Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.

Journal: Frontiers in immunology
Published Date: Apr 17, 2024

Abstract

OBJECTIVE: Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.

Authors

  • Erbao Chen
    Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
  • Zhilin Zou
    Department of Ophthalmology, Affiliated Eye Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Rongyue Wang
    Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Zhen Peng
    State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi, 330047, PR China; School of Food Science & Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi, 330047, PR China.
  • Zhe Gan
    Gannan Medical University, Ganzhou, China.
  • Zewei Lin
    Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
  • Jikui Liu

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