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用于结直肠癌诊断的个体化宏基因组网络模型:肠道微生物群生态的病毒调控见解

Individualized metagenomic network model for colorectal cancer diagnosis: insights into viral regulation of gut microecology.

作者信息

Qian Li-Mei, Wang Shi-Xiang, Zhou Wen, Qin Zi-Xin, Wang Ying-Nan, Zhao Qi, Xu Rui-Hua

机构信息

Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University, Guangzhou 510060, P. R. China.

Cancer Microbiome Platform, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

出版信息

Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf208.

Abstract

The role of gut microbiota, especially viruses, in colorectal cancer (CRC) pathogenesis remains unclear. This study investigated the interplay between gut microbiota and CRC development. We developed a viral/bacterial sequence analysis pipeline to reanalyze gut metagenomic datasets from eight CRC studies. A multisample co-occurrence network was constructed to delineate microbiota species interconnections. Our analysis confirmed dysbiosis in CRC patients and revealed enrichment of viral species, particularly those hosted by Lactococcus and Escherichia. These viruses were identified as central hubs in the multikingdom interaction network. We developed a network-based model using single sample networks (SSN) that distinguished CRC patients from controls with an area under the curve (AUC) of 0.93. Models combining relative abundance and SSN assessment achieved an AUC of 0.97, outperforming SSN-based models without viral data. This study highlights the crucial role of viruses in the gut microbiome network and their potential as targets for CRC prevention and intervention. Our approach offers a new perspective on noninvasive diagnostic criteria for CRC.

摘要

肠道微生物群,尤其是病毒,在结直肠癌(CRC)发病机制中的作用仍不清楚。本研究调查了肠道微生物群与CRC发展之间的相互作用。我们开发了一种病毒/细菌序列分析流程,以重新分析来自八项CRC研究的肠道宏基因组数据集。构建了一个多样本共现网络来描绘微生物物种之间的联系。我们的分析证实了CRC患者存在生态失调,并揭示了病毒物种的富集,特别是那些由乳酸乳球菌和大肠杆菌携带的病毒。这些病毒被确定为多界相互作用网络中的中心枢纽。我们使用单样本网络(SSN)开发了一种基于网络的模型,该模型区分CRC患者和对照的曲线下面积(AUC)为0.93。结合相对丰度和SSN评估的模型AUC达到0.97,优于没有病毒数据的基于SSN的模型。本研究强调了病毒在肠道微生物群网络中的关键作用及其作为CRC预防和干预靶点的潜力。我们的方法为CRC的非侵入性诊断标准提供了新的视角。

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