Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer

Chao Kong; Yu-Xiang Yao; Zhi-Tong Bing; Bing-Hui Guo; Liang Huang; Zi-Gang Huang; Ying-Cheng Lai

doi:10.1371/journal.pcbi.1007793

Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer

PLoS Comput Biol. 2020 May 19;16(5):e1007793. doi: 10.1371/journal.pcbi.1007793. eCollection 2020 May.

Authors

Chao Kong^{1

2

3}, Yu-Xiang Yao³, Zhi-Tong Bing^{4

5

6}, Bing-Hui Guo⁷, Liang Huang³, Zi-Gang Huang^{1

1}, Ying-Cheng Lai^{8

9}

Affiliations

¹ The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P. R. China.
² National Engineering Research Center for Healthcare Devices. Guangzhou, Guangdong, P.R. China.
³ Institute of Computational Physics and Complex Systems, School of Physical Science and Technology, Lanzhou University, Lanzhou, China.
⁴ Evidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
⁵ Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.
⁶ Department of Computational Physics, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
⁷ Beijing Advanced Innovation Center for Big Data and Brain Computing, LMIB and School of Mathematics and System Sciences, Beihang University, Beijing, China.
⁸ School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, United States of America.
⁹ Department of Physics, Arizona State University, Tempe, Arizona, United States of America.

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adenocarcinoma of Lung / genetics*
Biomarkers, Tumor / genetics
Computational Biology / methods
Databases, Genetic
Disease Progression
Gene Expression Profiling / methods*
Gene Expression Regulation, Neoplastic / genetics
Gene Regulatory Networks / genetics*
Humans
Kaplan-Meier Estimate
Lung Neoplasms / genetics
MicroRNAs / genetics
Prognosis
RNA, Long Noncoding / genetics
RNA, Messenger / genetics
Transcriptome / genetics

Substances

Biomarkers, Tumor
MicroRNAs
RNA, Long Noncoding
RNA, Messenger

Grants and funding

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.