Exploring the relationship between potential common biomarkers and related immune cell infiltration between metabolic syndrome and peri-implantitis based on bioinformatics
CHEN Yu-xiang
ZHAO An-na
YANG Xia
YANG Hao-ran
CHENG Ting-ting
RAO Xian-qi
LI Zi-liang
Abstract：Objective This study aims to identify potential common biomarkers and related immune cell infiltration between metabolic syndrome(MetS)and peri implantitis(PI).Additionally,a diagnostic model for PI will be constructed.Method Download transcriptome data of MetS and PI from the GEO database,use weighted gene co-expression network analysis and differential expression analysis to identify common disease-causing genes between the two diseases,categorize them,and conduct enrichment analysis.LASSO and Boruita algorithms were employed to select hub genes,which were then used to develop a diagnostic model for PI.Pearson correlation analysis was used to analyze the relationship between hub genes and periodontal and implant probing depth.CIBERSORT software was used to analyze the immune infiltration of MetS and PI samples,and to explore the relationship between pivot genes and immune cell infiltration.Result A total of 81 co-pathogenic genes were identified,with a significant proportion being related to diabetes and osteoporosis.These genes were mainly involved in signal pathways associated with chemokines and their receptors.Two hub genes(BATF and CD79B)were ultimately identified and a diagnostic model was constructed(AUC=0.932).Immune infiltration analysis showed significant differences in immune cell infiltration levels between the disease group and the healthy group of MetS and PI,and there was a significant correlation between hub genes and different immune cell populations.Conclusion BATF and CD79B are potential common biomarkers for MetS and PI,and they have close relationships with the host immune process.A diagnostic model based on this can provide assistance for the prevention and early diagnosis of PI in MetS patients.
Keywords：peri-implantitismetabolic syndromemachine learningbioinformaticsimmune infiltration analysis
Publication Date：2025-01-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 37-46 )