Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry

• Published in: Analytica chimica acta Vol. 648; no. 1; pp. 98 - 104
• Main Authors: Wu, Hao, Xue, Ruyi, Dong, Ling, Liu, Taotao, Deng, Chunhui, Zeng, Huazong, Shen, Xizhong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 19.08.2009; Elsevier
• Subjects: Adult; Alanine Transaminase - blood; Analytical chemistry; Biomarker; Carcinoma, Hepatocellular - metabolism; Carcinoma, Hepatocellular - urine; Chemistry; Chromatographic methods and physical methods associated with chromatography; Exact sciences and technology; Gas chromatographic methods; Gas Chromatography-Mass Spectrometry - methods; Gas chromatography/mass spectrometry; Hepatocellular carcinoma; Humans; Liver Neoplasms - metabolism; Liver Neoplasms - urine; Male; Metabolomic profiling; Metabolomics - methods; Middle Aged; Principal Component Analysis; ROC Curve; Spectrometric and optical methods; Metabolomic profiling; Hepatocellular carcinoma; Biomarker; Gas chromatography/mass spectrometry; Human; Urine; Metabolomics; Chemical analysis; Metabolite; Biological marker; Multivariate analysis; Derivatization; Statistics; Gas chromatography; Non destructive method; Serum; Mass spectrometry; Principal component analysis
• ISSN: 0003-2670; 1873-4324; 1873-4324
• DOI: 10.1016/j.aca.2009.06.033

With the technique of metabolomics, gas chromatography/mass spectrometry (GC/MS), urine or serum metabolites can be assayed to explore disease biomarkers. In this work, we present a metabolomic method to investigate the urinary metabolic difference between hepatocellular carcinoma (HCC, n = 20) male patients and normal male subjects ( n = 20). The urinary endogenous metabolome was assayed using chemical derivatization followed by GC/MS. After GC/MS analysis, 103 metabolites were detected, of which 66 were annotated as known compounds. By a two sample t-test statistics with p < 0.05, 18 metabolites were shown to be significantly different between the HCC and control groups. A diagnostic model was constructed with a combination of 18 marker metabolites or together with alphafetoprotein, using principal component analysis and receiver–operator characteristic curves. The multivariate statistics of the diagnostic model yielded a separation between the two groups with an area under the curve value of 0.9275. This non-invasive technique of identifying HCC biomarkers from urine may have clinical utility.