Evidence map›Paper›PMID 31623626›Full record

ArticleJournal of translational medicine2019

DNA methylation exploration for ARDS: a multi-omics and multi-microarray interrelated analysis.

Shi Zhang, Zongsheng Wu, Jianfeng Xie, Yi Yang, Lei Wang, Haibo Qiu

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

22 citing papers in PubMed, 36 citations in OpenAlex.

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  18. Molecular Mechanisms of Vascular Damage During Lung Injury.Advances in experimental medicine and biology · 2021
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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 1 institution in 1 country.

Shi ZhangDepartment of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Zongsheng WuDepartment of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Jianfeng XieDepartment of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Yi YangDepartment of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Lei WangDepartment of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Haibo QiuDepartment of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China. haiboq2000@163.com.
Zhongda Hospital Southeast University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite advances in clinical management, there are currently no novel therapeutic targets for acute respiratory distress syndrome (ARDS). DNA methylation, as a reversible process involved in the development and progression of many diseases, would be used as potential therapeutic targets to improve the treatment strategies of ARDS. However, the meaningful DNA methylation sites associated with ARDS still remain largely unknown. We sought to determine the difference in DNA methylation between ARDS patients and healthy participants, and simultaneously, the feasible DNA methylation markers for potential therapeutic targets were also explored.

methodsMicroarray data of human blood samples for ARDS and healthy participants up to June 2019 was searched in GEO database. The difference analyses between ARDS and healthy population were performed through limma R package, and furthermore, interrelated analyses of DNA methylation and transcript were accomplished by VennDiagram R package. Perl and sva R package were used to merge microarray data and decrease heterogeneities among different studies. The biological function of screened methylation sites and their regulating genes were annotated according to UniProt database and Pubmed database. GO term and KEGG pathway enrichment analyses were conducted using DAVID 6.8 and KOBAS 3.0. The meaningful DNA methylation markers to distinguish ARDS from healthy controls were explored through ROC (receiver operating characteristic curves) analyses.

resultsFive datasets in GEO databases (one DNA methylation dataset, three mRNA datasets, and one mRNA dataset of healthy people) were enrolled in present analyses finally, and the series were GSE32707, GSE66890, GSE10474, GSE61672, and GSE67530. These databases included 99 patients with ARDS (within 48 h of onset) and 136 healthy participants. Difference analyses indicated 44,439 DNA methylation alterations and 29 difference mRNAs between ARDS and healthy controls. 40 methylation variations regulated transcription of 16 genes was explored via interrelated analysis. According to the functional annotations, 30 DNA methylation sites were related to the imbalance of inflammation or immunity, endothelial function, epithelial function and/or coagulation function. cg03341377, cg24310395, cg07830557 and cg08418670, with AUC up to 0.99, might be the meaningful characteristics with the highest performance to distinguish ARDS from healthy controls.

conclusions44,439 DNA methylation alterations and 29 difference mRNAs exist between ARDS and healthy controls. 30 DNA methylation sites may regulate transcription of 10 genes, which take part in pathogenesis of ARDS. These findings could be intervention targets, with validation experiments to be warranted to assess these further.

Indexed as

DNA MethylationAdultAgedCase-Control StudiesDatabases, GeneticFemaleGene Expression ProfilingGenetic MarkersHealthy VolunteersHumansMaleMicroarray AnalysisMiddle AgedMolecular Sequence AnnotationOligonucleotide Array Sequence AnalysisRespiratory Distress SyndromeGenetic MarkersRNA, MessengerARDSDNA methylationInterrelated analysismRNAMulti-omics

Identifiers

PMID31623626
PMCPMC6796364
OpenAlexW2981132970

What OpenQuestion holds

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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.