Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

Background: Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has n...

Full description

Bibliographic Details
Main Authors: Hong-yu Shen, Jia-lin Xu, Zhen Zhu, Hai-ping Xu, Ming-xing Liang, Di Xu, Wen-quan Chen, Jin-hai Tang, Zheng Fang, Jian Zhang
Format: Article
Language:English
Published: Elsevier 2023-11-01
Series:Neoplasia: An International Journal for Oncology Research
Subjects:
Antigen processing and presentation machinery
Gene signatures
Breast cancer
Risk assessment
Gene mutation
Immunotherapy
Online Access:http://www.sciencedirect.com/science/article/pii/S1476558623000660

Internet

http://www.sciencedirect.com/science/article/pii/S1476558623000660

Similar Items