Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs

doi:10.1371/journal.pcbi.1007540

CSpace > 应用数学研究所

	Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs
	Wang, Yongcui 1,2; Chen, Shilong 1; Chen, Luonan 3; Wang, Yong4,5
	2019-12-01
发表期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
卷号	15 期号:12 页码:20
摘要	Long 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 .
DOI	10.1371/journal.pcbi.1007540
收录类别	SCI
语种	英语
资助项目	National 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研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000507310800007
出版者	PUBLIC LIBRARY SCIENCE
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50589
专题	应用数学研究所
通讯作者	Wang, Yongcui; Wang, Yong
作者单位	1.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
推荐引用方式 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.