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.2020 Jan 8;48(D1):D956-D963.
doi: 10.1093/nar/gkz711.

ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types

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ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types

Jiang Li et al. Nucleic Acids Res..

Abstract

Numerous studies indicate that non-coding RNAs (ncRNAs) have critical functions across biological processes, and single-nucleotide polymorphisms (SNPs) could contribute to diseases or traits through influencing ncRNA expression. However, the associations between SNPs and ncRNA expression are largely unknown. Therefore, genome-wide expression quantitative trait loci (eQTL) analysis to assess the effects of SNPs on ncRNA expression, especially in multiple cancer types, will help to understand how risk alleles contribute toward tumorigenesis and cancer development. Using genotype data and expression profiles of ncRNAs of >8700 samples from The Cancer Genome Atlas (TCGA), we developed a computational pipeline to systematically identify ncRNA-related eQTLs (ncRNA-eQTLs) across 33 cancer types. We identified a total of 6 133 278 and 721 122 eQTL-ncRNA pairs in cis-eQTL and trans-eQTL analyses, respectively. Further survival analyses identified 8312 eQTLs associated with patient survival times. Furthermore, we linked ncRNA-eQTLs to genome-wide association study (GWAS) data and found 262 332 ncRNA-eQTLs overlapping with known disease- and trait-associated loci. Finally, a user-friendly database, ncRNA-eQTL (http://ibi.hzau.edu.cn/ncRNA-eQTL), was developed for free searching, browsing and downloading of all ncRNA-eQTLs. We anticipate that such an integrative and comprehensive resource will improve our understanding of the mechanistic basis of human complex phenotypic variation, especially for ncRNA- and cancer-related studies.

© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Figures

Figure 1.
Figure 1.
Flowchart of the ncRNA-eQTL database. (A) Data collection of sequencing, genotype and GWAS data. (B) Processing of sequencing and gene expression data. (C) Database content of four types of eQTLs (cis-eQTLs, trans-eQTLs, survival-eQTLs, GWAS-eQTLs).
Figure 2.
Figure 2.
Case study of eQTLs in survival-eQTLs and GWAS-eQTLs. (A) nc-eQTL rs10965295 and rs3131997 affect patient overall survival times in LGG. (B) egene CDKN2B-AS1 of rs10965295 and egene hsa-miR-5699–5p of rs3131997 were significantly differentially expressed among genotypes in LGG. (C) nc-eQTL rs17191861 located in BRCA GWAS locus. (D) egeneLINC01235 of rs17191861 was significantly differentially expressed in paired tumour and normal samples.
Figure 3.
Figure 3.
Web interface of the ncRNA-eQTL database. (A) ncRNA-eQTL database header with navigation bar. (B) Four different searching modules we offer. (C) The results of a quick search. (D) Heat map of cis-eQTLs across 33 cancer types. The colour of each box indicates the values of the correlation coefficient (r). (E) Heat map of trans-eQTLs across 33 cancer types.
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