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A tumour‐associated macrophage‐based signature for deciphering prognosis and immunotherapy response in prostate cancer

Authors :
Jian Wang
Tao Guo
Yuanyuan Mi
Xiangyu Meng
Shuang Xu
Feng Dai
Chengwen Sun
Yi Huang
Jun Wang
Lijie Zhu
Jianquan Hou
Sheng Wu
Source :
IET Systems Biology, Vol 18, Iss 5, Pp 155-171 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour‐associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy‐sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC‐XENA, GEO and TISCH databases were extracted for analysis. Based on both single‐cell datasets and bulk transcriptome profiles, a macrophage‐related score (MRS) consisting of the 10‐gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial‐mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high‐MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E‐knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

Details

Language :
English
ISSN :
17518857 and 17518849
Volume :
18
Issue :
5
Database :
Directory of Open Access Journals
Journal :
IET Systems Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.077db27d99024a30ba052c84addda82b
Document Type :
article
Full Text :
https://doi.org/10.1049/syb2.12097