A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD

A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet cl...

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Bibliographic Details
Main Authors: Zhenyu Zhao, Wei Yin, Xiong Peng, Qidong Cai, Boxue He, Shuai Shi, Weilin Peng, Guangxu Tu, Yunping Li, Dateng Li, Yongguang Tao, Muyun Peng, Xiang Wang, Fenglei Yu
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-05-01
Series:Frontiers in Immunology
Subjects:
GGO (ground-glass opacity)
LUAD
TCGA
GEO
prognosis
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2022.872387/full

Internet

https://www.frontiersin.org/articles/10.3389/fimmu.2022.872387/full

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