Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.
BMC Cardiovasc Disord
; 24(1): 272, 2024 May 24.
Article
em En
| MEDLINE
| ID: mdl-38783198
ABSTRACT
BACKGROUND:
T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis.METHODS:
By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the "DrugBank" database to serve as potential therapeutic medications for future TEX-AMI basic research.RESULTS:
We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered.CONCLUSION:
Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Valor Preditivo dos Testes
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Biologia Computacional
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Perfilação da Expressão Gênica
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Bases de Dados Genéticas
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Transcriptoma
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Mapas de Interação de Proteínas
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Aprendizado de Máquina
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Infarto do Miocárdio
Limite:
Humans
Idioma:
En
Revista:
BMC Cardiovasc Disord
Assunto da revista:
ANGIOLOGIA
/
CARDIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China