Microbiome changes in the gastric mucosa and gastric juice in different histological stages of Helicobacter pylori-negative gastric cancers.
Microbiome changes in the gastric mucosa and gastric juice in different histological stages of Helicobacter pylori-negative gastric cancers.
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幽门螺杆菌阴性胃癌不同组织学阶段胃粘膜和胃液微生物组的变化
DOI：
10.3748/wjg.v28.i3.365
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发表时间：
2022-01-21
影响因子：
4.3
通讯作者：
Ding SG
中科院分区：
文献类型：
--
作者：
Sun QH;Zhang J;Shi YY;Zhang J;Fu WW;Ding SG
背景：胃癌患者的胃微生物区系已受到越来越多的关注，但对于胃肿瘤发生的组织学阶段，尤其是幽门螺杆菌阴性胃癌(HPNGC)患者，胃微生物组的变化仍知之甚少。目的研究HPNGC癌变过程中胃粘膜和胃液的微生物谱特征，明确癌前病变中不同的分类群。方法对134例幽门螺杆菌(Hp)阴性患者(浅表性胃炎56例，萎缩性胃炎9例，肠化生27例，异型增生29例，胃癌13例)的胃粘膜进行16S rRNA基因分析，研究不同病期胃粘膜微生物多样性和组成的差异。此外，还分析了来自18个SG、18个IM和18个Dys样本的胃粘膜和胃液配对样本。用Shannon指数和Chao1指数衡量α多样性，用偏最小二乘判别分析计算β多样性。使用线性判别分析效应大小来评估不同样本类型中不同疾病阶段的微生物组成的差异。结果胃粘膜细菌微生物区系的多样性和组成在不同的癌变阶段呈递增变化。IM组和Dys组的胃粘膜微生物区系多样性明显低于SG组，且GC患者的细菌群落丰富度最低(P<；0.05)。IM患者和Dys患者的胃粘膜微生物区系相似，优势菌属为葡萄球菌属和红球菌属。微生物网络分析显示，IM和Dys之间的关联强度增加(|相关阈值|≥0.5，P<；0.0 5)。GC及其癌前病变具有可区分的细菌分类；我们的结果鉴定出HPNGC相关细菌链球菌科和乳杆菌科(P<；0.05)。此外，在幽门螺杆菌阴性患者从AG到Dys的癌前病变阶段，Burkholdriaceae的丰度持续增加，而链球菌科和Prevoteaceae的丰度呈持续下降的趋势。此外，胃液中的微生物多样性(P<；0.001)高于粘膜中的微生物多样性，而偏最小二乘法显示两组之间的差异有统计学意义(ANOSIM，P=0.001)。在微生物结构上发现了显著的差异，变形杆菌在胃粘膜中更常见，而菲米库特在胃液中更丰富。结论我们的研究结果为HPNGC及其癌前病变分期提供了潜在的分类生物标志物，并有助于根据粘膜微生物区系特征预测IM和Dys的预后。
BACKGROUND The gastric microbiota in patients with gastric cancer (GC) has received increasing attention, but the profiling of the gastric microbiome through the histological stages of gastric tumorigenesis remains poorly understood, especially for patients with Helicobacter pylori-negative GC (HPNGC). AIM To characterize microbial profiles of gastric mucosa and juice for HPNGC carcinogenesis and identify distinct taxa in precancerous lesions. METHODS The 16S rRNA gene analysis was performed on gastric mucosa from 134 Helicobacter pylori-negative cases, including 56 superficial gastritis (SG), 9 atrophic gastritis (AG), 27 intestinal metaplasia (IM), 29 dysplasia (Dys), and 13 GC cases, to investigate differences in gastric microbial diversity and composition across the disease stages. In addition, paired gastric mucosa and juice samples from 18 SG, 18 IM, and 18 Dys samples were analyzed. α-Diversity was measured by Shannon and Chao1 indexes, and β-diversity was calculated using partial least squares discrimination analysis (PLS-DA). Differences in the microbial composition across disease stages in different sample types were assessed using the linear discriminant analysis effect size. RESULTS The diversity and composition of the bacterial microbiota in the gastric mucosa changed progressively across stages of gastric carcinogenesis. The diversity of the gastric mucosa microbiota was found to be significantly lower in the IM and Dys groups than in the SG group, and the patients with GC had the lowest bacterial community richness (P < 0.05). Patients with IM and those with Dys had similar gastric mucosa microbiota profiles with Ralstonia and Rhodococcus as the predominant genera. Microbial network analysis showed that there was increasing correlation strength between IM and Dys (|correlation threshold|≥ 0.5, P < 0.05). GC and its precancerous lesions have distinguishable bacterial taxa; our results identified HPNGC-associated bacteria Streptococcaceae and Lactobacillaceae (P < 0.05). Additionally, across precancerous lesion stages from AG to Dys in Helicobacter pylori-negative patients, Burkholderiaceae abundance continuously increased, while Streptococcaceae and Prevotellaceae abundance presented a continuous downward trend. Furthermore, the microbial diversity was higher in gastric juice (P < 0.001) than in the mucosa, while PLS-DA revealed a statistically significant difference between the two groups (ANOSIM, P = 0.001). A significant difference in the microbial structure was identified, with Proteobacteria being more prevalent in the gastric mucosa and Firmicutes being more abundant in gastric juice. CONCLUSION Our results provide insights into potential taxonomic biomarkers for HPNGC and its precancerous stages and assist in predicting the prognosis of IM and Dys based on the mucosal microbiota profile.
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