机器学习分析口腔鳞状细胞癌缺氧免疫模型中的 DNA 甲基化。
Machine learning analysis of DNA methylation in a hypoxia-immune model of oral squamous cell carcinoma.
机构信息
Department 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.
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, China.
出版信息
Int Immunopharmacol. 2020 Dec;89(Pt B):107098. doi: 10.1016/j.intimp.2020.107098. Epub 2020 Oct 19.
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.
背景
缺氧状态和免疫与口腔鳞状细胞癌（OSCC）的发展和预后有关。在这里，我们构建了一个缺氧免疫模型来探索其上游机制并确定潜在的 CpG 位点。
方法
通过 iCluster 算法构建和验证缺氧免疫模型。使用 LASSO、SVM-RFE 和 GA-ANN 筛选与缺氧免疫微环境相关的 CpG 位点。
结果
我们发现了七个与缺氧免疫相关的 CpG 位点。在三种机器学习算法中，Lasso 具有最佳的分类性能。
结论
我们探讨了缺氧免疫模型的临床意义，并通过多种机器学习算法发现了七个与缺氧免疫相关的 CpG 位点。该模型和候选 CpG 位点可能具有预测缺氧免疫微环境的临床应用价值。
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