Literature DB >> 23791722

Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks.

Edwin Wang1, Jinfeng Zou, Naif Zaman, Lenore K Beitel, Mark Trifiro, Miltiadis Paliouras.   

Abstract

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. Crown
Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cancer hallmark; Genome sequencing; Molecular network; Signaling network; Systems biology; Tumor clonal network

Mesh:

Year:  2013        PMID: 23791722     DOI: 10.1016/j.semcancer.2013.06.002

Source DB:  PubMed          Journal:  Semin Cancer Biol        ISSN: 1044-579X            Impact factor:   15.707


  40 in total

1.  RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.

Authors:  Xing Chen; Qiao-Feng Wu; Gui-Ying Yan
Journal:  RNA Biol       Date:  2017-04-19       Impact factor: 4.652

Review 2.  Biomarkers: Delivering on the expectation of molecularly driven, quantitative health.

Authors:  Jennifer L Wilson; Russ B Altman
Journal:  Exp Biol Med (Maywood)       Date:  2017-12-03

3.  Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations.

Authors:  Gauri A Patwardhan; Michal Marczyk; Vikram B Wali; David F Stern; Lajos Pusztai; Christos Hatzis
Journal:  NPJ Breast Cancer       Date:  2021-05-26

4.  A highly efficient approach to protein interactome mapping based on collaborative filtering framework.

Authors:  Xin Luo; Zhuhong You; Mengchu Zhou; Shuai Li; Hareton Leung; Yunni Xia; Qingsheng Zhu
Journal:  Sci Rep       Date:  2015-01-09       Impact factor: 4.379

5.  miRNA and mRNA expression analysis reveals potential sex-biased miRNA expression.

Authors:  Li Guo; Qiang Zhang; Xiao Ma; Jun Wang; Tingming Liang
Journal:  Sci Rep       Date:  2017-01-03       Impact factor: 4.379

6.  KATZLDA: KATZ measure for the lncRNA-disease association prediction.

Authors:  Xing Chen
Journal:  Sci Rep       Date:  2015-11-18       Impact factor: 4.379

Review 7.  The clinical utilization of circulating cell free DNA (CCFDNA) in blood of cancer patients.

Authors:  Yahya I Elshimali; Husseina Khaddour; Marianna Sarkissyan; Yanyuan Wu; Jaydutt V Vadgama
Journal:  Int J Mol Sci       Date:  2013-09-13       Impact factor: 5.923

8.  Identifying potential cancer driver genes by genomic data integration.

Authors:  Yong Chen; Jingjing Hao; Wei Jiang; Tong He; Xuegong Zhang; Tao Jiang; Rui Jiang
Journal:  Sci Rep       Date:  2013-12-18       Impact factor: 4.379

9.  Integrating reductive and synthetic approaches in biology using man-made cell-like compartments.

Authors:  Wataru Aoki; Masato Saito; Ri-ichiroh Manabe; Hirotada Mori; Yoshinori Yamaguchi; Eiichi Tamiya
Journal:  Sci Rep       Date:  2014-04-17       Impact factor: 4.379

10.  Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes.

Authors:  Hongbo Shi; Juan Xu; Guangde Zhang; Liangde Xu; Chunquan Li; Li Wang; Zheng Zhao; Wei Jiang; Zheng Guo; Xia Li
Journal:  BMC Syst Biol       Date:  2013-10-08
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