Machine learning analysis of DNA methylation in a hypoxia-immune model of oral squamous cell carcinoma

Background: Hypoxia status and immunity are related with the development and prognosis of oral squamous cell carcinoma (OSCC). Here, we constructed a hypoxia-immune model to explore its upstream mechanism and identify potential CpG sites. Methods: The hypoxia-immune model was developed and validated by the iCluster algorithm. The LASSO, SVM-RFE and GA-ANN were performed to screen CpG sites correlated to the hypoxia-immune microenvironment. Results: We found seven hypoxia-immune related CpG sites. Lasso had the best classification performance among three machine learning algorithms. Conclusion: We explored the clinical significance of the hypoxia-immune model and found seven hypoxia-immune related CpG sites by multiple machine learning algorithms. This model and candidate CpG sites may have clinical applications to predict the hypoxia-immune microenvironment.