Abstract
Periodontitis, a prevalent global oral health issue, is primarily characterized by chronic inflammation resulting from bacterial infection. Periodontitis primarily affects the tissues surrounding and supporting the teeth, encompassing the gingival tissue, periodontal attachment apparatus, and the bony socket. The disease mechanism results from intricate interactions between hereditary factors, the body's defense mechanisms, and shifts in the composition of oral microbiota, with each element playing a crucial role in the initiation and advancement of the pathological process. The early symptoms of periodontitis are often not obvious, resulting in patients often not seeking medical attention until they are seriously ill, so finding biomarkers for periodontitis is essential for timely diagnosis and treatment. In this study, we selected two datasets (GSE10334 and GSE16134) by in-depth analysis of publicly available sequencing data of affected and unaffected gum tissue in periodontitis patients in the GEO database. To identify key genes associated with periodontitis pathogenesis and explore potential therapeutic biomarkers, we employed two complementary computational approaches: Random Forest, a robust machine learning algorithm for feature selection, and Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method for identifying co-expressed gene modules. Through comprehensive analysis of these combined datasets, our objective is to elucidate the underlying molecular pathways governing periodontal disease progression, thereby identifying novel therapeutic targets that may facilitate the design of improved clinical interventions for this condition. This study establishes a substantial scientific foundation that contributes to both clinical applications and fundamental research in periodontitis. The findings not only offer valuable insights for developing early diagnostic strategies and therapeutic interventions but also provide a robust theoretical framework to guide future investigations into the molecular mechanisms underlying this complex disease.