Identifying MS4A6A

Purpose: Concrete epidemiological evidence has suggested the mutually-contributing effect respectively between nonalcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM), and periodontitis (PD); however, their shared crosstalk mechanism remains an open issue. Method: The NAFLD, PD, and T2DM-related datasets were obtained from the NCBI GEO repository. Their common differentially expressed genes (DEGs) were identified and the functional enrichment analysis performed by the DAVID platform determined relevant biological processes and pathways. Then, the STRING database established a PPI network of such DEGs and topological analysis through Cytoscape 3.7.1 software along with the machine-learning analysis by the least absolute shrinkage and selection operator (LASSO) algorithm screened out hub characteristic genes. Their efficacy was validated by external datasets using the receiver operating characteristic (ROC) curve, and gene expression and location of the most robust one was determined using single-cell sequencing and immunohistochemical staining. Finally, the promising drugs were predicted through the CTD database, and the CB-DOCK 2 and Pymol platform mimicked molecular docking. Result: Intersection of differentially expressed genes from three datasets identified 25 shared DEGs of the three diseases, which were enriched in MHC II-mediated antigen presenting process. PPI network and LASSO machine-learning analysis determined 4 feature genes, of which the MS4A6A gene mainly expressed by macrophages was the hub gene and key immune cell type. Molecular docking simulation chosen fenretinide as the most promising medicant for MS4A6A Conclusion: MS4A6A