Prediction of interactome hub genes in oral cancer and chronic inflammatory periodontitis

Oral infections that cause inflammation typically affect the gingival tissues. The immuneinflammatory reactions significantly influence the patient's vulnerability to periodontal diseases. Numerous studies have found a correlation between persistent inflammation and an increased risk of developing cancer in the afflicted oral epithelium. New research demonstrates a startling connection between periodontal conditions and various forms of cancer, including oral cancer.

The aim of the study was to use bioinformatics techniques in order to predict interatomic hub genes in oral cancer and periodontitis.

The datasets were screened for differentially expressed genes (DEGs) in periodontitis and oral cancer using the Gene Expression Omnibus (GEO) database, a gene expression data analysis tool. GeneMANIA was used to identify hub genes between oral cancer and periodontitis. Orange machine learning was conducted for hub gene prediction using random forest, decision tree, AdaBoost, and neural network.

The top 5 hub genes (RSPO4, CDHR2, DDAH2, HLA-J, and IRF3) were prioritized to explore their relationship with oral cancer and periodontal disease. The receiver operating characteristic (ROC) curve was constructed, with the area under the curve (AUC) for random forest at 0.999, for the decision tree at 0.998, for AdaBoost at 1.000, and for the neural network model at 0.865. The AdaBoost model, followed by random forest and decision tree, exhibited the highest level of accuracy (1.000). These results suggest that the 3 models demonstrate good predictability and may facilitate the early detection of periodontitis and oral cancer.

The insights derived from this study may contribute to the development of novel diagnostic and therapeutic techniques for chronic inflammatory periodontitis and oral cancer by utilizing computational approaches and integrating multi-omics data. The identification of interactome hub genes in these diseases has important clinical ramifications. The obtained outcomes may help decipher disease pathways, promote early detection, and create targeted treatments for better patient outcomes. The accurate prediction of hub genes may promote their utilization as biomarkers in the development of individualized treatment plans for both illnesses.