# Systems Metabolomics for HCC Biomarker Discovery

> **NIH NIH R01** · GEORGETOWN UNIVERSITY · 2020 · $311,000

## Abstract

PROJECT SUMMARY
Metabolomics offers a comprehensive analysis of thousands of small molecules in biological samples. It is a
crucial component of systems biology, which characterizes an organism as an integrated and interactive network
of various biomolecules. In particular, metabolomic characterization of complex diseases such as hepatocellular
carcinoma (HCC) plays an indispensable role in the growing systems biology approaches to identify reliable
biomarkers and improved disease treatment strategies. Liquid chromatography coupled to mass spectrometry
(LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been extensively used for high-
throughput comparison of the levels of thousands of metabolites among biological samples. However, the
potential values of many HCC-associated analytes discovered by these platforms have been inadequately
explored in systems biology research due to lack of computational tools and resources to: (1) accurately
determine the identity of most of the analytes; (2) investigate the rewiring and conserved interactions among
metabolites in the progression of the disease, and (3) integrate multi-omic data to evaluate the relationship
between disease and metabolites at the systems level. Partly due to these limitations, poor reproducibility of
previously identified metabolite biomarker candidates for HCC has been observed, especially when they are
evaluated through independent platforms and validation sets. Therefore, new methods are sought to find more
potent biomarkers for HCC. This project aims to address this challenge with the help of network-based
approaches for: (1) prioritizing putative IDs to assist in metabolite identification; (2) performing differential
analysis to uncover relationships between HCC and metabolites; and (3) integrating metabolomic data with
transcriptomic, proteomic, and glycomic data to identify highly promising metabolites as biomarker candidates.
The clinical relevance of these candidates will be evaluated using isotope dilution by selected reaction monitoring
(SRM) and selected ion monitoring (SIM) in sera collected from independent subjects recruited in multiple sites.
Also, the performance of the candidates in detecting HCC will be compared against established blood biomarkers
such as alpha-fetoprotein (AFP), AFP-L3, des-gamma-carboxy prothrombin (DCP), and golgi protein-73 (GP73).
In summary, this project seeks to find metabolite biomarkers for HCC by capitalizing on the power of network
modeling, multi-omic data integration, targeted quantitative analysis, and a multicenter repository of
biospecimens. We strongly believe that the project will lead to reliable serological biomarkers that are likely to
succeed in future large-scale biomarker validation studies.

## Key facts

- **NIH application ID:** 9963282
- **Project number:** 5R01GM123766-04
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** Habtom W Ressom
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $311,000
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963282

## Citation

> US National Institutes of Health, RePORTER application 9963282, Systems Metabolomics for HCC Biomarker Discovery (5R01GM123766-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9963282. Licensed CC0.

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