Transcriptomic analysis reveals shared gene signatures and molecular mechanisms between obesity and periodontitis

Abstract

BackgroundBoth obesity (OB) and periodontitis (PD) are chronic non-communicable diseases, and numerous epidemiological studies have demonstrated the association between these two diseases. However, the molecular mechanisms that could explain the association between OB and PD are largely unclear. This study aims to investigate the common gene signatures and biological pathways in OB and PD through bioinformatics analysis of publicly available transcriptome datasets.MethodsThe RNA expression profile datasets of OB (GSE104815) and PD (GSE106090) were used as training data, and GSE152991 and GSE16134 as validation data. After screening for differentially expressed genes (DEGs) shared by OB and PD, gene enrichment analysis, protein-protein interaction (PPI) network construction, GeneMANIA analysis, immune infiltration analysis and gene set enrichment analysis (GSEA) were performed. In addition, receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the hub gene. Finally, we constructed the hub gene-associated TF-miRNA-mRNA regulatory network.ResultsWe identified a total of 147 DEGs shared by OB and PD (38 down-regulated and 109 up-regulated). Functional analysis showed that these genes were mainly enriched in immune-related pathways such as B cell receptor signalling, leukocyte migration and cellular defence responses. 14 hub genes (FGR, MNDA, NCF2, FYB1, EVI2B, LY86, IGSF6, CTSS, CXCR4, LCK, FCN1, CXCL2, P2RY13, MMP7) showed high sensitivity and specificity in the ROC curve analysis. The results of immune infiltration analysis showed that immune cells such as macrophages, activated CD4 T cells and immune B cells were present at high infiltration levels in both OB and PD samples.The results of GeneMANIA analysis and GSEA analysis suggested that five key genes (FGR, LCK, FYB1, LY86 and P2RY13) may be strongly associated with macrophages. Finally, we constructed a TF-miRNA-mRNA regulatory network consisting of 233 transcription factors (TFs), 8 miRNAs and 14 mRNAs based on the validated information obtained from the database.ConclusionsFive key genes (FGR, LCK, FYB1, LY86, P2RY13) may be important biomarkers of OB and PD. These genes may play an important role in the pathogenesis of OB and PD by affecting macrophage activity and participating in immune regulation and inflammatory responses.

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources

“…Yang et al explored the relationship between MitoEVs and the immune microenvironment in periodontitis by using machine learning and bioinformatics methods ( Yang et al, 2024 ). Cai et al found a common pattern of gene expression between obesity and periodontitis by analyzing transcriptomic data and identified five important biomarkers ( Cai et al, 2023 ). Huang et al used machine learning combined with L1 regularisation and the LIME model interpreter to identify genes associated with periodontitis ( Huang et al, 2023 ).…”

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

“…While P2RY13 is particularly sensitive to ADP, it can be triggered by both ADP and ATP. Research indicates a crucial function of P2RY13 in inflammation and immune imbalance 15 . Several studies have reported that P2RY13 is a key regulator of cholesterol transport and hepatic HDL endocytosis and is involved in bone formation, remodeling, cell survival, and neuroprotection 13 .…”

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“…Yang et al explored the relationship between MitoEVs and the immune microenvironment in periodontitis by using machine learning and bioinformatics methods (Yang et al, 2024). Cai et al found a common pattern of gene expression between obesity and periodontitis by analyzing transcriptomic data and identified five important biomarkers (Cai et al, 2023). Huang et al used machine learning combined with L1 regularisation and the LIME model interpreter to identify genes associated with periodontitis (Huang et al, 2023).…”