Profiling The Urinary Microbiota In Male Patients With Bladder Cancer By High Throughput Sequencing

BackgroundBladder cancer is a complex disease arising from the acquisition of multiple genetic and epigenetic changes.Unfortunately,the etiology and pathophysiology of bladder cancer remain unknown.Previous researches of bladder cancer were focus on bladder but ignored the effects of urinary tract microenvironment.Microorganism,the ensemble of symbiotic bacteria,fungi,parasites,and viruses that inhabit the epithelial barrier surfaces of our body,is an important part of human microenvironment,which affects human physiological functions,such as metabolism,immunity and haematopoiesis.In addition,the microbiome also plays a role in the development of malignancies both at epithelial barriers and in tissues.Recently,emerging evidence overturns the dogma that urine in healthy individuals must be sterile.Dysbiosis of the urinary microbiome has been revealed responsible for various urological disorders,such as overactive bladder,urgency urinary incontinence,prostate cancer and so on.Furthermore,enormous evidences strongly support the hypothesis that microbiome might be involved in bladder carcinogenesis,progression and relapse.Our primary purpose was to characterize urinary microbiota associated with bladder cancer in China and to explore the role of microbiome in bladder carcinogenesis.Materials and methods1.Subject Recruitment and Specimen CollectionUrine specimens were collected from male patients with bladder cancer and healthy controls admitted to Nanfang hospital in China between March 2017 and September 2017.All subjects were required to finish a structured questionnaire to collect information on socio-demographic characteristics.Data collection followed the principles outlined in the Declaration of Helsinki.All participants had signed a written informed consent to contribute their own anonymous information to this study.Our study was approved by the Medicine Institutional Review Board of Southern Medical University..2.DNA Isolation and 16S rRNA Gene SequencingTo avoid contamination,DNA isolation was performed using the cultured cells protocol supplied with the DNeasy Blood and Tissue Kit(Qiagen,Germany)in a laminar flow hood.The concentration of extracted DNA was determined through a Nanodrop ND-1000 spectrophotometer(Thermo Electron Corporation,USA).The genomic DNA isolated from the clinical samples was amplified using primer sets specific for V4 region.In order to evaluate contribution of extraneous DNA from reagents,extraction negative controls(no urine)and PCR negative controls(no template)were included.The resultant PCR products were purified by Qiaquick PCR purification kit(Qiagen,Valencia,CA).Finally,purified samples were normalized to equal DNA concentration and sequenced using the Illumina Miseq sequencer(Illumina,Inc.,USA).3.Bioinformatics AnalysisRaw data were filtered to eliminate reads with adapter pollution and low quality to obtain clean reads by using QIIME.Filtered sequences were clustered by 97%identity into operational taxonomic units(OTUs)using UPARSE and subsequently,a single representative sequence from each clustered OTU was used to align to the SILVA database and the Greengenes database by Ribosomal Database Project Classifier.QIIME was used to evaluate alpha diversity,which is composed of the Observed Species,Chao1,Shannon,Simpson and Ace indexes.Among them,the Observed Species,Chaol and Ace indexes are indicators of species richness,while Shannon and Simpson indexes are indicators of species diversity.The difference of alpha diversity between groups was evaluated by Wilcoxon Rank-Sum Test(group number = 2)and Kruskal-Wallis test(n>2)using SPSS(version22).To compare microbial composition between groups,beta diversity was evaluated by calculating the Bray Curtis,weighted UniFrac and unweighted UniFrac distances.Principal coordinate analysis(PCoA)was applied to generate three-dimensional plots in QIIME based on these distance matrices.The PERMANOVA was performed to test for statistical significance between groups using 999 permutations in QIIME.To identify significantly different bacteria between groups,taxa summaries were reformatted and input into Linear discriminant analysis effect size(LEfSe)via the Huttenhower Lab Galaxy Server.The Kruskal-Wallis rank sum test and Wilcoxon test were used to identify biomarkers,and linear discriminant analysis(LDA)was used to score them.Only taxa with logarithmic LDA score greater than 2 at a P<0.05 were considered significantly enriched.To predict the functional pathways from microbiota composition data,Phylogenetic Investigation of Communities by Reconstruction of Unobserved States(PICRUSt)was performed for reconstruction of metagenome.Predicted functional genes were categorized into Kyoto Encyclopedia of Genes and Genomes(KEGG)orthology and compared across patient groups using STAMP(version)4.Statistical AnalysisData are presented as median(first quartile to the third quartile)for continuous variables or number of cases(%)for counts data.The statistical significance of differences between groups were evaluated using Mann-Whitney U-test for continuous variables and Pearson's chi-square test or Fisher's Exact Test for count data through SPSS software(Version 22.0).Results1.Higher Observed Species index(richness,P = 0.008),Chaol index(richness,P =0.008),Ace index(richness,P = 0.003),Shannon index(diversity,P>0.05)and lower Simpson index(diversity,P>0.05)were presented in cancer group,which indicates that bacterial richness significantly increased in cancer patients while difference of species diversity was not significant.2.We also found that bacterial richness increased in HER group(high risk of recurrence)and HEP(high risk of progression)group,compared to LER(low risk of recurrence)group and LEP(low risk of progression)group,respectively.3.A comparison of urine from cancer patients and that from controls showed significantly different bacterial profiles on unweighted UniFrac PCoA plots.The PERMANOVA performed on the data set showed that the observed differences were statistically significant(F = 1.97,P<0.05;F = 2.53,P<0.001;and F =1.61.,P<0.05,for weighted UniFrac,unweighted UniFrac and Bray)4.The LEfSe,which allows for identifying specific taxa associated with cancer,showed significantly higher compositional abundances of Acinetobacter,Anaerococcus,Rubrobacter,Sphingobacterium,Atopostipes,Geobacillus in cancer patients and Serratia,Proteus,Roseomonas,Ruminiclostridium-6,and Eubacterium-xylanophilum in control group at genus level.The results showed that 6 genera were overrepresented in patients with high risk of recurrence and 4 genera in patients with high risk of progression,including Herbaspirillum,Gemella,Bacteroides,Porphyrobacter,Faecalibacterium,Aeromonas in HER group and Herbaspirillum,Porphyrobacter,Bacteroides,Marmoricola in HEP group.5.The predicted KEGG pathways significantly enriched in bladder cancer included Staphylococcus aureus infection,glycerolipid metabolism,retinol metabolism,ethylbenzene degradation and carotenoid biosynthesis.Conclusion1.The urinary microbial profile associated with bladder cancer is significantly different from healthy controls,which suggests that aberrant urinary microbiome may serve as disease biomarkers.2.Among non-invasive bladder cancer patients,higher bacterial richness was observed in HEP group and HER group,compared to LEP group and LER group,which indicates bacterial richness may be associated with prognosis of bladder cancer.3.It is not possible to determine the cause-effect relationship between microbiome and bladder cancer for retrospective study and low number of cases.Thus,prospective follow-up studies with a larger sample number and animal experiment studies will be needed to clarify the role of microbiome in development and progression of bladder cancer.