High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers
Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin’s lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomark...
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1257079/full |
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| author | Jingjing Ma Kun Chen Yun Ding Xiao Li Qiming Tang Bo Jin Ruben Y. Luo Sheeno Thyparambil Zhi Han C. James Chou Ashlee Zhou James Schilling Zhiguang Lin Yan Ma Qing Li Mengxue Zhang Karl G. Sylvester Seema Nagpal Doff B. McElhinney Xuefeng B. Ling Bobin Chen |
| author_facet | Jingjing Ma Kun Chen Yun Ding Xiao Li Qiming Tang Bo Jin Ruben Y. Luo Sheeno Thyparambil Zhi Han C. James Chou Ashlee Zhou James Schilling Zhiguang Lin Yan Ma Qing Li Mengxue Zhang Karl G. Sylvester Seema Nagpal Doff B. McElhinney Xuefeng B. Ling Bobin Chen |
| author_sort | Jingjing Ma |
| collection | DOAJ |
| description | Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin’s lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL).Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling.Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients.Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL. |
| first_indexed | 2024-03-11T14:30:17Z |
| format | Article |
| id | doaj.art-7fd2a5974ad749da8d00e6f20aaa8cb5 |
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| language | English |
| last_indexed | 2024-03-11T14:30:17Z |
| publishDate | 2023-10-01 |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-7fd2a5974ad749da8d00e6f20aaa8cb52023-10-31T10:11:03ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2023-10-011010.3389/fmolb.2023.12570791257079High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengersJingjing Ma0Kun Chen1Yun Ding2Xiao Li3Qiming Tang4Bo Jin5Ruben Y. Luo6Sheeno Thyparambil7Zhi Han8C. James Chou9Ashlee Zhou10James Schilling11Zhiguang Lin12Yan Ma13Qing Li14Mengxue Zhang15Karl G. Sylvester16Seema Nagpal17Doff B. McElhinney18Xuefeng B. Ling19Bobin Chen20Department of Hematology, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, ChinamProbe Inc., Palo Alto, CA, United StatesmProbe Inc., Palo Alto, CA, United StatesmProbe Inc., Palo Alto, CA, United StatesmProbe Inc., Palo Alto, CA, United StatesDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, United StatesDepartment of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, United StatesDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, United StatesmProbe Inc., Palo Alto, CA, United StatesmProbe Inc., Palo Alto, CA, United StatesDepartment of Hematology, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Hematology, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Hematology, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Hematology, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, United StatesDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United StatesDepartments of Cardiothoracic Surgery and Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA, United StatesDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, United StatesDepartment of Hematology, Huashan Hospital, Fudan University, Shanghai, ChinaBackground: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin’s lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL).Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling.Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients.Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL.https://www.frontiersin.org/articles/10.3389/fmolb.2023.1257079/fullPCNSLcerebrospinal fluidamino acidacylcarnitineUHPLC-MS/MS |
| spellingShingle | Jingjing Ma Kun Chen Yun Ding Xiao Li Qiming Tang Bo Jin Ruben Y. Luo Sheeno Thyparambil Zhi Han C. James Chou Ashlee Zhou James Schilling Zhiguang Lin Yan Ma Qing Li Mengxue Zhang Karl G. Sylvester Seema Nagpal Doff B. McElhinney Xuefeng B. Ling Bobin Chen High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers Frontiers in Molecular Biosciences PCNSL cerebrospinal fluid amino acid acylcarnitine UHPLC-MS/MS |
| title | High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers |
| title_full | High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers |
| title_fullStr | High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers |
| title_full_unstemmed | High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers |
| title_short | High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers |
| title_sort | high throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid identification of pcnsl biomarkers and potential metabolic messengers |
| topic | PCNSL cerebrospinal fluid amino acid acylcarnitine UHPLC-MS/MS |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1257079/full |
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