Evidence map›Paper›PMID 39997913›Full record

ArticleToxics2025

Transcriptome-Wide Analysis of N6-Methyladenosine-Modified Long Noncoding RNAs in Particulate Matter-Induced Lung Injury.

Yingying Zeng, Guiping Zhu, Wenjun Peng, Hui Cai, Chong Lu, Ling Ye, Meiling Jin, Jian Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Toxics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Epitranscriptomic alterations induced by environmental toxins: implications for RNA modifications and disease.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2025
    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

8 authors.

Yingying ZengDepartment of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200030, China.ORCID 0000-0003-1728-820X
Guiping ZhuDepartment of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200030, China.
Wenjun PengDepartment of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200030, China.
Hui CaiDepartment of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200030, China.
Chong LuDepartment of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200030, China.
Ling YeDepartment of Allergy, Zhongshan Hospital, Fudan University, Shanghai 200030, China.
Meiling JinDepartment of Allergy, Zhongshan Hospital, Fudan University, Shanghai 200030, China.
Jian WangDepartment of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200030, China.

Funding

National Natural Science Foundation of China 82000013National Natural Science Foundation of China 82100028National Natural Science Foundation of China 82270028Shanghai "Rising Stars of Medical Talents" Youth Development Program SHWSRS(2023)_062
6 · The paper itself

Abstract

backgroundN6-methyladenosine (m

methodsHere, we establish a mouse model of PM-induced lung injury. We utilized m6A-modified RNA immunoprecipitation sequencing (MeRIP-seq) to identify differentially expressed m6A peaks on long non-coding RNAs (lncRNAs). Concurrently, we performed lncRNA sequencing (lncRNA-seq) to determine the differentially expressed lncRNAs. The candidate m6A-modified lncRNAs in the lung tissues of mice were identified through the intersection of the data obtained from these two sequencing approaches.

resultsA total of 664 hypermethylated m

conclusionsThis study first provides a comprehensive transcriptome-wide analysis of m

Indexed as

long noncoding RNAslung injurym6A methylationparticulate matter

Identifiers

PMID39997913
PMCPMC11860755

What OpenQuestion holds

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Registered trials

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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.