Identification of laryngeal cancer prognostic biomarkers using an inflammatory gene-related, competitive endogenous RNA network
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Qun He2, Linli Tian1, Hao Jiang3, Jiarui Zhang1, Qiang Li4, Yanan Sun1, Jiannan Zhao2, Huijun Li2, Ming Liu1
1Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
2Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital, Harbin Medical University, Harbin, China
3The Affiliated Hongqi Hospital of Mudanjiang Medical University, Mudanjiang, China
4The Second Affiliated Hospital, Harbin Medical University, Harbin, China
Ming Liu, email: firstname.lastname@example.org
Huijun Li, email: email@example.com
Keywords: inflammatory gene, laryngeal cancer, microRNA, competetive endogenous RNA, prognostic biomarker
Received: August 26, 2016 Accepted: November 08, 2016 Published: November 25, 2016
Competitive endogenous RNAs (ceRNAs) act as molecular sponges for microRNAs (miRNAs), and are associated with tumorigenesis in various cancers, including laryngeal cancer (LC). In this work, we constructed an LC-specific inflammatory gene-related ceRNA network (IceNet). In IceNet, ceRNAs targeting inflammation-related genes tended to be network hubs. Additionally, the betweenness centralities of these hub ceRNAs were higher than those of the inflammation-related genes themselves, indicating that the hub ceRNAs in this study played critical roles in communication between IceNet molecules. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that IceNet molecules are associated with multiple cancer-related functions and signaling pathways. Using cFinder software and survival analyses, we identified a potential prognostic module within IceNet that contains 18 mRNAs and a long non-coding RNA (lncRNA), and we effectively stratified patients into high- and low-risk subgroups with different survival outcomes, independent of patient age and tumor grade. This 18-mRNA and one-lncRNA module provides a novel mechanism for potentially improving LC patient prognostic predictions. Applying the module clinically to differentiate high- and low-risk patients could inform therapeutic decision making and ultimately improve patient outcomes. In addition, these results demonstrate the potential importance of IceNet hub ceRNAs in LC development and progression.
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