Identification of Periodontal Disease Diagnostic Markers Via Data Cross-Validation.

ABSTRACT:

Introduction and aims

Periodontitis is a globally prevalent disease that is clinically diagnosed when the periodontal tissues are pathologically affected. Therefore, it is vital to identify novel periodontitis-associated biomarkers that will aid in diagnosing or treating potential patients with periodontitis.

Methods

The GSE16134 and GSE10334 datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes between periodontitis and healthy samples. Single-sample gene set enrichment analysis was performed to identify significantly involved signalling pathways. Weighted gene correlation network analysis was used to identify key molecular modules. Hub genes were screened using key genes to construct a diagnosis and prediction model o

SUBMITTER: Du J 

PROVIDER: S-EPMC12142786 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature