| Literature DB >> 30450847 |
Misu Lee1,2, Haeyong Ko1, Mijin Yun3.
Abstract
Various molecular targeted therapies and diagnostic modalities have been developed for the treatment of hepatocellular carcinoma (HCC); however, HCC still remains a difficult malignancy to cure. Recently, the focus has shifted to cancer metabolism for the diagnosis and treatment of various cancers, including HCC. In addition to conventional diagnostics, the measurement of enhanced tumor cell metabolism using F-18 fluorodeoxyglucose (18F-FDG) for increased glycolysis or C-11 acetate for fatty acid synthesis by positron emission tomography/computed tomography (PET/CT) is well established for clinical management of HCC. Unlike tumors displaying the Warburg effect, HCCs vary substantially in terms of 18F-FDG uptake, which considerably reduces the sensitivity for tumor detection. Accordingly, C-11 acetate has been proposed as a complementary radiotracer for detecting tumors that are not identified by 18F-FDG. In addition to HCC diagnosis, since the degree of 18F-FDG uptake converted to standardized uptake value (SUV) correlates well with tumor aggressiveness, 18F-FDG PET/CT scans can predict patient outcomes such as treatment response and survival with an inverse relationship between SUV and survival. The loss of tumor suppressor genes or activation of oncogenes plays an important role in promoting HCC development, and might be involved in the "metabolic reprogramming" of cancer cells. Mutations in various genes such as TERT, CTNNB1, TP53, and Axin1 are responsible for the development of HCC. Some microRNAs (miRNAs) involved in cancer metabolism are deregulated in HCC, indicating that the modulation of genes/miRNAs might affect HCC growth or metastasis. In this review, we will discuss cancer metabolism as a mechanism for treatment resistance, as well as an attractive potential therapeutic target in HCC. © Copyright: Yonsei University College of Medicine 2018.Entities:
Keywords: Hepatocellular carcinoma; cancer metabolism; drug resistance; positron emission tomography/computed tomography (PET/CT)
Mesh:
Substances:
Year: 2018 PMID: 30450847 PMCID: PMC6240564 DOI: 10.3349/ymj.2018.59.10.1143
Source DB: PubMed Journal: Yonsei Med J ISSN: 0513-5796 Impact factor: 2.759
Fig. 1Hepatocellular carcinoma positive for F-18 fluorodeoxyglucose (A), but negative for C-11 acetate (B).
Fig. 2Hepatocellular carcinoma negative for F-18 fluorodeoxyglucose (A), but positive C-11 acetate (B).
Fig. 3Differences in the expression of glucose transport 1 (A and C) and monocarboxylate transporter 1 (B and D) in hepatocellular carcinoma (HCC) samples, based on 18F-fluorodeoxyglucose and 11C-acetate uptake. Human HCC samples were used. Immunohistochemistry (IHC) was performed as described previously.16 After antigen retrieval, IHC was performed using indicated antibodies. Scale bars: 40 µm.
List of the Most Relevant Mutations in Hepatocellular Carcinoma
| De-regulated pathway | Gene | Frequency (%) | Etiology enrichment | |
|---|---|---|---|---|
| Telomere maintenanc | 70 | Gain of function | Alcohol | |
| Cell cycle control | 30 | Loss of function | HBV | |
| 8 | Loss of function | |||
| 8 | Loss of function | Alcohol | ||
| 7 | Gain of function | |||
| 5 | Gain of function | |||
| Wnt signaling | CTNNB1 | 30 | Gain of function | Alcohol |
| AXIN1 | 11 | Loss of function | ||
| ZNRF3 | 3 | Loss of function | ||
| AXIN2 | 1 | |||
| APC | 1 | Loss of function | ||
| Chromatin remodeling | 13 | Loss of function | Alcohol | |
| 10 | ||||
| ARID2 | 7 | |||
| KMT2D | 6 | |||
| 3 | ||||
| 2 | ||||
| PI3K/mTOR signaling | TSC2 | 5 | Loss of function | |
| TSC1 | 3 | Loss of function | ||
| DAPK1 | 3 | Loss of function | ||
| PI3CA | 3 | Gain of function | ||
| mTOR | 2 | Gain of function | ||
| RAS/MAPK signaling | RPS6KA3 | 7 | Loss of function | |
| FGF19 | 4 | Gain of function | ||
| NTRK3 | 3 | |||
| EPHA4 | 3 | |||
| JAK/STAT signaling | IL6ST | 3 | Gain of function | |
| JAK1 | 1 | |||
| Oxidative stress | NFE2L2 | 6 | Gain of function | |
| KEAP1 | 4 | Loss of function |