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Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery
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| Zeitschriftentitel: | Cancer Epidemiology, Biomarkers & Prevention |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , , , |
| In: | Cancer Epidemiology, Biomarkers & Prevention, 26, 2017, 5, S. 675-683 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Association for Cancer Research (AACR)
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| Schlagwörter: |
| author_facet |
Di Poto, Cristina Ferrarini, Alessia Zhao, Yi Varghese, Rency S. Tu, Chao Zuo, Yiming Wang, Minkun Nezami Ranjbar, Mohammad R. Luo, Yue Zhang, Chi Desai, Chirag S. Shetty, Kirti Tadesse, Mahlet G. Ressom, Habtom W. Di Poto, Cristina Ferrarini, Alessia Zhao, Yi Varghese, Rency S. Tu, Chao Zuo, Yiming Wang, Minkun Nezami Ranjbar, Mohammad R. Luo, Yue Zhang, Chi Desai, Chirag S. Shetty, Kirti Tadesse, Mahlet G. Ressom, Habtom W. |
|---|---|
| author |
Di Poto, Cristina Ferrarini, Alessia Zhao, Yi Varghese, Rency S. Tu, Chao Zuo, Yiming Wang, Minkun Nezami Ranjbar, Mohammad R. Luo, Yue Zhang, Chi Desai, Chirag S. Shetty, Kirti Tadesse, Mahlet G. Ressom, Habtom W. |
| spellingShingle |
Di Poto, Cristina Ferrarini, Alessia Zhao, Yi Varghese, Rency S. Tu, Chao Zuo, Yiming Wang, Minkun Nezami Ranjbar, Mohammad R. Luo, Yue Zhang, Chi Desai, Chirag S. Shetty, Kirti Tadesse, Mahlet G. Ressom, Habtom W. Cancer Epidemiology, Biomarkers & Prevention Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery Oncology Epidemiology |
| author_sort |
di poto, cristina |
| spelling |
Di Poto, Cristina Ferrarini, Alessia Zhao, Yi Varghese, Rency S. Tu, Chao Zuo, Yiming Wang, Minkun Nezami Ranjbar, Mohammad R. Luo, Yue Zhang, Chi Desai, Chirag S. Shetty, Kirti Tadesse, Mahlet G. Ressom, Habtom W. 1055-9965 1538-7755 American Association for Cancer Research (AACR) Oncology Epidemiology http://dx.doi.org/10.1158/1055-9965.epi-16-0366 <jats:title>Abstract</jats:title> <jats:p>Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.</jats:p> <jats:p>Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.</jats:p> <jats:p>Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).</jats:p> <jats:p>Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.</jats:p> <jats:p>Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675–83. ©2016 AACR.</jats:p> Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery Cancer Epidemiology, Biomarkers & Prevention |
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American Association for Cancer Research (AACR), 2017 |
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American Association for Cancer Research (AACR), 2017 |
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1055-9965 1538-7755 |
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Cancer Epidemiology, Biomarkers & Prevention |
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| title |
Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_unstemmed |
Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_full |
Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_fullStr |
Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_full_unstemmed |
Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_short |
Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_sort |
metabolomic characterization of hepatocellular carcinoma in patients with liver cirrhosis for biomarker discovery |
| topic |
Oncology Epidemiology |
| url |
http://dx.doi.org/10.1158/1055-9965.epi-16-0366 |
| publishDate |
2017 |
| physical |
675-683 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.</jats:p>
<jats:p>Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.</jats:p>
<jats:p>Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).</jats:p>
<jats:p>Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.</jats:p>
<jats:p>Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675–83. ©2016 AACR.</jats:p> |
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| author | Di Poto, Cristina, Ferrarini, Alessia, Zhao, Yi, Varghese, Rency S., Tu, Chao, Zuo, Yiming, Wang, Minkun, Nezami Ranjbar, Mohammad R., Luo, Yue, Zhang, Chi, Desai, Chirag S., Shetty, Kirti, Tadesse, Mahlet G., Ressom, Habtom W. |
| author_facet | Di Poto, Cristina, Ferrarini, Alessia, Zhao, Yi, Varghese, Rency S., Tu, Chao, Zuo, Yiming, Wang, Minkun, Nezami Ranjbar, Mohammad R., Luo, Yue, Zhang, Chi, Desai, Chirag S., Shetty, Kirti, Tadesse, Mahlet G., Ressom, Habtom W., Di Poto, Cristina, Ferrarini, Alessia, Zhao, Yi, Varghese, Rency S., Tu, Chao, Zuo, Yiming, Wang, Minkun, Nezami Ranjbar, Mohammad R., Luo, Yue, Zhang, Chi, Desai, Chirag S., Shetty, Kirti, Tadesse, Mahlet G., Ressom, Habtom W. |
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| description | <jats:title>Abstract</jats:title> <jats:p>Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.</jats:p> <jats:p>Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.</jats:p> <jats:p>Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).</jats:p> <jats:p>Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.</jats:p> <jats:p>Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675–83. ©2016 AACR.</jats:p> |
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| imprint_str_mv | American Association for Cancer Research (AACR), 2017 |
| institution | DE-Zi4, DE-Gla1, DE-15, DE-Pl11, DE-Rs1, DE-14, DE-105, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161 |
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| spelling | Di Poto, Cristina Ferrarini, Alessia Zhao, Yi Varghese, Rency S. Tu, Chao Zuo, Yiming Wang, Minkun Nezami Ranjbar, Mohammad R. Luo, Yue Zhang, Chi Desai, Chirag S. Shetty, Kirti Tadesse, Mahlet G. Ressom, Habtom W. 1055-9965 1538-7755 American Association for Cancer Research (AACR) Oncology Epidemiology http://dx.doi.org/10.1158/1055-9965.epi-16-0366 <jats:title>Abstract</jats:title> <jats:p>Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.</jats:p> <jats:p>Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.</jats:p> <jats:p>Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).</jats:p> <jats:p>Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.</jats:p> <jats:p>Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675–83. ©2016 AACR.</jats:p> Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery Cancer Epidemiology, Biomarkers & Prevention |
| spellingShingle | Di Poto, Cristina, Ferrarini, Alessia, Zhao, Yi, Varghese, Rency S., Tu, Chao, Zuo, Yiming, Wang, Minkun, Nezami Ranjbar, Mohammad R., Luo, Yue, Zhang, Chi, Desai, Chirag S., Shetty, Kirti, Tadesse, Mahlet G., Ressom, Habtom W., Cancer Epidemiology, Biomarkers & Prevention, Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery, Oncology, Epidemiology |
| title | Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_full | Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_fullStr | Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_full_unstemmed | Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_short | Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| title_sort | metabolomic characterization of hepatocellular carcinoma in patients with liver cirrhosis for biomarker discovery |
| title_unstemmed | Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery |
| topic | Oncology, Epidemiology |
| url | http://dx.doi.org/10.1158/1055-9965.epi-16-0366 |