A prospective study to examine the association of the urinary and fecal microbiota with prostate cancer diagnosis after transrectal biopsy of the prostate using 16sRNA gene analysis

Abstract

INTRODUCTION: There is accumulating evidence that variations in the human microbiota may promote disease states including cancer. Our goal was to examine the association between urinary and fecal microbial profiles and the diagnosis of prostate cancer (PC) in patients undergoing transrectal biopsy of the prostate. MATERIALS AND METHODS: We extracted total DNA from urine and fecal samples collected before a prostate biopsy performed for elevated prostatic specific antigen in patients suspected of having PC. We then amplified the extracted DNA and sequenced it using bacterial 16S rRNA gene high-throughput next-generation sequencing platform, and analyzed microbial profiles for taxonomy comparing those patients diagnosed with PC with those who did not receive that diagnosis. RESULTS: We included 30 patients in our analysis (60 samples, one urine and one fecal per patient). The majority of patients with PC (10/14) had similar bacterial communities within their urinary sample profile and clustered separately than patients without cancer (n = 16). Differential analysis of the operational taxonomical units (OTUs) in urine samples revealed decreased abundance of several bacterial species in patients with prostate cancer. Analysis of the bacterial taxonomies of the fecal samples did not reveal any clustering in concordance with benign or malignant prostate biopsies. Patients who had a Gleason score (GS) of 6 (n = 11) were present in both urine bacterial community clusters, but patients with GS 7 or higher (n = 3) did not cluster tightly with non-cancer subjects. CONCLUSIONS: The urinary microbiota of patients with PC tends to cluster separately from those without this disease. Further research is needed to investigate the urinary microbiome potential of serving as a biomarker that could be used to improve the accuracy of pre-biopsy models predicting the presence of PC in post-biopsy tissue examination.