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Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alteratio...

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Autores principales: Ma, Xin, Botros, Andro, Yun, Sylvia R., Park, Eun Young, Kim, Olga, Chen, Ruihong, Palaniappan, Murugesan, Matzuk, Martin M., Kim, Jaeyeon, Fernández, Facundo M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634942/
https://www.ncbi.nlm.nih.gov/pubmed/37961688
http://dx.doi.org/10.1101/2023.10.30.564760
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author Ma, Xin
Botros, Andro
Yun, Sylvia R.
Park, Eun Young
Kim, Olga
Chen, Ruihong
Palaniappan, Murugesan
Matzuk, Martin M.
Kim, Jaeyeon
Fernández, Facundo M.
author_facet Ma, Xin
Botros, Andro
Yun, Sylvia R.
Park, Eun Young
Kim, Olga
Chen, Ruihong
Palaniappan, Murugesan
Matzuk, Martin M.
Kim, Jaeyeon
Fernández, Facundo M.
author_sort Ma, Xin
collection PubMed
description No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and a triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide a direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.
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spelling pubmed-106349422023-11-13 Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models Ma, Xin Botros, Andro Yun, Sylvia R. Park, Eun Young Kim, Olga Chen, Ruihong Palaniappan, Murugesan Matzuk, Martin M. Kim, Jaeyeon Fernández, Facundo M. bioRxiv Article No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and a triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide a direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis. Cold Spring Harbor Laboratory 2023-11-02 /pmc/articles/PMC10634942/ /pubmed/37961688 http://dx.doi.org/10.1101/2023.10.30.564760 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Ma, Xin
Botros, Andro
Yun, Sylvia R.
Park, Eun Young
Kim, Olga
Chen, Ruihong
Palaniappan, Murugesan
Matzuk, Martin M.
Kim, Jaeyeon
Fernández, Facundo M.
Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models
title Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models
title_full Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models
title_fullStr Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models
title_full_unstemmed Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models
title_short Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models
title_sort ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634942/
https://www.ncbi.nlm.nih.gov/pubmed/37961688
http://dx.doi.org/10.1101/2023.10.30.564760
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