title: Reverse engineering of modified genes by Bayesian network analysis defines molecular determinants critical to the development of glioblastoma.
abstract: In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and 'key genes' within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96-100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential 'hubs of activity'. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several 'key genes' may be required for the development of glioblastoma. Further studies are needed to validate these 'key genes' as useful tools for early detection and novel therapeutic options for these tumors.
authors.affiliation: Department of Environmental and Occupational Health, Florida International University, Miami, Florida, USA.
authors.forename: Brian W
authors.initials: BW
authors.lastname: Kunkle
authors.forename: Changwon
authors.initials: C
authors.lastname: Yoo
authors.forename: Deodutta
authors.initials: D
authors.lastname: Roy
mesh_terms: D001499:Bayes Theorem; D063646:Carcinogenesis / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D018450:Disease Progression; D005260:Female; D053263:Gene Regulatory Networks; D052138:Genes, Neoplasm / Q000235:genetics*; D005909:Glioblastoma / Q000235:genetics* / Q000473:pathology*; D006801:Humans; D008297:Male; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D060787:Neoplasm Grading; D015203:Reproducibility of Results; D059467:Transcriptome