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肺腺癌患者瘤内微生物-免疫相互作用对生存的可预测性调控

Predictable regulation of survival by intratumoral microbe-immune crosstalk in patients with lung adenocarcinoma.

作者信息

Shi Shuo, Chu Yuwen, Liu Haiyan, Yu Lan, Sun Dejun, Yang Jialiang, Tian Geng, Ji Lei, Zhang Cong, Lu Xinxin

机构信息

The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.

Geneis Beijing Co., Ltd., Beijing 100102, China.

出版信息

Microb Cell. 2024 Feb 19;11:29-40. doi: 10.15698/mic2024.02.813. eCollection 2024.

Abstract

Intratumoral microbiota can regulate the tumor immune microenvironment (TIME) and mediate tumor prognosis by promoting inflammatory response or inhibiting anti-tumor effects. Recent studies have elucidated the potential role of local tumor microbiota in the development and progression of lung adenocarcinoma (LUAD). However, whether intratumoral microbes are involved in the TIME that mediates the prognosis of LUAD remains unknown. Here, we obtained the matched tumor microbiome and host transcriptome and survival data of 478 patients with LUAD in The Cancer Genome Atlas (TCGA). Machine learning models based on immune cell marker genes can predict 1- to 5-year survival with relative accuracy. Patients were stratified into high- and low-survival-risk groups based on immune cell marker genes, with significant differences in intratumoral microbial communities. Specifically, patients in the high-risk group had significantly higher alpha diversity (p < 0.05) and were characterized by an enrichment of lung cancer-related genera such as . However, network analysis highlighted a more active pattern of dominant bacteria and immune cell crosstalk in TIME in the low-risk group compared to the high-risk group. Our study demonstrated that intratumoral microbiota-immune crosstalk was strongly associated with prognosis in LUAD patients, which would provide new targets for the development of precise therapeutic strategies.

摘要

肿瘤内微生物群可调节肿瘤免疫微环境(TIME),并通过促进炎症反应或抑制抗肿瘤作用来介导肿瘤预后。最近的研究阐明了局部肿瘤微生物群在肺腺癌(LUAD)发生发展中的潜在作用。然而,肿瘤内微生物是否参与介导LUAD预后的TIME仍不清楚。在此,我们获取了癌症基因组图谱(TCGA)中478例LUAD患者的匹配肿瘤微生物组、宿主转录组和生存数据。基于免疫细胞标记基因的机器学习模型能够相对准确地预测1至5年生存率。根据免疫细胞标记基因将患者分为高生存风险组和低生存风险组,两组肿瘤内微生物群落存在显著差异。具体而言,高风险组患者的α多样性显著更高(p < 0.05),其特征是富集了与肺癌相关的属,如 。然而,网络分析表明,与高风险组相比,低风险组在TIME中优势细菌与免疫细胞的串扰模式更为活跃。我们的研究表明,肿瘤内微生物群与免疫的串扰与LUAD患者的预后密切相关,这将为精准治疗策略的开发提供新的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b162/10876218/6a387c137e24/mic-11-029-g001.jpg

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