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Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs
Wang, Yongcui1,2; Chen, Shilong1; Chen, Luonan3; Wang, Yong4,5
2019-12-01
Source PublicationPLOS COMPUTATIONAL BIOLOGY
ISSN1553-734X
Volume15Issue:12Pages:20
AbstractLong noncoding RNA (lncRNA) transcripts have emerging impacts in cancer studies, which suggests their potential as novel therapeutic agents. However, the molecular mechanism behind their treatment effects is still unclear. Here, we designed a computational model to Associate LncRNAs with Anti-Cancer Drugs (ALACD) based on a bilevel optimization model, which optimized the gene signature overlap in the upper level and imputed the missing lncRNA-gene association in the lower level. ALACD predicts genes coexpressed with lncRNAs mean while matching drug's gene signatures. This model allows us to borrow the target gene information of small molecules to understand the mechanisms of action of lncRNAs and their roles in cancer. The ALACD model was systematically applied to the 10 cancer types in The Cancer Genome Atlas (TCGA) that had matched lncRNA and mRNA expression data. Cancer type-specific lncRNAs and associated drugs were identified. These lncRNAs show significantly different expression levels in cancer patients. Follow-up functional and molecular pathway analysis suggest the gene signatures bridging drugs and lncRNAs are closely related to cancer development. Importantly, patient survival information and evidence from the literature suggest that the lncRNAs and drug-lncRNA associations identified by the ALACD model can provide an alternative choice for cancer targeting treatment and potential cancer pognostic biomarkers. The ALACD model is freely available at .
DOI10.1371/journal.pcbi.1007540
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11671396] ; National Natural Science Foundation of China[31270270] ; National Natural Science Foundation of China[61671444] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[11871463] ; Qinghai Sciences and Technology Department for Basic Research Program[2017-ZJ-Y14] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB13050100] ; National Key Research and Development Program of China[2017YFC0908400]
WOS Research AreaBiochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Mathematical & Computational Biology
WOS IDWOS:000507310800007
PublisherPUBLIC LIBRARY SCIENCE
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/50589
Collection应用数学研究所
Corresponding AuthorWang, Yongcui; Wang, Yong
Affiliation1.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining, Peoples R China
2.Chinese Acad Sci, Northwest Inst Plateau Biol, Qinghai Prov Key Lab Crop Mol Breeding, Xining, Peoples R China
3.Chinese Acad Sci, Innovat Ctr Cell Signaling Network, Inst Biochem & Cell Biol, Key Lab Syst Biol,Shanghai Inst Biol Sci, Shanghai, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, CEMS, NCMIS,MDIS, Beijing, Peoples R China
5.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yongcui,Chen, Shilong,Chen, Luonan,et al. Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs[J]. PLOS COMPUTATIONAL BIOLOGY,2019,15(12):20.
APA Wang, Yongcui,Chen, Shilong,Chen, Luonan,&Wang, Yong.(2019).Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs.PLOS COMPUTATIONAL BIOLOGY,15(12),20.
MLA Wang, Yongcui,et al."Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs".PLOS COMPUTATIONAL BIOLOGY 15.12(2019):20.
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