School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
Guangdong Hongyuan Pukang Medical Technology Co, Ltd, Guangzhou, 510000, China.
BMC Gastroenterol. 2024 Sep 17;24(1):315. doi: 10.1186/s12876-024-03408-3.
Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition.
In this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results.
These results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups.
结直肠癌（CRC）是一种常见的癌症，每年在全球范围内导致数百万人死亡。目前，大量研究证实肠道微生物在 CRC 过程中发挥着关键作用。此外，研究表明，CRC 可以根据肿瘤基因表达分为几个共识分子亚型（CMS），并且已经报道 CRC 微生物组与 CMS 相关。然而，大多数先前关于 CRC 肠道微生物组的研究仅比较了患者与健康对照组，而没有根据肠道微生物组成对 CRC 患者进行分类。
本研究选择了包括 339 例 CRC 样本和 333 例健康对照的 CRC 队列作为发现集，并使用 PAM 聚类算法基于肠道微生物组成将 CRC 样本分为两个亚组（234 个 Subgroup1 和 105 个 Subgroup2）。我们发现，不仅微生物多样性有显著差异（Shannon 指数，p 值 < 0.05），而且两个 CRC 亚组之间有 129 个共有属发生改变（p 值 < 0.05），包括 CRC 中的几个标志性属，如梭杆菌属和拟杆菌属。随机森林算法用于构建诊断模型，当将 CRC 样本分为亚组时，该模型显示出更高的效率。然后选择了一个包括 187 例 CRC 样本（分为 153 个 Subgroup1 和 34 个 Subgroup2）和 123 例健康对照的独立队列来验证模型，并确认了结果。
这些结果表明，分组后的 CRC 亚组可以提高疾病诊断的效率，亚组中的微生物组成存在差异。
