Molecular Determinants of Osseointegration in Implant-Supported Prostheses: A Narrative Review of Gene Expression Signatures, Signaling Pathways, and Bioinformatic Insights.

Osseointegration is a biologically complex process that determines the long-term success of implant-supported prostheses. Advances in molecular biology have shown that gene expression programs and signaling networks, rather than mechanical fixation alone, govern bone healing and implant integration. Yet, these molecular determinants are still seldom used to guide clinical decisions, which continue to rely on mechanical and histologic assessments. This narrative review addresses that gap by analyzing the molecular determinants of osseointegration through five connected perspectives: the cellular cascade that follows implant placement; the principal osteogenic signaling pathways (bone morphogenetic protein (BMP), Wnt/β-catenin, nuclear factor kappa B (NF-κB), and Runt-related transcription factor 2 (RUNX2)); transcriptomic signatures across the inflammatory, repair, and remodeling phases; bioinformatic gene regulatory and protein-protein interaction networks associated with implant success or failure; and the influence of implant surface properties on the molecular response. Across these perspectives, osteogenic and angiogenic genes such as RUNX2, collagen type I alpha 1 chain (COL1A1), bone gamma-carboxyglutamate protein (BGLAP), and vascular endothelial growth factor A (VEGFA) are consistently linked to successful integration, whereas sustained inflammatory and osteoclastogenic signatures such as IL6, tumor necrosis factor (TNF), matrix metalloproteinase-9 (MMP9), and nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (NFKB1) characterize failure. The review then considers how this knowledge may be translated into gene expression-based biomarkers, peri-implant crevicular fluid monitoring, patient risk stratification, and precision implant therapy, and identifies the main barriers to clinical adoption of an emerging implantogenomics framework.