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Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches

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SYSTEMS BIOLOGY IN REPRODUCTIVE MEDICINE 2021年第3期67卷 209-220页

作者： Sahly, Nora Naif Banaganapalli, Babajan Sahly, Ahmed N. Aligiraigri, Ali H. Nasser, Khalidah K. Shinawi, Thoraia Mohammed, Arif Alamri, Abdulhakeem S. Bondagji, Nabeel Elango, Ramu Shaik, Noor Ahmad King Abdulaziz Univ Fac Med Dept Obstet & Gynecol Jeddah Saudi Arabia King Abdulaziz Univ Fac Med Dept Genet Med Jeddah Saudi Arabia King Abdulaziz Univ Princess Al Jawhara Al Brahim Ctr Excellence Res Jeddah Saudi Arabia King Faisal Specialist Hosp & Res Ctr Dept Neurosci Jeddah Saudi Arabia King Abdulaziz Univ Hosp Dept Hematol Jeddah Saudi Arabia King Abdulaziz Univ Fac Appl Med Sci Dept Med Lab Technol Jeddah Saudi Arabia Univ Jeddah Coll Sci Dept Biol Jeddah Saudi Arabia Taif Univ Coll Appl Med Sci Dept Clin Labs Sci At Taif Saudi Arabia Taif Univ Ctr Biomed Sci Res CBSR Deanship Sci Res At Taif Saudi Arabia

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p <= 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value <= 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value <= 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.

关键词： Uterine leiomyosarcoma protein interaction network microarray data analysis differentially expressed genes

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A statistical framework for expression-based molecular classification in cancer

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2002年第4期64卷 717-736页

作者： Parmigiani, G Garrett, ES Anbazhagan, R Gabrielson, E Johns Hopkins Univ Sch Med Div Oncol & Baltimore Baltimore MD 21205 USA

Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.

关键词： microarray data analysis mixture distributions molecular classification of cancer

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Mathematical modelling in the post-genome era: understanding genome expression and regulation - a system theoretic approach

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BIOSYSTEMS 2002年第1期65卷 1-18页

作者： Wolkenhauer, O Univ Manchester Control Syst Ctr Dept Elect Engn & Elect Dept Biomol Sci Manchester M60 1QD Lancs England

This paper introduces a mathematical framework for modelling genome expression and regulation. Starting with a philosophical foundation, causation is identified as the principle of explanation of change in the realm of matter. Causation is, therefore, a relationship, not between components, but between changes of states of a system. We subsequently view genome expression (formerly known as 'gene expression') as a dynamic process and model aspects of it as dynamic systems using methodologies developed within the areas of systems and control theory. We begin with the possibly most abstract but general formulation in the setting of category theory. The class of models realised are state-space models, input-output models, autoregressive models or automata. We find that a number of proposed 'gene network' models are, therefore, included in the framework presented here. The conceptual framework that integrates all of these models defines a dynamic system as a family of expression profiles. It becomes apparent that the concept of a 'gene' is less appropriate when considering mathematical models of genome expression and regulation. The main claim of this paper is that we should treat (model) the organisation and regulation of genetic pathways as what they are: dynamic systems. microarray technology allows Lis to generate large sets of time series data and is, therefore, discussed with regard to its use in mathematical modelling of gene expression and regulation. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

关键词： genome expression systems biology microarray data analysis gene network modelling

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microarray analysis using bioinformatics analysis audit trails (BAATs)

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COMPTES RENDUS BIOLOGIES 2003年第10-11期326卷 1083-1087页

作者： Bellgard, M Hunter, A Kenworthy, W Murdoch Univ Ctr Bioinformat & Biol Comp Perth WA 6150 Australia

Bioinformatics analysis plays an integrative role in genomics and functional genomics. The ability to conduct quality managed, hypothesis-driven bioinformatics analysis with the plethora of data available is mandatory. Biological interpretation of this data is dependent on versions of databases, programs and the parameters used. Thus, tracking and auditing the analyses process is important. This paper outlines what we term Bioinformatics analysis Audit Trails (BAATs) and describes YABI, a bioinformatics environment that implements BAATs. YABI can incorporate most bioinformatics tools within the same environment, making it a valuable resource. (C) 2003 Published by Elsevier SAS on behalf of Academie des sciences.

关键词： audit trails bioinformatics analysis pipelines comparative genomic sequence analysis microarray data analysis

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data-dependent kernel machines for microarray data classification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第4期4卷 583-595页

作者： Xiong, Huilin Zhang, Ya Chen, Xue-Wen Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

One important application of gene expression analysis is to classify tissue samples according to their gene expression levels. Gene expression data are typically characterized by high dimensionality and small sample size, which makes the classification task quite challenging. In this paper, we present a data-dependent kernel for microarray data classification. This kernel function is engineered so that the class separability of the training data is maximized. A bootstrapping-based resampling scheme is introduced to reduce the possible training bias. The effectiveness of this adaptive kernel for microarray data classification is illustrated with a k-Nearest Neighbor (KNN) classifier. Our experimental study shows that the data-dependent kernel leads to a significant improvement in the accuracy of KNN classifiers. Furthermore, this kernel-based KNN scheme has been demonstrated to be competitive to, if not better than, more sophisticated classifiers such as Support Vector Machines (SVMs) and the Uncorrelated Linear Discriminant analysis (ULDA) for classifying gene expression data.

关键词： microarray data analysis cancer classification kernel machines kernel optimization bootstrapping resampling

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Identification of potential biomarkers from microarray experiments using multiple criteria optimization

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CANCER MEDICINE 2013年第2期2卷 253-265页

作者： Sanchez-Pena, Matilde L. Isaza, Clara E. Perez-Morales, Jaileene Rodriguez-Padilla, Cristina Castro, Jose M. Cabrera-Rios, Mauricio Univ Puerto Rico Mayaguez Dept Ind Engn Bio IE Lab Mayaguez PR 00680 USA Univ Autonoma Nuevo Leon Immunol & Virol Lab Monterrey Mexico Ohio State Univ Columbus OH 43210 USA

microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedu

关键词： Cancer biomarkers cervical cancer data envelopment analysis microarray data analysis multiple criteria optimization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2004年第4期1卷 181-190页

作者： Ye, JP Li, T Xiong, T Janardan, R Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Florida Int Univ Sch Comp Sci Miami FL 33199 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds);that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.

关键词： microarray data analysis discriminant analysis generalized singular value decomposition classification

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OVA scheme vs. single machine approach in feature selection for microarray datasets

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6th Industrial Conference on data Mining (ICDM 2006)

作者： Ooi, Chia Huey Chetty, Madhu Teng, Shyh Wei Monash Univ Gippsland Sch Informat Technol Churchill Vic 3842 Australia

ISBN: (纸本)3540360360

The large number of genes in microarray data makes feature selection techniques more crucial than ever. From rank-based filter techniques to classifier-based wrapper techniques, many studies have devised their own feature selection techniques for microarray datasets. By combining the OVA (one-vs.-all) approach and differential prioritization in our feature selection technique, we ensure that class-specific relevant features are selected while guarding against redundancy in predictor set at the same time. In this paper we present the OVA version of our differential prioritization-based feature selection technique and demonstrate how it works better than the original SMA (single machine approach) version.

关键词： molecular classification microarray data analysis feature selection

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Mining Breast Cancer Genetic data

Mining Breast Cancer Genetic Data

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9th International Conference on Natural Computation (ICNC)

作者： Mansour, Nashat Zardout, Rouba El-Sibai, Mirvat Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Lebanese Amer Univ Dept Nat Sci Beirut Lebanon

ISBN: (纸本)9781467347143

Analyzing breast cancer gene expression data is a very challenging problem due to the large amount of genes examined. Computational techniques have proved reliable to make sense of large amounts of data like the data obtained from microarray analysis. In this study, we present a method to find a clustering pattern of the genes involved in breast cancer. We design a growing hierarchical self-organizing map (GHSOM) to mine gene microarray data. We have applied GHSOM to 24,481 genes of DNA microarray of breast tumor samples. Our results have revealed 17 genes that are likely to be correlated with four breast cancer marker genes.

关键词： Bioinformatics Growing hierarchical self-organizing map Breast cancer microarray data analysis Clustering Self-organizing map

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Restricted Boltzmann Machines for Unsupervised Feature Selection with Partial Least Square Feature Extractor for microarray datasets 9

Restricted Boltzmann Machines for Unsupervised Feature Selec...

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9th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Sutawika, Lintang Adyuta Wasito, Ito Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781538631720

Feature selection is a key component in microarray data analysis. This is due to the fact that microarray datasets consists of features that are far exceed the number of instances. High dimensional data are also known to contain significant amount of noise and irrelevant variables that do not contribute to classification tasks and may even hinder classification performance. In this paper, a feature selection method which consists of two stages is proposed. At the first step, feature selection is done through a stacked Restricted Boltzmann Machines by means of comparing the error between reconstructed data and the original data. The next stage will use Partial Least Square to extract synthesis features from the previously selected features that will be then used for classification. The performance of the proposed method is done through the classification of ten microarray datasets that are widely used. The proposed model is able to out perform state-of-the-art in 2 datasets, namely 82.11% for GLIOMA and 72.39% for Breast datasets.

关键词： restricted boltzmann machine deep learning microarray data analysis gene expression

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