口腔颌面外科杂志2025，Vol.35Issue(5)：356-364,9.DOI:10.12439/kqhm.1005-4979.2025.05.004
基于机器学习筛选和鉴定与头颈部鳞状细胞癌预后相关的关键基因
Machine learning-based screening and identification of key genes associated with the prognosis of head and neck squamous cell carcinoma
摘要
Abstract
Objective:To screen and identify key genes associated with prognosis in head and neck squamous cell carcinoma(HNSCC).Methods:The clinical data and RNA sequencing(RNA-Seq)data of HNSCC patients from the Cancer Genome Atlas(TCGA)database were randomly divided into training set(cohortⅠ,n=228)and validation set(cohortⅡ,n=98).The prognostic seed genes were determined using random survival forest(RSF)models and Cox proportional hazards models,and the key genes related to prognosis were further screened using a forward selection modes.The survival risk scoring system was constructed using the selected key genes,and these genes were subsequently validated and subjected to bioinformatics analysis.The expression of the key genes was detected by real-time quantitative polymerase chain reaction(RT-qPCR)in the human oral epithelial keratinocytes(HOK cell line)and the human tongue squamous carcinoma cell(CAL27 cell line).Results:Twelve prognosis-related key genes were identified.Patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group,with a hazard ratio(HR)of 4.19 in CohortⅡ(P<0.05).There was a significant difference in the expression level of the key genes between the HOK cell line and the CAL27 cell line(P<0.05).Conclusion:Twelve key genes affecting the prognosis of HNSCC patients were identified through a machine learning model and may serve as prognostic biomarkers for HNSCC.关键词
机器学习/头颈部鳞状细胞癌/随机生存森林模型/Cox比例风险模型/生存风险Key words
machine learning/head and neck squamous cell carcinoma/random survival forest model/Cox proportional hazards model/survival risk分类
口腔医学引用本文复制引用
姚佳,党琳琳,屠军波,那思家..基于机器学习筛选和鉴定与头颈部鳞状细胞癌预后相关的关键基因[J].口腔颌面外科杂志,2025,35(5):356-364,9.基金项目
陕西省卫生健康口腔颌面外科科研创新团队(2024TD-19) （2024TD-19）
