Adaptation of MALDI-TOF MS Technique for Tracking Changes in the Urinary Microbiome During and After Radiotherapy for Prostate Cancer.
기술보고
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
환자: prostate cancer who underwent RT were included
I · Intervention 중재 / 시술
RT were included
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Glucose levels in urine and blood significantly affect microbial composition, suggesting that metabolic factors modulate RT-related microbiome shifts. These findings highlight the interplay between RT, host metabolism, and urinary microbiota, supporting the potential value of glucose monitoring to maintain microbial balance after RT.
[PURPOSE] The urinary microbiome may influence the development of radiation-induced complications in prostate cancer.
APA
Złoch M, Sibińska E, et al. (2026). Adaptation of MALDI-TOF MS Technique for Tracking Changes in the Urinary Microbiome During and After Radiotherapy for Prostate Cancer.. Cancer management and research, 18, 573379. https://doi.org/10.2147/CMAR.S573379
MLA
Złoch M, et al.. "Adaptation of MALDI-TOF MS Technique for Tracking Changes in the Urinary Microbiome During and After Radiotherapy for Prostate Cancer.." Cancer management and research, vol. 18, 2026, pp. 573379.
PMID
41883995
Abstract
[PURPOSE] The urinary microbiome may influence the development of radiation-induced complications in prostate cancer. However, its dynamics during and after radiotherapy (RT) remain unclear. This study aimed to use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to characterize and monitor urinary microbiome changes during RT for prostate cancer.
[PATIENTS AND METHODS] Eighty-eight patients with prostate cancer who underwent RT were included. Midstream urine and blood samples were collected at six time points: before gold fiducial implantation (t1), at the start (t2) and end of RT (t3), and at 1, 4, and 7 months post-RT (t4-t6). Microorganisms were cultured under diverse conditions and identified by MALDI-TOF MS. Statistical analyses were used to assess the associations between microbial profiles, RT stages, and biochemical parameters in the urine and blood.
[RESULTS] A total of 1773 microbial isolates were identified in 89% of urine samples, with 79% showing a polymicrobial composition. The microbiota was dominated by (51.6%), , and . Biodiversity decreased at the end of RT but gradually recovered up to seven months post-treatment. Genera such as , and were significantly correlated with study time course, whereas the abundance of increased over time. Changes in microbiome composition were strongly associated with glucose levels in urine and blood.
[CONCLUSION] RT triggers a dynamic response in the urinary microbiome, with an initial decline in diversity followed by progressive recolonization. Glucose levels in urine and blood significantly affect microbial composition, suggesting that metabolic factors modulate RT-related microbiome shifts. These findings highlight the interplay between RT, host metabolism, and urinary microbiota, supporting the potential value of glucose monitoring to maintain microbial balance after RT.
[PATIENTS AND METHODS] Eighty-eight patients with prostate cancer who underwent RT were included. Midstream urine and blood samples were collected at six time points: before gold fiducial implantation (t1), at the start (t2) and end of RT (t3), and at 1, 4, and 7 months post-RT (t4-t6). Microorganisms were cultured under diverse conditions and identified by MALDI-TOF MS. Statistical analyses were used to assess the associations between microbial profiles, RT stages, and biochemical parameters in the urine and blood.
[RESULTS] A total of 1773 microbial isolates were identified in 89% of urine samples, with 79% showing a polymicrobial composition. The microbiota was dominated by (51.6%), , and . Biodiversity decreased at the end of RT but gradually recovered up to seven months post-treatment. Genera such as , and were significantly correlated with study time course, whereas the abundance of increased over time. Changes in microbiome composition were strongly associated with glucose levels in urine and blood.
[CONCLUSION] RT triggers a dynamic response in the urinary microbiome, with an initial decline in diversity followed by progressive recolonization. Glucose levels in urine and blood significantly affect microbial composition, suggesting that metabolic factors modulate RT-related microbiome shifts. These findings highlight the interplay between RT, host metabolism, and urinary microbiota, supporting the potential value of glucose monitoring to maintain microbial balance after RT.