机器学习在胃癌的分子特征筛选及预后模型建立应用的回顾
A Review of the Application of Machine Learning in Molecular Feature Screening and Prognostic Model Establishment of Gastric Cancer
DOI: 10.12677/ACM.2024.143788, PDF,    科研立项经费支持
作者: 宝 仰：大理大学临床医学院，云南 大理；和红阳, 管云飞, 张 阳, 方俊伟：大理大学第一附属医院普外一科，云南 大理
关键词: 胃癌；机器学习；MSI；Gastric Cancer； Machine Learning； MSI
摘要: 胃癌在我国恶性肿瘤的死亡率中位列第三，严重危害我们的生命和健康，目前治疗方式多以手术治疗为主，但许多患者发现时已经是晚期，基本无手术机会。微卫星不稳定是一种在胃癌中的遗传变异类型，与基因组不稳定性和肿瘤进展相关。机器学习作为一种近年来常用于肿瘤的数据分析工具，可以对大规模的基因表达数据进行分析，筛选出与胃癌微卫星不稳定相关的特征基因，建立预后模型，有助于指导胃癌的预后评估和治疗决策。本文综合归纳了机器学习在肿瘤中分子特征筛选及预后模型建立的应用，分析其在胃癌诊断、治疗及预后判断中的应用价值，并对今后的研究方向进行展望。
Abstract: Gastric cancer ranks third in the mortality rate of malignant tumors in China, which seriously en-dangers our lives and health. Microsatellite instability is a type of genetic variant in gastric cancer that is associated with genomic instability and tumor progression. As a data analysis tool commonly used in tumors in recent years, machine learning can analyze large-scale gene expression data, screen out characteristic genes associated with microsatellite instability of gastric cancer, and es-tablish prognostic models, which will help guide the prognosis assessment and treatment decisions of gastric cancer. This article comprehensively summarizes the application of machine learning in the screening of molecular features and the establishment of prognostic models in tumors, analyzes its application value in the diagnosis, treatment and prognosis of gastric cancer, and looks forward to future research directions.
文章引用：宝仰, 和红阳, 管云飞, 张阳, 方俊伟. 机器学习在胃癌的分子特征筛选及预后模型建立应用的回顾[J]. 临床医学进展, 2024, 14(3): 894-899. https://doi.org/10.12677/ACM.2024.143788

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