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

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
Jian Wang, Tao Guo, Yuanyuan Mi, Xiangyu Meng, Shuang Xu, Feng Dai, Chengwen Sun, Yi Huang, Jun Wang, Lijie Zhu, Jianquan Hou, Sheng Wu
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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.

基于肿瘤相关巨噬细胞的特征,用于解读前列腺癌的预后和免疫疗法反应。
前列腺癌(PCa)的多期进展和对免疫疗法的抵抗,肿瘤相关巨噬细胞是一个重要因素。尽管免疫疗法是一种重要且前景广阔的癌症治疗方式,但大多数前列腺癌患者对免疫疗法并不敏感。除了探索新的治疗靶点,当务之急是确定对免疫疗法高度敏感的个体。这项研究旨在建立一个源自巨噬细胞的特征风险模型,以评估免疫治疗反应并预测预后。研究人员从 UCSC-XENA、GEO 和 TISCH 数据库中提取数据进行分析。基于单细胞数据集和大容量转录组图谱,利用基因组变异分析构建了由10个基因组成的巨噬细胞相关评分(MRS)。MRS与缺氧、血管生成和上皮-间质转化高度相关,表明其具有作为风险指标的潜力。此外,在各种免疫疗法队列中观察到,高MRS组的免疫疗法反应较差,预后表现较差。此外,MRS的组成基因之一APOE也影响了巨噬细胞的极化。特别是在植入载脂蛋白E基因敲除小鼠体内的PCa异种移植物中观察到了M2巨噬细胞水平的降低和肿瘤进展的抑制。构建的MRS有可能成为一种可靠的预后预测工具,并有助于选择PCa的治疗方法,尤其是免疫疗法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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