Decoding the Genetic Interplay: Integrated Computational Insights into the Interplay Between Peri-Implantitis and Osteoporosis

Background: Peri-implantitis (PI) and osteoporosis (OP) are prevalent chronic conditions with substantial health impacts, yet the genetic and molecular links between these diseases are not fully understood. Clarifying the shared genetic similarities between PI and OP is crucial for enhancing diagnostic and therapeutic strategies. Objective: The study aims to elucidate the shared genetic and molecular pathways between PI and OP through an integrated computational biology analysis, contributing to the development of more effective diagnostic and therapeutic approaches. Methods: The research utilized PI-related datasets GSE33774 and GSE106090 from the GEO database and genes associated with PI and OP from DisGeNET. Differential expression analysis was conducted using the "limma" package in R. Protein-Protein Interaction (PPI) networks were constructed with Cytoscape, identifying cross-talk genes between PI and OP. The LASSO model was applied for marker gene selection and GO Biological Process and KEGG pathway analyses were conducted. Analyses of drug susceptibility and immune infiltration were also included to understand potential therapeutic implications and the role of the immune environment. Results: The analysis identified key differentially expressed genes (DEGs) in PI and significant cross-talk genes between PI and OP. The PPI network highlighted central genes like HIF1A, TNF, TGM2, and SPP1. Marker genes (i.e., PIK3CG, SFRP4, CCR5, and PRLR) pinpointed through LASSO and logistic regression showed significant correlations with the cross-talk genes. Insights into drug susceptibility and the immune infiltration landscape in both conditions were provided. Single cell analysis further delineated the expression patterns of these marker genes in relevant cell types. Conclusion: This study uncovers novel insights into the shared genetic landscape of peri-implantitis (PI) and osteoporosis (OP) by identifying four key marker genes—PIK3CG, SFRP4, CCR5, and PRLR—and their roles in the pathogenesis of these conditions. The integrated computational biology approach employed in this research contributes to the current body of literature by offering a deeper understanding of the genetic and molecular mechanisms underlying the interplay between PI and OP.