Urine microbiome changes during and after radiotherapy for prostate cancer

BackgroundThe urinary microbiome may play a new important role in the development of complications, but still, there is no information about their changes during and after radiotherapy (RT). This study aimed to use the matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) technique to identify the microbiome and assess its changes in urine samples of 88 patients irradiated for prostate cancer. Material and methodsBlood for biochemical analysis and urine samples for MALDI were collected at various time points before gold fiducial implantation (t1) at the beginning (t2) and end of radiotherapy (t3); during follow-up, 1 (t4), 4 (t5), 7 (t6) months after the end of treatment. ResultsWe identified 1801 different microbial isolates, in 89% (470/528) samples revealed the presence of at least one microbial species among which 79% (373/470) were polymicrobial. Species level: 136 G+, 29 G-, 2 Candida have been noted. The far most abundant group of the identified microorganisms was Staphylococcus members -51.6% of all isolates followed by Micrococcus (9.1%), Enterococcus (7.6%), Kocuria (5.6%), Corynebacterium (5.4%), and Streptococcus (2.2%). A lower variety of microorganisms incident was observed at the end of RT. The total number of species (TNS) was 50 at t1, increased up to 61 at t2, and then fell to the initial value of 52 at t3. The increase in biodiversity was noted after radiotherapy t4-68, t5-86, and t6-75 (p<0.05). Changes in the biodiversity of the urinary microbiota were also reflected in the differences in the total number of isolates (TNI) - 261, 281, and 273 for time points t1-t3 compared to the 292, 362, and 332 for time points t4-t6 as well as in the total number of detected genera (TNG) - 25, 29, 23 (t1-t3) and 28, 38, 31 (t4-t6). Actinomyces, Corynebacterium, Staphylococcus, Streptococcus, demonstrated significant correlation with the RT stages. Concerning individual species, only K. rhizophila abundance significantly increased with time (p=0.045). Bacteria incidence was strongly correlated with glucose levels in urine. The same correlation was observed for glucose levels in blood, but in a weak manner. Staphylococcus presence was related to higher tPSA. ConclusionRT for prostate cancer induces a dynamic response in the urinary microbiome, characterized by an initial reduction in diversity post-RT followed by a subsequent increase. Our findings highlight the significant influence of glucose levels in both urine and blood on the urinary microbiota. These insights contribute to the evolving understanding of the interplay between RT, the urinary microbiome, and patient health, paving the way for more targeted interventions and personalized approaches in prostate cancer treatment.