Hepatocellular carcinoma (HCC) is a significant health problem in the United States. Compared to European Americans (EA), the incidence of HCC is higher in African Americans (AA) and is associated with more advanced tumor stage at diagnosis and lower survival rates. It has been reported that the sensitivity of ?- fetoprotein (AFP) for the diagnosis of HCC in African Americans with hepatitis C virus (HCV) infection is lower than that of patients of all other racial groups combined. We previously performed preliminary investigation into racial disparities through metabolomics profiling of sera from HCC cases and patients with liver cirrhosis by using both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Through stratified analysis of the LC/GC-MS data, we identified different candidates that distinguish HCC cases from the cirrhotic controls among AA and EA. This application builds on these promising preliminary results to find and validate race-specific metabolites as biomarkers for HCC. This will be accomplished by targeted analysis of metabolites in liver tissues and sera from HCC cases and patients with liver cirrhosis representing AA and EA. Metabolites that differentiate HCC cases from cirrhotic controls in a race-specific manner will be selected by statistical and network-based methods. Then, a systems-oriented approach that combines these metabolites with other candidate biomarkers (genes, glycans, and proteins) will be utilized for the selection of key signaling pathways perturbed in HCC. Following validation of the candidates via independent samples from HCC cases, patients with liver cirrhosis, and healthy subjects, we will elucidate their functional roles through both in vitro and in vivo experiments. Successful completion of this research will enable us to identify HCC biomarkers and perturbed pathways that are race-specific as well as those that are shared by AA and EA. These findings will contribute not only to a greater understanding of racial disparities in HCC but also to improving diagnosis of HCC through race-specific biomarkers.

Public Health Relevance

Currently used biomarkers for detecting hepatocellular carcinoma (HCC) in high-risk cirrhotic patients lack the desired sensitivity and specificity, particularly in African American patients. The goal of this project is to find and validate race-specific biomarkers for HCC and identify perturbed intercellular signaling pathways in HCC. If successful, the study will (1) contribute to a greater understanding of racial disparities in HCC, (2) enable accurate detection of HCC by using race as a factor, and (3) give insights into the mechanisms underlying the behavior of potential biomarkers and therapeutic targets.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA185188-04
Application #
9527764
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Spalholz, Barbara A
Project Start
2015-08-01
Project End
2020-07-31
Budget Start
2018-08-01
Budget End
2019-07-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Georgetown University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
049515844
City
Washington
State
DC
Country
United States
Zip Code
20057
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Di Poto, Cristina; Ferrarini, Alessia; Zhao, Yi et al. (2017) Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery. Cancer Epidemiol Biomarkers Prev 26:675-683
Afsari, Ali; Lee, Edward; Shokrani, Babak et al. (2017) Clinical and Pathological Risk Factors Associated with Liver Fibrosis and Steatosis in African-Americans with Chronic Hepatitis C. Dig Dis Sci 62:2159-2165
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Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina et al. (2016) Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies. BMC Genomics 17 Suppl 4:545
Yao, Zhixing; Sherif, Zaki A (2016) The effect of epigenetic silencing and TP53 mutation on the expression of DLL4 in human cancer stem disorder. Oncotarget 7:62976-62988
Zuo, Yiming; Cui, Yi; Di Poto, Cristina et al. (2016) INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery. Methods 111:12-20
Ranjbar, Mohammad R Nezami; Di Poto, Cristina; Wang, Yue et al. (2015) SIMAT: GC-SIM-MS data analysis tool. BMC Bioinformatics 16:259