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瘤微环境特征增强肺腺癌预后:瘤内微生物群的影响

Fei Zhao ^1,2, Lei Wang ^3,4,5,2, Dongjie Du ⁶

¹School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China.
²The authors contributed equally to this work.
³Hebei Key Laboratory of Molecular Oncology, Hebei, 063000, China.
⁴Tangshan Key Laboratory of Cancer Prevention and Therapy, Hebei, 063000, China.
⁵Department of Pathology, Tangshan People's Hospital, Hebei, 063000, China.
⁶Department of Vascular Surgery, Hebei General Hospital, Shijiazhuang, 050051, China.
⁷Geneis Beijing Co., Ltd., Beijing, 100102, China.
⁸Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China.
⁹The Cancer Institute, Tangshan People's Hospital, Hebei, 063000, China.
¹⁰Tangshan Key Laboratory of Precision Medicine Testing, Hebei, 063000, China.
¹¹Department of Chemoradiotherapy, Tangshan People's Hospital, Hebei, 063000, China.
¹²Department of Thoracic Surgery, Jiamusi Central Hospital, Jiamusi, 154000, China.
¹³Department of Gastrointestinal Surgery, Tangshan People's Hospital, Hebei, 063000, China.

⁰School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China.

Microbial Cell (graz, Austria) +

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2025年八月27日

在PubMed上查看摘要

概括

瘤微环境 (TME) 和肺腺癌微生物组 (LUAD) 相互作用,影响患者的生存. 整合TME和微生物数据的新模型改善了LUAD预后和免疫治疗预测.

科学领域

癌症学
微生物组研究
生物信息学

背景情况

瘤微环境 (TME) 显著影响癌症的进展和治疗反应.
了解内微生物组在肺腺癌 (LUAD) 中的作用对于有效的治疗至关重要.
目前关于LUAD中TME与微生物群的相互作用的知识有限.

研究的目的

在LUD患者中研究TME和内微生物组之间的交叉声.
开发对LUAD患者生存率和免疫治疗反应的预测模型.
确定与LUAD进展相关的特定微生物特征.

主要方法

通过癌症基因组图谱 (TCGA) 收集了478名LUAD患者的转录组,微生物组和临床数据.
采用多种经济学整合,并开发了基于TME的预测模型 (TMEindex).
开发了一个综合模型 (IMTMEindex),结合了TME特征和微生物丰富度.

主要成果

根据临床结果,存活率和免疫治疗反应,TMEindex模型有效地区分了LUAD患者的子组.
通过TME特征定义的高风险组和低风险组之间观察到微生物多样性的显著差异.
类型<i>Luteibacter</i>和<i>Chryseobacterium</i>与LUAD患者的生存风险存在相反的关联.
IMTME指数模型在1,3年和5年的总生存预测中表现出优异.

结论

TME与内微生物组之间的相互作用是LUAD进展和患者预后的关键决定因素.
TME特征和特定的微生物特征可以预测患者的LUAD结果和免疫治疗反应.
将微生物组数据与TME签名整合为改善LUAD管理和治疗开发提供了一个有希望的策略.

关键词:

内微生物组肺腺癌肺腺癌机器学习预后情况瘤微环境

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