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. 2017 Dec 1;77(23):6777-6787.
doi: 10.1158/0008-5472.CAN-17-1296.

Oral Microbiome Composition Reflects Prospective Risk for Esophageal Cancers

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Oral Microbiome Composition Reflects Prospective Risk for Esophageal Cancers

Brandilyn A Peters et al. Cancer Res. .
Free PMC article

Abstract

Bacteria may play a role in esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), although evidence is limited to cross-sectional studies. In this study, we examined the relationship of oral microbiota with EAC and ESCC risk in a prospective study nested in two cohorts. Oral bacteria were assessed using 16S rRNA gene sequencing in prediagnostic mouthwash samples from n = 81/160 EAC and n = 25/50 ESCC cases/matched controls. Findings were largely consistent across both cohorts. Metagenome content was predicted using PiCRUST. We examined associations between centered log-ratio transformed taxon or functional pathway abundances and risk using conditional logistic regression adjusting for BMI, smoking, and alcohol. We found the periodontal pathogen Tannerella forsythia to be associated with higher risk of EAC. Furthermore, we found that depletion of the commensal genus Neisseria and the species Streptococcus pneumoniae was associated with lower EAC risk. Bacterial biosynthesis of carotenoids was also associated with protection against EAC. Finally, the abundance of the periodontal pathogen Porphyromonas gingivalis trended with higher risk of ESCC. Overall, our findings have potential implications for the early detection and prevention of EAC and ESCC. Cancer Res; 77(23); 6777-87. ©2017 AACR.

Conflict of interest statement

Conflict of interest disclosure: The authors declare no potential conflicts of interest

Figures

Figure 1
Figure 1. Forest plot of odds ratios (OR) and 95% confidence intervals (95% CI) for associations of clr-transformed periodontal pathogen (a priori), genus, and species abundance with EAC and ESCC risk in conditional logistic regression models
See Tables 2 and 3 for numeric display of the OR (95% CI) estimates. Taxa names are colored by phylum; odds ratio estimates are colored only if nominally statistically significant (p<0.05).
Figure 2
Figure 2. Ecological networks among bacterial species associated with EAC or ESCC risk
The SPIEC-EASI algorithm (33) was used to infer microbial ecological networks. In (a) algorithm was applied to EAC cases and matched controls (n=241), and only networks related to EAC-associated species or a priori periodontal pathogens are shown. In (b) algorithm was applied to ESCC cases and matched controls (n=75), and only networks related to ESCC-associated species or a priori periodontal pathogens are shown. Species associated with EAC or ESCC are colored by phylum; other species in networks are indicated by small gray-outlined circles. Lines connecting species are colored by sign (positive: green, negative: red).
Figure 3
Figure 3. Correlations of bacterial species and inferred metagenomic functions
Species and KEGG pathway counts were clr-transformed. Partial Spearman’s correlation coefficients were estimated for each pairwise comparison of species and KEGG pathway abundance, adjusting for age, sex, cohort, race, and smoking. Only KEGG pathways relating to metabolism, and periodontal pathogens or species associated with EAC or ESCC (p<0.05), are included in the heatmap.

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