Yan Ranxin, Zheng Rui, Han Yucheng, Song Ge, Huo Ban, Sun Han
College of Economics and Management, Henan Agricultural University, Zhengzhou 450046, PR China.
College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, PR China.
J Med Microbiol. 2025 Jul;74(7). doi: 10.1099/jmm.0.002042.
Colorectal cancer (CRC) is a malignant tumour in which dysbiosis of the gut microbiome is a contributing factor in the development of cancer. However, the species composition and species-specific changes in the gut microbiome related to CRC still require comprehensive investigation. There is a significant difference in gut microbiome between CRC patients and healthy individuals. The microbiome-based association test methods are used for the association between the microbiome and host phenotypes, and linear discriminant analysis effect size (LEfSe) analysis is employed to search for microbial biomarkers associated with CRC. We conducted a meta-analysis of microbiome data from multiple cohorts, totalling 1,462 samples and 320 genus-level features. Considering the data obtained under different experimental conditions, we removed the batch effect using conditional quantile regression. Then, we employed the common analysis processes and methods of microbiome data, including microbial diversity analysis, microbiome-based association test analysis and microbial differential abundance analysis. The experimental results showed that there were significant differences in -diversity between the CRC group and the healthy group, as well as in the overall microbial community (PERMANOVA -value less than 0.05). LEfSe analysis also demonstrated the genus-level features enriched in the gut of CRC patients and the genus-level features enriched in the gut of healthy individuals. Notably, the batch effect-corrected data exhibit more significant performance than the raw data. Gut microbiome composition is a significant factor associated with the development of CRC. and enriched in the gut of CRC patients may be CRC-related microbial biomarkers, while and enriched in the gut of healthy individuals are core genera of the healthy gut. In addition, batch effects in microbiome data caused by differences in sample handling may lead to false discoveries, especially large-scale microbiome data. These findings could deepen the understanding of the role played by gut microbes in CRC and are expected to provide recommendations for the diagnosis of cancer and the development of new microbial therapies.
结直肠癌（CRC）是一种恶性肿瘤，其中肠道微生物群失调是癌症发生的一个促成因素。然而，与CRC相关的肠道微生物群的物种组成和物种特异性变化仍需要全面研究。CRC患者与健康个体的肠道微生物群存在显著差异。基于微生物群的关联测试方法用于微生物群与宿主表型之间的关联，线性判别分析效应大小（LEfSe）分析用于寻找与CRC相关的微生物生物标志物。我们对来自多个队列的微生物群数据进行了荟萃分析，总共1462个样本和320个属水平特征。考虑到在不同实验条件下获得的数据，我们使用条件分位数回归消除了批次效应。然后，我们采用了微生物群数据的常见分析流程和方法，包括微生物多样性分析、基于微生物群的关联测试分析和微生物差异丰度分析。实验结果表明，CRC组与健康组之间在α多样性以及整体微生物群落方面存在显著差异（PERMANOVA P值小于0.05）。LEfSe分析还展示了CRC患者肠道中富集的属水平特征和健康个体肠道中富集的属水平特征。值得注意的是，经过批次效应校正的数据表现出比原始数据更显著的性能。肠道微生物群组成是与CRC发生相关的一个重要因素。CRC患者肠道中富集的 和 可能是与CRC相关的微生物生物标志物，而健康个体肠道中富集的 和 是健康肠道的核心属。此外，样本处理差异导致的微生物群数据批次效应可能会导致错误发现，尤其是大规模微生物群数据。这些发现可以加深对肠道微生物在CRC中所起作用的理解，并有望为癌症诊断和新的微生物疗法开发提供建议。
