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Computational Assessment of the Expression-modulating Potential for Non-coding Variants.

ABSTRACT: Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

SUBMITTER: Shi FY

PROVIDER: S-EPMC10787178 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Computational Assessment of the Expression-modulating Potential for Non-coding Variants.

Shi Fang-Yuan FY Wang Yu Y Huang Dong D Liang Yu Y Liang Nan N Chen Xiao-Wei XW Gao Ge G

Genomics, proteomics & bioinformatics 20211207 3

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the ...[more]

PMID: 34890839

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Project description:Recent studies show that long non-coding RNAs (lncRNAs) play crucial roles in human cancers. However, functional lncRNAs and their downstream mechanisms are largely unknown in the molecular pathogenesis of intrahepatic cholangiocarcinoma (ICC) and its progression. In the present study, we performed transcriptomic profiling of five ICC and paired adjacent noncancerous tissues (N) using lncRNA and mRNA microarrays to identify relevant biomarkers in ICC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the microarray results. We sought correlations between the expression levels of lncRNAs and those of target genes. Clinicopathological characteristics and overall survival were compared using the t-test and the Kaplan–Meier method, respectively. A total of 3,054 and 2,111 lncRNAs were significantly up- and down-regulated(fold change?2, p?0.05) in ICC tissues compared to the adjacent NT samples. Bioinformatic analysis indicated that most such genes were related to carcinogenesis, hepatic system disease, and signal transduction. Positive correlations were evident between four pairs of lncRNAs and target mRNAs (RNA43085 and SULF1, RNA47504 and KDM8, RNA58630 and PCSK6, and RNA40057 and CYP2D6). In addition, some lncRNAs and mRNAs were significantly associated with clinicopathological characteristics. The cumulative overall survival rate was significantly associated with low-level expression of CYP2D6 (p=0.005) and PCSK6 (p=0.038). And patients with high expression levels of CYP2D6 and RNA40057 have significant better prognosis (p=0.014). Our results suggested that lncRNA expression profile in ICC tissues is profoundly different from that in NT samples. The lncRNA signature could be used as a biomarker for the prognosis of patients with ICC. Furthermore, the combination of lncRNA and mRNA can reliably predict the survival. The lncRNA expression profiles of cancer and adjacent normal tissues form 5 ICC patients were studied by microarray and an combination of lncRNA and mRNA could be used as a biomarker for the prognosis of patients with ICC

Dataset Information

Computational Assessment of the Expression-modulating Potential for Non-coding Variants.

Publications

Computational Assessment of the Expression-modulating Potential for Non-coding Variants.

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