Background: Periodontitis is among the most prevalent inflammatory conditions and greatly impacts oral health. This study aimed to elucidate the role of basement membrane-related genes in the pathogenesis and diagnosis of periodontitis.
Methods: GSE10334 was used for identification of hub genes via the differential analysis, protein-protein interaction network, MCC and DMNC algorithms, and evaluation via LASSO regression and support vector machine analysis to identify basement membrane-related markers in patients with periodontitis. Findings were validated by analysis of the GSE16134 dataset and quantitative reverse transcription PCR. The regulatory interplay among lncRNAs, miRNAs, and mRNAs was investigated through multiple databases. Immune infiltration analysis was performed to assess the immune landscape in periodontitis.
Results: ITGA7 was identified as a key gene for periodontitis, as supported by machine learning analysis, validation of expression, and receiver operating characteristic analysis from external datasets. Immune infiltration analysis revealed significant associations between ITGA7 expression and the infiltration of numerous immune cells implicated in periodontitis. Additionally, our findings suggest that the expression of the lncRNA LINC-PINT is significantly increased in periodontitis, and that it can modulate ITGA7 expression through hsa-miR-1293.
Conclusion: ITGA7 is a potential diagnostic and therapeutic target for periodontitis. The LINC-PINT/hsa-miR-1293/ITGA7 axis and the relationship between ITGA7 and immune infiltration provide new insights into the molecular mechanisms underlying periodontitis and highlight potential avenues for clinical intervention.
Keywords: Basement membranes; Biological marker; Immune; Machine learning; Periodontitis.
© 2024. The Author(s).