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Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer

EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY 2016年第1期2016卷 6页

作者： Solvang, Hiroko K. Frigessi, Arnoldo Kaveh, Fateme Riis, Margit L. H. Luders, Torben Bukholm, Ida R. K. Kristensen, Vessela N. Andreassen, Bettina K. Inst Marine Res Dept Marine Mammals C Sundts Gate 64 N-5004 Bergen Norway Univ Oslo Inst Basic Med Sci Dept Biostat Oslo Norway Stat Innovat Sfi 2 Oslo Norway Oslo Univ Hosp Ulleval Med Genet Dept Oslo Norway Akershus Univ Hosp Dept Surg Lorenskog Norway Akershus Univ Hosp Inst Clin Med Dept Mol Biol & Lab Sci EpiGen Lorenskog Norway Univ Oslo Norwegian Ctr HPH Network Inst Clin Med Oslo Norway Oslo Univ Hosp Radiumhosp Inst Canc Res Dept Genet Oslo Norway Canc Registry Norway Inst Populat Based Res Oslo Norway

Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is common practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule from a new point of view. Here, we try to find the optimal threshold based on the differences between the gene expression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to measure the overall differential gene expression between patients with smaller tumors and those with larger tumors among multiple expression datasets from different studies. We confirmed the performance of the proposed algorithm by a simulation study and then applied it to three different studies conducted at two Norwegian hospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2-2.4 cm, and we confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly available expression datasets. Furthermore, we observed a significant differentiation between the two threshold groups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated network and canonical pathway analyses for the genes differentially expressed between tumors below and above the given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a cellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an enrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds.

关键词： Breast cancer T-category Differentially expressed microarray data Two-group comparison statistical test Optimization algorithm

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Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

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BIOINFORMATICS 2007年第16期23卷 2096-2103页

作者： Elo, Laura L. Jaervenpaeae, Henna Oresic, Matej Lahesmaa, Riitta Aittokallio, Tero Univ Turku Dept Math FI-20014 Turku Finland Turku Ctr Biotechnol FI-20521 Turku Finland VTT Biotechnol FI-02044 Espoo Finland Inst Pasteur Syst Biol Unit F-75724 Paris France

Motivation: Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared. Results: We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process. Contact: laliel@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： microarray data REGULATORY NETWORKS RELEVANCE NETWORKS EXPRESSION IDENTIFICATION CANCER TRANSCRIPTION LYMPHOCYTES VALIDATION AFFYMETRIX

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Projection-pursuit approach to robust linear discriminant analysis

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JOURNAL OF MULTIVARIATE ANALYSIS 2010年第10期101卷 2464-2485页

作者： Pires, Ana M. Branco, Joao A. Univ Tecn Lisboa Dept Math P-1049001 Lisbon Portugal Univ Tecn Lisboa CEMAT IST P-1049001 Lisbon Portugal

Discriminant analysis plays an important role in multivariate statistics as a prediction and classification method. It has been successfully applied in many fields of work and research. As it happens with other multivariate methods, discriminant analysis is highly vulnerable to the presence of outliers that commonly occur in many real world data sets. The lack of robustness of the classical estimators on which the linear discriminant function is based is a severe disadvantage and several authors have worked to find efficient ways to prevent the damage that outliers can cause. This paper focuses on the projection-pursuit approach to discriminant analysis. The projection-pursuit estimators are described and theoretical properties are deduced and their relevance is highlighted. These include Fisher consistency, affine equivariance, partial influence functions and asymptotic distributions. Application to real data and a simulation study reveal the robustness of the projection-pursuit approach. In both analyses the data relates to a large number of variables, a situation that is becoming common when new technology is applied to data gathering. (c) 2010 Elsevier Inc. All rights reserved.

关键词： Projection-pursuit Robustness Linear discriminant analysis Influence function Asymptotic variance High-dimensional data microarray data

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Survival analysis with high-dimensional covariates (vol 19, pg 29, 2010)

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STATISTICAL METHODS IN MEDICAL RESEARCH 2010年第2期19卷 200-200页

作者： Witten, Daniela M. Tibshirani, Robert Department of Statistics Stanford University Stanford CA 94305 USA dwitten{at}stanford.edu Departments of Health Research and Policy & Statistics Stanford University Stanford CA 94305 USA

Daniela M. Witten and Robert Tibshirani. Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 2010;19: 29-51 (DOI: 10.1177/0962280209105024). On page 36, Figure 1 is incorrect. Please note the following correction in the figure: The 'Modified Cox' and 'LPC' legends in the artwork were reversed. The correct figure is given below.

关键词： GENE-EXPRESSION data GENOME-WIDE ASSOCIATION B-CELL LYMPHOMA microarray data PRINCIPAL COMPONENTS COX REGRESSION VARIABLE SELECTION STATISTICAL TESTS PREDICT SURVIVAL BREAST-CANCER

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Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles

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BIOINFORMATICS 2008年第11期24卷 1359-1366页

作者： Bhattacharya, Anindya De, Rajat K. Indian Stat Inst Machine Intelligence Unit Kolkata India Netaji Subhash Engn Coll Dept Comp Sci & Engn Garia India

Motivation: Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we focus on several shortcomings of conventional clustering algorithms and propose a new one that is able to produce better clustering solution than that produced by some others. Results: We present the Divisive Correlation Clustering Algorithm (DCCA) that is suitable for finding a group of genes having similar pattern of variation in their expression values. To detect clusters with high correlation and biological significance, we use the correlation clustering concept introduced by Bansal et al. Our proposed algorithm DCCA produces a clustering solution without taking number of clusters to be created as an input. DCCA uses the correlation matrix in such a way that all genes in a cluster have highest average correlation with genes in that cluster. To test the performance of the DCCA, we have applied DCCA and some well-known conventional methods to an artificial dataset, and nine gene-expression datasets, and compared the performance of the algorithms. The clustering results of the DCCA are found to be more significantly relevant to the biological annotations than those of the other methods. All these facts show the superiority of the DCCA over some others for the clustering of gene-expression data.

关键词： microarray data

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Differential Expression of MicroRNAs and Predicted Drug Target in Amyotrophic Lateral Sclerosis

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JOURNAL OF MOLECULAR NEUROSCIENCE 2023年第6期73卷 375-390页

作者： Patel, Riya Ben Bajpai, Akhilesh Kumar Thirumurugan, Kavitha Vellore Inst Technol Sch Biosci & Technol Struct Biol Lab 412JPearl Res Pk Vellore 632014 India Univ Tennessee Hlth Sci Ctr Dept Genet Genom & Informat Memphis TN 38163 USA

ALS (Amyotrophic Lateral Sclerosis) is a rare type of neurodegenerative disease. It shows progressive degradation of motor neurons in the brain and spinal cord. At present, there is no treatment available that can completely cure ALS. The available treatments can only increase a patient's life span by a few months. Recently, microRNAs (miRNAs), a sub-class of small non-coding RNAs have been shown to play an essential role in the diagnosis, prognosis, and therapy of ALS. Our study focuses on analyzing differential miRNA profiles and predicting drug targets in ALS using bioinformatics and computational approach. The study identifies eight highly differentially expressed miRNAs in ALS patients, four of which are novel. We identified 42 hub genes for these eight highly expressed miRNAs with Amyloid Precursor Protein (APP) as a candidate gene among them for highly expressed down-regulated miRNA, hsa-miR-455-3p using protein-protein interaction network and Cytoscape analysis. A novel association has been found between hsa-miR-455-3p/APP/serotonergic pathway using KEGG pathway analysis. Also, molecular docking studies have revealed curcumin as a potential drug target that may be used for the treatment of ALS. Thus, the present study has identified four novel miRNA biomarkers: hsa-miR-3613-5p, hsa-miR-24, hsa-miR-3064-5p, and hsa-miR-4455. There is a formation of a novel axis, hsa-miR-455-3p/APP/serotonergic pathway, and curcumin is predicted as a potential drug target for ALS.

关键词： Amyotrophic lateral sclerosis MicroRNAs microarray data Target genes Drug targets

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Visualization of microarray results to assist interpretation

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TUBERCULOSIS 2004年第3-4期84卷 275-281页

作者： Papatheodorou, I Sergot, M Randall, M Stewart, GR Robertson, BD Univ London Imperial Coll Sci & Technol Ctr Mol Microbiol & Infect London SW7 2AZ England Univ London Imperial Coll Sci & Technol Dept Comp London SW7 2AZ England

Whole genome microarrays allow assessment of the profile of genes expressed under particular experimental conditions, including external stimuti such as pH or temperature, and internal changes brought about by deleting or overexpressing a gene. Such experiments produce large data sets, for which sophisticated analysis software is available. What is tacking are toots for analysing data sets from different experiments, in order to test and generate hypotheses about the links between regulatory networks. We describe here a method for presenting results from different experiments as a directed graph constructed using an automated graph drawing program xneato, enhanced by a logic program designed to cluster data and aid in the generation of hypotheses about possible gene interactions. A web-based front-end to the system has been constructed to explore and manipulate the graphical displays produced. Results of microarray experiments on Mycobacterium tuberculosis were used to develop and evaluate the visualization too[ and initiate the development of an inference system for gene interactions based on such data. The GeneGraph project can be accessed at: *** (C) 2004 Elsevier Ltd. All rights reserved.

关键词： directed graphs visualization microarray data

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A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data

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BIOINFORMATICS 2005年第13期21卷 3025-3033页

作者： Teschendorff, AE Wang, YZ Barbosa-Morais, NL Brenton, JD Caldas, C Univ Cambridge Hutchinson MRC Res Ctr Canc Genom Program Dept Oncol Cambridge CB2 2XZ England

Motivation: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. Results: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.

关键词： microarray data CLASS DISCOVERY BREAST-CANCER CLASSIFICATION PATTERNS

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An error model for protein quantification

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BIOINFORMATICS 2007年第20期23卷 2747-2753页

作者： Kreutz, C. Rodriguez, M. M. Bartolome Maiwald, T. Seidl, M. Blum, H. E. Mohr, L. Timmer, J. Freiburg Ctr Data Anal & Modeling FDM D-79014 Freiburg Germany Univ Freiburg Dept Phys D-79014 Freiburg Germany Univ Hosp Freiburg Dept Med 2 D-79106 Freiburg Germany

Motivation: Quantitative experimental data is the critical bottleneck in the modeling of dynamic cellular processes in systems biology. Here, we present statistical approaches improving reproducibility of protein quantification by immunoprecipitation and immunoblotting. Results: Based on a large data set with more than 3600 data points, we unravel that the main sources of biological variability and experimental noise are multiplicative and log-normally distributed. Therefore, we suggest a log-transformation of the data to obtain additive normally distributed noise. After this transformation, common statistical procedures can be applied to analyze the data. An error model is introduced to account for technical as well as biological variability. Elimination of these systematic errors decrease variability of measurements and allow for a more precise estimation of underlying dynamics of protein concentrations in cellular signaling. The proposed error model is relevant for simulation studies, parameter estimation and model selection, basic tools of systems biology.

关键词： DIFFERENTIALLY-EXPRESSED GENES microarray data QUANTITATIVE data IDENTIFICATION NORMALIZATION VARIANCE

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Identification the Low Oxidative Stress Subtype of Periodontitis

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INTERNATIONAL DENTAL JOURNAL 2024年第1期74卷 119-128页

作者： Wu, Yuchen Zhang, Xianfang Chen, Yunong Chen, Weiting Qian, Wenhao Shanghai Xuhui Dist Dent Ctr Dept Prosthodont Shanghai Peoples R China Shanghai Xuhui Dist Dent Ctr Dept Periodontol Shanghai Peoples R China Shanghai Xuhui Dist Dent Ctr Dept Oral Implantol Shanghai Peoples R China 500 Fenglin Rd Shanghai 200032 Peoples R China

Objective: The aim of this research was to identify the low oxidative stress-related genes expression (L-OS) subtype in patients with periodontitis. Methods: microarray data (MA) were retrieved from the Gene Expression Omnibus database. The different oxidative stress (OS) subtypes of periodontitis were identified by K-means clustering analysis and gene set variation analysis (GSVA). Differentially expressed genes (DEGs) (|Log fold change (FC)| >1, q < 0.05) amongst the OS subtypes and healthy controls (HCs) were identified by Limma R package. The genomic feature of L-OS subtype and corresponding medicines were evaluated and visualised with Drug-Gene Interaction database and cytoscape-v3.7.2 software (Pearson correlation coefficient > 0.4). Finally, the LASSOLogistic regression model was adopted to evaluate and predict patients' OS phenotype in routine clinical practice. Results: The 241 periodontitis samples and 69 HCs were included. Thirty-three DEGs between the L-OS and high oxidative stress-related genes expression (H-OS) subtypes and 96 DEGs, including 8 transcription factors, between L-OS subtype and HCs were identified, respectively. Then, the network of TFs-Genes-Drugs was constructed to determine genomic feature of L-OS subtype. Finally, a 4-gene signature formula and the cutoff value were identified by ML with LASSO model to predict patients' classification. Conclusions: For the first time, we identified L-OS subtype of periodontitis and evaluated its genomic feature with MA. (c) 2023 The Authors. Published by Elsevier Inc. on behalf of FDI World Dental Federation. (http://***/licenses/by-nc-nd/4.0/)

关键词： Periodontitis Low oxidative stress microarray data TFs-Genes-Medicines network Machine learning

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