Evidence map›Paper›PMID 39747333›Full record

ArticleScientific reports2025

Prognostic signature and therapeutic drug identification for dilated cardiomyopathy based on necroptosis via bioinformatics and experimental validation.

Han Yang, Zhenwei Wang, Yawei Xu, Yimei Du, Haibo Yang, Yang Lu

Abstract read
In one paragraph

Article in Scientific reports, 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

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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. Article
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.

Han Yang *Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zhenwei Wang *Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yawei Xu *Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yimei DuDepartment of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. yimeidu@mail.hust.edu.cn.
Haibo YangDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. yanghaibo1975@126.com.
Yang LuDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 164355353@qq.com.

Funding

the National Natural Science Foundation of China 82170326the National Natural Science Foundation of China 82300395
6 · The paper itself

Abstract

Necroptosis, a type of programmed cell death, has been increasingly linked to cardiovascular disease development, yet its role in dilated cardiomyopathy (DCM) remains unclear. In this study, we analyzed the GSE5406 dataset from the GEO database to explore necroptosis-related prognostic signatures in DCM using LASSO regression. We identified five necroptosis-related genes (BID, CAMK2B, GLUL, HSP90AB1, CHMP5) that define a necroptosis-related signature with strong predictive value, evidenced by ROC curve areas of 0.852 and 0.957 in training and test sets, respectively. Our analyses, including GO and GSEA enrichment, focused on pathways associated with high necroptosis-related scores (NRS) and revealed significant immune cell infiltration. Notably, nTreg and iTreg cells were enriched in the high NRS group, while CD8 naive T cells and CD8 T cells positively correlated with NRS. Small molecule drugs fenofibrate, procyclidine, and tienilic acid emerged as potential therapeutic agents for high-risk patients, with fenofibrate showing efficacy in inhibiting DCM progression in an inflammatory animal model. These findings underscore the clinical relevance of necroptosis-related genes in assessing DCM progression and prognosis and highlight their potential for targeted therapeutic development.

Indexed as

Cardiomyopathy, DilatedComputational BiologyNecroptosisAnimalsGene Expression ProfilingHumansMicePrognosisDilated cardiomyopathyFenofibrateNecroptosisNecroptosis-related genesPrognostic assessment

Identifiers

PMID39747333
PMCPMC11696111

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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.