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Liang, P., Chen, J., Yao, L., Hao, Z. and Chang, Q. (2023) A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes. Biomedicines, 11, Article No. 1479.
https://doi.org/10.3390/biomedicines11051479

has been cited by the following article:

• TITLE: Entrectinib in Lung Adenocarcinoma: Network Toxicology Identification of Prognostic Genes, Toxicity Targets and Combination Drugs

  AUTHORS: Yanting Xiao, Bin Ning, Fangwei Xiong, Jiale Shao, Liangcheng Huang

  KEYWORDS: Lung Adenocarcinoma, Entrectinib, Network Toxicology, Prognostic Model, Molecular Docking, Drug Sensitivity

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.14 No.6, June 2, 2026

  ABSTRACT: Entrectinib is a potent targeted agent for ROS1-positive lung adenocarcinoma (LUAD), but its side effects, resistance mechanisms and combination therapies remain understudied. Here, we integrated network toxicology and bioinformatics to explore its core gene signatures, prognostic value, toxic targets and potential combinations using TCGA transcriptomic data, PPI network, Cox model with SHAP analysis, molecular docking and GDSC drug screening. We identified 93 candidate and 20 hub genes enriched in cell cycle pathways. The prognostic model showed robust performance (HR = 2.034, P