Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PMID: 38914821

Abstract

PURPOSE: PANoptosis is considered a novel type of cell death that plays important roles in tumor progression. In this study, we applied machine learning algorithms to explore the relationships between PANoptosis-related lncRNAs (PRLs) and head and neck squamous cell carcinoma (HNSCC) and established a neural network model for prognostic prediction.

Authors

  • Zhenzhen Wang
    Department of Nursing, Union Hospital, Fujian Medical University, 29 Xinquan Road, Gulou District, 350001, Fuzhou, China.
  • Lixin Cheng
    Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China.
  • Juntao Huang
    Department of Otolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China. 798749265@qq.com.
  • Yi Shen
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China. Electronic address: shenyi_777@126.com.

Keywords

External Resources

Popular Topics
Recent Journals