Literature DB >> 25982164

Cerebrospinal fluid protein dynamic driver network: At the crossroads of brain tumorigenesis.

Zhou Tan1, Rui Liu2, Le Zheng3, Shiying Hao4, Changlin Fu5, Zhen Li4, Xiaohong Deng4, Taichang Jang4, Milton Merchant4, John C Whitin4, Minyi Guo6, Harvey J Cohen4, Lawrence Recht4, Xuefeng B Ling7.   

Abstract

To get a better understanding of the ongoing in situ environmental changes preceding the brain tumorigenesis, we assessed cerebrospinal fluid (CSF) proteome profile changes in a glioma rat model in which brain tumor invariably developed after a single in utero exposure to the neurocarcinogen ethylnitrosourea (ENU). Computationally, the CSF proteome profile dynamics during the tumorigenesis can be modeled as non-smooth or even abrupt state changes. Such brain tumor environment transition analysis, correlating the CSF composition changes with the development of early cellular hyperplasia, can reveal the pathogenesis process at network level during a time before the image detection of the tumors. In our controlled rat model study, matched ENU- and saline-exposed rats' CSF proteomics changes were quantified at approximately 30, 60, 90, 120, 150 days of age (P30, P60, P90, P120, P150). We applied our transition-based network entropy (TNE) method to compute the CSF proteome changes in the ENU rat model and test the hypothesis of the critical transition state prior to impending hyperplasia. Our analysis identified a dynamic driver network (DDN) of CSF proteins related with the emerging tumorigenesis progressing from the non-hyperplasia state. The DDN associated leading network CSF proteins can allow the early detection of such dynamics before the catastrophic shift to the clear clinical landmarks in gliomas. Future characterization of the critical transition state (P60) during the brain tumor progression may reveal the underlying pathophysiology to device novel therapeutics preventing tumor formation. More detailed method and information are accessible through our website at http://translationalmedicine.stanford.edu.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Critical transition; Dynamical driver network (DDN); Network entropy; Transition state; Tumorigenesis progressing

Mesh:

Substances:

Year:  2015        PMID: 25982164     DOI: 10.1016/j.ymeth.2015.05.004

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  6 in total

1.  Detecting the tipping points in a three-state model of complex diseases by temporal differential networks.

Authors:  Pei Chen; Yongjun Li; Xiaoping Liu; Rui Liu; Luonan Chen
Journal:  J Transl Med       Date:  2017-10-26       Impact factor: 5.531

2.  Defining and characterizing the critical transition state prior to the type 2 diabetes disease.

Authors:  Bo Jin; Rui Liu; Shiying Hao; Zhen Li; Chunqing Zhu; Xin Zhou; Pei Chen; Tianyun Fu; Zhongkai Hu; Qian Wu; Wei Liu; Daowei Liu; Yunxian Yu; Yan Zhang; Doff B McElhinney; Yu-Ming Li; Devore S Culver; Shaun T Alfreds; Frank Stearns; Karl G Sylvester; Eric Widen; Xuefeng B Ling
Journal:  PLoS One       Date:  2017-07-07       Impact factor: 3.240

3.  A proteomic clock for malignant gliomas: The role of the environment in tumorigenesis at the presymptomatic stage.

Authors:  Le Zheng; Yan Zhang; Shiying Hao; Lin Chen; Zhen Sun; Chi Yan; John C Whitin; Taichang Jang; Milton Merchant; Doff B McElhinney; Karl G Sylvester; Harvey J Cohen; Lawrence Recht; Xiaoming Yao; Xuefeng B Ling
Journal:  PLoS One       Date:  2019-10-10       Impact factor: 3.240

4.  Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers.

Authors:  Pei Chen; Rui Liu; Luonan Chen; Kazuyuki Aihara
Journal:  Front Genet       Date:  2015-07-28       Impact factor: 4.599

5.  The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system.

Authors:  Pei Chen; Yongjun Li
Journal:  BMC Syst Biol       Date:  2016-08-01

6.  Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers.

Authors:  Rui Liu; Jinzeng Wang; Masao Ukai; Ki Sewon; Pei Chen; Yutaka Suzuki; Haiyun Wang; Kazuyuki Aihara; Mariko Okada-Hatakeyama; Luonan Chen
Journal:  J Mol Cell Biol       Date:  2019-08-19       Impact factor: 6.216

  6 in total

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