Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework

Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework

Abstract Background Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory system, with high incidence and mortality rates. Cellular senescence (CS) widely affects the tumor microenvironment (TME) and tumor growth, and is related to the invasion and immune escape of tumor cells...

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Bibliographic Details
Main Authors: Ting Ge, Guixin He, Qian Cui, Shuangcui Wang, Zekun Wang, Yingying Xie, Yuanyuan Tian, Juyue Zhou, Jianchun Yu, Jinmin Hu, Wentao Li
Format: Article
Language:English
Published: Springer 2025-04-01
Series:Discover Oncology
Subjects:
Cellular senescence
Lung adenocarcinoma
Machine learning
Bioinformatics analysis
Immunotherapy
Online Access:https://doi.org/10.1007/s12672-025-02262-3
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