Salivary Microbiota for Gastric Cancer Prediction: An Exploratory Study

Abstract

To characterize the salivary microbiota in patients at different progressive histological stages of gastric carcinogenesis and identify microbial markers for detecting gastric cancer, two hundred and ninety-three patients were grouped into superficial gastritis (SG; n = 101), atrophic gastritis (AG; n = 93), and gastric cancer (GC; n = 99) according to their histology. 16S rRNA gene sequencing was used to access the salivary microbiota profile. A random forest model was constructed to classify gastric histological types based on the salivary microbiota compositions. A distinct salivary microbiota was observed in patients with GC when comparing with SG and AG, which was featured by an enrichment of putative proinflammatory taxa including Corynebacterium and Streptococcus. Among the significantly decreased oral bacteria in GC patients including Haemophilus, Neisseria, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella, Haemophilus, and Neisseria are known to reduce nitrite, which may consequently result in an accumulation of carcinogenic N-nitroso compounds. We found that GC can be distinguished accurately from patients with AG and SG (AUC = 0.91) by the random forest model based on the salivary microbiota profiles, and taxa belonging to unclassified Streptophyta and Streptococcus have potential as diagnostic biomarkers for GC. Remarkable changes in the salivary microbiota functions were also detected across three histological types, and the upregulation in the isoleucine and valine is in line with a higher level of these amino acids in the gastric tumor tissues that reported by other independent studies. Conclusively, bacteria in the oral cavity may contribute gastric cancer and become new diagnostic biomarkers for GC, but further evaluation against independent clinical cohorts is required. The potential mechanisms of salivary microbiota in participating the pathogenesis of GC may include an accumulation of proinflammatory bacteria and a decline in those reducing carcinogenic N-nitroso compounds.

Keywords: 16S rRNA; gastric cancer; high-throughput sequencing; precancerous lesions; salivary microbiota.

Figure 1

The salivary microbiota biodiversity in patients with malignant and non-malignant gastric lesions. The alpha diversity of salivary microbiota was measured at the ASV level by using (A) Shannon index, (B) Chao1. Significance was determined by using Kruskal–Wallis rank sum test, and Wilcoxon rank-sum tests for each of the two groups. PCoA based on (C) unweighted UniFrac distance matrix, and (D) weighted UniFrac distance matrix revealed distinct clustering of GC samples.

Figure 2

The salivary microbiota composition in patients with malignant and non-malignant gastric lesions. The salivary microbiota composition in patients with malignant and non-malignant gastric lesions. Relative abundance of salivary taxa of the three groups were compared at the genus level. Significance was determined by using Kruskal-Wallis rank sum test with BH-adjusted P < 0.001, and Wilcoxon rank-sum tests for each of the two groups with *BH-adjusted P < 0.05, **BH-adjusted P < 0.01, and ***BH-adjusted P < 0.001.

Figure 3

Heatmap summarizing the significant associations between oral bacteria and metadata. Color key: -log(q-value)*sign(coefficient). Cells that denote significant associations are colored (red or blue) and overlaid with a plus (+) or minus (−) sign that indicates the direction of association.

Figure 4

Salivary bacterial biomarkers for classifying different stages of gastric carcinogenesis. (A) Microbiological features of the salivary microbiota associated with different histological stages of gastric tumorigenesis. Bacteria taxa that enriched in each histological stage were determined by LEfSe with Kruskal–Wallis test P <0.05 and log 10 LDA score >3.4. (B) ROC curves analysis to evaluate the discriminatory potential of salivary bacteria in identifying GC out of pre-malignant lesions. (C) The top 10 bacterial genera that are most important for discriminating GC with non-malignant types. Each genus is ranked according to an importance score (mean decrease accuracy).