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- W3048532839 abstract "Background The pulmonary microbiome is closely related to the occurrence of pulmonary diseases. The morbidity and mortality of lung cancer are relatively high in the world. It has been confirmed that lung microecology changes in lung cancer patients compared with healthy individuals. Furthermore, the abundance of some bacterial species shows obvious changes, suggesting their potential use as a microbial marker for the detection of lung cancer. The composition of the pulmonary microbiome in patients with different histological types of lung cancer has not been determined. We aim to study the correlation and difference of microbiome between different histological types of lung cancer. Methods Illumina HiSeq high-throughput sequencing technology was used to sequenced the 16S rDNA V3-V4 region of bacterial in sputum samples of patients with advanced lung cancer. Results A total of 371 OTU samples were obtained. It was found that Streptococcus, Neisseria and Prevotella were the main bacteria of lung cancer patients. Advantage bacterium group differ between different histological types of lung cancer. Adenocarcinoma (AD) group was dominated by Streptococcus and Neisseria, followed by Veillonella. Small cell lung cancer (SCLC) group was dominated by Neisseria, followed by Streptococcus. Squamous carcinoma (SCC) group was dominated by Streptococcus, followed by Veillonella. Combined small cell lung cancer (C-SCLC) group was dominated by Streptococcus, followed by Prevotella. Conclusions The pulmonary bacterial microbiome of lung cancer of different histological types is different. This experiment enriched the pulmonary bacterial microbiome data of lung cancer and filled the gap of pulmonary microbiome of small cell lung cancer." @default.
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- W3048532839 date "2020-08-06" @default.
- W3048532839 modified "2023-09-23" @default.
- W3048532839 title "[Analysis of Pulmonary Microbial Diversity in Patients with Advanced Lung Cancer Based on High-throughput Sequencing Technology]." @default.
- W3048532839 doi "https://doi.org/10.3779/j.issn.1009-3419.2020.103.16" @default.
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