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lnCaNet: pan-cancer co-expression network for human lncRNA and cancer genes
Journal article   Peer reviewed

lnCaNet: pan-cancer co-expression network for human lncRNA and cancer genes

Yining Liu and Min Zhao
Bioinformatics, Vol.32(10), pp.1595-1597
2016
url
https://doi.org/10.1093/bioinformatics/btw017View
Published Version

Abstract

Summary: Thousands of human long non-coding RNAs (lncRNAs) have been identified in cancers and played important roles in a wide range of tumorigenesis. However, the functions of vast majority of human lncRNAs are still elusive. Emerging studies revealed that the expression level of majority lncRNAs shows discordant expression pattern with their protein-coding gene neighbors in various model organisms. Therefore, it may be useful to infer lncRNAs' potential biological function in cancer development by more comprehensive functional views of co-expressed cancer genes beyond mere physical proximity of genes. To this aim, we performed thorough searches and analyses of the interactions between lncRNA and non-neighboring cancer genes and provide a comprehensive co-expression data resource, LnCaNet. In current version, LnCaNet contains the pre-computed 8 494 907 significant co-expression pairs of 9641 lncRNAs and 2544 well-classified cancer genes in 2922 matched TCGA samples. In detail, we integrated 10 cancer gene lists from public database and calculate the co-expression with all the lncRNAs in 11 TCGA cancer types separately. Based on the resulted 110 co-expression networks, we identified 17 common regulatory pairs related to extracellular space shared in 11 cancers. We expect LnCaNet will enable researcher to explore lncRNA expression pattern, their affected cancer genes and pathways, biological significance in the context of specific cancer types and other useful annotation related to particular kind of lncRNA-cancer gene interaction.

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Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability

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