Evidence map›Paper›PMID 23285118›Full record

ArticlePloS one2012

DNA methylation in multiple myeloma is weakly associated with gene transcription.

Sungwon Jung, Seungchan Kim, Molly Gale, Irene Cherni, Rafael Fonseca, John Carpten, Bodour Salhia

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
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

12 citing papers in PubMed, 21 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Sungwon JungIntegrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA.
Seungchan Kim
Molly Gale
Irene Cherni
Rafael Fonseca
John Carpten
Bodour Salhia
Translational Genomics Research Institute · USMayo Clinic in Arizona · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous studies have now demonstrated that both genic and global hypomethylation characterizes the multiple myeloma (MM) epigenome. Whether these methylation changes are associated with global and corresponding increases (or decreases) in transcriptional activity are poorly understood. The purpose of our current study was to correlate DNA methylation levels in MM to gene expression. We analyzed matching datasets generated by the GoldenGate methylation BeadArray and Affymetrix gene expression platforms in 193 MM samples. We subsequently utilized two independent statistical approaches to identify methylation-expression correlations. In the first approach, we used a linear correlation parameter by computing a Pearson correlation coefficient. In the second approach, we discretized samples into low and high methylation groups and then compared the gene expression differences between the groups. Only methylation of 2.1% and 25.3% of CpG sites on the methylation array correlated to gene expression by Pearson correlation or the discretization method, respectively. Among the genes with methylation-expression correlations were IGF1R, DLC1, p16, and IL17RB. In conclusion, DNA methylation may directly regulate relatively few genes and suggests that additional studies are needed to determine the effects of genome-wide methylation changes in MM.

Indexed as

DNA MethylationGene Expression Regulation, NeoplasticTranscription, GeneticChromosome AberrationsComputational BiologyCpG IslandsEpigenesis, GeneticEpigenomicsGene Expression ProfilingHumansMultiple MyelomaReproducibility of Results

Identifiers

PMID23285118
PMCPMC3527579
OpenAlexW2030396992

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.