Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation

Abstract

Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle–late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.

Keywords:

• hepatocellular carcinoma
• metabolomics
• biomarker
• diagnosis and therapy

Acknowledgments

The study was financially supported by grants from the research council of the University of Hong Kong (project codes 104004092, 104003919, 104004460, 104004745, and 104004746), the Research Grants Committee (RGC) of Hong Kong, HKSAR (project codes 766211, 17152116), Wong’s Donation for Modern Oncology of Chinese Medicine (project code 200006276), and the Shenzhen Basic Research Program (project code JCYJ20140903112959964).

Disclosure

The authors report no conflicts of interest in this work.