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Journal of Forensic Medicine ; (6): 433-440, 2023.
Article in English | WPRIM | ID: wpr-1009374

ABSTRACT

OBJECTIVES@#The common differentially expressed mRNAs in brain, heart and liver tissues of deceased sudden infant death syndrome (SIDS) and infectious sudden death in infancy (ISDI) confirmed by autopsy was screened by bioinformatics to explore the common molecular markers and pathogenesis of SIDS and ISDI.@*METHODS@#The datasets of GSE70422 and GSE136992 were downloaded, the limma of R software was used to screen differentially expressed mRNA in different tissue samples of SIDS and ISDI decedents for overlapping analysis. The clusterProfiler of R software was used to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The protein-protein interaction (PPI) network was constructed by STRING database, while the hub gene was screened by cytoHubba plug-in.@*RESULTS@#Compared with the control group, there were 19 significant differentially expressed genes in the tissue samples of SIDS and ISDI decedents, among which 16 in the heart tissue and 3 in the liver tissue, and the astrotactin 1 (ASTN1) gene expression difference in the heart tissue was most significant. The PPI network identified Ras homolog family member A (RHOA), integrin subunit alpha 1 (ITGA1), and H2B clustered histone 5 (H2BC5) were hub genes. The analysis of GO and KEGG showed that differentially expressed genes were enriched in the molecular pathways of actin cytoskeleton regulation, focal adhesion and response to mycophenolic acid.@*CONCLUSIONS@#ASTN1, RHOA and ITGA1 may participate in the development of SIDS and ISDI. The enrichment of differentially expressed genes in immune and inflammatory pathways suggests a common molecular regulatory mechanism between SIDS and ISDI. These findings are expected to provide new biomarkers for molecular anatomy and forensic identification of SIDS and ISDI.


Subject(s)
Humans , Infant , Gene Expression Profiling , Sudden Infant Death/genetics , Gene Regulatory Networks , Protein Interaction Maps/genetics , Computational Biology
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