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A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress

Di Liddo, Antonella and de Oliveira Freitas Machado, Camila and Fischer, Sandra and Ebersberger, Stefanie and Heumüller, Andreas W. and Weigand, Julia E. and Müller-McNicoll, Michaela and Zarnack, Kathi (2019):
A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress.
In: Journal of molecular cell biology, 11 (10), pp. 829-844. Oxford Academic, ISSN 1759-4685,
DOI: 10.1093/jmcb/mjz094,
[Article]

Abstract

Hypoxia is associated with several diseases, including cancer. Cells that are deprived of adequate oxygen supply trigger transcriptional and post-transcriptional responses, which control cellular pathways such as angiogenesis, proliferation, and metabolic adaptation. Circular RNAs (circRNAs) are a novel class of mainly non-coding RNAs, which have been implicated in multiple cancers and attract increasing attention as potential biomarkers. Here, we characterize the circRNA signatures of three different cancer cell lines from cervical (HeLa), breast (MCF-7), and lung (A549) cancer under hypoxia. In order to reliably detect circRNAs, we integrate available tools with custom approaches for quantification and statistical analysis. Using this consolidated computational pipeline, we identify ~12000 circRNAs in the three cancer cell lines. Their molecular characteristics point to an involvement of complementary RNA sequences as well as trans-acting factors in circRNA biogenesis, such as the RNA-binding protein HNRNPC. Notably, we detect a number of circRNAs that are more abundant than their linear counterparts. In addition, 64 circRNAs significantly change in abundance upon hypoxia, in most cases in a cell type-specific manner. In summary, we present a comparative circRNA profiling in human cancer cell lines, which promises novel insights into the biogenesis and function of circRNAs under hypoxic stress.

Item Type: Article
Erschienen: 2019
Creators: Di Liddo, Antonella and de Oliveira Freitas Machado, Camila and Fischer, Sandra and Ebersberger, Stefanie and Heumüller, Andreas W. and Weigand, Julia E. and Müller-McNicoll, Michaela and Zarnack, Kathi
Title: A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress
Language: English
Abstract:

Hypoxia is associated with several diseases, including cancer. Cells that are deprived of adequate oxygen supply trigger transcriptional and post-transcriptional responses, which control cellular pathways such as angiogenesis, proliferation, and metabolic adaptation. Circular RNAs (circRNAs) are a novel class of mainly non-coding RNAs, which have been implicated in multiple cancers and attract increasing attention as potential biomarkers. Here, we characterize the circRNA signatures of three different cancer cell lines from cervical (HeLa), breast (MCF-7), and lung (A549) cancer under hypoxia. In order to reliably detect circRNAs, we integrate available tools with custom approaches for quantification and statistical analysis. Using this consolidated computational pipeline, we identify ~12000 circRNAs in the three cancer cell lines. Their molecular characteristics point to an involvement of complementary RNA sequences as well as trans-acting factors in circRNA biogenesis, such as the RNA-binding protein HNRNPC. Notably, we detect a number of circRNAs that are more abundant than their linear counterparts. In addition, 64 circRNAs significantly change in abundance upon hypoxia, in most cases in a cell type-specific manner. In summary, we present a comparative circRNA profiling in human cancer cell lines, which promises novel insights into the biogenesis and function of circRNAs under hypoxic stress.

Journal or Publication Title: Journal of molecular cell biology
Journal volume: 11
Number: 10
Publisher: Oxford Academic
Divisions: 10 Department of Biology
10 Department of Biology > RNA Biochemistry
Date Deposited: 05 Mar 2021 08:08
DOI: 10.1093/jmcb/mjz094
Official URL: https://academic.oup.com/jmcb/article/11/10/829/5575096
Identification Number: pmid:31560396
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