Evidence map›Paper›PMID 40660343›Full record

ArticleJournal of cardiothoracic surgery2025

Identification and analysis of pyroptosis-related key genes in heart failure.

Jing Zhang, Zhijie Yue, Na Zhu, Na Zhao

Abstract read
In one paragraph

Article in Journal of cardiothoracic surgery, 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jing ZhangDepartment of Cardiovascular Internal Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University,Tongji Shanxi Hospital, Taiyuan, 030032, China.
Zhijie YueDepartment of Cardiovascular Internal Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University,Tongji Shanxi Hospital, Taiyuan, 030032, China.
Na ZhuDepartment of Medical Record Management, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University,Tongji Shanxi Hospital, Taiyuan, 030032, China.
Na ZhaoDepartment of Imaging Medicine and Nuclear Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, 030053, China. 635427848@qq.com.

Funding

Mechanism of L-type calcium channel self-inhibitory dysfunction accelerating cell death during myocardial ischemia-reperfusion 2022RC08
6 · The paper itself

Abstract

backgroundPyroptosis plays a pivotal role in the pathogenesis of Heart Failure (HF). However, the current understanding of how pyroptosis-related genes (PRGs) influence HF is scarce. This study aimed to explore the link between PRGs and HF based on bioinformatics.

methodsThree datasets of HF were involved in this study.Candidate genes were identified by overlapping two sets of genes. The first set consisted of differentially expressed genes from differential expression analysis. The second set included critical module genes from weighted gene co-expression network analysis. Further, the key genes were screened based on machine learning algorithms. Furthermore, immune infiltration analysis and mRNA-Transcription factor (TF)/drug regulatory networks construction were implemented. Ultimately, we also verified the expression of key genes.

resultsIn this study, we pinpointed seven key genes (SNORD76, RPS3A, SNORD1A, CCDC159, AMT, RANBP6, and CRAT) exhibiting superior diagnostic potential in HF. We found five distinct immune cell types to be significantly associated with these key genes. Moreover, CRAT and AMT were subject to regulation by PHF8. Additionally, AMT, RPS3A, and CRAT corresponded to eight potential therapeutic drugs. Importantly, the expression of CCDC159, CRAT, and AMT was consistent with the dataset.

conclusionWe identified the seven key genes that were intimately associated with HF, offering novel insights into the therapeutic targets for HF.

Indexed as

Heart FailurePyroptosisComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansBioinformatic analysisHeart failureImmune infiltration analysisMachine learningPyroptosis

Identifiers

PMID40660343
PMCPMC12257851

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