{"title":"Selective inhibition of TGF-β-induced epithelial-mesenchymal transition overcomes chemotherapy resistance in high-risk lung squamous cell carcinoma.","authors":"Liangdong Sun, Jue Wang, Huansha Yu, Xinsheng Zhu, Jing Zhang, Junjie Hu, Yilv Yan, Xun Zhang, Yuming Zhu, Gening Jiang, Ming Ding, Peng Zhang, Lele Zhang","doi":"10.1038/s42003-025-07595-x","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10552,"journal":{"name":"Communications Biology","volume":"8 1","pages":"152"},"PeriodicalIF":5.2000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787392/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s42003-025-07595-x","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Communications Biology is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the biological sciences. Research papers published by the journal represent significant advances bringing new biological insight to a specialized area of research.