Selective inhibition of TGF-β-induced epithelial-mesenchymal transition overcomes chemotherapy resistance in high-risk lung squamous cell carcinoma
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...
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Main Authors: | , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
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Series: | Communications Biology |
Online Access: | https://doi.org/10.1038/s42003-025-07595-x |
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Summary: | 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. |
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ISSN: | 2399-3642 |