Human liver cancer organoids: Biological applications, current challenges, and prospects in hepatoma therapy

Investigating the mechanism of tricyclic decyl benzoxazole -induced apoptosis in liver Cancer cells through p300-mediated FOXO3 activation

OBJECTIVE

To investigate whether tricyclic decylbenzoxazole (TDB) regulates liver cancer cell proliferation and apoptosis through p300-mediated FOXO acetylation.

METHODS

Sequencing, adenovirus, and lentivirus transfection were performed in human liver cancer cell line SMMC-7721 and apoptosis was detected by Tunel, Hoechst, and flow cytometry. TEM for mitochondrial morphology, MTT for cell proliferation ability, Western blot, and PCR were used to detect protein levels and mRNA changes.

RESULTS

Sequencing analysis and cell experiments confirmed that TDB can promote the up-regulation of FOXO3 expression. TDB induced FOXO3 up-regulation in a dose-dependent manner, promoted the expression of p300 and Bim, and enhanced the acetylation and dephosphorylation of FOXO3, thus promoting apoptosis. p300 promotes apoptosis of cancer cells through Bim and other proteins, while HAT enhances the phosphorylation of FOXO3 and inhibits apoptosis. Overexpression of FOXO3 can increase the expression of exo-apoptotic pathways (FasL, TRAIL), endo-apoptotic pathways (Bim), and acetylation at the protein level and inhibit cell proliferation and apoptotic ability, while FOXO3 silencing or p300 mutation can partially reverse apoptosis. In tumor tissues with overexpression of FOXO3, TDB intervention can further increase the expression of p53 and caspase-9 proteins in tumor cells, resulting in loss of mitochondrial membrane integrity during apoptosis, the release of cytoplasm during signal transduction, activation of caspase-9 and synergistic inhibition of growth.

CONCLUSION

TDB induces proliferation inhibition and promotes apoptosis of SMMC-7721 cells by activating p300-mediated FOXO3 acetylation.

The Prospect of Hepatic Decellularized Extracellular Matrix as a Bioink for Liver 3D Bioprinting

The incidence of liver diseases is high worldwide. Many factors can cause liver fibrosis, which in turn can lead to liver cirrhosis and even liver cancer. Due to the shortage of donor organs, immunosuppression, and other factors, only a few patients are able to undergo liver transplantation. Therefore, how to construct a bioartificial liver that can be transplanted has become a global research hotspot. With the rapid development of three-dimensional (3D) bioprinting in the field of tissue engineering and regenerative medicine, researchers have tried to use various 3D bioprinting technologies to construct bioartificial livers in vitro. In terms of the choice of bioinks, liver decellularized extracellular matrix (dECM) has many advantages over other materials for cell-laden hydrogel in 3D bioprinting. This review mainly summarizes the acquisition of liver dECM and its application in liver 3D bioprinting as a bioink with respect to availability, printability, and biocompatibility in many aspects and puts forward the current challenges and prospects.

Hepatocellular carcinoma and lipid metabolism: Novel targets and therapeutic strategies

Hepatocellular carcinoma (HCC) is an increasingly prevalent disease that is associated with high and continually rising mortality rates. Lipid metabolism holds a crucial role in the pathogenesis of HCC, in which abnormalities pertaining to the delicate balance of lipid synthesis, breakdown, and storage, predispose for the pathogenesis of the nonalcoholic fatty liver disease (NAFLD), a disease precursor to HCC. If caught early enough, HCC treatment may be curative. In later stages, treatment is only halting the inevitable outcome of death, boldly prompting for novel drug discovery to provide a fighting chance for this patient population. In this review, we begin by providing a summary of current local and systemic treatments against HCC. From such we discuss hepatic lipid metabolism and highlight novel targets that are ripe for anti-cancer drug discovery. Lastly, we provide a targeted summary of current known risk factors for HCC pathogenesis, providing key insights that will be essential for rationalizing future development of anti-HCC therapeutics.

Developing Liver Microphysiological Systems for Biomedical Applications

Microphysiological systems (MPSs), also known as organ chips, are micro-units that integrate cells with diverse physical and biochemical environmental cues. In the field of liver MPSs, cellular components have advanced from simple planar cell cultures to more sophisticated 3D formations such as spheroids and organoids. Additionally, progress in microfluidic devices, bioprinting, engineering of matrix materials, and interdisciplinary technologies have significant promise for producing MPSs with biomimetic structures and functions. This review provides a comprehensive summary of biomimetic liver MPSs including their clinical applications and future developmental potential. First, the key components of liver MPSs, including the principal cell types and engineered structures utilized for cell cultivation, are briefly introduced. Subsequently, the biomedical applications of liver MPSs, including the creation of disease models, drug absorption, distribution, metabolism, excretion, and toxicity, are discussed. Finally, the challenges encountered by MPSs are summarized, and future research directions for their development are proposed.

Harnessing CD8+ T cell dynamics in hepatitis B virus-associated liver diseases: Insights, therapies and future directions

Hepatitis B virus (HBV) infection playsa significant role in the etiology and progression of liver-relatedpathologies, encompassing chronic hepatitis, fibrosis, cirrhosis, and eventual hepatocellularcarcinoma (HCC). Notably, HBV infection stands as the primary etiologicalfactor driving the development of HCC. Given the significant contribution ofHBV infection to liver diseases, a comprehensive understanding of immunedynamics in the liver microenvironment, spanning chronic HBV infection,fibrosis, cirrhosis, and HCC, is essential. In this review, we focused on thefunctional alterations of CD8+ T cells within the pathogenic livermicroenvironment from HBV infection to HCC. We thoroughly reviewed the roles ofhypoxia, acidic pH, metabolic reprogramming, amino acid deficiency, inhibitory checkpointmolecules, immunosuppressive cytokines, and the gut-liver communication in shapingthe dysfunction of CD8+ T cells in the liver microenvironment. Thesefactors significantly impact the clinical prognosis. Furthermore, we comprehensivelyreviewed CD8+ T cell-based therapy strategies for liver diseases,encompassing HBV infection, fibrosis, cirrhosis, and HCC. Strategies includeimmune checkpoint blockades, metabolic T-cell targeting therapy, therapeuticT-cell vaccination, and adoptive transfer of genetically engineered CD8+ T cells, along with the combined usage of programmed cell death protein-1/programmeddeath ligand-1 (PD-1/PD-L1) inhibitors with mitochondria-targeted antioxidants.Given that targeting CD8+ T cells at various stages of hepatitis Bvirus-induced hepatocellular carcinoma (HBV + HCC) shows promise, we reviewedthe ongoing need for research to elucidate the complex interplay between CD8+ T cells and the liver microenvironment in the progression of HBV infection toHCC. We also discussed personalized treatment regimens, combining therapeuticstrategies and harnessing gut microbiota modulation, which holds potential forenhanced clinical benefits. In conclusion, this review delves into the immunedynamics of CD8+ T cells, microenvironment changes, and therapeuticstrategies within the liver during chronic HBV infection, HCC progression, andrelated liver diseases.

Clinical significance of RNA methylation in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a primary liver malignancy with high mortality rates and poor prognosis. Recent advances in high-throughput sequencing and bioinformatic technologies have greatly enhanced the understanding of the genetic and epigenetic changes in liver cancer. Among these changes, RNA methylation, the most prevalent internal RNA modification, has emerged as a significant contributor of the development and progression of HCC. Growing evidence has reported significantly abnormal levels of RNA methylation and dysregulation of RNA-methylation-related enzymes in HCC tissues and cell lines. These alterations in RNA methylation play a crucial role in the regulation of various genes and signaling pathways involved in HCC, thereby promoting tumor progression. Understanding the pathogenesis of RNA methylation in HCC would help in developing prognostic biomarkers and targeted therapies for HCC. Targeting RNA-methylation-related molecules has shown promising potential in the management of HCC, in terms of developing novel prognostic biomarkers and therapies for HCC. Exploring the clinical application of targeted RNA methylation may provide new insights and approaches for the management of HCC. Further research in this field is warranted to fully understand the functional roles and underlying mechanisms of RNA methylation in HCC. In this review, we described the multifaceted functional roles and potential mechanisms of RNA methylation in HCC. Moreover, the prospects of clinical application of targeted RNA methylation for HCC management are discussed, which may provide the basis for subsequent in-depth research on RNA methylation in HCC.

CK2B is a Prognostic Biomarker and a Potential Drug Target for Hepatocellular Carcinoma

BACKGROUND

Although casein kinase II subunit beta (CK2B) was previously reported to be involved in human cancers, such as hepatocellular carcinoma (HCC), there has been no systematic assessment of CK2B in HCC.

OBJECTIVE

To assess the potential function of CK2B as a prognostic biomarker and possible druggable target in HCC.

METHODS

The Cancer Genome Atlas database was accessed to investigate the potential oncogenic and prognostic roles of CK2B in HCC. Diverse analytical methods were used to obtain a fuller understanding of CK2B, including CIBERSORT, The Tumor Immune Estimation Resource (TIMER), gene set enrichment analyses (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene ontology (GO). Furthermore, the Comparative Toxicogenomic Database (CTD) was used to identify potential drugs to treat CK2B-overexpressing HCC. Patents for these drugs were reviewed using Patentscope® and Worldwide Espacenet®.

RESULTS

Upregulated CK2B expression was markedly associated with more aggressive pathological features, including G3, G4 (vs. G1, G2), and T2, T3 (vs. T1). Kaplan-Meier survival curves indicated that patients with HCC with higher expression of CK2B had worse overall survival (P = 0.005), progression-free interval (P = 0.001), and disease-specific survival (P = 0.011). GO and KEGG analysis revealed that CK2B dysregulation affects mitotic chromosome condensation, protein stabilization and binding, regulation of signal transduction of p53 class mediator, and cancer-related pathways. GSEA identified six well-known pathways, including MAPK, WNT, Hedgehog, and TGFβ signaling pathways. Finally, CTD identified six compounds that might represent targeted drugs to treat HCC with CK2B overexpression. A review of patents indicated these compounds showed promising anticancer results; however, whether CK2B interacts with these drugs and improves drug outcomes for patients with HCC was not confirmed.

CONCLUSION

CK2B is a biomarker for HCC prognosis and could be a potential new drug target. Moreover, the association between infiltrating immune cells and CK2B in the HCC tumor microenvironment might provide a solid basis for further investigation and a potent strategy for immunotherapy of HCC.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

A state-of-the-art review on the NRF2 in Hepatitis virus-associated liver cancer

According to a paper released and submitted to WHO by IARC scientists, there would be 905,700 new cases of liver cancer diagnosed globally in 2020, with 830,200 deaths expected as a direct result. Hepatitis B virus (HBV) hepatitis C virus (HCV), and hepatitis D virus (HDV) all play critical roles in the pathogenesis of hepatocellular carcinoma (HCC), despite the rising prevalence of HCC due to non-infectious causes. Liver cirrhosis and HCC are devastating consequences of HBV and HCV infections, which are widespread worldwide. Associated with a high mortality rate, these infections cause about 1.3 million deaths annually and are the primary cause of HCC globally. In addition to causing insertional mutations due to viral gene integration, epigenetic alterations and inducing chronic immunological dysfunction are all methods by which these viruses turn hepatocytes into cancerous ones. While expanding our knowledge of the illness, identifying these pathways also give possibilities for novel diagnostic and treatment methods. Nuclear factor erythroid 2-related factor 2 (NRF2) activation is gaining popularity as a treatment option for oxidative stress (OS), inflammation, and metabolic abnormalities. Numerous studies have shown that elevated Nrf2 expression is linked to HCC, providing more evidence that Nrf2 is a critical factor in HCC. This aberrant Nrf2 signaling drives cell proliferation, initiates angiogenesis and invasion, and imparts drug resistance. As a result, this master regulator may be a promising treatment target for HCC. In addition, the activation of Nrf2 is a common viral effect that contributes to the pathogenesis, development, and chronicity of virus infection. However, certain viruses suppress Nrf2 activity, which is helpful to the virus in maintaining cellular homeostasis. In this paper, we discussed the influence of Nrf2 deregulation on the viral life cycle and the pathogenesis associated with HBV and HCV. We summed up the mechanisms for the modulation of Nrf2 that are deregulated by these viruses. Moreover, we describe the molecular mechanism by which Nrf2 is modulated in liver cancer, liver cancer stem cells (LCSCs), and liver cancer caused by HBV and HCV. Video Abstract.

Comparative analysis between 2D and 3D colorectal cancer culture models for insights into cellular morphological and transcriptomic variations

Drug development is a time-consuming and expensive process, given the low success rate of clinical trials. Now, anticancer drug developments have shifted to three-dimensional (3D) models which are more likely to mimic tumor behavior compared to traditional two-dimensional (2D) cultures. A comparative study among different aspects was conducted between 2D and 3D cultures using colorectal cancer (CRC) cell lines, in addition, Formalin-Fixed Paraffin-Embedded (FFPE) block samples of patients with CRC were used for evaluation. Compared to the 2D culture, cells grown in 3D displayed significant (p < 0.01) differences in the pattern of cell proliferation over time, cell death phase profile, expression of tumorgenicity-related genes, and responsiveness to 5-fluorouracil, cisplatin, and doxorubicin. Epigenetically, 3D cultures and FFPE shared the same methylation pattern and microRNA expression, while 2D cells showed elevation in methylation rate and altered microRNA expression. Lastly, transcriptomic study depending on RNA sequencing and thorough bioinformatic analyses showed significant (p-adj < 0.05) dissimilarity in gene expression profile between 2D and 3D cultures involving thousands of genes (up/down-regulated) of multiple pathways for each cell line. Taken together, the study provides insights into variations in cellular morphologies between cells cultured in 2D and 3D models.

Determination of five mTOR inhibitors in human plasma for hepatocellular carcinoma treatment using QuEChERS-UHPLC-MS/MS

A fast and reliable QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method for pre-processing combined with Ultra - high performance liquid chromatography - tandem mass spectrometry (UHPLC-MS/MS) was established for the analysis of five mammalian rapamycin target protein (mTOR) inhibitors (vistusertib, AZD8055, pictilisib, everolimus, temsirolimus)in human plasma. Extraction was achieved by addition of acetonitrile to the sample followed by anhydrous magnesium sulfate and 30 mg C18 for salting out and purification, respectively. MTOR inhibitors were detected using selective response monitoring (SRM) under positive ion electrospray mode. Vistusertib, AZD8055 and pictilisib showed good linearity with a range of 1-80 ng/ml, Additionally, the concentration of everolimus and temsirolimus was 2.5-200 ng/ml and10-800 ng/ml, respectively. The linear correlation coefficient (R2) of each analysis was ≥ 0.9950. The limit of detection (LOD) and Limit of Quantitation (LOQ) were 0.015-0.75 ng/ml and 1-10 ng/ml, respectively. This method showed a high accuracy with high recovery rate and excellent stability. This method is fast, accurate and reliable, suitable for quantitative detection of mTOR inhibitors in human plasma.

Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network

Segmenting the liver and liver tumors in computed tomography (CT) images is an important step toward quantifiable biomarkers for a computer-aided decision-making system and precise medical diagnosis. Radiologists and specialized physicians use CT images to diagnose and classify liver organs and tumors. Because these organs have similar characteristics in form, texture, and light intensity values, other internal organs such as the heart, spleen, stomach, and kidneys confuse visual recognition of the liver and tumor division. Furthermore, visual identification of liver tumors is time-consuming, complicated, and error-prone, and incorrect diagnosis and segmentation can hurt the patient's life. Many automatic and semi-automatic methods based on machine learning algorithms have recently been suggested for liver organ recognition and tumor segmentation. However, there are still difficulties due to poor recognition precision and speed and a lack of dependability. This paper presents a novel deep learning-based technique for segmenting liver tumors and identifying liver organs in computed tomography maps. Based on the LiTS17 database, the suggested technique comprises four Chebyshev graph convolution layers and a fully connected layer that can accurately segment the liver and liver tumors. Thus, the accuracy, Dice coefficient, mean IoU, sensitivity, precision, and recall obtained based on the proposed method according to the LiTS17 dataset are around 99.1%, 91.1%, 90.8%, 99.4%, 99.4%, and 91.2%, respectively. In addition, the effectiveness of the proposed method was evaluated in a noisy environment, and the proposed network could withstand a wide range of environmental signal-to-noise ratios (SNRs). Thus, at SNR = -4 dB, the accuracy of the proposed method for liver organ segmentation remained around 90%. The proposed model has obtained satisfactory and favorable results compared to previous research. According to the positive results, the proposed model is expected to be used to assist radiologists and specialist doctors in the near future.

Application of 3D Bioprinting in Liver Diseases

Liver diseases are the primary reason for morbidity and mortality in the world. Owing to a shortage of organ donors and postoperative immune rejection, patients routinely suffer from liver failure. Unlike 2D cell models, animal models, and organoids, 3D bioprinting can be successfully employed to print living tissues and organs that contain blood vessels, bone, and kidney, heart, and liver tissues and so on. 3D bioprinting is mainly classified into four types: inkjet 3D bioprinting, extrusion-based 3D bioprinting, laser-assisted bioprinting (LAB), and vat photopolymerization. Bioinks for 3D bioprinting are composed of hydrogels and cells. For liver 3D bioprinting, hepatic parenchymal cells (hepatocytes) and liver nonparenchymal cells (hepatic stellate cells, hepatic sinusoidal endothelial cells, and Kupffer cells) are commonly used. Compared to conventional scaffold-based approaches, marked by limited functionality and complexity, 3D bioprinting can achieve accurate cell settlement, a high resolution, and more efficient usage of biomaterials, better mimicking the complex microstructures of native tissues. This method will make contributions to disease modeling, drug discovery, and even regenerative medicine. However, the limitations and challenges of this method cannot be ignored. Limitation include the requirement of diverse fabrication technologies, observation of drug dynamic response under perfusion culture, the resolution to reproduce complex hepatic microenvironment, and so on. Despite this, 3D bioprinting is still a promising and innovative biofabrication strategy for the creation of artificial multi-cellular tissues/organs.

Esophageal organoids: applications and future prospects

Organoids have been developed in the last decade as a new research tool to simulate organ cell biology and disease. Compared to traditional 2D cell lines and animal models, experimental data based on esophageal organoids are more reliable. In recent years, esophageal organoids derived from multiple cell sources have been established, and relatively mature culture protocols have been developed. Esophageal inflammation and cancer are two directions of esophageal organoid modeling, and organoid models of esophageal adenocarcinoma, esophageal squamous cell carcinoma, and eosinophilic esophagitis have been established. The properties of esophageal organoids, which mimic the real esophagus, contribute to research in drug screening and regenerative medicine. The combination of organoids with other technologies, such as organ chips and xenografts, can complement the deficiencies of organoids and create entirely new research models that are more advantageous for cancer research. In this review, we will summarize the development of tumor and non-tumor esophageal organoids, the current application of esophageal organoids in disease modeling, regenerative medicine, and drug screening. We will also discuss the future prospects of esophageal organoids.

Patient-derived organoids in translational oncology and drug screening

Patient-derived organoids (PDO) are a new biomedical research model that can reconstruct phenotypic and genetic characteristics of the original tissue and are useful for research on pathogenesis and drug screening. To introduce the progression in this field, we review the key factors of constructing organoids derived from epithelial tissues and cancers, covering culture medium and matrix, morphological characteristics, genetic profiles, high-throughput drug screening, and application potential. We also discuss the co-culture system of cancer organoids with tumor microenvironment (TME) associated cells. The co-culture system is widely used in evaluating crosstalk of cancer cells with TME components, such as fibroblasts, endothelial cells, immune cells, and microorganisms. The article provides a prospective for standardized cultivation mode, automatic morphological evaluation, and drug sensitivity screening using high-throughput methods.