Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

[1]  D. Steindler,et al.  Aldehyde dehydrogenase-expressing colon stem cells contribute to tumorigenesis in the transition from colitis to cancer. , 2009, Cancer research.

[2]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[3]  Tao Zhang,et al.  Aldehyde dehydrogenase 1 is a marker for normal and malignant human colonic stem cells (SC) and tracks SC overpopulation during colon tumorigenesis. , 2009, Cancer research.

[4]  Stefano Moretti,et al.  Game Theory applied to gene expression analysis , 2009, 4OR.

[5]  Vito Fragnelli,et al.  A game theoretical approach to the classification problem in gene expression data analysis , 2008, Comput. Math. Appl..

[6]  S. Vajda,et al.  Contribution to the Theory of Games , 1951 .

[7]  S. Drăghici,et al.  Analysis of microarray experiments of gene expression profiling. , 2006, American journal of obstetrics and gynecology.

[8]  Stefano Bonassi,et al.  Identification of low intratumoral gene expression heterogeneity in neuroblastic tumors by genome‐wide expression analysis and game theory , 2008, Cancer.

[9]  W. Weichert,et al.  Cytoplasmic CD24 Expression in Colorectal Cancer Independently Correlates with Shortened Patient Survival , 2005, Clinical Cancer Research.

[10]  D. Choi,et al.  Cancer stem cell markers CD133 and CD24 correlate with invasiveness and differentiation in colorectal adenocarcinoma. , 2009, World journal of gastroenterology.

[11]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Wolfgang Busch,et al.  Omics meet networks - using systems approaches to infer regulatory networks in plants. , 2010, Current opinion in plant biology.

[13]  Terence P. Speed,et al.  Normalization for cDNA microarry data , 2001, SPIE BiOS.

[14]  S. Jackson,et al.  Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis. , 2015, Trends in plant science.

[15]  P. Herrlich,et al.  CD44 in colon cancer. , 1995, European journal of cancer.

[16]  Juan A. Nepomuceno,et al.  An application of the Shapley value to the analysis of co-expression networks , 2018, Appl. Netw. Sci..

[17]  Marco Slikker.  A characterization of the position value* , 2005, Int. J. Game Theory.

[18]  The Prognostic and Clinical Value of CD44 in Colorectal Cancer: A Meta-Analysis , 2019, Front. Oncol..

[19]  Fioravante Patrone,et al.  The class of microarray games and the relevance index for genes , 2007 .

[20]  Songyot Nakariyakul,et al.  A hybrid gene selection algorithm based on interaction information for microarray-based cancer classification , 2019, PloS one.

[21]  Surajit Borkotokey,et al.  A solution concept for network games: The role of multilateral interactions , 2015, Eur. J. Oper. Res..

[22]  Matthew O. Jackson,et al.  Allocation Rules for Network Games , 2003, Games Econ. Behav..

[23]  Sandrine Dudoit,et al.  Classification in microarray experiments , 2003 .

[24]  Vito Fragnelli,et al.  Using coalitional games on biological networks to measure centrality and power of genes , 2010, Bioinform..

[25]  João Pedro de Magalhães,et al.  Gene co-expression analysis for functional classification and gene–disease predictions , 2017, Briefings Bioinform..

[26]  Yoshiyuki Ogata,et al.  Approaches for extracting practical information from gene co-expression networks in plant biology. , 2007, Plant & cell physiology.

[27]  M. Stojkovic,et al.  Phenotypic Characterization of Murine Primitive Hematopoietic Progenitor Cells Isolated on Basis of Aldehyde Dehydrogenase Activity , 2004, Stem cells.

[28]  G Spagnoli,et al.  Prognostic impact of the expression of putative cancer stem cell markers CD133, CD166, CD44s, EpCAM, and ALDH1 in colorectal cancer , 2010, British Journal of Cancer.

[29]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[30]  C. Chen,et al.  The biology and role of CD44 in cancer progression: therapeutic implications , 2018, Journal of Hematology & Oncology.

[31]  Terry Speed,et al.  Normalization of cDNA microarray data. , 2003, Methods.

[32]  Lei Du,et al.  CD44 is of Functional Importance for Colorectal Cancer Stem Cells , 2008, Clinical Cancer Research.

[33]  S. Horvath,et al.  Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks , 2006, BMC Genomics.

[34]  I. B. Borel Rinkes,et al.  ALDH1A1 expression is associated with poor differentiation, ‘right-sidedness’ and poor survival in human colorectal cancer , 2018, PloS one.

[35]  Staffan Persson,et al.  Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. , 2009, Plant, cell & environment.

[36]  Elise A. R. Serin,et al.  Learning from Co-expression Networks: Possibilities and Challenges , 2016, Front. Plant Sci..

[37]  L. Shapley.  A Value for n-person Games , 1988 .

[38]  Surajit Borkotokey,et al.  Network Games: The Cooperative Approach , 2019, Network Theory and Agent-Based Modeling in Economics and Finance.

[39]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[40]  Juan A. Nepomuceno,et al.  A Game Theoretic Neighbourhood-Based Relevance Index , 2017, COMPLEX NETWORKS.

[41]  T. Fitzgerald,et al.  The impact of Aldehyde dehydrogenase 1 expression on prognosis for metastatic colon cancer. , 2014, The Journal of surgical research.

[42]  Oscar Franzén,et al.  PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data , 2019, Database J. Biol. Databases Curation.

[43]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[44]  S. Sarangi,et al.  A Survey of Player-based and Link-based Allocation Rules for Network Games , 2014 .

[45]  Feng Li,et al.  CellMarker: a manually curated resource of cell markers in human and mouse , 2018, Nucleic Acids Res..

[46]  Dennis P. Wall,et al.  Using game theory to detect genes involved in Autism Spectrum Disorder , 2011 .

[47]  Itziar Irigoien,et al.  Identification of differentially expressed genes by means of outlier detection , 2018, BMC Bioinformatics.

[48]  C. Cooper,et al.  Applications of microarray technology in breast cancer research , 2001, Breast Cancer Research.

[49]  P. Chu,et al.  Characterization of a subpopulation of colon cancer cells with stem cell‐like properties , 2009, International journal of cancer.

[50]  D. Edwards,et al.  Statistical Analysis of Gene Expression Microarray Data , 2003 .

[51]  Peter Borm,et al.  On the Position Value for Communication Situations , 1992, SIAM J. Discret. Math..

[52]  H. Wang,et al.  A subpopulation of CD24⁺ cells in colon cancer cell lines possess stem cell characteristics. , 2012, Neoplasma.

[53]  M. Koch,et al.  Expression Analysis of Aldehyde Dehydrogenase 1A1 (ALDH1A1) in Colon and Rectal Cancer in Association with Prognosis and Response to Chemotherapy , 2012, Annals of Surgical Oncology.

[54]  Guang Bai,et al.  Expression of Aldehyde Dehydrogenase 1A1 (ALDH1A1) as a Prognostic Biomarker in Colorectal Cancer Using Immunohistochemistry , 2018, Medical science monitor : international medical journal of experimental and clinical research.

[55]  Marco Slikker,et al.  An axiomatic characterization of the position value for network situations , 2012, Math. Soc. Sci..

[56]  Sara Häggblad Sahlberg,et al.  Evaluation of Cancer Stem Cell Markers CD133, CD44, CD24: Association with AKT Isoforms and Radiation Resistance in Colon Cancer Cells , 2014, PloS one.

[57]  D Bentley,et al.  Highly parallel SNP genotyping. , 2003, Cold Spring Harbor symposia on quantitative biology.