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Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis
The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255691/ https://www.ncbi.nlm.nih.gov/pubmed/34235148 http://dx.doi.org/10.3389/fcell.2021.682269 |
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author | Jiang, Nan Zhang, Zhenya Chen, Xianyang Zhang, Guofen Wang, Ying Pan, Lijie Yan, Chengping Yang, Guoshan Zhao, Li Han, Jiarui Xue, Teng |
author_facet | Jiang, Nan Zhang, Zhenya Chen, Xianyang Zhang, Guofen Wang, Ying Pan, Lijie Yan, Chengping Yang, Guoshan Zhao, Li Han, Jiarui Xue, Teng |
author_sort | Jiang, Nan |
collection | PubMed |
description | The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC. |
format | Online Article Text |
id | pubmed-8255691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82556912021-07-06 Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis Jiang, Nan Zhang, Zhenya Chen, Xianyang Zhang, Guofen Wang, Ying Pan, Lijie Yan, Chengping Yang, Guoshan Zhao, Li Han, Jiarui Xue, Teng Front Cell Dev Biol Cell and Developmental Biology The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC. Frontiers Media S.A. 2021-06-21 /pmc/articles/PMC8255691/ /pubmed/34235148 http://dx.doi.org/10.3389/fcell.2021.682269 Text en Copyright © 2021 Jiang, Zhang, Chen, Zhang, Wang, Pan, Yan, Yang, Zhao, Han and Xue. 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 | Cell and Developmental Biology Jiang, Nan Zhang, Zhenya Chen, Xianyang Zhang, Guofen Wang, Ying Pan, Lijie Yan, Chengping Yang, Guoshan Zhao, Li Han, Jiarui Xue, Teng Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis |
title | Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis |
title_full | Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis |
title_fullStr | Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis |
title_full_unstemmed | Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis |
title_short | Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis |
title_sort | plasma lipidomics profiling reveals biomarkers for papillary thyroid cancer diagnosis |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255691/ https://www.ncbi.nlm.nih.gov/pubmed/34235148 http://dx.doi.org/10.3389/fcell.2021.682269 |
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