A novel algorithm for network-based prediction of cancer recurrence

被引:9
|
作者
Ruan, Jianhua [1 ,2 ,3 ]
Jahid, Md Jamiul [1 ]
Gu, Fei [2 ]
Lei, Chengwei [3 ]
Huang, Yi-Wen [4 ]
Hsu, Ya-Ting [2 ]
Mutch, David G. [5 ]
Chen, Chun-Liang [2 ]
Kirma, Nameer B. [2 ]
Huang, Tim H-M [2 ,6 ]
机构
[1] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
[2] Univ Texas Hlth Sci Ctr San Antonio, Dept Mol Med, San Antonio, TX 78229 USA
[3] McNeese State Univ, Dept Elect Engn & Comp Sci, Lake Charles, IA USA
[4] Med Coll Wisconsin, Dept Obstet & Gynecol, Milwaukee, WI 53226 USA
[5] Washington Univ, Sch Med, Dept Obstet & Gynecol, St Louis, MO 63110 USA
[6] Univ Texas Hlth Sci Ctr San Antonio, Canc Therapy Fa Res Ctr, San Antonio, TX 78229 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
DNA METHYLATION; GENE; EXPRESSION; IDENTIFICATION; AMPLIFICATION; GENOME; ROBUST;
D O I
10.1016/j.ygeno.2016.07.005
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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 tumorgnesis 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. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:17 / 23
页数:7
相关论文
共 50 条
  • [21] Network-based prediction of protein function
    Sharan, Roded
    Ulitsky, Igor
    Shamir, Ron
    MOLECULAR SYSTEMS BIOLOGY, 2007, 3 (1) : 1 - 13
  • [22] Network-based prediction of protein interactions
    István A. Kovács
    Katja Luck
    Kerstin Spirohn
    Yang Wang
    Carl Pollis
    Sadie Schlabach
    Wenting Bian
    Dae-Kyum Kim
    Nishka Kishore
    Tong Hao
    Michael A. Calderwood
    Marc Vidal
    Albert-László Barabási
    Nature Communications, 10
  • [23] Network-based prediction of drug combinations
    Feixiong Cheng
    István A. Kovács
    Albert-László Barabási
    Nature Communications, 10
  • [24] Network-based drug sensitivity prediction
    Khandakar Tanvir Ahmed
    Sunho Park
    Qibing Jiang
    Yunku Yeu
    TaeHyun Hwang
    Wei Zhang
    BMC Medical Genomics, 13
  • [25] Corrigendum: Network-based prediction of anti-cancer drug combinations
    Jiang, Jue
    Wei, Xuxu
    Lu, Yukang
    Li, Simin
    Xu, Xue
    FRONTIERS IN PHARMACOLOGY, 2024, 15
  • [26] FERAL: network-based classifier with application to breast cancer outcome prediction
    Allahyar, Amin
    de Ridder, Jeroen
    BIOINFORMATICS, 2015, 31 (12) : 311 - 319
  • [27] Improved Network-Based Recommendation Algorithm
    Shan, Xiao-fei
    Mi, Chuan-min
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING (AISE 2014), 2014, : 297 - 301
  • [28] The Recursive Network-Based Routing Algorithm
    Choi, Dongmin
    Chung, Ilyong
    SEPADS'10: PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, PARALLEL AND DISTRIBUTED SYSTEMS, 2010, : 78 - 80
  • [29] A novel recurrent neural network-based prediction system for option trading and hedging
    Quek, C.
    Pasquier, M.
    Kumar, N.
    APPLIED INTELLIGENCE, 2008, 29 (02) : 138 - 151
  • [30] A network-based predictive gene expression signature for recurrence risks in stage Ⅱ colorectal cancer
    YANG Wen-jing
    WANG Hai-bo
    WANG Wen-da
    ZHANG Gan-lin
    YANG Guo-wang
    中国药理学与毒理学杂志, 2019, (10) : 890 - 890