Dataset Information

Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis.

ABSTRACT: Circular RNAs are highly stable molecules present in all eukaryotes generated by distinct transcript processing. We have exploited poly(A-) RNA-Seq data generated in our lab in MCF-7 breast cancer cells to define a compilation of exonic circRNAs more comprehensive than previously existing lists. Development of a novel computational tool, named CircHunter, allowed us to more accurately characterize circRNAs and to quantitatively evaluate their expression in publicly available RNA-Seq data from breast cancer cell lines and tumor tissues. We observed and confirmed, by ChIP analysis, that exons involved in circularization events display significantly higher levels of the histone post-transcriptional modification H3K36me3 than non-circularizing exons. This result has potential impact on circRNA biogenesis since H3K36me3 has been involved in alternative splicing mechanisms. By analyzing an Ago-HITS-CLIP dataset we also found that circularizing exons overlapped with an unexpectedly higher number of Ago binding sites than non-circularizing exons. Finally, we observed that a subset of MCF-7 circRNAs are specific to tumor versus normal tissue, while others can distinguish Luminal from other tumor subtypes, thus suggesting that circRNAs can be exploited as novel biomarkers and drug targets for breast cancer.

SUBMITTER: Coscujuela Tarrero L

PROVIDER: S-EPMC5865691 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis.

Coscujuela Tarrero Lucia L Ferrero Giulio G Miano Valentina V De Intinis Carlo C Ricci Laura L Arigoni Maddalena M Riccardo Federica F Annaratone Laura L Castellano Isabella I Calogero Raffaele A RA Beccuti Marco M Cordero Francesca F De Bortoli Michele M

Oncotarget 20180219 18

Circular RNAs are highly stable molecules present in all eukaryotes generated by distinct transcript processing. We have exploited poly(A-) RNA-Seq data generated in our lab in MCF-7 breast cancer cells to define a compilation of exonic circRNAs more comprehensive than previously existing lists. Development of a novel computational tool, named <i>CircHunter</i>, allowed us to more accurately characterize circRNAs and to quantitatively evaluate their expression in publicly available RNA-Seq data ...[more]

PMID: 29581865

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Project description:Triple-negative breast cancer (TNBC) is a highly heterogeneous group of cancers, and molecular subtyping is necessary to better identify molecular-based therapies. While some classifiers have been established, no one has integrated the expression profiles of long noncoding RNAs (lncRNAs) into such subtyping criterions. Considering the emerging important role of lncRNAs in cellular processes, a novel classification integrating transcriptome profiles of both messenger RNA (mRNA) and lncRNA would help us better understand the heterogeneity of TNBC.Using human transcriptome microarrays, we analyzed the transcriptome profiles of 165 TNBC samples. We used k-means clustering and empirical cumulative distribution function to determine optimal number of TNBC subtypes. Gene Ontology (GO) and pathway analyses were applied to determine the main function of the subtype-specific genes and pathways. We conducted co-expression network analyses to identify interactions between mRNAs and lncRNAs.All of the 165 TNBC tumors were classified into four distinct clusters, including an immunomodulatory subtype (IM), a luminal androgen receptor subtype (LAR), a mesenchymal-like subtype (MES) and a basal-like and immune suppressed (BLIS) subtype. The IM subtype had high expressions of immune cell signaling and cytokine signaling genes. The LAR subtype was characterized by androgen receptor signaling. The MES subtype was enriched with growth factor signaling pathways. The BLIS subtype was characterized by down-regulation of immune response genes, activation of cell cycle, and DNA repair. Patients in this subtype experienced worse recurrence-free survival than others (log rank test, P = 0.045). Subtype-specific lncRNAs were identified, and their possible biological functions were predicted using co-expression network analyses.We developed a novel TNBC classification system integrating the expression profiles of both mRNAs and lncRNAs and determined subtype-specific lncRNAs that are potential biomarkers and targets. If further validated in a larger population, our novel classification system could facilitate patient counseling and individualize treatment of TNBC.

Dataset Information

Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis.

Publications

Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis.

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