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基于缺氧相关基因的口腔癌化疗预测模型的建立

Establishment of Chemotherapy Prediction Model Based on Hypoxia-Related Genes for Oral Cancer.

作者信息

Zhou Chuhuan, Jia Hanqi, Jiang Nan, Zhao Jingli, Nan Xinrong

机构信息

Shanxi Medical University School and Hospital of Stomatology, Taiyuan, 030001, China.

Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, 030001, China.

出版信息

J Cancer. 2024 Aug 13;15(16):5191-5203. doi: 10.7150/jca.96654. eCollection 2024.

DOI:10.7150/jca.96654
PMID:39247585
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11375540/
Abstract

Identify the hypoxia genes related to chemotherapy resistance of oral cancer, and construct a chemotherapy response model by machine learning algorithm. 72 oral cancer patients with complete chemotherapy records and chemotherapy reactions were screened from the Cancer Genome Atlas (TCGA) database. According to the chemotherapy reactions, they were divided into chemotherapy sensitive group and chemotherapy resistant group. The differential genes were screened by Limma package. Then the chemotherapy response gene were screened by univariate analysis. Based on the gene expression profile of chemotherapy response, four machine learning algorithms were used to construct the prediction model of chemotherapy response. The core genes were screened by lasso regression analysis. Finally, the prognosis and immune infiltration of the core genes were analyzed. The results were verified by immunohistochemistry (IHC). We obtained 22 hypoxia related differential genes. Univariate analysis found 6 Chemotherapy response genes. Machine learning algorithms show that XGBoost have the best predictive performance for chemotherapy response. ALDOA is the core gene of chemotherapy resistance. Successfully constructed a chemotherapy prediction model for oral cancer by machine learning algorithm. Under hypoxia, the high expression of ALDOA is associated with chemotherapy resistance in oral cancer.

摘要

鉴定与口腔癌化疗耐药相关的缺氧基因，并通过机器学习算法构建化疗反应模型。从癌症基因组图谱（TCGA）数据库中筛选出72例有完整化疗记录和化疗反应的口腔癌患者。根据化疗反应，将他们分为化疗敏感组和化疗耐药组。使用Limma软件包筛选差异基因。然后通过单因素分析筛选化疗反应基因。基于化疗反应的基因表达谱，使用四种机器学习算法构建化疗反应预测模型。通过lasso回归分析筛选核心基因。最后分析核心基因的预后和免疫浸润情况。结果通过免疫组织化学（IHC）验证。我们获得了22个与缺氧相关的差异基因。单因素分析发现6个化疗反应基因。机器学习算法表明，XGBoost对化疗反应具有最佳预测性能。ALDOA是化疗耐药的核心基因。通过机器学习算法成功构建了口腔癌化疗预测模型。在缺氧条件下，ALDOA的高表达与口腔癌化疗耐药相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d944/11375540/8b06617573a8/jcav15p5191g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d944/11375540/8b06617573a8/jcav15p5191g008.jpg

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