衰老是导致癌症的因素之一。肠道微生物与衰老和结直肠癌（CRC）密切相关。我们从R包福利定制的11个与CRC相关的宏基因组数据集中收集了数据。经过批次效应校正后，健康个体和CRC样本分为三个年龄组。在健康个体和CRC样本中，使用ggplot2和Microbiota Process软件包对物种组成和PCA进行了视觉描述。根据年龄，对健康/CRC组中的物种相对丰度数据执行了LEfSe分析。分别计算了健康个体和CRC样本中不同年龄的细菌的Spearman相关系数。最后，基于不同年龄细菌构建了年龄预测模型和CRC风险预测模型。肠道微生物群落的结构和组成在三个组之间有显著差异。例如，老年组中拟杆菌丰度较低，而青年组和中年组中拟杆菌丰度增加。此外，筛选出了与年龄相关丰度增加的七种细菌。此外，在CRC中，致病菌（大肠埃希菌、病毒卵圆杆菌、双环球肠球菌、脆弱拟杆菌和耐震链球菌）的丰度随年龄增加而增加。益生菌（真拟杆菌）的丰度在CRC中随年龄减少。基于80个与年龄相关的差异细菌的健康个体年龄预测模型及基于58个与年龄相关的差异细菌的CRC患者模型的表现良好，分别具有0.79和0.71的曲线下面积（AUC）。基于45个疾病差异细菌构建的CRC风险预测模型的AUC为0.83。从健康样本中除去疾病差异细菌与年龄差异细菌的交集后，基于剩余31个细菌的CRC风险预测模型的AUC为0.8。每个年龄组的CRC风险预测模型的准确度没有显著差异（年轻组：AUC=0.82，中年组：AUC=0.83，老年组：AUC=0.85）。考虑到年龄对微生物组成的影响，将其应用于预测CRC风险时应予以考虑。© 2023 作者们。由John Wiley & Sons Ltd.出版的《癌症医学》刊登。

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.© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.