Shuo Shi, Yuwen Chu, Haiyan Liu, Lan Yu, Dejun Sun, Jialiang Yang, Geng Tian, Lei Ji, Cong Zhang, Xinxin Lu
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引用次数: 0
摘要
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)发生和发展中的潜在作用。然而,瘤内微生物是否参与了介导 LUAD 预后的 TIME 仍是未知数。在此,我们从癌症基因组图谱(TCGA)中获得了478名LUAD患者的匹配肿瘤微生物组和宿主转录组及生存数据。基于免疫细胞标记基因的机器学习模型可以相对准确地预测1至5年的生存率。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,肿瘤内微生物群落存在显著差异。具体来说,高风险组患者的α多样性明显更高(p < 0.05),其特点是富含链球菌等肺癌相关菌属。然而,网络分析显示,与高风险组相比,低风险组 TIME 中的优势菌和免疫细胞串扰模式更为活跃。我们的研究表明,瘤内微生物与免疫细胞间的串扰与肺癌患者的预后密切相关,这将为制定精确的治疗策略提供新的靶点。
Predictable regulation of survival by intratumoral microbe-immune crosstalk in patients with lung adenocarcinoma.
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 Streptococcus. 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.