An Immune Interaction Network driven approach for identifying biomarkers for Peri-implantitis

J1FDX ePOSTER BASIC RESEARCH

Background: Identifying valuable biomarkers to combat peri-implant diseases is an imperative and demanding task in biomedical research now. Molecular facts of peri-implant diseases are increasingly abundant due to the rapid advances in High-throughput data. However, a great gap remains in ide the use in implant dentistry. ntifying the potential molecular data associated with the phenotype. Hence, an interaction network based on differential expression pattern in peri-implantitis has been used to identify small RNA based biomarkers.

Aim/Hypothesis: Peri-implantitis is chronic inflammatory diseases linked to the accretion of a pathogenic biofilm on the implant surface, which are characterized by resorption of the implant-bone. Aim of the study is to identify sRNA biomarkers in peri-implantitis using system biology-based approach.

Materials and Methods: A meta-analysis of differential expression patter in peri-implantitis performed using GEO2R an interactive online tool to identify DEGs from GEO series. Six Gene expression profiles of peri-implant disease patients acquired from GSE57631. Following KEGG and GO analysis, a functional annotation for DEGs was performed. P < 0.05 was considered to indicate a statistically significant. Top 250 upregulated genes in peri-implantitis were retrieved. The gene ontology on immune regulation was constructed using Cytoscape V3.2, and density-based clustering was performed to identify highly dense hubs having a strong association in peri-implantitis phenotype. Further, the interaction network was enriched with small RNA associated with coexpression pattern, and its role in phenotype regulation was determined.

Results: The meta-analysis of six peri-implantitis dataset was performed, and the top 250 genes retrieved. Among the top 250 genes, those regulating immunomodulatory phenotypes determined using gene ontology interaction network. The interaction network was further clustered based on the density of interactions, and potential regulatory hubs were identified. Among the top regulatory hubs the genes PSMA6, PSMB2, PSMB3, PSMD4, PSME3 regulating antigen processing and presentation to MHC II and genes IFNA2, IFNA10, IFNA5 and POU1F1 regulating natural killer cells were determined to be key driver genes of peri-implantitis inflammatory phenotype. The interaction network was further enriched with coexpression genes and small RNA data to determine potential biomarkers. Among them, hsa-miR-31-5p associated with PSMB2 can be a potential biomarker for peri-implantitis diagnosis.

Conclusions and Clinical Implications: Molecular diagnosis of peri-implantitis is the essential process for efficient clinical management of a chronic inflammatory state. From the current study, we determined miRNA hsa-miR-31-5p could be a potential diagnostic biomarker for peri-implantitis.

Acknowledgements: We acknowledge SRMIST and SRM kattankulathur dental college for the support.

Keywords: Peri-implantitis, Biomarker, Interaction Network, miRNA, chronic inflammation