Meta-analysis of gut microbiome reveals patterns of dysbiosis in colorectal cancer patients

Introduction. 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. Hypothesis. There is a significant difference in gut microbiome between CRC patients and healthy individuals. Aim. 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. Methodology. 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. Results. The experimental results showed that there were significant differences in α -diversity between the CRC group and the healthy group, as well as in the overall microbial community (PERMANOVA P -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. Conclusion. Gut microbiome composition is a significant factor associated with the development of CRC. Enterobacter and Fusobacterium enriched in the gut of CRC patients may be CRC-related microbial biomarkers, while Bacteroides and Faecalibacterium 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.