| Literature DB >> 34040698 |
Ming-Cheng Guan1, Wei Ouyang1, Ming-Da Wang2, Lei Liang3, Na Li1, Ting-Ting Fu1, Feng Shen2, Wan-Yee Lau2, Qiu-Ran Xu3, Dong-Sheng Huang3, Hong Zhu1, Tian Yang2.
Abstract
Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma (HCC). Overwhelming evidence from published studies on HCC has documented that multiple molecular biomarkers detected in body fluids and feces can be utilized in early-diagnosis, predicting responses to specific therapies, evaluating prognosis before or after therapy, as well as serving as novel therapeutic targets. Detection and analysis of proteins, metabolites, circulating nucleic acids, circulating tumor cells, and extracellular vesicles in body fluids (e.g., blood and urine) and gut microbiota (e.g., in feces) have excellent capabilities to improve different aspects of management of HCC. Numerous studies have been devoted in identifying more promising candidate biomarkers and therapeutic targets for diagnosis, treatment, and monitoring responses of HCC to conventional therapies, most of which may improve diagnosis and management of HCC in the future. This review aimed to summarize recent advances in utilizing these biomarkers in HCC and discuss their clinical significance. ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.Entities:
Keywords: Biomarker; Blood; Feces; Gut microbiota; Hepatocellular carcinoma; Urine
Year: 2021 PMID: 34040698 PMCID: PMC8131906 DOI: 10.4251/wjgo.v13.i5.351
Source DB: PubMed Journal: World J Gastrointest Oncol
Figure 1Multiple molecular biomarkers derived from blood, urine, and feces, including proteins, metabolites, circulating nucleic acids, circulating tumor cells, extracellular vesicles, and gut microbiota, have great potential to diagnose early hepatocellular carcinoma, predict responses to specific therapies, evaluate prognoses before or after therapies, and be developed as new therapeutic targets for this tumor. P: Proteins; M: Metabolites.
The major values of circulating tumor cells and extracellular vesicles for management of hepatocellular carcinoma
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| CTCs | Recurrence prediction | Chen | A close relationship between postoperative CTC levels and early recurrence in HCC patients after liver transplantation or partial hepatectomy was revealed |
| Recurrence prediction | Qi | A preoperative CTC count ≥ 16 and a mesenchymal-CTC percentage ≥ 2% were significant risk factors associated with early HCC recurrence, multi-intrahepatic recurrence, and lung metastasis | |
| Prognostic evaluation | Shen | An EpCAM-positive CTC count detected before therapy could predict poor survival of patients with HCC | |
| Prognostic evaluation | Hamaoka | A GPC3-positive CTC count detected before therapy could predict poor survival of patients with HCC | |
| Prognostic evaluation | Luo | The presence of CTC-associated white blood cell clusters detected before therapy could predict poor survival of patients with HCC | |
| Diagnosis/management | Guo | A CTC panel including four putative stem cell biomarkers showed great potential in HCC diagnosis, outcome prediction, as well as treatment response evaluation | |
| Monitoring response to therapy | Rau | Changes in some CTCs could reflect treatment response to regional therapies, particularly helpful in monitoring AFP-negative HCCs | |
| EVs | Therapeutic targets | Son | A new strategy of transferring the sodium/iodide symporter protein to cells |
| Pomatto | Mild electroporation allowed a more efficient and functional miRNA encapsulation in EVs and achieved better protection of antitumor miRNAs from RNase degradation | ||
| Liu | Hepatic stellate cell-derived EVs loaded with therapeutic nucleic acids such as miR-30a-3p and miR-335-5p decelerated the progress of HCC by directly regulating targets | ||
| Lu | AFP-enriched exosomes derived from dendritic cells allowed the stimulation of antitumor immune responses in autochthonous HCC mouse models in eliciting tumor regression |
AFP: Alpha-fetoprotein; HCC: Hepatocellular carcinoma; CTC: Circulating tumor cell; EpCAM: Epithelial cell adhesion molecule; GPC3: Glypican-3; HBV: Hepatitis B virus; EV: Extracellular vesicles.
Multiple potential urinary biomarkers used for hepatocellular carcinoma prevention, diagnosis, and recurrence prediction
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| HCC prevention | Wu | 8-oxodeoxyguanosine |
| Wu | 15-F2t-isoprostane | |
| Yuan | 8-epi-prostaglandin F2α | |
| Mahmoud | 8-hydroxy-deoxyguanosine | |
| Detection/diagnosis | Abdalla and Haj-Ahmad[ | DJ-1, Chromatin Assembly Factor-1, Heat Shock Protein 60 |
| Zhan | AFP and orosomucoid 1 | |
| Zhao | 7 urinary protein features | |
| Ladep | A panel including inosine, indole-3-acetate, galactose, and N-acetylated amino acid | |
| Abdalla and Haj-Ahmad[ | miR-618 and miR-650 | |
| Jeng | Adenosine, cytidine, and inosine | |
| Recurrence prediction | Kikuchi | Trypsin inhibitor |
| Ye | Ethanolamine, lactic acid, acotinic acid, phenylalanine and ribose |
HCC: Hepatocellular carcinoma; AFP: Alpha-fetoprotein.
The diagnostic, prognostic, and therapeutic values of gut microbiota
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| Diagnosis | Ren | The selected panel of 30 optimal gut microbial markers showed a powerful diagnostic performance for early HCC, achieving an AUC of 80.64% between 75 early HCC and 105 non-HCC subjects |
| Lapidot | The environmental factors leading to fatty liver, consumption of artificial sweeteners, and high-sugar foods were significantly associated with changes in the microbiota of cirrhotic patients with HCC | |
| Monitoring response to therapy | Zheng | Gut microbiota could influence the response to immunotherapy in patients with HCC |
| Prognostic evaluation | Huang | Six microbial biomarkers related to tumor immune microenvironment or bile acid metabolism could predict clinical outcome |
| Iida | The intestinal anaerobic bacteria including | |
| Therapeutic targets | Rattan | The use of probiotics in murine HCC models resulted in reducing HCC development |
HCC: Hepatocellular carcinoma; AUC: Area under curve.