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Identification of human papillomavirus status specific biomarker in head and neck cancer

HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK 2015年第9期37卷 1310-1318页

作者： Kim, Ki-Yeol Zhang, Xianglan Cha, In-Ho Yonsei Univ Coll Dent Oral Canc Res Inst Seoul 120752 South Korea Yanbian Univ Dept Pathol Coll Med Yanji Jilin Province Peoples R China Yonsei Univ Dept Oral & Maxillofacial Surg Coll Dent Seoul 120752 South Korea

BackgroundIn an attempt to improve the prognosis of patients with head and neck cancer, it could be necessary to identify biomarkers in homogeneous subgroups of patients with head and neck cancer. MethodsWe explored the necessity of human papillomavirus (HPV) status-specific biomarkers in patients with head and neck cancer and identified an HPV status-specific gene set. The identified gene set was validated with the external microarray datasets. For this purpose, we used 4 microarray datasets obtained from a public database. One dataset was used for identifying the HPV status specific gene set, and the other datasets were used for validation. ResultsThe identified gene set for distinguishing head and neck cancer from head and neck normal, did not accurately discriminate the HPV specific patients with head and neck cancer from the head and neck normal group. Also, it was demonstrated that some HPV-positive (HPV+) head and neck cancer-specific genes may not be significant in HPV-negative (HPV-) head and neck cancer patients. The identified HPV specific gene sets showed good performance in validation datasets. The HPV+ head and neck cancer-specific gene set included various metabolism-related pathways, and the HPV- head and neck cancer-specific gene set included several pathways;fibroblast, collagen degradation, actin-binding, and actin cytoskeleton. ConclusionThe identification of HPV status-specific significant gene set was necessary. It is expected that HPV status-specific biomarkers can be helpful in deciding therapeutic methods for patients with head and neck cancer, after biological validation and large prospective trials. (c) 2014 Wiley Periodicals, Inc. Head Neck 37: 1310-1318, 2015

关键词： head and neck cancer human papillomavirus human papillomavirus (HPV) status-specific gene set prognosis microarray data

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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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Transcriptomic signatures in breast cancer

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MOLECULAR BIOSYSTEMS 2007年第7期3卷 466-472页

作者： Fu, Jianjiang Jeffrey, Stefanie S. Stanford Univ Med Ctr Stanford CA 94305 USA

High throughput DNA microarray technology has been broadly applied to the study of breast cancer to classify molecular subtypes, to predict outcome, survival, response to treatment, and for the identification of novel therapeutic targets. Although results are promising, this technology will not have a full impact on routine clinical practice until there is further standardization of techniques and optimal clinical trial design. Due to substantial disease heterogeneity and the number of genes being analyzed, collaborative, multi-institutional studies are required to accrue enough patients for sufficient statistical power. Newer bioinformatic approaches are being developed to assist with the analysis of this important data.

关键词： GENE-EXPRESSION SIGNATURE NOTTINGHAM PROGNOSTIC INDEX microarray data MOLECULAR CLASSIFICATION ONCOGENIC PATHWAYS HISTOLOGIC GRADE ADJUVANT THERAPY METASTASIS SURVIVAL TUMORS

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