Genetic and Epigenetic Biomarkers for the Early Oral Cancerization Risk in Periodontitis Patients

Abstract

Oral squamous cell carcinoma (OSCC) remains one of the most prevalent and aggressive malignancies worldwide, with late diagnosis contributing to poor survival rates. Recent evidence suggests that periodontitis may act as a co-factor in development of OSCC through persistent inflammation, microbial dysbiosis, and subsequent tissue remodeling. Identifying molecular signatures that link periodontitis with early oral cancerization is therefore of paramount importance for risk assessment, prevention, and timely intervention. This narrative review aims to provide an integrative overview of the current knowledge on molecular, genetic, and epigenetic biomarkers associated with oral cancer risk in patients with periodontitis. Specifically, periodontal pathogens such as Porphyromonas gingivalis and Fusobacterium nucleatum promote oral cancerization by modulating molecular, genetic, and epigenetic pathways, including p53, Cyclin D1, Ki-67, p16INK4A, DNA methylation, histone modifications, and microRNA regulation. Therefore, this review provides a discussion about the role of inflammatory mediators, oxidative stress-related molecules, microbial-derived products, genetic markers and epigenetic mechanisms as early molecular signals of malignant transformation. The study of these salivary biomarkers (salivaomics) has emerged as a promising non-invasive diagnostic tool, although variability in sampling, biomarker stability, and confounding factors such as coexisting periodontal disease remain significant limitations. By synthesizing the available evidence, this review summarizes recent evidence linking periodontitis to oral cancerization, highlights potential salivary, proteomic, and inflammatory biomarkers, and considers the role of periodontal therapy in improving inflammatory profiles and modulating tumor-related biomarkers. Finally, it explores future perspectives, including the integration of Artificial Intelligence (AI) to enhance biomarker-based diagnosis and risk stratification in OSCC patients.