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Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST
OBJECTIVE: Low-intensity shockwave therapy (LiST) has been applied in the clinical treatment of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), but few studies have focused on the prediction of its therapeutic effect before treatment. METHODS: Seventy-five CP/CPPS patients from our insti...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332892/ https://www.ncbi.nlm.nih.gov/pubmed/35911714 http://dx.doi.org/10.3389/fimmu.2022.953403 |
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author | Meng, Jialin Jin, Chen Li, Jiawei Zhang, Song Zhang, Meng Hao, Zongyao Chen, Xianguo Song, Zhengyao Zhang, Li Liang, Chaozhao |
author_facet | Meng, Jialin Jin, Chen Li, Jiawei Zhang, Song Zhang, Meng Hao, Zongyao Chen, Xianguo Song, Zhengyao Zhang, Li Liang, Chaozhao |
author_sort | Meng, Jialin |
collection | PubMed |
description | OBJECTIVE: Low-intensity shockwave therapy (LiST) has been applied in the clinical treatment of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), but few studies have focused on the prediction of its therapeutic effect before treatment. METHODS: Seventy-five CP/CPPS patients from our institute between July 2020 and May 2021 were enrolled and received 3 Hz, 0.25 mJ/mm(2) LiST once a week over the course of four weeks. The scores of the NIH-CPSI, IPSS questionnaire and demographic features before treatment were recorded. The plasma before LiST treatment was also collected, while liquid chromatography-tandem mass spectrometry was used to detect the metabolites. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to identify the prediction metabolites and generate the metabolism score. Receiver operating characteristic curves and calibration curves were drawn to assess the prediction accuracy of the nomogram. RESULTS: Twelve metabolites were identified at incomparable levels before and after LiST treatment. The metabolism score generated by LASSO analysis presented a perfect prediction value (AUC: 0.848, 95% CI: 0.719-0.940) in the training cohort and further increased to 0.892 (95% CI: 0.802-0.983) on the nomogram, which accompanied with the NIH-CPSI scores and age. Similar results of the metabolism score (AUC: 0.732, 95% CI: 0.516-0.889) and total nomogram (AUC: 0.968, 95% CI: 0.909-1.000) were obtained in the testing cohort. Further enrichment of the 12 metabolites indicated that the glycine and serine metabolism pathway was involved in the LiST treatment. CONCLUSION: We used our system to accurately and quantitatively measure plasma metabolites and establish a predictive model to identify suitable patients for LiST treatment. |
format | Online Article Text |
id | pubmed-9332892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93328922022-07-29 Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST Meng, Jialin Jin, Chen Li, Jiawei Zhang, Song Zhang, Meng Hao, Zongyao Chen, Xianguo Song, Zhengyao Zhang, Li Liang, Chaozhao Front Immunol Immunology OBJECTIVE: Low-intensity shockwave therapy (LiST) has been applied in the clinical treatment of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), but few studies have focused on the prediction of its therapeutic effect before treatment. METHODS: Seventy-five CP/CPPS patients from our institute between July 2020 and May 2021 were enrolled and received 3 Hz, 0.25 mJ/mm(2) LiST once a week over the course of four weeks. The scores of the NIH-CPSI, IPSS questionnaire and demographic features before treatment were recorded. The plasma before LiST treatment was also collected, while liquid chromatography-tandem mass spectrometry was used to detect the metabolites. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to identify the prediction metabolites and generate the metabolism score. Receiver operating characteristic curves and calibration curves were drawn to assess the prediction accuracy of the nomogram. RESULTS: Twelve metabolites were identified at incomparable levels before and after LiST treatment. The metabolism score generated by LASSO analysis presented a perfect prediction value (AUC: 0.848, 95% CI: 0.719-0.940) in the training cohort and further increased to 0.892 (95% CI: 0.802-0.983) on the nomogram, which accompanied with the NIH-CPSI scores and age. Similar results of the metabolism score (AUC: 0.732, 95% CI: 0.516-0.889) and total nomogram (AUC: 0.968, 95% CI: 0.909-1.000) were obtained in the testing cohort. Further enrichment of the 12 metabolites indicated that the glycine and serine metabolism pathway was involved in the LiST treatment. CONCLUSION: We used our system to accurately and quantitatively measure plasma metabolites and establish a predictive model to identify suitable patients for LiST treatment. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9332892/ /pubmed/35911714 http://dx.doi.org/10.3389/fimmu.2022.953403 Text en Copyright © 2022 Meng, Jin, Li, Zhang, Zhang, Hao, Chen, Song, Zhang and Liang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Meng, Jialin Jin, Chen Li, Jiawei Zhang, Song Zhang, Meng Hao, Zongyao Chen, Xianguo Song, Zhengyao Zhang, Li Liang, Chaozhao Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST |
title | Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST |
title_full | Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST |
title_fullStr | Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST |
title_full_unstemmed | Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST |
title_short | Metabolomics Analysis Reveals the Differential Metabolites and Establishes the Therapeutic Effect Prediction Nomogram Among CP/CPPS Patients Who Respond or Do Not Respond to LiST |
title_sort | metabolomics analysis reveals the differential metabolites and establishes the therapeutic effect prediction nomogram among cp/cpps patients who respond or do not respond to list |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332892/ https://www.ncbi.nlm.nih.gov/pubmed/35911714 http://dx.doi.org/10.3389/fimmu.2022.953403 |
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