title: A novel algorithm for network-based prediction of cancer recurrence.
abstract: To develop accurate prognostic models is one of the biggest challenges in "omics"-based cancer research. Here, we propose a novel computational method for identifying dysregulated gene subnetworks as biomarkers to predict cancer recurrence. Applying our method to the DNA methylome of endometrial cancer patients, we identified a subnetwork consisting of differentially methylated (DM) genes, and non-differentially methylated genes, termed Epigenetic Connectors (EC), that are topologically important for connecting the DM genes in a protein-protein interaction network. The ECs are statistically significantly enriched in well-known tumorgenesis and metastasis pathways, and include known epigenetic regulators. Importantly, combining the DMs and ECs as features using a novel random walk procedure, we constructed a support vector machine classifier that significantly improved the prediction accuracy of cancer recurrence and outperformed several alternative methods, demonstrating the effectiveness of our network-based approach.
authors.affiliation: Department of Computer Science, University of Texas, San Antonio, TX, USA; Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA; Department of Electrical Engineering and Computer Science, McNeese State University, Lake Charles, LA, USA. Electronic address: jianhua.ruan@utsa.edu.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ruan
authors.affiliation: Department of Computer Science, University of Texas, San Antonio, TX, USA.
authors.forename: Md Jamiul
authors.initials: MJ
authors.lastname: Jahid
authors.affiliation: Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA.
authors.forename: Fei
authors.initials: F
authors.lastname: Gu
authors.affiliation: Department of Electrical Engineering and Computer Science, McNeese State University, Lake Charles, LA, USA.
authors.forename: Chengwei
authors.initials: C
authors.lastname: Lei
authors.affiliation: Department of Obstetrics and Gynecology, Medical College of Wisconsin, Milwaukee, WI, USA.
authors.forename: Yi-Wen
authors.initials: YW
authors.lastname: Huang
authors.affiliation: Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA.
authors.forename: Ya-Ting
authors.initials: YT
authors.lastname: Hsu
authors.affiliation: Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: David G
authors.initials: DG
authors.lastname: Mutch
authors.affiliation: Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA.
authors.forename: Chun-Liang
authors.initials: CL
authors.lastname: Chen
authors.affiliation: Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA.
authors.forename: Nameer B
authors.initials: NB
authors.lastname: Kirma
authors.affiliation: Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA; Cancer Therapy & Research Center, University of Texas Health Science Center, San Antonio, TX, USA. Electronic address: huangt3@uthscsa.edu.
authors.forename: Tim H-M
authors.initials: TH
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor; D018899:CpG Islands; D019175:DNA Methylation; D004273:DNA, Neoplasm; D016889:Endometrial Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D057890:Epigenomics; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008957:Models, Genetic; D009364:Neoplasm Recurrence, Local; D011379:Prognosis; D054730:Protein Interaction Domains and Motifs; D017422:Sequence Analysis, DNA
chemical_list: D014408:Biomarkers, Tumor; D004273:DNA, Neoplasm