Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma.

Journal: Discover oncology
Published Date: May 23, 2025

Abstract

BACKGROUND: Lung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality, and tumor heterogeneity could result in diverse prognostic subtypes. Traditional prognostic factors, like tumor, node, and metastasis (TNM) staging, offer limited predictive accuracy. This study aims to identify LUSC subtypes and develop predictive models that have the potential to improve prognosis prediction accuracy and support personalized treatment.

Authors

  • Guangliang Duan
    Department of Oncology, The Affiliated Hospital of Hangzhou Normal University, Zhengzhou, Zhejiang, People's Republic of China. Electronic address: dgl20171926@hznu.edu.cn.
  • Qi Huo
    Department of Oncology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, People's Republic of China.
  • Wei Ni
    Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Marsfield, NSW 2122, Australia.
  • Fei Ding
    Information Processing and Communication Technology Lab, Shanghai Institute of Satellite Engineering, Shanghai, China.
  • Yuefang Ye
    Department of Gastroenterology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, People's Republic of China.
  • Tingting Tang
    Department of Internal Medicine, Jinping District People's Hospital of Shantou, Shantou, China.
  • Huiping Dai
    Department of Proctology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, People's Republic of China. dhp20221316@hznu.edu.cn.

Keywords

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