Experimental gerontology · 2025
Bioinformatics analysis based on microarray data reveals molecular crosstalk and immune relationship between sarcopenia and atherosclerosis.
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Yang Jinghong, Zhong Jun, Du Yimin, Liu Jialin, Li Zhong, Liu Yanshi
Аннотация
BACKGROUND: Several global studies have investigated the association between sarcopenia and atherosclerosis. However, the potential common pathogenesis, molecular mechanisms, and their relationship remain elusive. Through bioinformatic analysis, we aim to identify potential biomarkers and therapeutic targets for sarcopenia and atherosclerosis, providing a theoretical foundation for future research.
METHODS: We screened microarray data from the Gene Expression Omnibus to explore the relationship between atherosclerosis and sarcopenia. We employed multiple statistical methods and bioinformatics tools to identify shared differentially expressed genes (DEGs). Subsequently, we conducted functional enrichment analysis, protein-protein interaction analysis, and TF-gene interaction network analysis, as well as TF-miRNA coregulatory network analysis. Drug compounds were predicted using the Drug Signatures database based on the common DEGs. Immune infiltration analysis was conducted on sarcopenia and atherosclerosis datasets. Finally, ROC curves were plotted to verify the reliability of our results using external databases.
RESULT: We identified 11 upregulated and 17 downregulated DEGs that were enriched in microglial cell activation, plasma membrane raft, and phosphatidylinositol-3,4-bisphosphate binding. PPI network analysis identified 6 hub genes: ADA, AIM2, CSF1R, C1QA, NCF1, and ITGAM. Notably, Isotretinoin HL60 UP is considered to be the best candidate drug for the treatment of sarcopenia and atherosclerosis, and some immune cells associated with atherosclerosis and sarcopenia were identified.
CONCLUSION: Through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for atherosclerosis and sarcopenia, providing a theoretical basis for future studies.
METHODS: We screened microarray data from the Gene Expression Omnibus to explore the relationship between atherosclerosis and sarcopenia. We employed multiple statistical methods and bioinformatics tools to identify shared differentially expressed genes (DEGs). Subsequently, we conducted functional enrichment analysis, protein-protein interaction analysis, and TF-gene interaction network analysis, as well as TF-miRNA coregulatory network analysis. Drug compounds were predicted using the Drug Signatures database based on the common DEGs. Immune infiltration analysis was conducted on sarcopenia and atherosclerosis datasets. Finally, ROC curves were plotted to verify the reliability of our results using external databases.
RESULT: We identified 11 upregulated and 17 downregulated DEGs that were enriched in microglial cell activation, plasma membrane raft, and phosphatidylinositol-3,4-bisphosphate binding. PPI network analysis identified 6 hub genes: ADA, AIM2, CSF1R, C1QA, NCF1, and ITGAM. Notably, Isotretinoin HL60 UP is considered to be the best candidate drug for the treatment of sarcopenia and atherosclerosis, and some immune cells associated with atherosclerosis and sarcopenia were identified.
CONCLUSION: Through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for atherosclerosis and sarcopenia, providing a theoretical basis for future studies.