A comparison of the microbiome composition in lower respiratory tract at different sites in early lung cancer patients

Abstract

Background: Lung microbiome dysbiosis has been associated with lung carcinogenesis. However, the differences in the microbiome composition at different lung sites of lung cancer patients remain little understood. Studying the whole lung microbiome in cancer patients could provide new insights for interpreting the complex interplay between the microbiome and lung cancer and finding new targets for more effective therapies and preventive measures.

Methods: A total of 16 patients with non-small cell lung cancer (NSCLC) were recruited for this study. Samples were obtained from four sites, including lung tumor tissues (TT), para-tumor tissues (PT), distal normal lung tissues (DN), and bronchial tissues (BT). The DNA was isolated from the tissues, and the V3-V4 regions were amplified. Sequencing libraries were generated and sequenced on an Illumina NovaSeq6000 platform.

Results: The richness and evenness of the microbiome were generally consistent among the TT, PT, DN, and BT groups in lung cancer patients. Principal coordinate analysis (PCoA) and nonmetric multidimensional scaling (NMDS) based on Bray-Curtis, weighted and unweighted UniFrac distance showed no distinct separation trend among the four groups. Proteobacteria, Firmicutes, Bacteroidota, and Desulfobacterota were the most common phyla in all four groups, while TT showed the highest abundance of Proteobacteria and the lowest abundance of Firmicutes. At the genus level, Rubellimicrobium and Fictibacillus were higher in the TT group. In the predicted functional analysis by PICRUSt, there were no specifically discrepant pathways among the four groups. In addition, an inverse relationship between body mass index (BMI) and alpha diversity was observed in this study.

Conclusions: A non-significant result was obtained from the microbiome diversity comparison between different tissues. However, we demonstrated that lung tumors were enriched with specific bacterial species, which might contribute to tumorigenesis. Moreover, we found an inverse relationship between BMI and alpha diversity in these tissues, providing a new clue for deciphering the mechanisms of lung carcinogenesis.

Keywords: 16S rRNA sequencing; Lung microbiome; microbiota dysbiosis; non-small cell lung cancer (NSCLC).

Figure 1

Correlation between BMI and alpha diversity. The alpha diversity was indicated by Chao, ACE, Shannon, and Simpson. The figures illustrate the respective relationships in tissues of TT (A), PT (B), DN (C), and BT (D). BMI, body mass index; TT, lung tumor tissues; PT, para-tumor tissues; DN, distal normal lung tissues; BT, bronchial tissues.

Figure 2

Comparison of alpha and beta diversity of the four tissue sites in lung cancer patients. (A) Alpha diversity indicated by Chao1, Richness, Shannon, and Simpson index was compared among four tissue sites. The P values were derived from Wilcoxon rank sum test; (B) Beta diversity, which was used to estimate the difference in microbiome composition between groups, was visualized by PCoA and NMDS and estimated based on Bray-Curtis distance. TT, lung tumor tissues; PT, para-tumor tissues; DN, distal normal lung tissues; BT, bronchial tissues; PCoA, principal coordinate analysis; NMDS, nonmetric multidimensional scaling.

Figure 3

Taxonomic profiles of the four tissue sites (BT, TT, DN, and PT) in lung cancer patients. (A) The relative frequency of microbiome in each site at the phylum level; (B) the relative frequency of microbiome in each site at the genus levels; (C) discrepant genus of four tissue sites based on LDA scores using LEfSe software. TT, lung tumor tissues; PT, para-tumor tissues; DN, distal normal lung tissues; BT, bronchial tissues; LDA, linear discriminant analysis.

Figure 4

Prediction of the microbiome function of each sample by PICRUSt. Each column represents one sample. The first two character of the x-axis labels represents the tissue sites and the remained characters are the subjects’ codes. TT, lung tumor tissues; PT, para-tumor tissues; DN, distal normal lung tissues; BT, bronchial tissues; PICRUSt, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.