Hepatocellular carcinoma: a systems biology perspective

Melatonin in Cancer Treatment: Current Knowledge and Future Opportunities

Melatonin is a pleotropic molecule with numerous biological activities. Epidemiological and experimental studies have documented that melatonin could inhibit different types of cancer in vitro and in vivo. Results showed the involvement of melatonin in different anticancer mechanisms including apoptosis induction, cell proliferation inhibition, reduction in tumor growth and metastases, reduction in the side effects associated with chemotherapy and radiotherapy, decreasing drug resistance in cancer therapy, and augmentation of the therapeutic effects of conventional anticancer therapies. Clinical trials revealed that melatonin is an effective adjuvant drug to all conventional therapies. This review summarized melatonin biosynthesis, availability from natural sources, metabolism, bioavailability, anticancer mechanisms of melatonin, its use in clinical trials, and pharmaceutical formulation. Studies discussed in this review will provide a solid foundation for researchers and physicians to design and develop new therapies to treat and prevent cancer using melatonin.

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

BACKGROUND

Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility.

RESULTS

The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems.

CONCLUSION

This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipeand contains as testing data all the data used in this paper.

Melatonin for the prevention and treatment of cancer

The epidemiological studies have indicated a possible oncostatic property of melatonin on different types of tumors. Besides, experimental studies have documented that melatonin could exert growth inhibition on some human tumor cells in vitro and in animal models. The underlying mechanisms include antioxidant activity, modulation of melatonin receptors MT1 and MT2, stimulation of apoptosis, regulation of pro-survival signaling and tumor metabolism, inhibition on angiogenesis, metastasis, and induction of epigenetic alteration. Melatonin could also be utilized as adjuvant of cancer therapies, through reinforcing the therapeutic effects and reducing the side effects of chemotherapies or radiation. Melatonin could be an excellent candidate for the prevention and treatment of several cancers, such as breast cancer, prostate cancer, gastric cancer and colorectal cancer. This review summarized the anticancer efficacy of melatonin, based on the results of epidemiological,experimental and clinical studies, and special attention was paid to the mechanisms of action.

Emodin-Loaded PLGA-TPGS Nanoparticles Combined with Heparin Sodium-Loaded PLGA-TPGS Nanoparticles to Enhance Chemotherapeutic Efficacy Against Liver Cancer

PURPOSE

Heparin sodium (HS)-loaded polylactic-co-glycolic acid-D-α-tocopheryl polyethylene glycol 1000 succinate (PLGA-TPGS) nanoparticles (HPTNs) were prepared as a sustained and targeting delivery carrier and combined with emodin (EMO)-loaded PLGA-TPGS nanoparticles (EPTNs), which were investigated previously to form a combination therapy system for the treatment of liver cancer.

METHODS

To assess cellular uptake and evaluate the liver-targeting capacity by analyzing the drug concentrations and frozen slices, HS/eosin-loaded PLGA-TPGS nanoparticles, HS/fluorescein- loaded PLGA-TPGS nanoparticles and EMO/C6-loaded PLGA-TPGS nanoparticles, which contained eosin, fluorescein and C6 as fluorescent probes, respectively, were also prepared. All of these nanoparticles were characterized in terms of their size, size distribution, surface charge, drug loading, encapsulation efficiency, in vitro release profile and cellular uptake. The apoptosis of HepG2 cells induced by EPTNs in combination with HPTNs was determined by Annexin V-FITC staining and PI labelling.

RESULTS

Transmission electron microscopy indicated that these nanoparticles were stably dispersed spheres with sizes ranging from 100 to 200 nm. The results demonstrated that fluorescent nanoparticles were internalized into HepG2 and HCa-F cells efficiently and had improved liver-targeting properties. The combination of EPTNs and HPTNs effectively inhibited cell growth in vitro and had a remarkable synergistic anticancer effect in vivo. EPTNs combined with HPTNs induced HepG2 cell apoptosis with synergistic effects. The liver H&E slice images of a hepatocarcinogenic mouse model indicated that EPTNs in combination with HPTNs significantly suppressed tumour growth in vivo.

CONCLUSIONS

The research suggests that the combination therapy system of EPTNs and HPTNs could be a new direction for liver cancer therapy.

Advanced mass spectrometry-based multi-omics technologies for exploring the pathogenesis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

Apelin-13 induces autophagy in hepatoma HepG2 cells through ERK1/2 signaling pathway-dependent upregulation of Beclin1

The aim of the present study was to investigate the effect of Apelin-13 on autophagy in hepatocellular carcinoma HepG2 cells and the underlying mechanism of the effect. The HepG2 cells were treated with Apelin-13 at a final concentration of 0.0001, 0.001, 0.01 and 0.1 µmol/l for 24 h. Cells were also treated with 10 µmol/l PD98059 for 24 h. The expression of the extracellular signal-regulated kinase (ERK)1/2, phosphorylated ERK1/2 (pERK1/2) and Beclin1 proteins were detected by western blot analysis. Beclin1 mRNA expression was also detected by reverse transcription-polymerase chain reaction. Autophagy was observed using fluorescence microscopy subsequent to monodansylcadaverine (MDC) staining. Following treatment with the various concentrations of Apelin-13, the expression of the ERK1/2 protein remained at a similar level, whereas the expression of pERK1/2 increased in a dose-dependent manner. Compared with the control group, the increase was significant (P<0.05). Similarly, Beclin1 expression was upregulated at the protein and mRNA levels by Apelin-13 treatment in a dose-dependent manner and was significantly increased compared with the control group. However, following treatment with the Apelin-13 inhibitor PD98059, the expression of pERK1/2, Beclin1 protein and Beclin1 mRNA were significantly decreased (P<0.05). In addition, Apelin-13 induced the autophagy of HepG2 cells in a dose-dependent manner, as revealed by MDC staining. PD98059 inhibited autophagy of HepG2 cells induced by Apelin-13. Therefore, Apelin-13 may promote autophagy in HepG2 cells by inducing the phosphorylation of ERK1/2 and upregulating the expression of Beclin1.

Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.