The accumulation of omics data at multiple levels provides an opportunity to better understand the progression of chronic liver disease (CLD) to hepatocellular carcinoma (HCC). A variety of HCC- associated molecular alterations have been detected. However, due to the lack of good diagnostic markers and treatment strategies, and because of the disease heterogeneity in human populations, a coherent understanding of the mechanism of HCC development is still limited. The assessment of complex multigenic molecular pathways in HCC remains a difficult challenge. This project brings together experts in bioinformatics, biostatistics, biochemistry, clinical cancer research, and mass spectrometry to EM Algorithm, Posterior Mode

Public Health Relevance

Defining clinically applicable biomarkers that detect early-stage hepatocellular carcinoma (HCC) in a high-risk population of cirrhotic patients has potentially far-reaching consequences for disease management and patient health. This project is important because most HCC patients are diagnosed at a late stage, where the treatment options are limited. There is a pressing need to identify biomarkers that could be used for early detection of HCC. This project will capitalize on markers identified in this and other studies to investigate fingerprints that may be related to the progression of HCC. In addition to screening high-risk populations for early signs of disease, the identified biomarkers and knowledge of their functional involvement in metabolic and signaling pathways could be used to design and test improved treatment strategies.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-OBT-Z (55))
Program Officer
Wali, Anil
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Georgetown University
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Xiao, Junfeng; Zhao, Yi; Varghese, Rency S et al. (2014) Evaluation of metabolite biomarkers for hepatocellular carcinoma through stratified analysis by gender, race, and alcoholic cirrhosis. Cancer Epidemiol Biomarkers Prev 23:64-72
Zhou, Bin; Xiao, Jun Feng; Ressom, Habtom W (2013) Prioritization of putative metabolite identifications in LC-MS/MS experiments using a computational pipeline. Proteomics 13:248-60
Xiao, Jun Feng; Zhou, Bin; Ressom, Habtom W (2012) Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends Analyt Chem 32:1-14
Xiao, Jun Feng; Varghese, Rency S; Zhou, Bin et al. (2012) LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort. J Proteome Res 11:5914-23
Zhou, Bin; Xiao, Jun Feng; Tuli, Leepika et al. (2012) LC-MS-based metabolomics. Mol Biosyst 8:470-81
Ressom, Habtom W; Xiao, Jun Feng; Tuli, Leepika et al. (2012) Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis. Anal Chim Acta 743:90-100
Zhou, Bin; Wang, Jinlian; Ressom, Habtom W (2012) MetaboSearch: tool for mass-based metabolite identification using multiple databases. PLoS One 7:e40096