
Immunogenetic features and their regulatory mechanisms on immune cells in peri-implantitis:a bioinformatics analysis
Zhu Xingyu, Tang Han, Chen Tao, Ji Ping
Immunogenetic features and their regulatory mechanisms on immune cells in peri-implantitis:a bioinformatics analysis
Objective To investigate the immune cells with significant infiltration and key immune-related genes in the progression of peri-implantitis based on bioinformatics analysis. Methods The GSE106090,GSE33774,and GSE57631 datasets from the NCBI Gene Expression Omnibus(GEO) were integrated. The single-sample gene set enrichment analysis(ssGSEA) was used to assess the immune cell infiltration score of peri-implantitis tissue and healthy gingival tissue,and the least absolute shrinkage and selection operator(LASSO) regression analysis was used to identify key immune genes. Results After the three datasets were integrated and the batch effect was removed,the ClusterProfiler package was used to perform gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) Gene Set Enrichment Analysis(GSEA) for peri-implantitis to identify significantly upregulated and downregulated signaling pathways and biological processes. The differentially expressed genes were intersected with the immune-related genes obtained from the ImmPort database,and key immune genes of the disease were successfully identified by the LASSO regression analysis,including C-C motif chemokine ligand 18(CCL18),interleukin-1β(IL1B),interleukin-6(IL6),complement C3(C3),natriuretic peptide receptor 3(NPR3),peptidase inhibitor 3(PI3),leukocyte immunoglobulin like receptor B3(LILRB3),and leucine rich repeat containing G protein-coupled receptor 4(LGR4). Subsequently,a correlation analysis was conducted with ssGSEA immune infiltration score,and the results showed varying degrees of correlation between these genes and the 23 types of immune cells with a significant increase in peri-implant soft tissue. GO and KEGG enrichment analyses showed that the genes such as IL1B,IL6,CCL18,C3,LGR4,PI3,and LILRB3 were mainly involved in the biological processes such as humoral immunity,adaptive immunity,leukocyte migration,and skin epidermal development,while NPR3 was mainly associated with the biological processes such as leukocyte proliferation and body fluid regulation. Conclusion Differentially expressed immune-related genes are obtained by the bioinformatics method,and eight key immune genes are identified,which participate in multiple links of immune response and inflammatory response in peri-implantitis and exhibit high sensitivity to the disease background of peri-implantitis. The identification of these immune genes provides important molecular targets for a deeper understanding of the pathogenesis of peri-implantitis and the development of novel therapeutic strategies.
peri-implantitis / bioinformatics / immunology / machine learning
1 |
|
2 |
|
3 |
|
4 |
|
5 |
|
6 |
|
7 |
|
8 |
|
9 |
|
10 |
|
11 |
|
12 |
|
13 |
|
14 |
|
15 |
|
16 |
|
17 |
|
18 |
|
19 |
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
|
25 |
|
26 |
|
27 |
|
/
〈 |
|
〉 |