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

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ID: 204870
2020
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Ranked #24 of 92 articles by views in international immunopharmacology

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Abstract
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.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.We found seven hypoxia-immune related CpG sites. Lasso had the best classification performance among three machine learning algorithms.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.
Reference Key
zeng2020machineinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zeng, Hao;Luo, Meng;Chen, Linyan;Ma, Xinyu;Ma, Xuelei;
Journal international immunopharmacology
Year 2020
DOI
S1567-5769(20)32415-2
URL
Keywords
Hypoxia Machine learning dna methylation oral squamous cell carcinoma tumor immune microenvironment

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