摘要
食管癌是全球高发恶性肿瘤,其高侵袭性与早期诊断率低导致患者预后较差,5年生存率不足20%。随着高通量测序技术的发展,海量组学数据为食管癌分子机制研究提供了丰富资源,而生物信息学与机器学习的交叉融合正成为解析肿瘤异质性、构建精准预后模型的核心路径。本文系统综述了近年来利用生物信息学挖掘食管癌关键基因、结合机器学习算法构建预后模型的研究进展,深入分析多组学整合策略、模型验证方法及临床转化挑战,为食管癌精准医学研究提供理论参考与技术路径。
Esophageal cancer is a highly prevalent malignant tumor worldwide.Its high invasiveness and low early diagnosis rate result in poor prognosis for patients,with a 5-year survival rate of less than 20%.With the development of high-throughput sequencing technology,massive omics data provides abundant resources for the study of molecular mechanisms of esophageal cancer,and the cross fusion of bioinformatics and machine learning is becoming the core path for analyzing tumor heterogeneity and constructing accurate prognostic models.This article systematically reviews the research progress in recent years on using bioinformatics to mine key genes in esophageal cancer and combining machine learning algorithms to construct prognostic models.It deeply analyzes multi omics integration strategies,model validation methods,and clinical translation challenges,providing theoretical references and technical paths for precision medicine research on esophageal cancer.
作者
应书岩
Shuyan Ying(The first Clinical Medical of School,Gannan Medical University,Ganzhou,Jiangxi 341000)
出处
《医学研究前沿》
2025年第7期85-87,共3页
Frontiers of Medical Research
关键词
食管癌
生物信息学
机器学习
关键基因
预后模型
多组学整合
esophageal cancer
bioinformatics
machine learning
key genes
prognostic model
multi omics integration
