Shared diagnostic biomarkers in metabolic syndrome and coronary artery disease identified by integrated bioinformatics and machine learning

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Background: Metabolic syndrome (MetS) comprises various complicated metabolic disorders. Coronary artery disease (CAD) is a major cardiovascular disease worldwide. These two diseases are the principal causes of morbidity and mortality in older adults. Previous studies have suggested a potential link between these two diseases. To date, finding sensitive and effective diagnostic biomarkers of MetS and CAD remains challenging. This research examined the potential shared biomarkers of MetS and CAD using a comprehensive bioinformatics approach. Methods: Five microarray datasets about MetS and CAD were used in our analysis. Integrated bioinformatics methods, such as differentially expressed gene (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and machine learning (ML) algorithms, were utilized to discern hub genes of MetS and CAD. Meanwhile, single-sample gene enrichment analysis and single-cell analysis were leveraged to analyze immune cell infiltration and the abundance of gene expression in immune cells. Finally, the expression levels of key biomarkers were primarily verified by RT-qPCR. Results: We identified 666 DEGs associated with MetS and 762 DEGs related to CAD, with 22 overlapping genes. Meanwhile, 3 hub genes related to MetS and CAD were screened by WGCNA, which were APOBEC3B, SGSM2, and LRRC32. Two hub genes, ADRB2 and KDM6A, were downregulated in the two diseases. ROC curves confirmed their diagnostic value. Furthermore, single-cell sequencing analysis and RT-qPCR obtained consistent findings. Conclusion: ADRB2 and KDM6A are identified as hub candidate diagnostic biomarkers for MetS and CAD. These genes may offer new targets for MetS and CAD and assist in exploring the molecular mechanisms underlying both diseases.

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