BackgroundAcute myocardial infarction (AMI) is the main clinical cause of death and cardiovascular disease and thus has high rates of morbidity and mortality. The increase in cardiovascular disease with aging is partly the result of vascular endothelial cell senescence and associated vascular dysfunction. This study was performed to identify potential key cellular senescence-related genes (SRGs) as biomarkers for the diagnosis of AMI using bioinformatics.MethodsUsing the CellAge database, we identified cellular SRGs. GSE66360 and GSE48060 for AMI patients and healthy controls and GSE19322 for mice were downloaded from the Gene Expression Omnibus (GEO) database. The GSE66360 dataset was divided into a training set and a validation set. The GSE48060 dataset was used as another validation set. The GSE19322 dataset was used to explore the evolution of the screened diagnostic markers in the dynamic process of AMI. Differentially expressed genes (DEGs) of AMI were identified from the GSE66360 training set. Differentially expressed senescence-related genes (DESRGs) selected from SRGs and DEGs were analyzed using Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in DESRGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed and targeted drug prediction was performed.ResultsA total of 520 DEGs were screened from the GSE66360 training set, and 279 SRGs were identified from the CellAge database. The overlapping DEGs and SRGs constituted 14 DESRGs, including 4 senescence suppressor genes and 10 senescence inducible genes. The top 10 hub genes, including FOS, MMP9, CEBPB, CDKN1A, CXCL1, ETS2, BCL6, SGK1, ZFP36, and IGFBP3, were screened. Furthermore, three diagnostic genes were identified: MMP9, ETS2, and BCL6. The ROC analysis showed that the respective area under the curves (AUCs) of MMP9, ETS2, and BCL6 were 0.786, 0.848, and 0.852 in the GSE66360 validation set and 0.708, 0.791, and 0.727 in the GSE48060 dataset. In the GSE19322 dataset, MMP9 (AUC, 0.888) and ETS2 (AUC, 0.929) had very high diagnostic values in the early stage of AMI. Finally, based on these three diagnostic genes, we found that drugs such as acetylcysteine and genistein may be targeted for the treatment of age-related AMI.ConclusionThe results of this study suggest that cellular SRGs might play an important role in AMI. MMP9, ETS2, and BCL6 have potential as specific biomarkers for the early diagnosis of AMI.
Background Weight-adjusted waist index (WWI) is a new anthropometric indicator to assess adiposity. Current knowledge regarding its association with type 2 diabetes mellitus (T2DM) is limited. This present study aims to evaluate the association of WWI with the risk of T2DM in the Japanese population, and to compare its predictive ability with body mass index (BMI) and waist circumference (WC). Methods This was a secondary analysis of a retrospective cohort study involving 15,464 participants. Participants were divided into quartiles based on baseline WWI levels. Cox regression model, Kaplan–Meier curve, and smooth curve fitting were used to explore the relationship between WWI and T2DM. The discriminative ability of obesity indices in predicting T2DM was compared by the receiver operating characteristic (ROC) curve. Results After a mean follow-up of 6.05 years, 373 participants were diagnosed with T2DM. In fully adjusted models, the risk of incident T2DM was 1.96 times higher for each 1-unit increment in WWI levels (95% CI: 1.61–2.39, P < 0.001). Smooth curve fitting analysis showed a linear positive association between baseline WWI and new-onset T2DM. Subgroup analysis showed consistent results which subjects in the 4th WWI quartile had the highest risk of developing T2DM in different age, gender, and BMI groups. WWI did not exhibit better predictive ability compared with BMI and WC in the results of ROC curve. Conclusion WWI, a new metabolic index, can be used to predict new-onset T2DM in the Japanese population. However, its predictive capability was not superior to conventional anthropometric indices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.