High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort
Abstract Background Prostate cancer (PCa) remains a leading global malignancy, yet current diagnostic reliance on prostate-specific antigen (PSA) testing is limited by suboptimal sensitivity and specificity for early-stage detection. The present study aims to establish an effective high-throughput s...
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BMC
2025-07-01
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| Series: | Biomarker Research |
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| Online Access: | https://doi.org/10.1186/s40364-025-00804-z |
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| author | Xinrong Jiang Chen Zhang Jing Le Jie Zhang Shuo Cao Xinran Xu Xiaoming Chen Sheng Cheng Haitao Yu Haofei Jiang Ruichen Zang Kunyu Wang Weiwu Chen Haobo Fan Jianmin Wu Yanlan Yu Guoqing Ding |
| author_facet | Xinrong Jiang Chen Zhang Jing Le Jie Zhang Shuo Cao Xinran Xu Xiaoming Chen Sheng Cheng Haitao Yu Haofei Jiang Ruichen Zang Kunyu Wang Weiwu Chen Haobo Fan Jianmin Wu Yanlan Yu Guoqing Ding |
| author_sort | Xinrong Jiang |
| collection | DOAJ |
| description | Abstract Background Prostate cancer (PCa) remains a leading global malignancy, yet current diagnostic reliance on prostate-specific antigen (PSA) testing is limited by suboptimal sensitivity and specificity for early-stage detection. The present study aims to establish an effective high-throughput screening technique for accurate PCa diagnosis. Methods A large-scale cohort of 28,892 subjects was considered for inclusion in this study, and 1133 volunteers were finally selected, including 600 healthy controls, 160 patients diagnosed with other diseases of urinary system, 89 patients diagnosed with benign prostate hyperplasia (BPH), and 284 PCa patients. Discovery and internal validation phases of diagnostic models were conducted through machine learning of urine metabolic fingerprints obtained by laser desorption/ionization mass spectrometry (LDI-MS). Furthermore, the developed diagnostic model was verified in an external validation cohort. Results In retrospective cohort, the stepwise binary classification model achieved satisfactory diagnostic performance with areas under curves (AUCs) of 0.9599–0.9957 in the discovery (n = 567) and internal validation dataset (n = 284). In the external validation cohort (n = 282), AUC values from the ROC curves that differentiate Non-PD from PD, BPH from PCa, and HC from UD were 0.9815, 0.9705, and 0.9980, respectively. More than 95% significant PCa patients in the gray area (3 < tPSA < 10 ng/mL) were successfully identified from BPH subjects. Notably, four metabolite-related candidate genes were identified in this work, including AOX1, PON3, CBS and ASPA. Conclusions This study demonstrated the clinical potential of an LDI-MS-based non-invasive urine biopsy for early prostate cancer detection, particularly in improving diagnostic accuracy for patients with tPSA levels in the gray zone (3–10 ng/mL). |
| format | Article |
| id | doaj-art-c3be08666dc941dca067e6bb76a62dbe |
| institution | Kabale University |
| issn | 2050-7771 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Biomarker Research |
| spelling | doaj-art-c3be08666dc941dca067e6bb76a62dbe2025-08-20T03:46:11ZengBMCBiomarker Research2050-77712025-07-0113111310.1186/s40364-025-00804-zHigh-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohortXinrong Jiang0Chen Zhang1Jing Le2Jie Zhang3Shuo Cao4Xinran Xu5Xiaoming Chen6Sheng Cheng7Haitao Yu8Haofei Jiang9Ruichen Zang10Kunyu Wang11Weiwu Chen12Haobo Fan13Jianmin Wu14Yanlan Yu15Guoqing Ding16Department of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityInstitute of Analytical Chemistry, Department of Chemistry, Zhejiang UniversityInstitute of Analytical Chemistry, Department of Chemistry, Zhejiang UniversityWell-healthcare Technologies Co.Department of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityClinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityInstitute of Analytical Chemistry, Department of Chemistry, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityDepartment of Urology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang UniversityAbstract Background Prostate cancer (PCa) remains a leading global malignancy, yet current diagnostic reliance on prostate-specific antigen (PSA) testing is limited by suboptimal sensitivity and specificity for early-stage detection. The present study aims to establish an effective high-throughput screening technique for accurate PCa diagnosis. Methods A large-scale cohort of 28,892 subjects was considered for inclusion in this study, and 1133 volunteers were finally selected, including 600 healthy controls, 160 patients diagnosed with other diseases of urinary system, 89 patients diagnosed with benign prostate hyperplasia (BPH), and 284 PCa patients. Discovery and internal validation phases of diagnostic models were conducted through machine learning of urine metabolic fingerprints obtained by laser desorption/ionization mass spectrometry (LDI-MS). Furthermore, the developed diagnostic model was verified in an external validation cohort. Results In retrospective cohort, the stepwise binary classification model achieved satisfactory diagnostic performance with areas under curves (AUCs) of 0.9599–0.9957 in the discovery (n = 567) and internal validation dataset (n = 284). In the external validation cohort (n = 282), AUC values from the ROC curves that differentiate Non-PD from PD, BPH from PCa, and HC from UD were 0.9815, 0.9705, and 0.9980, respectively. More than 95% significant PCa patients in the gray area (3 < tPSA < 10 ng/mL) were successfully identified from BPH subjects. Notably, four metabolite-related candidate genes were identified in this work, including AOX1, PON3, CBS and ASPA. Conclusions This study demonstrated the clinical potential of an LDI-MS-based non-invasive urine biopsy for early prostate cancer detection, particularly in improving diagnostic accuracy for patients with tPSA levels in the gray zone (3–10 ng/mL).https://doi.org/10.1186/s40364-025-00804-zMetabolomicsProstate cancerBenign prostatic hyperplasiaNon-invasive diagnosisLaser desorption/Ionization mass spectrometry |
| spellingShingle | Xinrong Jiang Chen Zhang Jing Le Jie Zhang Shuo Cao Xinran Xu Xiaoming Chen Sheng Cheng Haitao Yu Haofei Jiang Ruichen Zang Kunyu Wang Weiwu Chen Haobo Fan Jianmin Wu Yanlan Yu Guoqing Ding High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort Biomarker Research Metabolomics Prostate cancer Benign prostatic hyperplasia Non-invasive diagnosis Laser desorption/Ionization mass spectrometry |
| title | High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort |
| title_full | High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort |
| title_fullStr | High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort |
| title_full_unstemmed | High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort |
| title_short | High-throughput LDI MS technology decodes the distinct metabolic landscape of prostate cancer in a large-scale cohort |
| title_sort | high throughput ldi ms technology decodes the distinct metabolic landscape of prostate cancer in a large scale cohort |
| topic | Metabolomics Prostate cancer Benign prostatic hyperplasia Non-invasive diagnosis Laser desorption/Ionization mass spectrometry |
| url | https://doi.org/10.1186/s40364-025-00804-z |
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