Evidence map›Paper›PMID 28155687›Full record

ArticleBMC systems biology2016

Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell lines.

Heejoon Chae, Sangseon Lee, Kenneth P Nephew, Sun Kim

Abstract read
In one paragraph

Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

4 authors.

Heejoon ChaeSchool of Informatics and Computing, Indiana University Bloomington, IN 47405, USA, Waterloo Road, Bloomington, IN, 47405, USA.
Sangseon LeeDepartment of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
Kenneth P NephewIndiana University School of Medicine, Department of Cellular and Integrative Physiology, Medical Sciences Program, Bloomington, USA.
Sun KimDepartment of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAberrant epigenetic modifications, including DNA methylation, are key regulators of gene activity in tumorigenesis. Breast cancer is a heterogeneous disease, and large-scale analyses indicate that tumor from normal and benign tissues, as well as molecular subtypes of breast cancer, can be distinguished based on their distinct genomic, transcriptomic, and epigenomic profiles. In this study, we used affinity-based methylation sequencing data in 30 breast cancer cell lines representing functionally distinct cancer subtypes to investigate methylation and mutation patterns at the whole genome level.

resultsOur analysis revealed significant differences in CpG island (CpGI) shore methylation and mutation patterns among breast cancer subtypes. In particular, the basal-like B type, a highly aggressive form of the disease, displayed distinct CpGI shore hypomethylation patterns that were significantly associated with downstream gene regulation. We determined that mutation rates at CpG sites were highly correlated with DNA methylation status and observed distinct mutation rates among the breast cancer subtypes. These findings were validated by using targeted bisulfite sequencing of differentially expressed genes (n=85) among the cell lines.

conclusionsOur results suggest that alterations in DNA methylation play critical roles in gene regulatory process as well as cytosine substitution rates at CpG sites in molecular subtypes of breast cancer.

Indexed as

DNA MethylationGenomicsMutationBreast NeoplasmsCell Line, TumorCpG IslandsHumansBreast cancerCpGI shoreDNA methylationMutationSubtype

Identifiers

PMID28155687
PMCPMC5259919

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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.