Cargando…
Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer
SIMPLE SUMMARY: In-time diagnosing ovarian cancer, intractable cancer that has no symptoms can increase the survival of women. The aim of this study was to discover biomarkers from liquid biopsy samples using multi-omics approach, metabolomics and proteomics for the diagnosis of ovarian cancer. To v...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709037/ https://www.ncbi.nlm.nih.gov/pubmed/33228226 http://dx.doi.org/10.3390/cancers12113447 |
_version_ | 1783617666400911360 |
---|---|
author | Ahn, Hee-Sung Yeom, Jeonghun Yu, Jiyoung Kwon, Young-Il Kim, Jae-Hoon Kim, Kyunggon |
author_facet | Ahn, Hee-Sung Yeom, Jeonghun Yu, Jiyoung Kwon, Young-Il Kim, Jae-Hoon Kim, Kyunggon |
author_sort | Ahn, Hee-Sung |
collection | PubMed |
description | SIMPLE SUMMARY: In-time diagnosing ovarian cancer, intractable cancer that has no symptoms can increase the survival of women. The aim of this study was to discover biomarkers from liquid biopsy samples using multi-omics approach, metabolomics and proteomics for the diagnosis of ovarian cancer. To verify our biomarker candidates, we conducted comparative analysis with other previous published studies. Despite the limitations of non-invasive samples, our findings are able to discover emerging properties through the interplay between metabolites and proteins and mechanism-based biomarkers through integrated protein and metabolite analysis. ABSTRACT: The 5-year survival rate in the early and late stages of ovarian cancer differs by 63%. In addition, a liquid biopsy is necessary because there are no symptoms in the early stage and tissue collection is difficult without using invasive methods. Therefore, there is a need for biomarkers to achieve this goal. In this study, we found blood-based metabolite or protein biomarker candidates for the diagnosis of ovarian cancer in the 20 clinical samples (10 ovarian cancer patients and 10 healthy control subjects). Plasma metabolites and proteins were measured and quantified using mass spectrometry in ovarian cancer patients and control groups. We identified the differential abundant biomolecules (34 metabolites and 197 proteins) and statistically integrated molecules of different dimensions to better understand ovarian cancer signal transduction and to identify novel biological mechanisms. In addition, the biomarker reliability was verified through comparison with existing research results. Integrated analysis of metabolome and proteome identified emerging properties difficult to grasp with the single omics approach, more reliably interpreted the cancer signaling pathway, and explored new drug targets. Especially, through this analysis, proteins (PPCS, PMP2, and TUBB) and metabolites (L-carnitine and PC-O (30:0)) related to the carnitine system involved in cancer plasticity were identified. |
format | Online Article Text |
id | pubmed-7709037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77090372020-12-03 Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer Ahn, Hee-Sung Yeom, Jeonghun Yu, Jiyoung Kwon, Young-Il Kim, Jae-Hoon Kim, Kyunggon Cancers (Basel) Article SIMPLE SUMMARY: In-time diagnosing ovarian cancer, intractable cancer that has no symptoms can increase the survival of women. The aim of this study was to discover biomarkers from liquid biopsy samples using multi-omics approach, metabolomics and proteomics for the diagnosis of ovarian cancer. To verify our biomarker candidates, we conducted comparative analysis with other previous published studies. Despite the limitations of non-invasive samples, our findings are able to discover emerging properties through the interplay between metabolites and proteins and mechanism-based biomarkers through integrated protein and metabolite analysis. ABSTRACT: The 5-year survival rate in the early and late stages of ovarian cancer differs by 63%. In addition, a liquid biopsy is necessary because there are no symptoms in the early stage and tissue collection is difficult without using invasive methods. Therefore, there is a need for biomarkers to achieve this goal. In this study, we found blood-based metabolite or protein biomarker candidates for the diagnosis of ovarian cancer in the 20 clinical samples (10 ovarian cancer patients and 10 healthy control subjects). Plasma metabolites and proteins were measured and quantified using mass spectrometry in ovarian cancer patients and control groups. We identified the differential abundant biomolecules (34 metabolites and 197 proteins) and statistically integrated molecules of different dimensions to better understand ovarian cancer signal transduction and to identify novel biological mechanisms. In addition, the biomarker reliability was verified through comparison with existing research results. Integrated analysis of metabolome and proteome identified emerging properties difficult to grasp with the single omics approach, more reliably interpreted the cancer signaling pathway, and explored new drug targets. Especially, through this analysis, proteins (PPCS, PMP2, and TUBB) and metabolites (L-carnitine and PC-O (30:0)) related to the carnitine system involved in cancer plasticity were identified. MDPI 2020-11-19 /pmc/articles/PMC7709037/ /pubmed/33228226 http://dx.doi.org/10.3390/cancers12113447 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ahn, Hee-Sung Yeom, Jeonghun Yu, Jiyoung Kwon, Young-Il Kim, Jae-Hoon Kim, Kyunggon Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer |
title | Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer |
title_full | Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer |
title_fullStr | Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer |
title_full_unstemmed | Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer |
title_short | Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer |
title_sort | convergence of plasma metabolomics and proteomics analysis to discover signatures of high-grade serous ovarian cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709037/ https://www.ncbi.nlm.nih.gov/pubmed/33228226 http://dx.doi.org/10.3390/cancers12113447 |
work_keys_str_mv | AT ahnheesung convergenceofplasmametabolomicsandproteomicsanalysistodiscoversignaturesofhighgradeserousovariancancer AT yeomjeonghun convergenceofplasmametabolomicsandproteomicsanalysistodiscoversignaturesofhighgradeserousovariancancer AT yujiyoung convergenceofplasmametabolomicsandproteomicsanalysistodiscoversignaturesofhighgradeserousovariancancer AT kwonyoungil convergenceofplasmametabolomicsandproteomicsanalysistodiscoversignaturesofhighgradeserousovariancancer AT kimjaehoon convergenceofplasmametabolomicsandproteomicsanalysistodiscoversignaturesofhighgradeserousovariancancer AT kimkyunggon convergenceofplasmametabolomicsandproteomicsanalysistodiscoversignaturesofhighgradeserousovariancancer |