Integrated bioinformatics and machine learning to explore the common mechanisms and potential biomarkers between periodontitis and preterm birth

Feng Mei¹Yutong Liu¹Wenting Xu¹Ruoyun Liu¹Xinlin Wang¹Tingting Li¹Yi Chen¹Tingting Wu^1*Wei Zhang^2*

• ¹College & Hospital of Stomatology, Anhui Medical University, Anhui Provincial Key Laboratory of Oral Diseases Research, Hefei, 230032, China, Hefei, China
• ²Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Rd, Hefei, Anhui Province, 230022, China, Hefei, China

Background: There is accumulating evidence suggesting an association between periodontitis (PD) and preterm birth (PTB), but the underlying mechanisms have not been fully elucidated. This study aims to explore potential biomarkers and mechanisms between PD and PTB through integrated bioinformatics and machine learning approaches. Methods: Datasets for PD (GSE16134 and GSE10334) and PTB (GSE203507, GSE174415, GSE18809, GSE73685 and GSE120480) were acquired from Gene Expression Omnibus (GEO). Then we performed Weighted gene co-expression network analysis (WGCNA), differential expressed genes (DEGs) analysis and three machine learning algorithms to identify cross-talk genes. To evaluate the potential of cross-talk genes as diagnostic biomarkers for PD and PTB, receiver operating characteristic (ROC) curve analysis and expression analysis were conducted. We then conducted functional enrichment analysis to elucidate the biological roles of the common DEGs. Single-sample gene set enrichment analysis (ssGSEA) assessed immune cell patterns of PD and PTB and biomarker-immune cell correlations. Additionally, we constructed a protein-protein interaction (PPI) network and further analyzed potential biomarkers using the cytoHubba plugin in Cytoscape software. Ultimately, the expression of the core genes in the PD animal model were validated. Results: We identified four cross-talk genes through the integrated analysis. Common DEGs were mainly concentrated in immune-related pathways. Following expression analysis and ROC curve analysis, we identified two genes (CD53 and BIN2) as potential biomarkers for PD and PTB. These genes were upregulated in disease groups compared to controls and exhibited strong diagnostic performance (AUC > 0.7) in both the training and validation cohorts. Moreover, CD53 and BIN2 displayed high connectivity within the PPI network. Immune cell infiltration analysis revealed that multiple immune cell types exhibited consistent upregulation in both diseases. In the PD model, consistent upregulation of CD53 and BIN2 was observed in the maxillary bone. Conclusion: We identified two potential biomarkers (CD53 and BIN2) for the concurrent diagnosis of PD and PTB, and suggested that the potential common mechanism of these two diseases may be correlated with the immune response. This study provides novel insights into the pathogenesis of both diseases, thereby informing future preventive, diagnostic and therapeutic strategies.