A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

[1]  Michael K. Ng,et al.  Multiple networks modules identification by a multi-dimensional Markov chain method , 2015, Network Modeling Analysis in Health Informatics and Bioinformatics.

[2]  Robin Foà,et al.  BIRC3 disruption and Copy Number Aberrations in Chronic Lymphocytic Leukemia (CLL) Patients with 11q Deletion , 2014 .

[3]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[4]  Xiaoke Ma,et al.  Long non-coding RNAs function annotation : a global prediction method based on bicolored networks , 2013 .

[5]  Martin J. van den Bent,et al.  Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.

[6]  Xiaoke Ma,et al.  Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks , 2012, Nucleic acids research.

[7]  Roded Sharan,et al.  Associating Genes and Protein Complexes with Disease via Network Propagation , 2010, PLoS Comput. Biol..

[8]  S. Bae,et al.  Tumor suppressor activity of RUNX3 , 2004, Oncogene.

[9]  M. Oti,et al.  The modular nature of genetic diseases , 2006, Clinical genetics.

[10]  Do-Hyun Nam,et al.  Wnt activation is implicated in glioblastoma radioresistance , 2012, Laboratory Investigation.

[11]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[12]  Gen Tamura,et al.  Promoter hypermethylation of RASSF1A and RUNX3 genes as an independent prognostic prediction marker in surgically resected non-small cell lung cancers. , 2007, Lung cancer.

[13]  Sol Efroni,et al.  Biomarker robustness reveals the PDGF network as driving disease outcome in ovarian cancer patients in multiple studies , 2012, BMC Systems Biology.

[14]  Yung-Yu Chuang,et al.  Affinity aggregation for spectral clustering , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Wei Tang,et al.  Clustering with Multiple Graphs , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[16]  Y. Moreau,et al.  Computational tools for prioritizing candidate genes: boosting disease gene discovery , 2012, Nature Reviews Genetics.

[17]  P. Robinson,et al.  Walking the interactome for prioritization of candidate disease genes. , 2008, American journal of human genetics.

[18]  Jason Y. Liu,et al.  Analysis of protein sequence and interaction data for candidate disease gene prediction , 2006, Nucleic acids research.

[19]  J. Uhm Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2009 .

[20]  Mitchel S. Berger,et al.  Inhibition of PI3K/mTOR pathways in glioblastoma and implications for combination therapy with temozolomide. , 2011, Neuro-oncology.

[21]  Hui Zhang,et al.  SHh-Gli1 signaling pathway promotes cell survival by mediating baculoviral IAP repeat-containing 3 (BIRC3) gene in pancreatic cancer cells , 2016, Tumor Biology.

[22]  和田 学,et al.  Frequent loss of RUNX3 gene expression in human bile duct and pancreatic cancer cell lines , 2004 .

[23]  Olof Akre,et al.  Promoter methylation in APC, RUNX3, and GSTP1 and mortality in prostate cancer patients. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  Jianhua Ruan,et al.  A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity , 2013, Bioinform..

[25]  B. Snel,et al.  Predicting disease genes using protein–protein interactions , 2006, Journal of Medical Genetics.

[26]  Il-Jin Kim,et al.  Promoter hypermethylation downregulates RUNX3 gene expression in colorectal cancer cell lines , 2004, Oncogene.

[27]  Pablo R. Freire,et al.  Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis. , 2013, Neuro-oncology.

[28]  Mei Mei,et al.  Downregulation of miR-21 inhibits EGFR pathway and suppresses the growth of human glioblastoma cells independent of PTEN status , 2010, Laboratory Investigation.

[29]  J. Kuo,et al.  Activation of multiple ERBB family receptors mediates glioblastoma cancer stem-like cell resistance to EGFR-targeted inhibition. , 2012, Neoplasia.

[30]  B. Ross,et al.  Mathematical Modeling of PDGF-Driven Glioblastoma Reveals Optimized Radiation Dosing Schedules , 2014, Cell.

[31]  Shuli Kang,et al.  Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network , 2011, Nucleic acids research.

[32]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[33]  G. Tortora,et al.  TAK1-regulated expression of BIRC3 predicts resistance to preoperative chemoradiotherapy in oesophageal adenocarcinoma patients , 2015, British Journal of Cancer.

[34]  Johan A. K. Suykens,et al.  Optimized data fusion for K-means Laplacian clustering , 2011, Bioinform..

[35]  L. Chin,et al.  Malignant astrocytic glioma: genetics, biology, and paths to treatment. , 2007, Genes & development.

[36]  Tao Jiang,et al.  Uncover disease genes by maximizing information flow in the phenome–interactome network , 2011, Bioinform..

[37]  Timothy C Ryken,et al.  Trends in brain cancer incidence and survival in the United States: Surveillance, Epidemiology, and End Results Program, 1973 to 2001. , 2006, Neurosurgical focus.

[38]  Yunming Ye,et al.  MultiComm: Finding Community Structure in Multi-Dimensional Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[39]  E. Snitkin,et al.  Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network , 2009, Genome Biology.

[40]  Christian von Mering,et al.  STRING: known and predicted protein–protein associations, integrated and transferred across organisms , 2004, Nucleic Acids Res..

[41]  Xing Chen,et al.  Drug-target interaction prediction by random walk on the heterogeneous network. , 2012, Molecular bioSystems.

[42]  Syed Haider,et al.  International Cancer Genome Consortium Data Portal—a one-stop shop for cancer genomics data , 2011, Database J. Biol. Databases Curation.

[43]  Michael K. Ng,et al.  Functional Module Analysis for Gene Coexpression Networks with Network Integration , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[44]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..