Meta-analysis of fecal viromes demonstrates high diagnostic potential of the gut viral signatures for colorectal cancer and adenoma risk assessment

人病毒体 结直肠癌 结直肠腺瘤 癌症 普氏粪杆菌 腺瘤 肿瘤科 内科学 医学 生物 肠道菌群 免疫学 基因组 遗传学 基因
作者
Fang Chen,Shenghui Li,Ruochun Guo,Fanghua Song,Yue Zhang,Xifan Wang,Xiaokui Huo,Qingbo Lv,Hayan Ullah,Guangyang Wang,Yufang Ma,Qiulong Yan,Xiaochi Ma
出处
期刊:Journal of Advanced Research [Elsevier BV]
卷期号:49: 103-114 被引量:30
标识
DOI:10.1016/j.jare.2022.09.012
摘要

Viruses have been reported as inducers of tumorigenesis. Little studies have explored the impact of the gut virome on the progression of colorectal cancer. However, there is still a problem with the repeatability of viral signatures across multiple cohorts.The present study aimed to reveal the repeatable gut vial signatures of colorectal cancer and adenoma patients and decipher the potential of viral markers in disease risk assessment for diagnosis.1,282 available fecal metagenomes from 9 published studies for colorectal cancer and adenoma were collected. A gut viral catalog was constructed via a reference-independent approach. Viral signatures were identified by cross-cohort meta-analysis and used to build predictive models based on machine learning algorithms. New fecal samples were collected to validate the generalization of predictive models.The gut viral composition of colorectal cancer patients was drastically altered compared with healthy, as evidenced by changes in some Siphoviridae and Myoviridae viruses and enrichment of Microviridae, whereas the virome variation in adenoma patients was relatively low. Cross-cohort meta-analysis identified 405 differential viruses for colorectal cancer, including several phages of Porphyromonas, Fusobacterium, and Hungatella that were enriched in patients and some control-enriched Ruminococcaceae phages. In 9 discovery cohorts, the optimal risk assessment model obtained an average cross-cohort area under the curve of 0.830 for discriminating colorectal cancer patients from controls. This model also showed consistently high accuracy in 2 independent validation cohorts (optimal area under the curve, 0.906). Gut virome analysis of adenoma patients identified 88 differential viruses and achieved an optimal area under the curve of 0.772 for discriminating patients from controls.Our findings demonstrate the gut virome characteristics in colorectal cancer and adenoma and highlight gut virus-bacterial synergy in the progression of colorectal cancer. The gut viral signatures may be new targets for colorectal cancer treatment. In addition, high repeatability and predictive power of the prediction models suggest the potential of gut viral biomarkers in non-invasive diagnostic tests of colorectal cancer and adenoma.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
miaowk完成签到,获得积分10
4秒前
刻苦不弱发布了新的文献求助10
4秒前
5秒前
ghtsmile完成签到 ,获得积分10
8秒前
Ccccn完成签到,获得积分10
8秒前
8秒前
世上僅有的榮光之路完成签到,获得积分0
8秒前
shlw完成签到,获得积分10
11秒前
大家觉得完成签到,获得积分20
12秒前
中微子完成签到 ,获得积分10
13秒前
silence完成签到 ,获得积分10
14秒前
Double_N完成签到,获得积分10
15秒前
MarvelerYB3完成签到,获得积分10
21秒前
孝顺的白枫完成签到 ,获得积分10
22秒前
巅峰囚冰完成签到,获得积分10
26秒前
1233330完成签到 ,获得积分10
28秒前
凌云揽月完成签到 ,获得积分10
28秒前
28秒前
聪明的破茧完成签到,获得积分10
28秒前
星星完成签到 ,获得积分10
30秒前
留猪完成签到,获得积分10
31秒前
简爱完成签到 ,获得积分10
33秒前
meilirenshengzcs完成签到,获得积分10
35秒前
arniu2008应助刻苦不弱采纳,获得20
36秒前
37秒前
38秒前
科目三应助lixinglei采纳,获得10
40秒前
小凤完成签到 ,获得积分10
40秒前
凌云揽月关注了科研通微信公众号
41秒前
柒月完成签到 ,获得积分10
44秒前
夜曦发布了新的文献求助10
44秒前
quixote完成签到,获得积分20
45秒前
tupos完成签到,获得积分10
48秒前
48秒前
Kao应助科研通管家采纳,获得10
48秒前
lizishu应助科研通管家采纳,获得10
49秒前
Kao应助科研通管家采纳,获得10
49秒前
Kao应助科研通管家采纳,获得10
49秒前
49秒前
认真的rain完成签到,获得积分10
50秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Health Psychology 800
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Electric machines: theory, operating applications, and controls 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
When Is Two-Stage Sample Robust Optimization Asymptotically Optimal? 500
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7592618
求助须知:如何正确求助?哪些是违规求助? 9169846
关于积分的说明 19626407
捐赠科研通 7170541
什么是DOI,文献DOI怎么找? 3267520
关于科研通互助平台的介绍 2432371
邀请新用户注册赠送积分活动 2260009