Profiling of lung microbiota discloses differences in adenocarcinoma and squamous cell carcinoma.
Sílvia GomesBruno CavadasJoana Catarina FerreiraPatrícia Isabel MarquesCatarina MonteiroMaria SucenaCatarina SousaLuís Vaz RodriguesGilberto TeixeiraPaula PintoTiago Tavares de AbreuCristina BárbaraJúlio SemedoLeonor MotaAna Sofia CarvalhoRune MatthiesenLuísa PereiraSusana SeixasPublished in: Scientific reports (2019)
The lung is a complex ecosystem of host cells and microbes often disrupted in pathological conditions. Although bacteria have been hypothesized as agents of carcinogenesis, little is known about microbiota profile of the most prevalent cancer subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). To characterize lung cancer (LC) microbiota a first a screening was performed through a pooled sequencing approach of 16S ribosomal RNA gene (V3-V6) using a total of 103 bronchoalveaolar lavage fluid samples. Then, identified taxa were used to inspect 1009 cases from The Cancer Genome Atlas and to annotate tumor unmapped RNAseq reads. Microbial diversity was analyzed per cancer subtype, history of cigarette smoking and airflow obstruction, among other clinical data. We show that LC microbiota is enriched in Proteobacteria and more diverse in SCC than ADC, particularly in males and heavier smokers. High frequencies of Proteobacteria were found to discriminate a major cluster, further subdivided into well-defined communities' associated with either ADC or SCC. Here, a SCC subcluster differing from other cases by a worse survival was correlated with several Enterobacteriaceae. Overall, this study provides first evidence for a correlation between lung microbiota and cancer subtype and for its influence on patient life expectancy.
Keyphrases
- squamous cell carcinoma
- papillary thyroid
- squamous cell
- lymph node metastasis
- single cell
- induced apoptosis
- computed tomography
- climate change
- escherichia coli
- magnetic resonance imaging
- pseudomonas aeruginosa
- magnetic resonance
- clinical trial
- cell death
- microbial community
- dna methylation
- endoplasmic reticulum stress
- deep learning
- machine learning
- transcription factor
- diffusion weighted imaging
- artificial intelligence
- radiation therapy
- cell proliferation
- resting state
- functional connectivity
- copy number
- klebsiella pneumoniae
- free survival
- pi k akt