Evidence map›Paper›PMID 27449149›Full record

ArticleBreast cancer research : BCR2016

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice.

Ruben Nogales-Cadenas, Ying Cai, Jhih-Rong Lin, Quanwei Zhang, Wen Zhang, Cristina Montagna, Zhengdong D Zhang

Abstract read
In one paragraph

Article in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

7 authors.

Ruben Nogales-CadenasDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Ying CaiDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Jhih-Rong LinDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Quanwei ZhangDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Wen ZhangDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Cristina MontagnaDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Zhengdong D ZhangDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA. zhengdong.zhang@einstein.yu.edu.

Funding

Gene regulation in metastasis and new methods to analyze its microarray profilesR00LM009770 · NLM · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI ZHANG, ZHENGDONG · 2010 to 2012
$727k
NLM NIH HHS R00 LM009770
6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated.

methodsIn this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs).

resultsWe identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes.

conclusionsIn this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.

Indexed as

Gene Expression Regulation, NeoplasticAnimalsBreast NeoplasmsCluster AnalysisComputational BiologyDisease Models, AnimalDisease ProgressionFatty AcidsFemaleGene Expression ProfilingGene OntologyGene Regulatory NetworksHigh-Throughput Nucleotide SequencingHumansMaleMiceFatty AcidsMicroRNAsRNA, MessengerBreast cancerCancer progressionMetastasisMicroRNAPyMT mouse modelRegulatory modules

Identifiers

PMID27449149
PMCPMC4957901

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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.