Recently, there are many researches on signature molecules of
periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze
periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing
periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential
protein profiles, functional enrichment analysis, and protein-protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and
periodontitis patients. DEPs correlating with
periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on
periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658
proteins and 115 DEPs, and the 115 DEPs are closely related to
inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from
periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of
periodontitis and are potential signature molecules involved in
periodontitis.