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Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics

BACKGROUND: The high drug resistance and metabolic reprogramming of clear cell renal cell carcinoma (ccRCC) are considered responsible for poor prognosis. In-depth research at multiple levels is urgently warranted to illustrate the lipid composition, distribution, and metabolic pathways of clinical...

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Autores principales: Li, Wen, Wang, Xiaobin, Zhang, Xianbin, Gong, Peng, Ding, Degang, Wang, Ning, Wang, Zhifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590225/
https://www.ncbi.nlm.nih.gov/pubmed/34774030
http://dx.doi.org/10.1186/s12944-021-01572-z
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author Li, Wen
Wang, Xiaobin
Zhang, Xianbin
Gong, Peng
Ding, Degang
Wang, Ning
Wang, Zhifeng
author_facet Li, Wen
Wang, Xiaobin
Zhang, Xianbin
Gong, Peng
Ding, Degang
Wang, Ning
Wang, Zhifeng
author_sort Li, Wen
collection PubMed
description BACKGROUND: The high drug resistance and metabolic reprogramming of clear cell renal cell carcinoma (ccRCC) are considered responsible for poor prognosis. In-depth research at multiple levels is urgently warranted to illustrate the lipid composition, distribution, and metabolic pathways of clinical ccRCC specimens. METHODS: In this project, a leading-edge targeted quantitative lipidomic study was conducted using 10 pairs of cancerous and adjacent normal tissues obtained from ccRCC patients. Accurate lipid quantification was performed according to a linear equation calculated using internal standards. Qualitative and quantitative analyses of lipids were performed with multiple reaction monitoring analysis based on ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS). Additionally, a multivariate statistical analysis was performed using data obtained on lipids. RESULTS: A total of 28 lipid classes were identified. Among them, the most abundant were triacylglycerol (TG), diacylglycerol (DG), phosphatidylcholine (PC), and phosphatidylethanolamine (PE). Cholesteryl ester (CE) was the lipid exhibiting the most considerable difference between normal samples and tumor samples. Lipid content, chain length, and chain unsaturation of acylcarnitine (CAR), CE, and DG were found to be significantly increased. Based on screening for variable importance in projection scores ≥1, as well as fold change limits between 0.5 and 2, 160 differentially expressed lipids were identified. CE was found to be the most significantly upregulated lipid, while TG was observed to be the most significantly downregulated lipid. CONCLUSION: Based on the absolute quantitative analysis of lipids in ccRCC specimens, it was observed that the content and change trends varied in different lipid classes. Upregulation of CAR, CE, and DG was observed, and analysis of changes in the distribution helped clarify the causes of lipid accumulation in ccRCC and possible carcinogenic molecular mechanisms. The results and methods described herein provide a comprehensive analysis of ccRCC lipid metabolism and lay a theoretical foundation for cancer treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12944-021-01572-z.
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spelling pubmed-85902252021-11-15 Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics Li, Wen Wang, Xiaobin Zhang, Xianbin Gong, Peng Ding, Degang Wang, Ning Wang, Zhifeng Lipids Health Dis Research BACKGROUND: The high drug resistance and metabolic reprogramming of clear cell renal cell carcinoma (ccRCC) are considered responsible for poor prognosis. In-depth research at multiple levels is urgently warranted to illustrate the lipid composition, distribution, and metabolic pathways of clinical ccRCC specimens. METHODS: In this project, a leading-edge targeted quantitative lipidomic study was conducted using 10 pairs of cancerous and adjacent normal tissues obtained from ccRCC patients. Accurate lipid quantification was performed according to a linear equation calculated using internal standards. Qualitative and quantitative analyses of lipids were performed with multiple reaction monitoring analysis based on ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS). Additionally, a multivariate statistical analysis was performed using data obtained on lipids. RESULTS: A total of 28 lipid classes were identified. Among them, the most abundant were triacylglycerol (TG), diacylglycerol (DG), phosphatidylcholine (PC), and phosphatidylethanolamine (PE). Cholesteryl ester (CE) was the lipid exhibiting the most considerable difference between normal samples and tumor samples. Lipid content, chain length, and chain unsaturation of acylcarnitine (CAR), CE, and DG were found to be significantly increased. Based on screening for variable importance in projection scores ≥1, as well as fold change limits between 0.5 and 2, 160 differentially expressed lipids were identified. CE was found to be the most significantly upregulated lipid, while TG was observed to be the most significantly downregulated lipid. CONCLUSION: Based on the absolute quantitative analysis of lipids in ccRCC specimens, it was observed that the content and change trends varied in different lipid classes. Upregulation of CAR, CE, and DG was observed, and analysis of changes in the distribution helped clarify the causes of lipid accumulation in ccRCC and possible carcinogenic molecular mechanisms. The results and methods described herein provide a comprehensive analysis of ccRCC lipid metabolism and lay a theoretical foundation for cancer treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12944-021-01572-z. BioMed Central 2021-11-13 /pmc/articles/PMC8590225/ /pubmed/34774030 http://dx.doi.org/10.1186/s12944-021-01572-z Text en © The Author(s) 2021 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
Li, Wen
Wang, Xiaobin
Zhang, Xianbin
Gong, Peng
Ding, Degang
Wang, Ning
Wang, Zhifeng
Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
title Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
title_full Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
title_fullStr Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
title_full_unstemmed Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
title_short Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
title_sort revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590225/
https://www.ncbi.nlm.nih.gov/pubmed/34774030
http://dx.doi.org/10.1186/s12944-021-01572-z
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