Oncotarget

Research Papers:

Construction and analysis of lncRNA-lncRNA synergistic networks to reveal clinically relevant lncRNAs in cancer

Yongsheng Li, Juan Chen, Jinwen Zhang, Zishan Wang, Tingting Shao, Chunjie Jiang, Juan Xu and Xia Li _

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Oncotarget. 2015; 6:25003-25016. https://doi.org/10.18632/oncotarget.4660

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Abstract

Yongsheng Li1,*, Juan Chen1,*, Jinwen Zhang1,*, Zishan Wang1, Tingting Shao1, Chunjie Jiang1, Juan Xu1, Xia Li1

1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China

*These authors have contributed equally to this work

Correspondence to:

Xia Li, e-mail: [email protected]

Juan Xu, e-mail: [email protected]

Keywords: long non-coding RNA, network hub, functional synergistic network, hallmark of cancer, prognostic module

Received: April 07, 2015     Accepted: July 15, 2015     Published: July 28, 2015

ABSTRACT

Long non-coding RNAs (lncRNAs) play key roles in diverse biological processes. Moreover, the development and progression of cancer often involves the combined actions of several lncRNAs. Here we propose a multi-step method for constructing lncRNA-lncRNA functional synergistic networks (LFSNs) through co-regulation of functional modules having three features: common coexpressed genes of lncRNA pairs, enrichment in the same functional category and close proximity within protein interaction networks. Applied to three cancers, we constructed cancer-specific LFSNs and found that they exhibit a scale free and modular architecture. In addition, cancer-associated lncRNAs tend to be hubs and are enriched within modules. Although there is little synergistic pairing of lncRNAs across cancers, lncRNA pairs involved in the same cancer hallmarks by regulating same or different biological processes. Finally, we identify prognostic biomarkers within cancer lncRNA expression datasets using modules derived from LFSNs. In summary, this proof-of-principle study indicates synergistic lncRNA pairs can be identified through integrative analysis of genome-wide expression data sets and functional information.


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