Evidence map›Paper›PMID 29405013›Full record

ArticleInterdisciplinary sciences, computational life sciences2018

Region-Specific Methylation Profiling in Acute Myeloid Leukemia.

Agnieszka Cecotka, Joanna Polanska

Open access · hybridAbstract read
In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Critical appraisal of epigenetic regulation of galectins in cancer.International journal of clinical oncology · 2022
    Review
  6. Article
  7. Targeting epigenetics for cancer therapy.Archives of pharmacal research · 2019
    Review
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

2 authors at 1 institution in 1 country.

Agnieszka CecotkaData Mining Division, Faculty of Automatic Control, Electronics and Computer Science, Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland.
Joanna PolanskaData Mining Division, Faculty of Automatic Control, Electronics and Computer Science, Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland. Joanna.Polanska@polsl.pl.ORCID http://orcid.org/0000-0001-8004-9864
Silesian University of Technology · PL

Funding

Narodowe Centrum Badań i Rozwoju GeCONiI POIG.02.03.01-24-099/13Narodowe Centrum Nauki 2015/19/B/ST6/01736Politechnika Śląska 02/010/BKM17/0083
6 · The paper itself

Abstract

Alteration of DNA methylation level in cancer diseases leads to deregulation of gene expression-silencing of tumor suppressor genes and enhancing of protooncogenes. There are several tools devoted to the problem of identification of CpG sites' demethylation but majority of them focuses on single site level and does not allow for quantification of region methylation changes. The aim was to create an adaptive algorithm supporting detection of differentially methylated CpG sites and genomic regions specific for acute myeloid leukemia. Knowledge on AML methylation fingerprint helps in better understanding the epigenetics of leukemogenesis. Proposed algorithm is data driven and does not use predefined quantification thresholds. Gaussian mixture modeling supports classification of CpG sites to several levels of demethylation. p value integration allows for translation from single site demethylation to the demethylation of gene promoter and body regions. Methylation profiles of healthy controls and AML patients were examined (GEO:GSE63409). The differences in whole genome methylation profiles were observed. The methylation profile differs significantly among genomic regions. The lowest methylation level was observed for promoter regions, while sites from intergenic regions were by average higher methylated. The observed number of AML related down methylated sites has not substantially exceeded the expected number by chance. Intergenic regions were characterized by the highest percentage of AML up methylated sites. Methylation enhancement/diminution is the most frequent for intergenic region while methylation compensation (positive or negative) is specific for promoter regions. Functional analysis performed for AML down methylated or extreme high up methylated genes showed strong connection to the leukemic processes.

Indexed as

CpG IslandsDNA DemethylationDNA, IntergenicDNA MethylationDNA Transposable ElementsEnhancer Elements, GeneticGene OntologyGenome, HumanHematopoietic Stem CellsHumansLeukemia, Myeloid, AcuteMicroRNAsRNA, Long NoncodingSoftwareDNA, IntergenicDNA Transposable ElementsMicroRNAsRNA, Long NoncodingAcute myeloid leukemiaAMLData driven algorithmDifferentially methylated regionsDMRDNA methylationEpigeneticsGaussian mixture modeling

Identifiers

PMID29405013
PMCPMC5838208
OpenAlexW2786826176

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.