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A stepwise data interpretation process for renal amyloidosis typing by LMD-MS

BACKGROUNDS: Systemic amyloidosis is classified according to the deposited amyloid fibril protein (AFP), which determines its best therapeutic scheme. The most common type of AFP found are immunoglobulin light chains. The laser microdissection combined with mass spectrometry (LMD-MS) technique is a...

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Autores principales: Ke, Ming, Li, Xin, Wang, Lin, Yue, Shuling, Zhao, Beibei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008935/
https://www.ncbi.nlm.nih.gov/pubmed/35418036
http://dx.doi.org/10.1186/s12882-022-02785-9
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author Ke, Ming
Li, Xin
Wang, Lin
Yue, Shuling
Zhao, Beibei
author_facet Ke, Ming
Li, Xin
Wang, Lin
Yue, Shuling
Zhao, Beibei
author_sort Ke, Ming
collection PubMed
description BACKGROUNDS: Systemic amyloidosis is classified according to the deposited amyloid fibril protein (AFP), which determines its best therapeutic scheme. The most common type of AFP found are immunoglobulin light chains. The laser microdissection combined with mass spectrometry (LMD-MS) technique is a promising approach for precise typing of amyloidosis, however, the major difficulty in interpreting the MS data is how to accurately identify the precipitated AFP from background. OBJECTIVES: The objective of the present study is to establish a complete data interpretation procedure for LMD-MS based amyloidosis typing. METHODS: Formalin-fixed paraffin-embedded specimens from patients with renal amyloidosis and non-amyloid nephropathies (including diabetic nephropathy, fibrillary glomerulonephritis, IgA nephropathy, lupus nephritis, membranous nephropathy, and normal tissue adjacent to tumors) were analyzed by LMD-MS. Forty-two specimens were used to train the data interpretation procedure, which was validated by another 50 validation specimens. Area under receiver operating curve (AUROC) analysis of amyloid accompanying proteins (AAPs, including apolipoprotein A-IV, apolipoprotein E and serum amyloid P-component) for discriminating amyloidosis from non-amyloid nephropathies was performed. RESULTS: A stepwise data interpretation procedure that includes or excludes the types of amyloidosis group by group was established. The involvement of AFPs other than immunoglobulin was determined by P-score, as well as immunoglobulin light chain by variable of λ-κ, and immunoglobulin heavy chain by H-score. This achieved a total of 88% accuracy in 50 validation specimens. The AAPs showed significantly different expression levels between amyloidosis specimens and non-amyloid nephropathies. Each of the single AAP had a AUROC value more than 0.9 for diagnosis of amyloidosis from non-amyloid control, and the averaged level of the three AAPs showed the highest AUROC (0.966), which might be an alternative indicator for amyloidosis diagnosis. CONCLUSIONS: The proteomic data interpretation procedure for LMD-MS based amyloidosis typing was established successfully that has a high practicability in clinical application. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02785-9.
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spelling pubmed-90089352022-04-15 A stepwise data interpretation process for renal amyloidosis typing by LMD-MS Ke, Ming Li, Xin Wang, Lin Yue, Shuling Zhao, Beibei BMC Nephrol Research BACKGROUNDS: Systemic amyloidosis is classified according to the deposited amyloid fibril protein (AFP), which determines its best therapeutic scheme. The most common type of AFP found are immunoglobulin light chains. The laser microdissection combined with mass spectrometry (LMD-MS) technique is a promising approach for precise typing of amyloidosis, however, the major difficulty in interpreting the MS data is how to accurately identify the precipitated AFP from background. OBJECTIVES: The objective of the present study is to establish a complete data interpretation procedure for LMD-MS based amyloidosis typing. METHODS: Formalin-fixed paraffin-embedded specimens from patients with renal amyloidosis and non-amyloid nephropathies (including diabetic nephropathy, fibrillary glomerulonephritis, IgA nephropathy, lupus nephritis, membranous nephropathy, and normal tissue adjacent to tumors) were analyzed by LMD-MS. Forty-two specimens were used to train the data interpretation procedure, which was validated by another 50 validation specimens. Area under receiver operating curve (AUROC) analysis of amyloid accompanying proteins (AAPs, including apolipoprotein A-IV, apolipoprotein E and serum amyloid P-component) for discriminating amyloidosis from non-amyloid nephropathies was performed. RESULTS: A stepwise data interpretation procedure that includes or excludes the types of amyloidosis group by group was established. The involvement of AFPs other than immunoglobulin was determined by P-score, as well as immunoglobulin light chain by variable of λ-κ, and immunoglobulin heavy chain by H-score. This achieved a total of 88% accuracy in 50 validation specimens. The AAPs showed significantly different expression levels between amyloidosis specimens and non-amyloid nephropathies. Each of the single AAP had a AUROC value more than 0.9 for diagnosis of amyloidosis from non-amyloid control, and the averaged level of the three AAPs showed the highest AUROC (0.966), which might be an alternative indicator for amyloidosis diagnosis. CONCLUSIONS: The proteomic data interpretation procedure for LMD-MS based amyloidosis typing was established successfully that has a high practicability in clinical application. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02785-9. BioMed Central 2022-04-13 /pmc/articles/PMC9008935/ /pubmed/35418036 http://dx.doi.org/10.1186/s12882-022-02785-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ke, Ming
Li, Xin
Wang, Lin
Yue, Shuling
Zhao, Beibei
A stepwise data interpretation process for renal amyloidosis typing by LMD-MS
title A stepwise data interpretation process for renal amyloidosis typing by LMD-MS
title_full A stepwise data interpretation process for renal amyloidosis typing by LMD-MS
title_fullStr A stepwise data interpretation process for renal amyloidosis typing by LMD-MS
title_full_unstemmed A stepwise data interpretation process for renal amyloidosis typing by LMD-MS
title_short A stepwise data interpretation process for renal amyloidosis typing by LMD-MS
title_sort stepwise data interpretation process for renal amyloidosis typing by lmd-ms
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008935/
https://www.ncbi.nlm.nih.gov/pubmed/35418036
http://dx.doi.org/10.1186/s12882-022-02785-9
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