ArticleArquivos brasileiros de cardiologia2024
Potential Biomarkers in Myocardial Fibrosis: A Bioinformatic Analysis.
Article in Arquivos brasileiros de cardiologia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deciphering myocardial fibrosis: a comprehensive bibliometric analysis of mechanism over the period 1992-2023.Journal of cardiothoracic surgery · 2025Article
- Challenges and Perspectives of Cardiac Fibrosis Biomarkers: From Diagnosis to Clinical Application.Arquivos brasileiros de cardiologia · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundMyocardial fibrosis (MF) occurs throughout the onset and progression of cardiovascular disease, and early diagnosis of MF is beneficial for improving cardiac function, but there is a lack of research on early biomarkers of MF.
objectivesUtilizing bioinformatics techniques, we identified potential biomarkers for MF.
methodsDatasets related to MF were sourced from the GEO database. After processing the data, differentially expressed genes were screened. Differentially expressed genes were enriched, and subsequently, protein-protein interaction (PPI) was performed to analyze the differential genes. The associated miRNAs and transcription factors were predicted for these core genes. Finally, ROC validation was performed on the core genes to determine their specificity and sensitivity as potential biomarkers. The level of significance adopted was 5% (p < 0.05).
resultsA total of 91 differentially expressed genes were identified, and PPI analysis yielded 31 central genes. Enrichment analysis showed that apoptosis, collagen, extracellular matrix, cell adhesion, and inflammation were involved in MF. One hundred and forty-two potential miRNAs were identified. the transcription factors JUN, NF-κB1, SP1, RELA, serum response factor (SRF), and STAT3 were enriched in most of the core targets. Ultimately, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2, and ITGB8 had higher diagnostic accuracy and sensitivity in predicting MF based on ROC curve analysis.
conclusionEight genes, IL11, GADD45B, GDF5, NOX4, IGFBP3, ACTC1, MYOZ2, and ITGB8, can serve as candidate biomarkers for MF. Processes such as cellular apoptosis, collagen protein synthesis, extracellular matrix formation, cellular adhesion, and inflammation are implicated in the development of MF.
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