Evidence map›Paper›PMID 27829232›Full record

ArticleOncotarget2016

Circular RNAs and their associations with breast cancer subtypes.

Asha A Nair, Nifang Niu, Xiaojia Tang, Kevin J Thompson, Liewei Wang, Jean-Pierre Kocher, Subbaya Subramanian, Krishna R Kalari

Abstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

94 citing papers in PubMed.

  1. Upregulation of a CircularInternational journal of molecular sciences · 2026
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34 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Asha A NairDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Nifang NiuDivision of Genomic and Molecular Pathology, University of Chicago, Chicago, IL, USA.
Xiaojia TangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Kevin J ThompsonDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Liewei WangDepartment of Pharmacology, Mayo Clinic, Rochester, MN, USA.
Jean-Pierre KocherDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Subbaya SubramanianDivision of Basic and Translational Research, University of Minnesota, Minneapolis, MN, USA.
Krishna R KalariDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI Felicity Enders · 1985 to 2026
$151.3M
NCI NIH HHS P30 CA015083
6 · The paper itself

Abstract

Circular RNAs (circRNAs) are highly stable forms of non-coding RNAs with diverse biological functions. They are implicated in modulation of gene expression thus affecting various cellular and disease processes. Based on existing bioinformatics approaches, we developed a comprehensive workflow called Circ-Seq to identify and report expressed circRNAs. Circ-Seq also provides informative genomic annotation along circRNA fused junctions thus allowing prioritization of circRNA candidates. We applied Circ-Seq first to RNA-sequence data from breast cancer cell lines and validated one of the large circRNAs identified. Circ-Seq was then applied to a larger cohort of breast cancer samples (n = 885) provided by The Cancer Genome Atlas (TCGA), including tumors and normal-adjacent tissue samples. Notably, circRNA results reveal that normal-adjacent tissues in estrogen receptor positive (ER+) subtype have relatively higher numbers of circRNAs than tumor samples in TCGA. Similar phenomenon of high circRNA numbers were observed in normal breast-mammary tissues from the Genotype-Tissue Expression (GTEx) project. Finally, we observed that number of circRNAs in normal-adjacent samples of ER+ subtype is inversely correlated to the risk-of-relapse proliferation (ROR-P) score for proliferating genes, suggesting that circRNA frequency may be a marker for cell proliferation in breast cancer. The Circ-Seq workflow will function for both single and multi-threaded compute environments. We believe that Circ-Seq will be a valuable tool to identify circRNAs useful in the diagnosis and treatment of other cancers and complex diseases.

Indexed as

Biomarkers, TumorBreast NeoplasmsCell ProliferationComputational BiologyDatabases, GeneticFemaleGene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseHumansMCF-7 CellsPhenotypeReceptors, EstrogenRNARNA, CircularSequence Analysis, RNAWorkflowBiomarkers, TumorReceptors, EstrogenRNARNA, Circularbreast cancercirc-seqcircular RNAmolecular subtypesproliferation

Identifiers

PMID27829232
PMCPMC5348369

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.