Evidence map›Paper›PMID 22709530›Full record

ArticleBMC medical genomics2012

Screening significantly hypermethylated genes in fetal tissues compared with maternal blood using a methylated-CpG island recovery assay-based microarray.

Aihua Yin, Xiangzhong Zhang, Jing Wu, Li Du, Tianwen He, Xiaozhuang Zhang

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC medical genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 8 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

6 authors at 2 institutions in 1 country.

Aihua YinPrenatal Diagnosis Centre, Guangdong Women and Children Hospital, Guangzhou, Guangdong 510010, China.
Xiangzhong Zhang
Jing Wu
Li Du
Tianwen He
Xiaozhuang Zhang
Guangdong Province Women and Children Hospital · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe noninvasive prenatal diagnosis procedures that are currently used to detect genetic diseases do not achieve desirable levels of sensitivity and specificity. Recently, fetal methylated DNA biomarkers in maternal peripheral blood have been explored for the noninvasive prenatal detection of genetic disorders. However, such efforts have covered only chromosomal aneuploidy, and fetal methylated DNA biomarkers in maternal whole blood for detecting single-gene diseases remain to be discovered.

methodsTo address this issue, we systematically screened significantly hypermethylated genes in fetal tissues and compared them with maternal peripheral blood potential in an attempt to detect fetal genes in maternal peripheral blood. First, the methylated-CpG island recovery assay combined with a CpG island array was performed for four fetus-toward placental tissues and the corresponding maternal peripheral bloods. Subsequently, direct bisulfite sequencing and combined bisulfite restriction analysis (COBRA) were carried out to validate the methylation status of the hypermethylated genes that were identified by the microarray analysis.

resultsThree hundred and ten significantly hypermethylated genes in the placental tissues were detected by microarray. From the top 15 hypermethylated genes detected by microarray, two were selected for sequencing validation in placental tissue and chorionic villus samples and four were selected for COBRA validation in four placental tissues, ten amniotic fluids and five chorionic villus samples. The six selected genes were confirmed to be hypermethylated in placental tissue and chorionic villus samples, but methylation of the genes could not be detected in the amniotic fluids.

conclusionsOf the many hypermethylated genes and methylation sites that were found in the fetal tissues, some have great potential to be developed into molecular markers for noninvasive prenatal diagnosis of monogenic disorders. Further clinical studies are warranted to confirm these findings.

Indexed as

Genetic TestingBase SequenceCluster AnalysisCpG IslandsDNADNA MethylationFetusHumansMolecular Sequence DataOligonucleotide Array Sequence AnalysisReproducibility of ResultsRestriction MappingSequence Analysis, DNASulfitesDNAhydrogen sulfiteSulfites

Identifiers

PMID22709530
PMCPMC3534415
OpenAlexW2010505094

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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.