In modern comprehensive dental care, dental implant treatment is one of the best solutions for missing teeth.^[1] Due to the increase in dental implant therapy, the biological complications and diseases related to implants have significantly increased. The soft and hard tissues around dental implants are impacted by the damaging inflammatory process known as peri-implantitis. With the progress of inflammation, the hard tissue (alveolar bone), which surrounds the implant for retention, gradually disappears. Bacterial biofilm proliferating plays a major role in the progress of this disease. Gram-negative anaerobic bacteria are the most common microorganisms in the environment of implants. Bacterial colonization occurs in the transmucosal attachment of osseointegrated dental implants. Titanium plays a pivotal role in implant material. Laboratory studies show the high affinity of several microorganisms to the titanium surface. To solve this problem, nanomaterials are used to cover titanium. Dental nanomaterials such as chlorhexidine, zinc oxide, hydroxyapatite, copper-oxide, and other metal oxide nanoparticles are used for titanium-based dental implant coatings to provide antimicrobial function and better integration of the implants into the bone. However, peri-implantitis remains one of the problems of implant treatment failure.^[2-7]

Genetic traits, history of periodontitis, smoking, systemic diseases, and oral hygiene are the risk factors for this type of infection.^[4,8] Based on the progression of the disease and peri-implant defects, peri-implantitis is classified as early, moderate, and advanced stage.^[9] Diagnosis of peri-implant diseases is based on analyses of peri-implant microbiota, clinical parameters such as the presence of swelling and suppuration, evaluation of peri-implant crevicular fluid or saliva, and radiographic examination of peri-implant bone loss. In this disease, untreated lesions and inflammatory processes can lead to progressive loss of peri-implant bone and implant failure. Early detection of infection, monitoring the course of inflammation, and rapid initiation of treatment are very effective for the successful treatment of peri-implantitis, which is possible through the assessment of biomarkers.^[10-12]

Interactome networks are remarkably complex and diverse and play an important role in every biological process. By identifying up/down proteins and constructing protein-protein interactions, prediction of biomarkers in various diseases is possible. In recent years, biomarkers or biological markers have become increasingly important for efficient disease diagnosis, target discovery, study of drug activity and mechanisms of action, identification of toxicity and safety, assessment of efficacy signals, and understanding of disease processes.^[13-15] In this research, artificial neural networks were used to detect new biological markers and drug-biomarker interaction in peri-implantitis.

Many studies have been done on peri-implantitis patients, in this field. A significant increase in levels of proinflammatory cytokines such as interleukin 1β (IL-1β), tumor necrosis factor alpha, and IL-6 was reported in peri-implantitis patients compared to individuals with healthy implants.^[16] The levels of osteoprotegerin (OPG), and receptor activator of nuclear factor kappa-Β ligand (RANKL) were shown to be significantly higher in peri-implantitis. Even upregulation of some enzymes such as matrix metalloproteinases, cathepsin K, and elastase have been mentioned as diagnostic biomarkers. Overactivity of these enzymes is associated with irreversible destruction of the connective tissue around the implant.^[17] In addition, miRNA-142-3p and miRNA-146a were reported as potential biomarkers in the meta-analysis study on the expression of microRNAs in periodontal and peri-implant diseases.^[18]

Despite numerous studies associated with identifying biomarkers of peri-implantitis, early detection of the onset of peri-implantitis remains a major challenge, and more exploratory research is needed to identify the specific biomarkers. The present study aimed to predict interactome networks of up/down regulated proteins and analyze drug-gene interaction in peri-implantitis. We used microarray data of peri-implantitis patients to detect the up-regulated and down-regulated proteins. Furthermore, we constructed protein-protein interaction networks and identified candidate hub genes as biomarkers. Finally, to find potential drug targets, a drug-gene interaction database (DGIdb) was used and drug hub genes for therapeutic targets in peri-implantitis were discovered.

Peri-implantitis is a common problem in dental implant treatment and a major cause of dental implant loss. Inflammatory conditions and the presence of harmful microorganisms cause this site-specific infectious disease.^[21,22] However, even with current diagnostic criteria, identifying the precise underlying cause and detecting the early stages of peri-implantitis remains challenging. Determination of disease status or response to therapy is a common use of molecular biomarkers in medicine.^[23] In previous studies, we worked on the use of bioinformatics methods in drug identification and vaccine design for various diseases.^[24-27] The objective of the present investigation was to predict potential key biomarkers associated with peri-implantitis by constructing interactome networks of up/down regulated proteins and drug-gene interaction analysis. Eighty-seven upregulated DEGs and 129 downregulated DEGs were successfully identified when comparing the gene expression matrix of peri-implantitis, and healthy samples. After further analysis of detected genes, 10 hub genes (5 up-regulated genes: FCGR3B, CSF3R, AQP9, TREM1, and P2RY13 and 5 down-regulated genes: CXCL10, OASL, IFIT1, RSAD2, and ISG15) were identified that may play a crucial role in the occurrence and development of peri-implantitis. Most of the obtained hub genes were consistent with previous peri-implantitis biomarker studies.

In this study results, Fc gamma receptor IIIb (FCGR3B) also known as cluster of differentiation 16b was identified as one of the hub genes involved in peri-implantitis. In a study conducted by Saremi et al., to evaluate the relationship between Fc gamma-receptor gene polymorphisms and peri-implantitis 50 cases of this disease were studied. Based on the results of their study, a significant association between FCGR3B and peri-implantitis was proven.^[28] Suzuki et al., analyzed 3 microarray gene expression data sets (GSE10334, GSE16134, and GSE23586) to identify molecular biomarker candidates for the diagnosis and prognosis of chronic periodontitis. Based on their results, the gene FCGR3B was introduced as a key gene along with other genes.^[29] The presence of the CSF3R gene in the protein network obtained based on the analysis of the GSE33774 microarray dataset was confirmed.^[30] Aquaporin-9 (AQP-9) is an integral membrane protein that facilitates water transport. This hydrophobic membrane protein plays important roles in tissue homeostasis, the specialized functions of leukocytes such as immune response and activity against bacteria. Studies have described the involvement of AQPs in pathological conditions in various organs. In the study by Buffoli et al., tissue samples from six patients, comprising paired peri-implant soft tissue and periodontal gingiva tissue, were procured to analyze DEGs. In this research, 3549 genes were detected as DEGs among them AQP9, which has been confirmed to exhibit upregulation in the soft tissue surrounding the implant site.^[31,32] P2Y purinoceptor 13 with 354 amino acids encoded by the P2RY13 gene belongs to the G protein-coupled receptor. This receptor increases the release of pro-inflammatory factors and is involved in various pathophysiological processes such as apoptosis, autophagy, proliferation, and metabolism. Based on the study of Cai et al., P2RY13 is the common gene signature among 5 key genes in both diseases, obesity and periodontitis.^[33]

The DGIdb database was employed to investigate the interactions between hub genes and related potential therapeutic drugs. This platform facilitates precise medical diagnosis and drug discovery. According to the results of this DGIdb, 7 genes among 10 hub genes were druggable and had approved drugs. Forty-nine potential drugs/compounds for peri-implantitis treatment were detected. Among them, 4 targeted drugs for AQP9, 12 targeted drugs for CSF3R, 11 targeted drugs for CXCL10, 2 targeted drugs for IFIT1, 1 targeted drug for ISG15, 2 targeted drugs for OASL, and 17 targeted drugs for FCGR3B.

The main difficulty in developing new drugs is identifying target proteins. Detected genes have potential applications beyond pri-implantitis screening. They may offer fresh perspectives on future directions in therapeutic drug development for clinical use. The current research has some limitations, we use available gene expression profiles and in silico approaches for data analysis. However, this study provides new insights into the understanding of genes involved in diagnosing and treating pri-implantitis.

In conclusion, seven genes, including AQP9, CSF3R, CXCL10, IFIT1, ISG15, OASL, and FCGR3B, have been identified as druggable genes in pri-implantitis, which play a crucial role in the progression and prognosis of this disease. However, due to the lack of in vivo and in vitro validation in this study, our results and identified genes need to be validated by clinical or experimental methods.