Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy.
Dilated cardiomyopathy
IGFBP2
Left ventricular ejection fraction
Machine learning
RNA modifications
Journal
Hereditas
ISSN: 1601-5223
Titre abrégé: Hereditas
Pays: England
ID NLM: 0374654
Informations de publication
Date de publication:
31 Oct 2023
31 Oct 2023
Historique:
received:
05
06
2023
accepted:
18
09
2023
medline:
2
11
2023
pubmed:
31
10
2023
entrez:
31
10
2023
Statut:
epublish
Résumé
RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined. In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis. A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10 In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.
Sections du résumé
BACKGROUND
BACKGROUND
RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.
METHODS
METHODS
In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.
RESULTS
RESULTS
A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10
CONCLUSION
CONCLUSIONS
In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.
Identifiants
pubmed: 37904201
doi: 10.1186/s41065-023-00298-5
pii: 10.1186/s41065-023-00298-5
pmc: PMC10617082
doi:
Substances chimiques
Biomarkers
0
Insulin-Like Growth Factor Binding Protein 2
0
RNA
63231-63-0
IGFBP2 protein, human
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
36Subventions
Organisme : National Natural Science Foundation of China
ID : 82170445
Informations de copyright
© 2023. The Author(s).
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