Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer.

Journal: Frontiers in immunology
Published Date: May 14, 2025

Abstract

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The tumor microenvironment (TME) plays a key role in cancer progression. Advances in single-cell transcriptomics have highlighted the impact of stromal cells on tumor progression, immune suppression, and immunotherapy. This study aims to identify stromal cell marker genes and develop a prognostic signature for predicting TNBC survival outcomes and immunotherapy response.

Authors

  • Fanrong Li
    Department of Genetics, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Congnan Jin
    Department of Genetics, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Yacheng Pan
    Department of Genetics, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Zheng Zhang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Liying Wang
    Laboratory of Nutrition and Functional Food, Jilin University, Changchun 130062, People's Republic of China.
  • Jieqiong Deng
    Department of Genetics, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Yifeng Zhou
  • Binbin Guo
    Department of Genetics, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Shenghua Zhang
    Department of Genetics, School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.

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