealed that ATHK1 is cotranscriptionally regulated with several Arabidopsis response regulators, together with two proteins containing novel sequences. Since overexpression of ATHK1 results in increased water stress tolerance, our observations suggest a new top-down route to increasing drought resistance via receptor-mediated increases in sensing water status, rather than through genetically engineered changes in downstream transcription factors or specific osmolytes.

关键词： ABSCISIC-ACID BIOSYNTHESIS PROBE LEVEL data RESPONSE REGULATORS GENE-EXPRESSION MASS-SPECTROMETRY microarray data SALT STRESS THALIANA PROTEIN PLANTS

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Gene subset selection using an iterative approach based on genetic algorithms

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ARTIFICIAL LIFE AND ROBOTICS 2009年第1期14卷 12-15页

作者： Mohamad, Mohd Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai 81310 Johore Malaysia

microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.

关键词： Gene selection Genetic algorithm Iterative approach microarray data

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