Suppr超能文献

非小细胞肺癌患者唾液、支气管肺泡灌洗液、非恶性、肿瘤周围和肿瘤组织中微生物组的特征:一项横断面临床试验。

Characterisation of microbiota in saliva, bronchoalveolar lavage fluid, non-malignant, peritumoural and tumour tissue in non-small cell lung cancer patients: a cross-sectional clinical trial.

机构信息

Université Clermont Auvergne, INRAE, UNH, F-63000, Clermont-Ferrand, France.

Greentech SA, Biopole Clermont-Limagne, 63360, Saint-Beauzire, France.

出版信息

Respir Res. 2020 May 25;21(1):129. doi: 10.1186/s12931-020-01392-2.

Abstract

BACKGROUND

While well-characterised on its molecular base, non-small cell lung cancer (NSCLC) and its interaction with local microbiota remains scarcely explored. Moreover, current studies vary in source of lung microbiota, from bronchoalveolar lavage fluid (BAL) to tissue, introducing potentially differing results. Therefore, the objective of this study was to provide detailed characterisation of the oral and multi-source lung microbiota of direct interest in lung cancer research. Since lung tumours in lower lobes (LL) have been associated with decreased survival, characteristics of the microbiota in upper (UL) and lower tumour lobes have also been examined.

METHODS

Using 16S rRNA gene sequencing technology, we analysed microbiota in saliva, BAL (obtained directly on excised lobe), non-malignant, peritumoural and tumour tissue from 18 NSCLC patients eligible for surgical treatment. Detailed taxonomy, diversity and core members were provided for each microbiota, with analysis of differential abundance on all taxonomical levels (zero-inflated binomial general linear model with Benjamini-Hochberg correction), between samples and lobe locations.

RESULTS

Diversity and differential abundance analysis showed clear separation of oral and lung microbiota, but more importantly, of BAL and lung tissue microbiota. Phylum Proteobacteria dominated tissue samples, while Firmicutes was more abundant in BAL and saliva (with class Clostridia and Bacilli, respectively). However, all samples showed increased abundance of phylum Firmicutes in LL, with decrease in Proteobacteria. Also, clades Actinobacteria and Flavobacteriia showed inverse abundance between BAL and extratumoural tissues depending on the lobe location. While tumour microbiota seemed the least affected by location, peritumoural tissue showed the highest susceptibility with markedly increased similarity to BAL microbiota in UL. Differences between the three lung tissues were however very limited.

CONCLUSIONS

Our results confirm that BAL harbours unique lung microbiota and emphasise the importance of the sample choice for lung microbiota analysis. Further, limited differences between the tissues indicate that different local tumour-related factors, such as tumour type, stage or associated immunity, might be the ones responsible for microbiota-shaping effect. Finally, the "shift" towards Firmicutes in LL might be a sign of increased pathogenicity, as suggested in similar malignancies, and connected to worse prognosis of the LL tumours.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT03068663. Registered February 27, 2017.

摘要

背景

尽管非小细胞肺癌(NSCLC)在分子基础上已经得到很好的描述,但它与局部微生物群的相互作用仍鲜有研究。此外,目前的研究在肺部微生物群的来源上存在差异,从支气管肺泡灌洗液(BAL)到组织,这可能导致结果存在差异。因此,本研究的目的是提供对肺癌研究中直接感兴趣的口腔和多源肺部微生物群的详细特征。由于下叶(LL)的肺肿瘤与生存率降低有关,因此还检查了上叶(UL)和肿瘤下叶的微生物群特征。

方法

使用 16S rRNA 基因测序技术,我们分析了 18 名符合手术治疗条件的 NSCLC 患者的唾液、BAL(直接从切除的肺叶中获得)、非恶性、肿瘤周围和肿瘤组织中的微生物群。为每个微生物群提供了详细的分类、多样性和核心成员,并在所有分类水平上进行了差异丰度分析(零膨胀二项式广义线性模型,采用 Benjamini-Hochberg 校正),以比较样本和叶位之间的差异。

结果

多样性和差异丰度分析表明,口腔和肺部微生物群之间存在明显的分离,但更重要的是,BAL 和肺部组织微生物群之间存在明显的分离。门级的 Proteobacteria 主要存在于组织样本中,而 Firmicutes 在 BAL 和唾液中更为丰富(分别为纲 Clostridia 和 Bacilli)。然而,所有样本在 LL 中均表现出较高的 Firmicutes 丰度,同时 Proteobacteria 减少。此外,根据叶位的不同,放线菌和黄杆菌纲在 BAL 和肿瘤周围组织之间的丰度呈相反趋势。尽管肿瘤微生物群似乎受位置影响最小,但肿瘤周围组织的易感性最高,与 UL 中的 BAL 微生物群相似度明显增加。然而,三种肺部组织之间的差异非常有限。

结论

我们的结果证实,BAL 中存在独特的肺部微生物群,并强调了选择肺部微生物群分析样本的重要性。此外,组织之间的差异非常有限,这表明可能是局部肿瘤相关因素,如肿瘤类型、分期或相关免疫,而不是其他因素,对微生物群的形成产生影响。最后,LL 中 Firmicutes 的“转移”可能是致病性增加的迹象,这在类似的恶性肿瘤中已有提示,并与 LL 肿瘤预后较差有关。

试验注册

ClinicalTrials.gov ID:NCT03068663。注册日期:2017 年 2 月 27 日。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f7/7249392/0dadb318677c/12931_2020_1392_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验