Bioinformatics-based identification of mitophagy related biomarkers in periodontitis

Periodontitis is a chronic inflammatory disease that leads to destruction of periodontal supporting tissues, and its early diagnosis remains challenging. Abnormal mitophagy is closely related to the pathogenesis of periodontitis, whereas mitophagy-related biomarkers are still lacking. This study aimed to identify and preliminarily validate mitophagy-related biomarkers for periodontitis using bioinformatics analysis and clinical sample verification. Transcriptomic data of periodontitis were retrieved from the GEO database (GSE16134 as training set, GSE10334 as validation set). Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), protein–protein interaction (PPI) network, and three machine learning algorithms (LASSO, SVM-RFE, Boruta) were applied to screen candidate biomarkers. Expression patterns and diagnostic efficacy were internally validated using boxplots and ROC analysis. Gingival tissues from 10 periodontitis patients and 10 healthy controls were collected for RT-qPCR validation. As results, mitophagy activity was significantly suppressed in periodontitis tissues. Two genes, FUNDC1 and TOMM20, were identified as mitophagy-related candidate biomarkers, both of which were markedly downregulated in periodontitis and showed favorable discriminatory ability in the datasets. RT-qPCR confirmed the consistent downregulation of FUNDC1 and TOMM20 in clinical periodontitis samples. In conclusion, FUNDC1 and TOMM20 are promising mitophagy-related candidate biomarkers for periodontitis. These findings provide preliminary clues for exploring mitophagy-based diagnosis and pathogenesis of periodontitis, yet further large‑scale and independent validation is required before clinical application.