Mo Zongchao, Huang Peide, Yang Chao, Xiao Sihao, Zhang Guojia, Ling Fei, Li Lin
School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China.
BGI Genomics, BGI-Shenzhen, Shenzhen, China.
mSystems. 2020 Apr 14;5(2):e00138-20. doi: 10.1128/mSystems.00138-20.
As research focusing on the colorectal cancer fecal microbiome using shotgun sequencing continues, increasing evidence has supported correlations between colorectal carcinomas (CRCs) and fecal microbiome dysbiosis. However, large-scale on-site and off-site (surrounding adjacent) tissue microbiome characterization of CRC was underrepresented. Here, considering each taxon as a feature, we demonstrate a machine learning-based method to investigate tissue microbial differences among CRC, colorectal adenoma (CRA), and healthy control groups using 16S rRNA data sets retrieved from 15 studies. A total of 2,099 samples were included and analyzed in case-control comparisons. Multiple methods, including differential abundance analysis, random forest classification, cooccurrence network analysis, and Dirichlet multinomial mixture analysis, were conducted to investigate the microbial signatures. We showed that the dysbiosis of the off-site tissue of colonic cancer was distinctive and predictive. The AUCs (areas under the curve) were 80.7%, 96.0%, and 95.8% for CRC versus healthy control random forest models using stool, tissue, and adjacent tissue samples and 69.9%, 91.5%, and 89.5% for the corresponding CRA models, respectively. We also found that the microbiota ecologies of the surrounding adjacent tissues of CRC and CRA were similar to their on-site counterparts according to network analysis. Furthermore, based on the enterotyping of tissue samples, the cohort-specific microbial signature might be the crux in addressing classification generalization problems. Despite cohort heterogeneity, the dysbiosis of lesion-adjacent tissues might provide us with further perspectives in demonstrating the role of the microbiota in colorectal cancer tumorigenesis. Turbulent fecal and tissue microbiome dysbiosis of colorectal carcinoma and adenoma has been identified, and some taxa have been proven to be carcinogenic. However, the microbiomes of surrounding adjacent tissues of colonic cancerous tissues were seldom investigated uniformly on a large scale. Here, we characterize the microbiome signatures and dysbiosis of various colonic cancer sample groups. We found a high correlation between colorectal carcinoma adjacent tissue microbiomes and their on-site counterparts. We also discovered that the microbiome dysbiosis in adjacent tissues could discriminate colorectal carcinomas from healthy controls effectively. These results extend our knowledge on the microbial profile of colorectal cancer tissues and highlight microbiota dysbiosis in the surrounding tissues. They also suggest that microbial feature variations of cancerous lesion-adjacent tissues might help to reveal the microbial etiology of colonic cancer and could ultimately be applied for diagnostic and screening purposes.
随着采用鸟枪法测序对结直肠癌粪便微生物群的研究不断深入，越来越多的证据支持结直肠癌（CRC）与粪便微生物群失调之间存在关联。然而，对CRC的大规模原位和异位（周围相邻）组织微生物群特征的研究较少。在此，将每个分类单元视为一个特征，我们展示了一种基于机器学习的方法，利用从15项研究中检索到的16S rRNA数据集，研究CRC、结直肠腺瘤（CRA）和健康对照组之间的组织微生物差异。在病例对照比较中，共纳入并分析了2099个样本。采用多种方法，包括差异丰度分析、随机森林分类、共现网络分析和狄利克雷多项混合分析，来研究微生物特征。我们发现结肠癌异位组织的失调具有独特性和预测性。使用粪便、组织和相邻组织样本的CRC与健康对照随机森林模型的曲线下面积（AUC）分别为80.7%、96.0%和95.8%，相应的CRA模型的AUC分别为69.9%、91.5%和89.5%。根据网络分析，我们还发现CRC和CRA周围相邻组织的微生物群生态与其原位对应组织相似。此外，基于组织样本的肠型分析，特定队列的微生物特征可能是解决分类泛化问题的关键。尽管存在队列异质性，但病变相邻组织的失调可能为我们进一步展示微生物群在结直肠癌发生中的作用提供视角。已确定结直肠癌和腺瘤的粪便和组织微生物群紊乱剧烈，并且一些分类单元已被证明具有致癌性。然而，结肠癌组织周围相邻组织的微生物群很少在大规模上进行统一研究。在此，我们描述了各种结肠癌样本组的微生物群特征和失调情况。我们发现结肠癌相邻组织微生物群与其原位对应组织之间存在高度相关性。我们还发现相邻组织中的微生物群失调能够有效地区分结直肠癌与健康对照。这些结果扩展了我们对结直肠癌组织微生物谱的认识，并突出了周围组织中的微生物群失调。它们还表明，癌性病变相邻组织的微生物特征变化可能有助于揭示结肠癌的微生物病因，并最终可应用于诊断和筛查目的。
