Oncotarget

Research Papers:

Systemically identifying and prioritizing risk lncRNAs through integration of pan-cancer phenotype associations

Chaohan Xu, Rui Qi, Yanyan Ping, Jie Li, Hongying Zhao, Li Wang, Michael Yifei Du, Yun Xiao and Xia Li _

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Oncotarget. 2017; 8:12041-12051. https://doi.org/10.18632/oncotarget.14510

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Abstract

Chaohan Xu1,*, Rui Qi1,*, Yanyan Ping1,*, Jie Li1, Hongying Zhao1, Li Wang1, Michael Yifei Du3, Yun Xiao1,2, Xia Li1

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

2Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, China

3Weston High School of Massachusetts, Massachusetts, USA

*These authors have contributed equally to this work

Correspondence to:

Xia Li, email: [email protected]

Yun Xiao, email: [email protected]

Keywords: disease phenotype association, risk lncRNA, pan cancer, identification and prioritization

Received: July 15, 2016     Accepted: December 12, 2016     Published: January 05, 2017

ABSTRACT

LncRNAs have emerged as a major class of regulatory molecules involved in normal cellular physiology and disease, our knowledge of lncRNAs is very limited and it has become a major research challenge in discovering novel disease-related lncRNAs in cancers. Based on the assumption that diverse diseases with similar phenotype associations show similar molecular mechanisms, we presented a pan-cancer network-based prioritization approach to systematically identify disease-specific risk lncRNAs by integrating disease phenotype associations. We applied this strategy to approximately 2800 tumor samples from 14 cancer types for prioritizing disease risk lncRNAs. Our approach yielded an average area under the ROC curve (AUC) of 80.66%, with the highest AUC (98.14%) for medulloblastoma. When evaluated using leave-one-out cross-validation (LOOCV) for prioritization of disease candidate genes, the average AUC score of 97.16% was achieved. Moreover, we demonstrated the robustness as well as the integrative importance of this approach, including disease phenotype associations, known disease genes and the numbers of cancer types. Taking glioblastoma multiforme as a case study, we identified a candidate lncRNA gene SNHG1 as a novel disease risk factor for disease diagnosis and prognosis. In summary, we provided a novel lncRNA prioritization approach by integrating pan-cancer phenotype associations that could help researchers better understand the important roles of lncRNAs in human cancers.


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