Dataset Information

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

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.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC6626872 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Zhang Yong Y Liao Gaoming G Bai Jing J Zhang Xinxin X Xu Liwen L Deng Chunyu C Yan Min M Xie Aimin A Luo Tao T Long Zhilin Z Xiao Yun Y Li Xia X

Molecular therapy. Nucleic acids 20190613

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-omic ...[more]

PMID: 31302496

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Dataset Information

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

Publications

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

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