MZB1-Driven Endoplasmic reticulum stress model as a predictor of breast cancer progression and survival.

Journal: Functional & integrative genomics
Published Date:

Abstract

Endoplasmic reticulum (ER) stress and its associated unfolded protein response (UPR) have been demonstrated to play a crucial role in cancer's progression, but their prognostic significance in breast cancer (BC) remains unclear. In this study, a reliable ER-related gene signature was developed for the purpose of predicting BC prognosis and investigating the associated immune landscape. By utilizing public datasets and analytical methods, we developed a 16 ER-related gene risk signature and verified its efficacy in predicting prognosis in independent patient groups. Patients in the high-risk group exhibited significantly poorer survival rates. Single-cell analysis revealed that the low-risk group exhibited stronger immune interactions. Conversely, the high-risk group exhibiting elevated immune checkpoints may signify an immunosuppressive microenvironment or heightened sensitivity to immune checkpoint inhibitor therapy. In vitro and vivo experiments confirmed that knocking down the expression of Marginal Zone B And B1 Cell Specific Protein (MZB1) significantly inhibited the proliferation, invasion, and tumorigenesis of breast cancer. The 16 ER-related gene signature is capable of effectively categorizing breast cancer patients into different risk levels, thereby providing a basis for personalized therapy. MZB1 has been identified as a significant regulatory factor, suggesting its potential as a target for the treatment of breast cancer.

Authors

  • Purong Zhang
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Yuying Wang
    Data Mining Research Center, Xiamen University, Xiamen, 361005, Fujian, China.
  • Ning Zhang
    Institute of Nuclear Agricultural Sciences, Zhejiang University, Hangzhou, 310058, China.
  • Ke Luo
    School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, Shandong Province, China.