万萍, 沈伟涛, 彭珍燕, 刘冉. 基于PacBio SMRT测序技术分析食管癌特征性肠道菌群及其生物标志物[J]. 环境与职业医学, 2022, 39(9): 1011-1020. DOI: 10.11836/JEOM22065
引用本文: 万萍, 沈伟涛, 彭珍燕, 刘冉. 基于PacBio SMRT测序技术分析食管癌特征性肠道菌群及其生物标志物[J]. 环境与职业医学, 2022, 39(9): 1011-1020. DOI: 10.11836/JEOM22065
WAN Ping, SHEN Weitao, PENG Zhenyan, LIU Ran. Characteristic intestinal flora and associated biomarkers in esophageal cancer based on PacBio SMRT sequencing[J]. Journal of Environmental and Occupational Medicine, 2022, 39(9): 1011-1020. DOI: 10.11836/JEOM22065
Citation: WAN Ping, SHEN Weitao, PENG Zhenyan, LIU Ran. Characteristic intestinal flora and associated biomarkers in esophageal cancer based on PacBio SMRT sequencing[J]. Journal of Environmental and Occupational Medicine, 2022, 39(9): 1011-1020. DOI: 10.11836/JEOM22065

基于PacBio SMRT测序技术分析食管癌特征性肠道菌群及其生物标志物

Characteristic intestinal flora and associated biomarkers in esophageal cancer based on PacBio SMRT sequencing

  • 摘要: 背景

    食管癌是常见的消化道肿瘤,中国是食管癌高发地区。有研究提示,肠道菌群与肿瘤等多种疾病的发生发展相关。受测序读长所限,传统的16S rDNA测序技术只能鉴定到属。

    目的

    本研究基于PacBio单分子实时(SMRT)测序技术在种水平筛选与食管癌相关的特征性微生物标志。

    方法

    招募首次诊断为食管癌的新发病例120例,和性别、年龄匹配的健康对照60例。采集所有研究对象的新鲜粪便样本。利用第三代测序PacBio SMRT技术对4例食管癌患者及1:1匹配的健康对照者样本进行16S rDNA全长测序,基于测序结果分析其肠道菌群结构差异。采用PICRUSt软件进行功能预测。通过线性判别分析和群落差异分析筛选差异肠道微生物进行大样本人群验证,识别食管癌相关肠道微生物。

    结果

    基于测序样本,α多样性分析中食管癌组的Ace、Chao1、Simpson Diversity、Shannon Wiener指数均高于健康对照组(P<0.05),β多样性分析显示食管癌组和健康对照组散点簇分离,二者肠道菌群结构存在差异。在门水平上,食管癌组肠道菌群中变形菌门、拟杆菌门、厚壁菌门丰度升高。在属水平上,食管癌组毛螺旋菌属、巴氏杆菌属、罗斯氏菌属和拟杆菌属相对丰度增加。在种水平上,食管癌组相对丰度增加的微生物是肠杆菌E.20、卵形拟杆菌V975、普氏栖粪杆菌等11种丰度降低的微生物是皮氏罗尔斯通氏菌肠杆菌未分类唾液链球菌JIM8777 3种。PICRUSt功能注释后发现食管癌组与健康组在丙氨酸、天冬氨酸和谷氨酸代谢(map00250)、肽聚糖生物合成(map00550)、叶酸一碳单位库(map00670)、硫胺素代谢(map00730)、氨基酸的生物合成(map01230)通路上存在差异。对验证人群的分析结果显示,与健康对照人群相比,食管癌人群的肠杆菌E.20、马赛拟杆菌的丰度升高,唾液链球菌JIM8777的丰度降低,差异具有统计学意义(P<0.05)。建立受试者工作特征分析发现,肠杆菌E.20、唾液链球菌JIM8777、马赛拟杆菌联合诊断的曲线下面积(AUC)为0.779,高于单一诊断结果(AUC分别为0.610、0.608、0.659)。

    结论

    食管癌组的肠道菌群与健康对照组存在差异。肠杆菌E.20、唾液链球菌JIM8777、马赛拟杆菌的联合应用对食管癌的诊断具有潜在应用价值。

     

    Abstract: Background

    Esophageal cancer is a common gastrointestinal tumor with a high incidence in China. Some studies suggest that intestinal flora is significantly related to the occurrence and development of tumors and other diseases. Traditional 16S rDNA sequencing technology only provides taxonomic resolution at genus level.

    Objective

    Based on PacBio single molecule real time (SMRT) sequencing technology to identify characteristic microbial biomarkers associated with esophageal cancer at the species level.

    Methods

    A total of 120 newly diagnosed cases of esophageal cancer were recruited and 60 healthy patients with matched sex and age were recruited as the control group. Fresh stool samples were collected from all subjects. Full-length 16S rDNA sequencing was performed on samples from 4 patients with esophageal cancer and 1:1 matched healthy controls using the third-generation sequencing PacBio SMRT technology, and the structural differences of intestinal flora were analyzed based on the sequencing results. Function prediction was performed by PICRUSt software. Large population samples were validated by screening different gut microbes by linear discriminant analysis and linear discriminant analysis effect size to identify esophageal cancer-associated gut microbes.

    Results

    Based on sequencing samples, the results of α diversity analysis showed that the Ace, Chao1, Simpson Diversity, and Shannon Wiener indices of the esophageal cancer group were higher than those of the healthy control group (P<0.05), and the results of β diversity showed that the scattered clusters of the esophageal cancer group and the healthy control group were separated, which meant that there were differences in the structure of intestinal flora between the two groups. It was found at the phylum level that the abundances of Proteobacteria, Bacteroidetes, and Firmicutes in the intestinal flora of the esophageal cancer group were increased. At the genus level, the relative abundances of Spirospira, Pasteurella, Roxella, and Bacteroides in the esophageal cancer group were increased. At the species level, there were 11 microbial species with increased relative abundances in the esophageal cancer group, including Enterobacter sp. E.20, Bacteroides ovatus V975, and Faecalibacterium prausnitzii, and the microbial species with decreased relative abundances in the esophageal cancer group were Ralstonia pickettii, Enterobacter unclassified, and Streptococcus salivarius JIM8777. The PICRUSt functional annotation found differences in alanine, aspartate and glutamate metabolism (map00250), peptidoglycan (map00550), one carbon pool by folate (map00670), thiamine metabolism (map00730), and biosynthesis of amino acids (map01230) between the two groups. The results of the population validation study showed that the abundances of Enterobacter sp E.20 and Bacteroides massilience in the esophageal cancer group were increased, the abundance of Streptococcus salivarius JIM8777 was decreased, and the differences between the two groups were statistically significant (P<0.05). By establishing receiver operating characteristic analysis for representative species level biomarkers, the area under curve (AUC) of combining Enterobacter sp E.20, Streptococcus salivarius JIM8777, and Bacteroides massilience was 0.779, higher than single diagnosis (AUC=0.610, 0.608, and 0.659, respectively).

    Conclusion

    There are significant differences in gut microbiota between the esophageal cancer group and the healthy control group. The combination of Enterobacter sp E.20, Streptococcus salivarius JIM8777, and Bacteroides Massilience has potential application value for the diagnosis of esophageal cancer.

     

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