Evidence map›Paper›PMID 38693740›Full record

ArticleCombinatorial chemistry & high throughput screening2025

miRNAs Influence m6A RNA Methylation through FTO and IGF2BP2 in Pressure Overload-Induced Heart Failure.

Yuanqi Wang, Linghao Xu, Md Sakibur Rahman Tapu, Tiantian Jiao, Xueqi Lin, Wei Song, Liang Wang, Lindong Nie, Jing Cheng, Lijian Xing and 2 more

Abstract read
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In one paragraph

Article in Combinatorial chemistry & high throughput screening, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
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4 · The record

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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

12 authors at 1 institution in 1 country.

Yuanqi WangDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Linghao XuDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.ORCID 0009-0008-7962-3871
Md Sakibur Rahman TapuDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Tiantian JiaoDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Xueqi LinDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Wei SongDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Liang WangDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Lindong NieDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Jing ChengDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Lijian XingDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Qi ZhangDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Jiming LiDepartment of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Shanghai East Hospital · CN

Funding

key disciplines group construction project of Shanghai pudong new area health commission PWZxq2022-02National Natural Science Foundation of China 82070416, 82104632, 81870296Shanghai key clinical specialty project shslczdzk06202Shanghai Pudong New Area Health System Excellent Young Medical Talents Training Program PWRq2021-24top-level clinical discipline project of Shanghai Pudong district PWYgf2021-01
6 · The paper itself

Abstract

backgroundN6-adenosine methylation (m6A) is a prevalent RNA modification associated with heart failure, alongside aberrant miRNA expression. Despite indications of miRNAs regulating m6A modification, their specific influence on m6A in heart failure remains unclear.

methodsThe initial analysis utilized transcriptome and methylation sequencing data from GSE131296 in mice to identify key m6A methylation enzymes in heart failure and construct an associated network. Integration of miRNA sequencing data from GSE231700 revealed miRNAs influencing m6A methylation enzymes, contributing to the formation of a comprehensive network. Furthermore, differential miRNA levels in human serum were assessed via qPCR, and the expression of m6A methyltransferases in the heart was confirmed using proteomic databases.

resultsIn pressure overload-induced heart failure mice, 217 mRNAs showed differential expression, with FTO and IGF2BP2 identified as m6A methylation enzymes. Subsequent methylation sequencing revealed 884 highly-methylated and 178 lowly-methylated peaks, establishing a network linking Fto and Igf2bp2 with these peaks. Additionally, miRNA sequencing identified 156 differentially expressed miRNAs, including let-7b-5p and miR-23b-3p, predicted as m6Aregulating miRNAs, both elevated in heart failure patients.

conclusionmiR-23b-3p and let-7b-5p are identified as potential regulators of RNA methylation in heart failure, acting via FTO and IGF2BP2, offering new insights into the role of miRNA- mediated RNA methylation and its potential therapeutic avenues for heart failure.

Indexed as

AdenosineAlpha-Ketoglutarate-Dependent Dioxygenase FTOHeart FailureMicroRNAsRNA-Binding ProteinsAnimalsHumansMaleMethylationMiceMice, Inbred C57BLRNA MethylationAdenosineAlpha-Ketoglutarate-Dependent Dioxygenase FTOFTO protein, humanFTO protein, mouseIGF2BP2 protein, humanMicroRNAsN-methyladenosineRNA-Binding Proteinscardiac remodelingFTOHeart failureIGF2BP2.m6AmicroRNARNA methylation

Identifiers

PMID38693740
OpenAlexW4396590569

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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.