生命科学研究2025，Vol.29Issue(3)：216-223,8.DOI:10.16605/j.cnki.1007-7847.2024.04.0146
肺腺癌分型方法及临床应用的研究进展
Progress of Classification Methods and Their Clinical Applications for Lung Adenocarcinoma
摘要
Abstract
As one of the prevalent subtypes of lung cancer,lung adenocarcinoma(LUAD)is associated with a relatively poor prognosis.Precise classification of this cancer is instrumental in guiding strategies of thera-peutic interventions.This article introduces the mainstream methods for LUAD classification.According to data sources,there are phenotype-based and molecular feature-based classification methods,and according to the number of data types,there are single-omics-based and multi-omics-integrated classification methods.Additionally,the article summarizes the applications and prospects of these different methods in clinical practice.The traditional phenotype-based classification methods play a certain role in the treatment of LUAD at present,but they also exhibit significant limitations.To comprehensively and precisely understand the pathological characteristics of LUAD,researchers are actively exploring the molecular subtypes of the cancer.By integrating data from genomics,transcriptomics,proteomics,and radiomics,researchers can achieve a more holistic understanding of the molecular mechanisms of LUAD.Deep learning algorithms provide crucial tech-nical support for the efficient integration of multi-omics information.They can interrelate data from different omics fields,enabling rapid and accurate analysis of vast datasets.Therefore,deep learning algorithms pro-vide a potent tool in oncology research,helping researchers to delve deeper into the complexities of LUAD,driving continuous progress in the cancer research and offering more precise guidance for personalized treat-ment strategies.关键词
肺腺癌(LUAD)/分子亚型/多组学/深度学习/多维数据整合Key words
lung adenocarcinoma(LUAD)/molecular subtype/multi-omics/deep learning/multidimensional data integration分类
生物科学引用本文复制引用
何海斌,马佳宏,唐妍,马明月..肺腺癌分型方法及临床应用的研究进展[J].生命科学研究,2025,29(3):216-223,8.基金项目
重庆市教委科学技术研究项目(KJ202200678822935) （KJ202200678822935）
