Metabolic Biosynthesis Pathways Identified from Fecal Microbiome Associated with Prostate Cancer

Abstract

Background: The fecal microbiome is associated with prostate cancer risk factors (obesity, inflammation) and can metabolize and produce various products that may influence cancer but have yet to be defined in prostate cancer.

Objective: To investigate gut bacterial diversity, identify specific metabolic pathways associated with disease, and develop a microbiome risk profile for prostate cancer.

Design, setting, and participants: After prospective collection of 133 rectal swab samples 2 wk before the transrectal prostate biopsy, we perform 16S rRNA amplicon sequencing on 105 samples (64 with cancer, 41 without cancer). Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was applied to infer functional categories associated with taxonomic composition. The p values were adjusted using the false discovery rate. The α- and β-diversity analyses were performed using QIIME. The Mann-Whitney U test was employed to evaluate the statistical significance of β-diversity distances within and between groups of interest, and least absolute shrinkage and selection operator (LASSO) regression analysis was used to determine pathway significance.

Outcome measurements and statistical analysis: The detection of prostate cancer on transrectal prostate needle biopsy and 16s microbiome profile.

Results and limitations: We identified significant associations between total community composition and cancer/non-cancer status (Bray-Curtis distance metric, p<0.01). We identified significant differences in enrichments of Bacteroides and Streptococcus species in cancer (all p<0.04). Folate (LDA 3.8) and arginine (LDA 4.1) were the most significantly altered pathways. We formed a novel microbiome-derived risk factor for prostate cancer based on 10 aberrant metabolic pathways (area under curve=0.64, p=0.02).

Conclusions: Microbiome analyses on men undergoing prostate biopsy noted mostly similar bacterial species diversity among men diagnosed with and without prostate cancer. The microbiome may have subtle influences on prostate cancer but are likely patient-specific and would require paired analysis and precise manipulation, such as improvement of natural bacterial folate production.

Patient summary: Microbiome evaluation may provide patients with personalized data regarding the presence or absence of particular bacteria that have metabolic functions and implications regarding prostate cancer risk. The study provides a basis to investigate the manipulation of aberrant microbiomes to reduce prostate cancer risk.

Keywords: B vitamins; Biomarker; Biotin; Folate; Microbiome; Prostate cancer.

Conflict of interest statement

Financial disclosures: Michael A. Liss certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: James Robert White is the founder of Resphera Biosciences that performed the microbiome analysis but did not inform the statistical analysis of the biomarker component of the manuscript. Dr. White was paid as a consultant on the microbiome analysis.

Published by Elsevier B.V.

Figures

Fig. 1 -

Consort diagram.

Fig. 2 -

Microbial diversity. Beta-diversity analysis reveals an altered intestinal microbiome composition associated with prostate cancer. Left: Mean beta-diversity distances (Bray-Curtis/Unweighted UniFrac) within and between groups by cancer status shows significant associations. Patients with prostate cancer are significantly more similar in taxonomic composition compared with the non-cancer group. Right: Principal coordinates analysis plot showing patients with and without cancer (based on Bray-Curtis distances). PCoA = principal coordinate analysis.

Fig. 3 -

LefSe analyses including the top differentially abundant PICRUSt categories. Predicted KEGG functional pathway differences between cancer (dark orange) and no cancer (orange) participants. PICRUSt was used to predict functional potential of microbiomes using 16s rRNA gene sequence data. (A) Indicates all PICRUSt variables and (B) indicates those specifically modifiable factors after removal of all human-associated functional categories. Differentially enriched bacterial functions among groups were identified using LefSe. Pathway differences plot as LDA score (log 10). Bars to the right of zero represent bacterial functions enriched in the microbiome of patients without prostate cancer, and bars to the left of zero represent bacterial functions in those diagnosed with prostate cancer with prostate needle biopsy. KEGG = Kyoto Encyclopedia of Genes and Genomes; LDA = linear discriminant analysis; LefSe = LDA effect size; PICRUSt = Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.

Fig. 4 -

Pyramid plot of microbiome-associated gut metabolic pathways (KEGG). Pyramid plot displaying the number of microbiome-associated gut metabolic pathways (KEGG) associated with cancer compared with no cancer on biopsy (Kendell’s Tau for ordinal assessment, p = 0.01). Pyramid plot in which the X axis is the number of cases (individual patients) and the Y axis is the number of aberrant microbiome metabolic profiles. The 10 important microbiome PICRUSt pathways are represented, and the asterisk (*) indicates those important on the LASSO regression analysis. KEGG = Kyoto Encyclopedia of Genes and Genomes; LASSO = least absolute shrinkage and selection operator; PICRUSt = Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.

Fig. 5 -

Microbiome score. Receiver operating curve for the presence of prostate cancer on prostate biopsy. We utilized PSA as there were no other known markers that are predictive in this cohort for prostate cancer. We excluded men with a PSA >20 for prediction purposes in this particular analysis. The two microbiome-associated lines are comparing two techniques to identify the most important microbiome KEGG metabolic pathways. The LASSO regression analysis identified four metabolic pathways with a point given for each association (score 0–4). The microbiome profile is a larger number of KEGG pathways associated with prostate cancer (n = 10) including vitamin B pathway and other pathways that could be targeted with probiotics or dietary interventions. AUC = area under the curve; CI = confidence interval; KEGG = Kyoto Encyclopedia of Genes and Genomes; LASSO = least absolute shrinkage and selection operator; PSA = prostate-specific antigen; ROC = receiver operating curve.