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Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis
The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were perf...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6826937/ https://www.ncbi.nlm.nih.gov/pubmed/31590436 http://dx.doi.org/10.3390/cancers11101497 |
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author | Kim, Da Jung Cho, Eun Ju Yu, Kyung-Sang Jang, In-Jin Yoon, Jung-Hwan Park, Taesung Cho, Joo-Youn |
author_facet | Kim, Da Jung Cho, Eun Ju Yu, Kyung-Sang Jang, In-Jin Yoon, Jung-Hwan Park, Taesung Cho, Joo-Youn |
author_sort | Kim, Da Jung |
collection | PubMed |
description | The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were performed for 53 and 47 patients with early HCC and cirrhosis, respectively, and 50 matched healthy controls. Results were validated in 82 and 80 patients with early HCC and cirrhosis, respectively. To retain a broad spectrum of metabolites, technically distinct analyses (global metabolomic profiling using gas chromatography time-of-flight mass spectrometry and targeted analyses using liquid chromatography with tandem mass spectrometry) were employed. Multivariate analyses classified distinct metabolites; logistic regression was employed to construct a prediction model for HCC diagnosis. Five metabolites (methionine, proline, ornithine, pimelylcarnitine, and octanoylcarnitine) were selected in a panel. The panel distinguished HCC from cirrhosis and normal controls, with an area under the receiver operating curve (AUC) of 0.82; this was significantly better than that of AFP (AUC: 0.75). During validation, the panel demonstrated significantly better predictability (AUC: 0.94) than did AFP (AUC: 0.78). Defects in ammonia recycling, the urea cycle, and amino acid metabolism, demonstrated on enrichment pathway analysis, may reliably distinguish HCC from cirrhosis. Compared with AFP alone, the metabolite panel substantially improved early-stage HCC detection. |
format | Online Article Text |
id | pubmed-6826937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68269372019-11-18 Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis Kim, Da Jung Cho, Eun Ju Yu, Kyung-Sang Jang, In-Jin Yoon, Jung-Hwan Park, Taesung Cho, Joo-Youn Cancers (Basel) Article The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were performed for 53 and 47 patients with early HCC and cirrhosis, respectively, and 50 matched healthy controls. Results were validated in 82 and 80 patients with early HCC and cirrhosis, respectively. To retain a broad spectrum of metabolites, technically distinct analyses (global metabolomic profiling using gas chromatography time-of-flight mass spectrometry and targeted analyses using liquid chromatography with tandem mass spectrometry) were employed. Multivariate analyses classified distinct metabolites; logistic regression was employed to construct a prediction model for HCC diagnosis. Five metabolites (methionine, proline, ornithine, pimelylcarnitine, and octanoylcarnitine) were selected in a panel. The panel distinguished HCC from cirrhosis and normal controls, with an area under the receiver operating curve (AUC) of 0.82; this was significantly better than that of AFP (AUC: 0.75). During validation, the panel demonstrated significantly better predictability (AUC: 0.94) than did AFP (AUC: 0.78). Defects in ammonia recycling, the urea cycle, and amino acid metabolism, demonstrated on enrichment pathway analysis, may reliably distinguish HCC from cirrhosis. Compared with AFP alone, the metabolite panel substantially improved early-stage HCC detection. MDPI 2019-10-06 /pmc/articles/PMC6826937/ /pubmed/31590436 http://dx.doi.org/10.3390/cancers11101497 Text en © 2019 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 Kim, Da Jung Cho, Eun Ju Yu, Kyung-Sang Jang, In-Jin Yoon, Jung-Hwan Park, Taesung Cho, Joo-Youn Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis |
title | Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis |
title_full | Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis |
title_fullStr | Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis |
title_full_unstemmed | Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis |
title_short | Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis |
title_sort | comprehensive metabolomic search for biomarkers to differentiate early stage hepatocellular carcinoma from cirrhosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6826937/ https://www.ncbi.nlm.nih.gov/pubmed/31590436 http://dx.doi.org/10.3390/cancers11101497 |
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