Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers

Yong Zhang; Gaoming Liao; Jing Bai; Xinxin Zhang; Liwen Xu; Chunyu Deng; Min Yan; Aimin Xie; Tao Luo; Zhilin Long; Yun Xiao; Xia Li

doi:10.1016/j.omtn.2019.05.030

Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers

Mol Ther Nucleic Acids. 2019 Sep 6:17:362-373. doi: 10.1016/j.omtn.2019.05.030. Epub 2019 Jun 13.

Authors

Yong Zhang¹, Gaoming Liao¹, Jing Bai¹, Xinxin Zhang¹, Liwen Xu¹, Chunyu Deng¹, Min Yan¹, Aimin Xie¹, Tao Luo¹, Zhilin Long¹, Yun Xiao², Xia Li³

Affiliations

¹ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
² College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China. Electronic address: xiaoyun@ems.hrbmu.edu.cn.
³ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China. Electronic address: lixia@hrbmu.edu.cn.

Abstract

The accumulation of somatic driver mutations in the human genome enables cells to gradually acquire a growth advantage and contributes to tumor development. Great efforts on protein-coding cancer drivers have yielded fruitful discoveries and clinical applications. However, investigations on cancer drivers in non-coding regions, especially long non-coding RNAs (lncRNAs), are extremely scarce due to the limitation of functional understanding. Thus, to identify driver lncRNAs integrating multi-omics data in human cancers, we proposed a computational framework, DriverLncNet, which dissected the functional impact of somatic copy number alteration (CNA) of lncRNAs on regulatory networks and captured key functional effectors in dys-regulatory networks. Applying it to 5 cancer types from The Cancer Genome Atlas (TCGA), we portrayed the landscape of 117 driver lncRNAs and revealed their associated cancer hallmarks through their functional effectors. Moreover, lncRNA RP11-571M6.8 was detected to be highly associated with immunotherapeutic targets (PD-1, PD-L1, and CTLA-4) and regulatory T cell infiltration level and their markers (IL2RA and FCGR2B) in glioblastoma multiforme, highlighting its immunosuppressive function. Meanwhile, a high expression of RP11-1020A11.1 in bladder carcinoma was predictive of poor survival independent of clinical characteristics, and CTD-2256P15.2 in lung adenocarcinoma responded to the sensitivity of methyl ethyl ketone (MEK) inhibitors. In summary, this study provided a framework to decipher the mechanisms of tumorigenesis from driver lncRNA level, established a new landscape of driver lncRNAs in human cancers, and offered potential clinical implications for precision oncology.

Keywords: cancer drivers; cancer hallmarks; copy number alterations; immunosuppression; long non-coding RNAs.