School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China.
Dig Dis Sci. 2021 Sep;66(9):2964-2980. doi: 10.1007/s10620-020-06637-0. Epub 2020 Oct 12.
Gastric cancer (GC) is one of the most common cancers, and the noninvasive diagnostic methods for monitoring GC are still lacking. Growing evidence shows that human microbiota has potential value for identifying digestive diseases.
The present study aimed to explore the association of the tongue coating microbiota with the serum metabolic features and inflammatory cytokines in GC patients and seek a potential, noninvasive biomarker for diagnosing GC.
The tongue coating microbiota was profiled by 16S rRNA and 18S rRNA genes sequencing technology in the original population with 181 GC patients and 112 healthy controls (HCs). Propensity score matching method was used to eliminate potential confounders including age, gender, and six lifestyle factors and a matching population with 66 GC patients and 66 HCs generated. Serum metabolomics profiling was performed by ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) in the matching population. Random forest model was constructed for the diagnosis of GC.
Linear discriminant analysis effect size (LEfSe) revealed that the differential bacterial taxa between GC patients and HCs in the matching population were similar to that in the original population, while the differential fungal taxa between GC patients and HCs dramatically changed before and after PSM. By random forest analysis, the combination of six bacterial genera (Peptostreptococcus, Peptococcus, Porphyromonas, Megamonas, Rothia, and Fusobacterium) was the optimal predictive model to distinguish GC patients from HCs effectively, with an area under the curve (AUC) value of 0.85. The model was verified with a high predictive potential (AUC = 0.76 to 0.96). In the matching population, eighteen specific HCs-enriched bacterial genera (Porphyromonas, Parvimonas, etc.) had negative correlations with lysophospholipids metabolites, and three of them had also negative correlations with serum IL-17α.
The alteration of tongue coating microbiota had a possible linkage with the inflammations and metabolome, and the tongue coating bacteria could be a potential noninvasive biomarker for diagnosing GC, which might be independent of lifestyle.
胃癌(GC)是最常见的癌症之一,目前仍缺乏用于监测 GC 的非侵入性诊断方法。越来越多的证据表明,人类微生物组在识别消化道疾病方面具有潜在价值。
本研究旨在探讨舌涂层微生物群与 GC 患者血清代谢特征和炎症细胞因子之间的关联,并寻找一种潜在的、非侵入性的 GC 诊断生物标志物。
采用 16S rRNA 和 18S rRNA 基因测序技术对 181 例 GC 患者和 112 例健康对照者(HCs)的原始人群进行舌涂层微生物组分析。采用倾向评分匹配法消除年龄、性别和 6 种生活方式因素等潜在混杂因素,生成匹配人群(66 例 GC 患者和 66 例 HCs)。采用超高效液相色谱串联四级杆飞行时间质谱(UPLC-Q-TOF/MS)对匹配人群进行血清代谢组学分析。构建随机森林模型进行 GC 诊断。
线性判别分析效应量(LEfSe)显示,匹配人群中 GC 患者与 HCs 之间的差异细菌分类群与原始人群相似,而 GC 患者与 HCs 之间的差异真菌分类群在 PSM 前后发生了显著变化。通过随机森林分析,6 种细菌属(消化链球菌属、消化球菌属、卟啉单胞菌属、巨单胞菌属、罗氏菌属和梭杆菌属)的组合是有效区分 GC 患者和 HCs 的最佳预测模型,曲线下面积(AUC)值为 0.85。该模型具有较高的预测潜力(AUC 值为 0.76 至 0.96),验证结果良好。在匹配人群中,18 种特定的 HCs 富集细菌属(卟啉单胞菌属、短小单胞菌属等)与溶血磷脂代谢物呈负相关,其中 3 种与血清白介素-17α也呈负相关。
舌涂层微生物群的改变与炎症和代谢组学可能有关,舌涂层细菌可能是诊断 GC 的一种潜在的非侵入性生物标志物,其可能与生活方式无关。