Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, China.
Cancer Med. 2023 Sep;12(17):17822-17834. doi: 10.1002/cam4.6414. Epub 2023 Aug 7.
Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC).
A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age-differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age-differentiated bacteria.
The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age-related differential bacteria and model of CRC patients based on the 58 age-related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease-differentiated bacteria and the age-differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85).
Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.
衰老也是导致癌症的因素之一。肠道微生物群与衰老和结直肠癌（CRC）有关。
从 R 包 curated Metagenomic Data 中收集了 11 个与 CRC 相关的宏基因组数据集。在进行批次效应校正后，将健康个体和 CRC 样本分为三组。使用 Ggplot2 和 Microbiota Process 包分别对健康个体和 CRC 样本中的物种组成和 PCA 进行可视化描述。根据年龄对健康/CRC 组中的物种相对丰度数据进行 LEfSe 分析。分别计算健康个体和 CRC 样本中年龄分化细菌的 Spearman 相关系数。最后，基于年龄分化细菌构建年龄预测模型和 CRC 风险预测模型。
三组之间肠道微生物群的结构和组成存在显著差异。例如，老年组中 Bacteroides vulgatus 的丰度低于其他两组，Bacteroides fragilis 的丰度随年龄增长而增加。此外，筛选出七种随着年龄增长而增加的细菌。此外，CRC 中致病性细菌（Escherichia_coli、Butyricimonas_virosa、Ruminococcus_bicirculans、Bacteroides_fragilis 和 Streptococcus_vestibularis）的丰度随年龄增加而增加。CRC 中益生菌（Eubacterium_eligens）的丰度随年龄增加而减少。基于 80 种与年龄相关的差异细菌的健康个体年龄预测模型和基于 58 种与年龄相关的差异细菌的 CRC 患者模型表现良好，AUC 分别为 0.79 和 0.71。基于 45 种疾病差异细菌的 CRC 风险预测模型的 AUC 为 0.83。从健康样本中去除疾病分化细菌与年龄分化细菌的交集后，基于剩余 31 种细菌的 CRC 风险预测模型的 AUC 为 0.8。三个年龄组的 CRC 风险预测模型的准确性没有显著差异（年轻组：AUC=0.82，中年组：AUC=0.83，老年组：AUC=0.85）。
在应用肠道微生物群预测 CRC 风险时，应考虑年龄这一影响微生物组成的因素。
