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Updated: Feb 2, 2026

Bioreactor Assembly for Continuous Culture of Complex Fecal Communities
Published on: April 25, 2025
Alterations of fecal bacterial communities in patients with lung cancer
Wei-Quan Zhang1,2, Shu-Kang Zhao1,2, Jun-Wen Luo1,2
1Department of Thoracic Surgery, The Second Hospital of Shandong University Shandong, P. R. China.
Abstract:
Emerging evidence suggests the microbiome may affect a number of diseases, including lung cancer. However, the direct relationship between gut bacteria and lung cancer remains uncharacterized. In this study, we directly sequenced the hypervariable V1-V2 regions of the 16S rRNA gene in fecal samples from patients with lung cancer and healthy volunteers. Unweighted principal coordinate analysis (PCoA) revealed a clear difference in the bacterial community membership between the lung cancer group and the healthy control group. The lung cancer group had remarkably higher levels of Bacteroidetes, Fusobacteria, Cyanobacteria, Spirochaetes, and Lentisphaerae but dramatically lower levels of Firmicutes and Verrucomicrobia than the healthy control group (P < 0.05). Despite significant interindividual variation, eight predominant genera were significantly different between the two groups. The lung cancer group had higher levels of Bacteroides, Veillonella, and Fusobacterium but lower levels of Escherichia-Shigella, Kluyvera, Fecalibacterium, Enterobacter, and Dialister than the healthy control group (P < 0.05). Most notably, correlations between certain specific bacteria and serum inflammatory biomarkers were identified. Our findings demonstrated an altered bacterial community in patients with lung cancer, providing a significant step in understanding the relationship between gut bacteria and lung cancer. To our knowledge, this is the first study to evaluate the correlations between certain specific bacteria and inflammatory indicators. To better understand this relationship, further studies should investigate the underlying mechanisms of gut bacteria in lung cancer animal models.
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