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Recoding RNA editing of AZIN1 predisposes to hepatocellular carcinoma

TLDR
This article showed that adenosine-to-inosine (A→I) RNA editing of AZIN1 (encoding antizyme inhibitor 1) is increased in hepatocellular carcinoma (HCC) specimens.
Abstract
A better understanding of human hepatocellular carcinoma (HCC) pathogenesis at the molecular level will facilitate the discovery of tumor-initiating events. Transcriptome sequencing revealed that adenosine-to-inosine (A→I) RNA editing of AZIN1 (encoding antizyme inhibitor 1) is increased in HCC specimens. A→I editing of AZIN1 transcripts, specifically regulated by ADAR1 (encoding adenosine deaminase acting on RNA-1), results in a serine-to-glycine substitution at residue 367 of AZIN1, located in β-strand 15 (β15) and predicted to cause a conformational change, induced a cytoplasmic-to-nuclear translocation and conferred gain-of-function phenotypes that were manifested by augmented tumor-initiating potential and more aggressive behavior. Compared with wild-type AZIN1 protein, the edited form has a stronger affinity to antizyme, and the resultant higher AZIN1 protein stability promotes cell proliferation through the neutralization of antizyme-mediated degradation of ornithine decarboxylase (ODC) and cyclin D1 (CCND1). Collectively, A→I RNA editing of AZIN1 may be a potential driver in the pathogenesis of human cancers, particularly HCC.

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Journal ArticleDOI

A-to-I editing of coding and non-coding RNAs by ADARs

TL;DR: Adenosine deaminases acting on RNA (ADARs) convert adenosine to inosine in double-stranded RNA, leading to reduced expression or altered function of mature miRNAs and certain microRNA precursors.
Journal ArticleDOI

Role of RNA modifications in cancer.

TL;DR: The evidence that RNA modification pathways are misregulated in cancer and suggests that they may be ideal targets for cancer therapy is presented.
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RADAR: a rigorously annotated database of A-to-I RNA editing

TL;DR: RADAR includes a comprehensive collection of A-to-I RNA editing sites identified in humans (Homo sapiens), mice and flies (Mus musculus) and flies, together with extensive manually curated annotations for each editing site.
Journal ArticleDOI

A-to-I RNA editing — immune protector and transcriptome diversifier

TL;DR: Next-generation sequencing technologies have enabled the comparison of editomes from multiple individuals and from multiple species and the results have changed the understanding of the extent and distribution of A-to-I editing and its role in evolution and disease.
References
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Journal ArticleDOI

Global cancer statistics

TL;DR: A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination, and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake.
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Fast and accurate short read alignment with Burrows–Wheeler transform

TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
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TL;DR: There are striking variations in the risk of different cancers by geographic area, most of the international variation is due to exposure to known or suspected risk factors related to lifestyle or environment, and provides a clear challenge to prevention.
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Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

TL;DR: The results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
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