RESUMEN

Pyroptosis plays a key role in the death of cells including cardiomyocytes, and it is associated with a variety of cardiovascular diseases. However, the role of pyroptosis-related genes (PRGs) in hypertrophic cardiomyopathy (HCM) is not well characterized. This study aimed to identify key biomarkers and explore the molecular mechanisms underlying the functions of the PRGs in HCM. The differentially expressed genes were identified by GEO2R, and the differentially expressed pyroptosis-related genes (DEPRGs) of HCM were identified by combining with PRGs. Enrichment analysis was performed using the "clusterProfiler" package of the R software. Protein-protein interactions (PPI) network analysis was performed using the STRING database, and hub genes were screened using cytoHubba. TF-miRNA coregulatory networks and protein-chemical interactions were analyzed using NetworkAnalyst. RT-PCR/WB was used for expression validation of HCM diagnostic markers. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western Blot (WB) were used to measure and compare the expression of the identified genes in the cardiac hypertrophy model and the control group. A total of 20 DEPRGs were identified, which primarily showed enrichment for the positive regulation of cytokine production, regulation of response to biotic stimulus, tumor necrosis factor production, and other biological processes. These processes primarily involved pathways related to Renin-angiotensin system, Adipocytokine signaling pathway and NF-kappa B signaling pathway. Then, a PPI network was constructed, and 8 hub genes were identified. After verification analysis, the finally identified HCM-related diagnostic markers were upregulated gene protein tyrosine phosphatase non-receptor type 11 (PTPN11), downregulated genes interleukin-1 receptor-associated kinase 3 (IRAK3), and annexin A2 (ANXA2). Further GSEA analysis revealed these 3 biomarkers primarily related to cardiac muscle contraction, hypertrophic cardiomyopathy, fatty acid degradation and ECM - receptor interaction. Moreover, we also elucidated the interaction network of these biomarkers with the miRNA network and known compounds, respectively. RT-PCR/WB results indicated that PTPN11 expression was significantly increased, and IRAK3 and ANXA2 expressions were significantly decreased in HCM. This study identified PTPN11, IRAK3, and ANXA2 as pyroptosis-associated biomarkers of HCM, with the potential to reveal the development and pathogenesis of HCM and could be potential therapeutic targets.

Asunto(s)

Cardiomiopatía Hipertrófica , MicroARNs , Humanos , Redes Reguladoras de Genes , Perfilación de la Expresión Génica/métodos , Piroptosis/genética , Biomarcadores , MicroARNs/genética , MicroARNs/metabolismo , Cardiomiopatía Hipertrófica/diagnóstico , Cardiomiopatía Hipertrófica/genética , Biología Computacional/métodos

RESUMEN

Objective@#To understand the development trend of poor vision among primary students through cross sectional, surveillance and longitudinal analysis, so as to put forward some suggestions on adolescents growth and health.@*Methods@#Visual data of 3 753 pupils were inclucled for analysis from Gui an New Distinct, Guizhou Province in autumn semester 2021, and were compared with data collected during the year of 2016-2021. The curve, increment and contribution rate of poor vision from each grade of the three designs were contrasted.@*Results@#In 2021, poor vision rate among pupils in this town was 25.6%. The curve of poor vision rate in cross sectional data was U shaped with significant rise followed by decline which was different from monitoring and longitudinal tracking data, and the trend of poor vision rate of monitoring and longitudinal tracking data were linear with continued increases. The cross sectional data in 2021 showed that the highest contribution rate of poor vision rate of pupils was in grade 1(87.0% ), while other data showed that those were both in grade 4(45.0%, 33.9%).@*Conclusion@#The accuracy of the development trend of poor vision is lowest in cross sectional analysis and highest in longitudinal analysis. However, data acquisition and preservation is easy in cross sectional study and difficult in longitudinal study. It is necessary to improve the electronic information system based on cross sectional data to gradually form a complete monitoring and longitudinal tracking data, and combine different data to provide more accurate information.