@article{cff214b86771437293295873c5db3fb5,
title = "Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer",
abstract = "Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD.",
author = "Chao Kong and Yao, {Yu Xiang} and Bing, {Zhi Tong} and Guo, {Bing Hui} and Liang Huang and Huang, {Zi Gang} and Lai, {Ying Cheng}",
note = "Funding Information: ZGH acknowledges supports from NNSF of China under Grants (Nos. 11975178, and 61431012), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2020JM-058), Fundamental Research Funds for the Central Universities (sxzd022020012), and support of K. C. Wong Education Foundation. LH acknowledges supports from NNSF of China under Grants Nos. 11775101, and 11422541. YCL would like to acknowledge support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through Grant No. N00014-16-1-2828. BHG acknowledges support from Artificial Intelligence Project (2018AAA0102301). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Prof. Celso Grebogi and Prof. Lei Yang for helpful discussions. Publisher Copyright: {\textcopyright} 2020 Kong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
year = "2020",
month = may,
doi = "10.1371/journal.pcbi.1007793",
language = "English (US)",
volume = "16",
journal = "PLoS computational biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "5",
}