Salivary Microbial Dysbiosis is Associated with Systemic Inflammatory Markers and Predicted Oral Metabolites in Non-Small Cell Lung Cancer Patients

Abstract

An increasing number of studies have suggested the dysbiosis of salivary microbiome has been linked to the advancement of multiple diseases and proved to be helpful for the diagnosis of them. Although epidemiological studies of salivary microbiota in carcinogenesis are mounting, no systemic study exists regarding the oral microbiota of non-small cell lung cancer (NSCLC) patients. In this study, we presented the characteristics of the salivary microbiota in patients from NSCLC and healthy controls by sequencing of the 16S rRNA microbial genes. Our result revealed distinct salivary microbiota composition in patients from NSCLC compared to the healthy controls. As principal co-ordinates analysis (PCoA) showed, saliva samples clearly differed between the two groups, considering the weighted (p = 0.001, R² = 0.17), and unweighted (p = 0.001, R² = 0.25) UniFrac distance. Phylum Firmicutes (31.69% vs 24.25%, p < 0.05) and its two genera Veillonella (15.51%% vs 9.35%, p < 0.05) and Streptococcus (9.96% vs 6.83%, p < 0.05) were strongly increased in NSCLC group compared to the controls. Additionally, the relative abundances of Fusobacterium (3.06% vs 4.92%, p = 0.08), Prevotella (1.45% vs 3.52%, p < 0.001), Bacteroides (0.56% vs 2.24%, p < 0.001), and Faecalibacterium (0.21% vs 1.00%, p < 0.001) in NSCLC group were generally decreased. Furthermore, we investigated the correlations between systemic inflammation markers and salivary microbiota. Neutrophil-lymphocyte ratio (NLR) positively correlated with the Veillonella (r =0.350, p = 0.007) and lymphocyte-monocyte ratio (LMR) negatively correlated with Streptococcus (r =-0.340, p = 0.008). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways inferred by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) showed that pathways related to xenobiotics biodegradation and metabolism (p < 0.05) and amino acid metabolism (p < 0.05) were enriched in the NSCLC group. Folate biosynthesis (p < 0.05) significantly decreased in NSCLC group. The specific correlations of clinical systemic inflammation markers and predicted KEGG pathways also could pronounce a broad understanding of salivary microbiota in patients with NSCLC. Moreover, our study extended the new sight into salivary microbiota-targeted interventions to clinically improve the therapeutic strategies for salivary dysbiosis in NSCLC patients. Further investigations of the potential mechanism of salivary microbiota in the progression of NSCLC are still in demand.

Figure 1

Principal coordinates analysis of the salivary microbiota. Overall salivary microbiota of patients with non-small cell lung cancer is statistically significantly different from that of healthy individuals as represented by the first two principal coordinates analysis of (a) weighted and (b) unweighted UniFrac distances. Each point represents a single sample, with plus sign and ellipses representing the fitted mean and 68% confidence interval of each group respectively. NSCLC represent non-small cell lung cancer group, and HC, healthy controls.

Figure 2

Relative abundance of significantly different genera at phylum (a), and genus (b) levels between non-small cell lung cancer patients and healthy controls. *p<0.05, ** p<0.01, ***p<0.001. NSCLC represent non-small cell lung cancer group, and HC, healthy controls.

Figure 3

Different structures of salivary microbiota between non-small cell lung cancer and healthy control groups. (a) Cladograms of bacterial lineages with significantly different representation in non-small cell lung cancer and the healthy control groups. Taxonomic cladogram obtained from LEfSe analysis of 16S sequences (The diameter of each circle is proportional to taxon abundance). (b) Histogram of the linear discriminant analysis (LDA) scores for differentially abundant bacterial taxa between non-small cell lung cancer patients and healthy controls. Only taxa meeting an LDA significant threshold > 4.0 are shown. Red (HC) indicates the healthy controls, and green (NSCLC), non-small cell lung cancer group

Figure 4

Canonical Correspondence Analysis (CCA) illustrating relations between bacteria taxa and systemic inflammatory markers in our study groups. Arrows indicate the direction and magnitude of the systemic inflammatory markers associated with bacterial community structures. The explained variance of the principal axes [Axis 1 (horizontally) and Axis 2 (vertically)] are 30.79% and 23.99%, respectively. NSCLC represent non-small cell lung cancer group, and HC, healthy controls.

Figure 5

The co-occurrence network of the salivary microbiota and the systemic inflammatory markers. Each green round node represents an OTU, and each red triangle represents an inflammatory marker. The solid and dashed edge represents a positive and negative correlation, respectively. The whole network could be divided into two subnetworks, where the positive correlation exists within each subnetwork, and the trans-subnetwork correlation was negative.

Figure 6

Different structures of predicted KEGG pathways between non-small cell lung cancer and healthy control groups. Histogram of the linear discriminant analysis (LDA) scores for differentially abundant bacterial taxa between non-small cell lung cancer patients and healthy controls. Only taxa meeting an LDA significant threshold >3.0 are shown. NSCLC represent non-small cell lung cancer group, and HC, healthy controls.