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Published on: February 1, 2020
Machine learning analysis of DNA methylation in a hypoxia-immune model of oral squamous cell carcinoma
Hao Zeng1, Meng Luo1, Linyan Chen1
1Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, China.
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.
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