Selective inhibition of TGF-β-induced epithelial-mesenchymal transition overcomes chemotherapy resistance in high-risk lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) represents a major subtype of lung cancer, and it demonstrates limited treatment options and worse survival. Identifications of a prognostic model and chemoresistance mechanism can be helpful for improving stratification and guiding therapy decisions. The integrative development of machine learning-based models reveals a random survival forest (RSF) prognostic model for LUSC. The 12-gene RSF model exhibits high prognostic power in more than 1,000 LUSC patients. High-risk LUSC patients are associated with worse survival and the activation of the epithelial-mesenchymal transition pathway. Additionally, high-risk LUSC patients are resistant to docetaxel or vinorelbine treatment. In vitro and in vivo drug sensitivity experiments indicates that high-risk HCC15/H226 tumour cells and cell line-derived xenograft models are more resistant to vinorelbine treatment. Furthermore, the combination of chemotherapy with transforming growth factor-β inhibition augments antitumour responses in LUSC tumours. Our study provides valuable insights into prognosis stratification and the development of therapeutic strategies for LUSC.

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Publication Details

Journal
Communications Biology
Published
2025-02-01
DOI
https://doi.org/10.1038/s42003-025-07595-x
Citations
4
Primary Topic
TGF-β signaling in diseases
Type
article
Field-Weighted Citation Impact
3.22

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article

Selective inhibition of TGF-β-induced epithelial-mesenchymal transition overcomes chemotherapy resistance in high-risk lung squamous cell carcinoma

Yuming Zhu, Gening Jiang, Ming Ding, Peng Zhang et al.
4 citations
Communications Biology
TGF-β signaling in diseases
3.22
article

Selective inhibition of TGF-β-induced epithelial-mesenchymal transition overcomes chemotherapy resistance in high-risk lung squamous cell carcinoma

Yuming Zhu, Gening Jiang, Ming Ding, Peng Zhang, Xun Zhang, Jue Wang, Lele Zhang, Jing Zhang, Xinsheng Zhu, Liangdong Sun, Huansha Yu, Junjie Hu, Yilv Yan
article en
4 citations

Abstract

Lung squamous cell carcinoma (LUSC) represents a major subtype of lung cancer, and it demonstrates limited treatment options and worse survival. Identifications of a prognostic model and chemoresistance mechanism can be helpful for improving stratification and guiding therapy decisions. The integrative development of machine learning-based models reveals a random survival forest (RSF) prognostic model for LUSC. The 12-gene RSF model exhibits high prognostic power in more than 1,000 LUSC patients. High-risk LUSC patients are associated with worse survival and the activation of the epithelial-mesenchymal transition pathway. Additionally, high-risk LUSC patients are resistant to docetaxel or vinorelbine treatment. In vitro and in vivo drug sensitivity experiments indicates that high-risk HCC15/H226 tumour cells and cell line-derived xenograft models are more resistant to vinorelbine treatment. Furthermore, the combination of chemotherapy with transforming growth factor-β inhibition augments antitumour responses in LUSC tumours. Our study provides valuable insights into prognosis stratification and the development of therapeutic strategies for LUSC.

Communications BiologyVol. 8(1)
Tongji University (CN), China Pharmaceutical University (CN), Shanghai Pulmonary Hospital (CN)
National Natural Science Foundation of China, Shanghai Municipal Education Commission, Shanghai Pulmonary Hospital
Good health and well-being
Openalex Percentile: Top 9%
TGF-β signaling in diseases
3.22
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