Circulating Exosomal miRNA Profiles Predict the Occurrence and Recurrence of Hepatocellular Carcinoma in Patients with Direct-Acting Antiviral-Induced Sustained Viral Response

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

BACKGROUND

Extracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray's 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.

RESULTS

Eighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.

CONCLUSIONS

This study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

Extracellular vesicles associated microRNAs: Their biology and clinical significance as biomarkers in gastrointestinal cancers

Gastrointestinal (GI) cancers, including colorectal, gastric, esophageal, pancreatic, and liver, are associated with high mortality and morbidity rates worldwide. One of the underlying reasons for the poor survival outcomes in patients with these malignancies is late disease detection, typically when the tumor has already advanced and potentially spread to distant organs. Increasing evidence indicates that earlier detection of these cancers is associated with improved survival outcomes and, in some cases, allows curative treatments. Consequently, there is a growing interest in the development of molecular biomarkers that offer promise for screening, diagnosis, treatment selection, response assessment, and predicting the prognosis of these cancers. Extracellular vesicles (EVs) are membranous vesicles released from cells containing a repertoire of biological molecules, including nucleic acids, proteins, lipids, and carbohydrates. MicroRNAs (miRNAs) are the most extensively studied non-coding RNAs, and the deregulation of miRNA levels is a feature of cancer cells. EVs miRNAs can serve as messengers for facilitating interactions between tumor cells and the cellular milieu, including immune cells, endothelial cells, and other tumor cells. Furthermore, recent years have witnessed considerable technological advances that have permitted in-depth sequence profiling of these small non-coding RNAs within EVs for their development as promising cancer biomarkers -particularly non-invasive, liquid biopsy markers in various cancers, including GI cancers. Herein, we summarize and discuss the roles of EV-associated miRNAs as they play a seminal role in GI cancer progression, as well as their promising translational and clinical potential as cancer biomarkers as we usher into the area of precision oncology.

Disto-TRP: An approach for identifying transient receptor potential (TRP) channels using structural information generated by AlphaFold

The ability to predict 3D protein structures computationally has significantly advanced biological research. The AlphaFold protein structure database, developed by DeepMind, has provided a wealth of predicted protein structures and has the potential to bring about revolutionary changes in the field of life sciences. However, directly determining the function of proteins from their structures remains a challenging task. The Distogram from AlphaFold is used in this study as a novel feature set to identify transient receptor potential (TRP) channels. Distograms feature vectors and pre-trained language model (BERT) features were combined to improve prediction performance for transient receptor potential (TRP) channels. The method proposed in this study demonstrated promising performance on many evaluation metrics. For five-fold cross-validation, the method achieved a Sensitivity (SN) of 87.00%, Specificity (SP) of 93.61%, Accuracy (ACC) of 93.39%, and a Matthews correlation coefficient (MCC) of 0.52. Additionally, on an independent dataset, the method obtained 100.00% SN, 95.54% SP, 95.73% ACC, and an MCC of 0.69. The results demonstrate the potential for using structural information to predict protein function. In the future, it is hoped that such structural information will be incorporated into artificial intelligence networks to explore more useful and valuable functional information in the biological field.

Hepatitis Viruses Control Host Immune Responses by Modifying the Exosomal Biogenesis Pathway and Cargo

The development of smart immune evasion mechanisms is crucial for the establishment of acute and chronic viral hepatitis. Hepatitis is a major health problem worldwide arising from different causes, such as pathogens, metabolic disorders, and xenotoxins, with the five hepatitis viruses A, B, C, D, and E (HAV, HBV, HCV, HDV, and HEV) representing the majority of the cases. Most of the hepatitis viruses are considered enveloped. Recently, it was reported that the non-enveloped HAV and HEV are, in reality, quasi-enveloped viruses exploiting exosomal-like biogenesis mechanisms for budding. Regardless, all hepatitis viruses use exosomes to egress, regulate, and eventually escape from the host immune system, revealing another key function of exosomes apart from their recognised role in intercellular communication. This review will discuss how the hepatitis viruses exploit exosome biogenesis and transport capacity to establish successful infection and spread. Then, we will outline the contribution of exosomes in viral persistence and liver disease progression.

A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection

BACKGROUND AND AIMS

Patients with hepatocellular carcinoma (HCC) surgically resected are at risk of recurrence; however, the risk factors of recurrence remain poorly understood. This study intended to establish a novel machine learning model based on clinical data for predicting early recurrence of HCC after resection.

METHODS

A total of 220 HCC patients who underwent resection were enrolled. Classification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and precision of the model were used to assess the model's accuracy and identify efficiency of the model.

RESULTS

Recurrent HCC developed in 89 (40.45%) patients at a median time of 14 months from primary resection. In principal component analysis, tumor size, tumor grade differentiation, portal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II (PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After comparing different machine learning methods, including logistic regression, decision tree, naïve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, precision of the K-NN model were 70.6%, 51.9%, 70.1%, respectively. The standard deviation was 0.020.

CONCLUSIONS

The K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.

Diagnostic value of exosomes in patients with liver cancer: a systematic review

BACKGROUND

Liver cancer is a disease with high morbidity and mortality. More and more studies have shown that exosomes can be used as biomarkers for the diagnosis of liver cancer, but their diagnostic accuracy is still unclear. Therefore, this meta-analysis summarizes various studies on the diagnostic value of exosomes for liver cancer.

METHODS

A comprehensive search was carried out based on the set search terms in PubMed, Web of Science and Wiley until April 1, 2022. All statistical analyses were performed by STATA 17 statistical software and Review Manager 5.4. Quality Assessment for Studies of Diagnostic Accuracy 2 tool was applied to evaluate the quality of included articles. Random effects model was used to calculate various diagnostic indicators.

RESULTS

A total of 47 studies were included in this meta-analysis. The number of participants was 3196. The combined sensitivity, specificity and the area under the curve with 95% confidence intervals (95% CI) were, respectively 0.80 (0.75-0.84), 0.83 (0.79-0.87), 0.89 (0.85-0.91).

CONCLUSIONS

This meta-analysis shows that exosomes have good diagnostic accuracy for liver cancer and can be used as an effective biomarker for the diagnosis of liver cancer.

Roles of small extracellular vesicles in the development, diagnosis and possible treatment strategies for hepatocellular carcinoma (Review)

Hepatocellular carcinoma (HCC) is the most common malignancy of hepatocytes accounting for 75-85% of primary hepatic carcinoma cases. Small extracellular vesicles (sEVs), previously known as exosomes with a diameter of 30-200 nm, can transport a variety of biological molecules between cells, and have been proposed to function in physiological and pathological processes. Recent studies have indicated that the cargos of sEVs are implicated in intercellular crosstalk among HCC cells, paratumor cells and the tumor microenvironment. sEV-encapsulated substances (including DNA, RNA, proteins and lipids) regulate signal transduction pathways in recipient cells and contribute to cancer initiation and progression in HCC. In addition, the differential expression of sEV cargos between patients facilitates the potential utility of sEVs in the diagnosis and prognosis of patients with HCC. Furthermore, the intrinsic properties of low immunogenicity and high stability render sEVs ideal vehicles for targeted drug delivery in the treatment of HCC. The present review article summarizes the carcinogenic and anti-neoplastic capacities of sEVs and discusses the potential and prospective diagnostic and therapeutic applications of sEVs in HCC.

Small extracellular vesicles and liver diseases: From diagnosis to therapy

Extracellular vesicles (EVs), especially small EVs (sEVs) derived from liver cells, have been the focus of much attention in the normal physiology and pathogenesis of various diseases affecting the liver. sEVs are approximately 100 nm in size, enclosed within lipid bilayers, and are very stable. The lipids, proteins, and nucleic acids, including miRNAs, contained within these vesicles are known to play important roles in intercellular communication. This mini-review summarizes the application of sEVs. First, liver diseases and the related diagnostic markers are described, and the current active status of miRNA research in diagnosis of hepatocellular carcinoma (HCC) is reported. Second, the biodistribution and pharmacokinetics of sEVs are described, and the liver is highlighted as the organ with the highest accumulation of sEVs. Third, the relationship between sEVs and the pathogenesis of liver disorders is described with emphesis on the current active status of miRNA research in HCC recurrence and survival. Finally, the possibility of future therapy using sEVs from mesenchymal stem (stromal) cells for cirrhosis and other diseases is described.

Biomarkers for the Detection and Management of Hepatocellular Carcinoma in Patients Treated with Direct-Acting Antivirals

Simple Summary

Chronic Hepatitis C virus (HCV) represents the main etiological factor for hepatocellular carcinoma (HCC) in developed countries. The introduction of direct-acting antivirals (DAAs) improved the eradication of the hepatitis C virus (HCV) but not the reduction in the incidence of HCV-associated HCC. Some patients still develop HCC, even after reaching a sustained virological response (SVR). This review is a summary of pre-clinical studies that investigated predictive biomarkers for HCC occurrence and recurrence in HCV-infected patients treated with DAAs. The presented biomarkers are found dysregulated in serum or tissue at specific time points (before, during, after DAA treatment or post SVR) and correlated with HCC-predisposing conditions. Thus, this review aims to improve the management of patients developing HCV-induced HCC.

Abstract

Hepatocellular carcinoma (HCC) is the sixth-most common type of cancer worldwide and chronic Hepatitis C virus (HCV) represents the main etiological factor in developed countries. HCV promotes hepatocarcinogenesis through persistent liver inflammation and dysregulation of cell signaling pathways. The introduction of direct-acting antivirals (DAAs) resulted in a significant improvement in the eradication of the virus, with an expected reduction of HCC incidence. However, the risk of HCC development can persist after DAA treatment. Recent studies have investigated the potential use of molecular biomarkers that predict HCC occurrence or recurrence helping the stratification of patients under surveillance. This review aimed to summarize all pre-clinical exploration of predictive biomarkers to identify DAA-treated patients at risk for HCC development. Dysregulated microRNAs, lncRNAs, histone modifications, cytokines, proteins, and sphingolipids represent various classes of HCC risk predictors identified in two different biological sources (tissue and serum). The non-invasive serum markers can provide a more accessible means to perform clinical monitoring and predict the risk of HCC. In addition, conditions like cirrhosis, predisposing to HCC, strongly correlate with most of the molecular predictors identified, supporting the value of these molecules as possible biomarkers of HCC in DAA-treated patients.

Features of patients who developed hepatocellular carcinoma after direct-acting antiviral treatment for hepatitis C Virus

Background

The features of hepatitis C virus patients with a sustained virologic response (SVR) who developed hepatocellular carcinoma (HCC) after direct-acting antiviral (DAA) therapy are unclear.

Methods

The study population included 1494 DAA-SVR patients without a history of HCC. The cumulative carcinogenesis rate after the end of treatment (EOT) and factors related to HCC were analyzed.

Results

Sixty (4.0%) patients developed HCC during a median observation period of 47.6 months. At four years, the cumulative carcinogenesis rate was 4.7%. A Cox proportional hazards analysis showed that age ≥73 years (hazard ratio [HR]: 2.148), male sex (HR: 3.060), hyaluronic acid (HA) ≥75 ng/mL (HR: 3.996), alpha-fetoprotein at EOT (EOT-AFP) ≥5.3 ng/mL (HR: 4.773), and albumin at EOT (EOT-Alb) <3.9 g/dL (HR: 2.305) were associated with HCC development. Especially, EOT-AFP ≥5.3 ng/mL was associated with HCC development after 3 years from EOT (HR: 6.237). Among patients who developed HCC, AFP did not increase in patients with EOT-AFP <5.3 ng/mL at the onset of HCC. Of these 5 factors, EOT-AFP ≥5.3 ng/mL was scored as 2 points; the others were scored as 1 point. The 4-year cumulative carcinogenesis rate for patients with total scores of 0–2, 3–4, and 5–6 points were 0.6%, 11.9%, and 27.1%, respectively (p<0.001).

Conclusions

EOT-AFP ≥5.3 ng/mL is useful for predicting HCC development after an SVR. However, AFP does not increase in patients with EOT-AFP <5.3 ng/mL at the onset of HCC. The combination of EOT-AFP, age, sex, HA, and EOT-Alb is important for predicting carcinogenesis.

Salivary miRNAs as non-invasive biomarkers of hepatocellular carcinoma: a pilot study

BACKGROUND

Improved detection of hepatocellular carcinoma (HCC) is needed, as current detection methods, such as alpha fetoprotein (AFP) and ultrasound, suffer from poor sensitivity. MicroRNAs (miRNAs) are small, non-coding RNAs that regulate many cellular functions and impact cancer development and progression. Notably, miRNAs are detectable in saliva and have shown potential as non-invasive biomarkers for a number of cancers including breast, oral, and lung cancers. Here, we present, to our knowledge, the first report of salivary miRNAs in HCC and compare these findings to patients with cirrhosis, a high-risk cohort for HCC.

METHODS

We performed small RNA sequencing in 20 patients with HCC and 19 with cirrhosis. Eleven patients with HCC had chronic liver disease, and analyses were performed with these samples combined and stratified by the presence of chronic liver disease. P values were adjusted for multiple comparisons using a false discovery rate (FDR) approach and miRNA with FDR P < 0.05 were considered statistically significant. Differential expression of salivary miRNAs was compared to a previously published report of miRNAs in liver tissue of patients with HCC vs cirrhosis. Support vector machines and leave-one-out cross-validation were performed to determine if salivary miRNAs have predictive potential for detecting HCC.

RESULTS

A total of 4,565 precursor and mature miRNAs were detected in saliva and 365 were significantly different between those with HCC compared to cirrhosis (FDR P < 0.05). Interestingly, 283 of these miRNAs were significantly downregulated in patients with HCC. Machine-learning identified a combination of 10 miRNAs and covariates that accurately classified patients with HCC (AUC = 0.87). In addition, we identified three miRNAs that were differentially expressed in HCC saliva samples and in a previously published study of miRNAs in HCC tissue compared to cirrhotic liver tissue.

CONCLUSIONS

This study demonstrates, for the first time, that miRNAs relevant to HCC are detectable in saliva, that salivary miRNA signatures show potential to be highly sensitive and specific non-invasive biomarkers of HCC, and that additional studies utilizing larger cohorts are needed.

TRP-BERT: Discrimination of transient receptor potential (TRP) channels using contextual representations from deep bidirectional transformer based on BERT

Transient receptor potential (TRP) channels are non-selective cation channels that act as ion channels and are primarily found on the plasma membrane of numerous animal cells. These channels are involved in the physiology and pathophysiology of a wide variety of biological processes, including inhibition and progression of cancer, pain initiation, inflammation, regulation of pressure, thermoregulation, secretion of salivary fluid, and homeostasis of Ca²⁺ and Mg²⁺. Increasing evidences indicate that mutations in the gene encoding TRP channels play an essential role in a broad array of diseases. Therefore, these channels are becoming popular as potential drug targets for several diseases. The diversified role of these channels demands a prediction model to classify TRP channels from other channel proteins (non-TRP channels). Therefore, we presented an approach based on the Support Vector Machine (SVM) classifier and contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) to represent protein sequences. BERT is a deeply bidirectional language model and a neural network approach to Natural Language Processing (NLP) that achieves outstanding performance on various NLP tasks. We apply BERT to generate contextualized representations for every single amino acid in a protein sequence. Interestingly, these representations are context-sensitive and vary for the same amino acid appearing in different positions in the sequence. Our proposed method showed 80.00% sensitivity, 96.03% specificity, 95.47% accuracy, and a 0.56 Matthews correlation coefficient (MCC) for an independent test set. We suggest that our proposed method could effectively classify TRP channels from non-TRP channels and assist biologists in identifying new potential TRP channels.

Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers because of diagnosis at late stage and inherent/acquired chemoresistance. Recent advances in genomic profiling and biology of this disease have not yet been translated to a relevant improvement in terms of disease management and patient’s survival. However, new possibilities for treatment may emerge from studies on key epigenetic factors. Deregulation of microRNA (miRNA) dependent gene expression and mRNA splicing are epigenetic processes that modulate the protein repertoire at the transcriptional level. These processes affect all aspects of PDAC pathogenesis and have great potential to unravel new therapeutic targets and/or biomarkers. Remarkably, several studies showed that they actually interact with each other in influencing PDAC progression. Some splicing factors directly interact with specific miRNAs and either facilitate or inhibit their expression, such as Rbfox2, which cleaves the well-known oncogenic miRNA miR-21. Conversely, miR-15a-5p and miR-25-3p significantly downregulate the splicing factor hnRNPA1 which acts also as a tumour suppressor gene and is involved in processing of miR-18a, which in turn, is a negative regulator of KRAS expression. Therefore, this review describes the interaction between splicing and miRNA, as well as bioinformatic tools to explore the effect of splicing modulation towards miRNA profiles, in order to exploit this interplay for the development of innovative treatments. Targeting aberrant splicing and deregulated miRNA, alone or in combination, may hopefully provide novel therapeutic approaches to fight the complex biology and the common treatment recalcitrance of PDAC.

Tertiary Prevention of HCC in Chronic Hepatitis B or C Infected Patients

Simple Summary

Hepatocellular carcinoma (HCC) recurrence is the major obstacle concerning patients’ survival. Tertiary prevention by antiviral therapies could reduce HCC recurrence rate in both chronic hepatitis B virus (HBV) or hepatitis C virus (HCV) infected patients. In chronic hepatitis B (CHB) patients, nucleos(t)ide analogues (Nuc) provide a more effective HCC tertiary prevention effect than an interferon (IFN)-based regimen. In chronic hepatitis C (CHC) patients, the tertiary prevention effect by direct acting antiviral agents (DAAs) was reported non-inferior to that by IFN-based therapy. Chronic hepatitis C patients left untreated had the worst survival benefit as well as shorted recurrence-free interval than those treated by either type of antiviral regimen. Although the risk of HCC recurrence could only be decreased but not diminished by antiviral therapies due to host and microenvironmental factors beyond virus infection, antiviral therapy helps to preserve and improve liver function which makes multi-modality anticancer treatment feasible to improve survival.

Abstract

Hepatocellular carcinoma (HCC) ranks as a leading cause of common cancer and cancer-related death. The major etiology of HCC is due to chronic hepatitis virus including HBV and HCV infections. Scheduled HCC surveillance in high risk populations improves the early detection rate and the feasibility of curative treatment. However, high HCC recurrence rate still accounts for the poor prognosis of HCC patients. In this article, we critically review the pathogenesis of viral hepatitis-related hepatocellular carcinoma and the evidence of tertiary prevention efficacy by current available antiviral treatment, and discuss the knowledge gap in viral hepatitis-related HCC tertiary prevention.

An integrated workflow for biomarker development using microRNAs in extracellular vesicles for cancer precision medicine

EV-miRNAs are microRNA (miRNA) molecules encapsulated in extracellular vesicles (EVs), which play crucial roles in tumor pathogenesis, progression, and metastasis. Recent studies about EV-miRNAs have gained novel insights into cancer biology and have demonstrated a great potential to develop novel liquid biopsy assays for various applications. Notably, compared to conventional liquid biomarkers, EV-miRNAs are more advantageous in representing host-cell molecular architecture and exhibiting higher stability and specificity. Despite various available techniques for EV-miRNA separation, concentration, profiling, and data analysis, a standardized approach for EV-miRNA biomarker development is yet lacking. In this review, we performed a substantial literature review and distilled an integrated workflow encompassing important steps for EV-miRNA biomarker development, including sample collection and EV isolation, EV-miRNA extraction and quantification, high-throughput data preprocessing, biomarker prioritization and model construction, functional analysis, as well as validation. With the rapid growth of "big data", we highlight the importance of efficient mining of high-throughput data for the discovery of EV-miRNA biomarkers and integrating multiple independent datasets for in silico and experimental validations to increase the robustness and reproducibility. Furthermore, as an efficient strategy in systems biology, network inference provides insights into the regulatory mechanisms and can be used to select functionally important EV-miRNAs to refine the biomarker candidates. Despite the encouraging development in the field, a number of challenges still hinder the clinical translation. We finally summarize several common challenges in various biomarker studies and discuss potential opportunities emerging in the related fields.

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test); Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28); and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation; however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.

Extracellular vesicles as biomarkers in liver diseases: A clinician's point of view

Extracellular vesicles are membrane-bound vesicles containing proteins, lipids, RNAs and microRNAs. They can originate from both healthy and stressed cells, and provide a snapshot of the cell of origin in physiological and pathological circumstances. Various processes that may give rise to the release of extracellular vesicles occur in liver diseases, including hepatocyte apoptosis, hepatic stellate cell activation, liver innate immune system activation, systemic inflammation, and organelle dysfunction (mitochondrial dysfunction and endoplasmic reticulum stress). Numerous studies have therefore investigated the potential role of extracellular vesicles as biomarkers in liver diseases. This review provides an overview of the methods that can be used to measure extracellular vesicle concentrations in clinical settings, ranging from plasma preparation to extracellular vesicle measurement techniques, as well as looking at the challenges of using extracellular vesicles as biomarkers. We also provide a comprehensive review of studies that test extracellular vesicles as diagnostic, severity and prognostic biomarkers in various liver diseases, including non-alcoholic and alcoholic steatohepatitis, viral hepatitis B and C infections, cirrhosis, primary liver cancers, primary sclerosing cholangitis and acute liver failure. In particular, extracellular vesicles could be useful tools to evaluate activity and fibrosis in non-alcoholic fatty liver disease, predict risk of hepatitis B virus reactivation, predict complications and mortality in cirrhosis, detect early hepatocellular carcinoma, detect malignant transformation in primary sclerosing cholangitis and predict outcomes in acute liver failure. While most studies draw on data derived from pilot studies, which still require clinical validation, some extracellular vesicle subpopulations have already been evaluated in solid prospective studies.

EVs as Potential New Therapeutic Tool/Target in Gastrointestinal Cancer and HCC

For more than a decade, extracellular vesicles (EVs) have been in focus of science. Once thought to be an efficient way to eliminate undesirable cell content, EVs are now well-accepted as being an important alternative to cytokines and chemokines in cell-to-cell communication route. With their cargos, mainly consisting of functional proteins, lipids and nucleic acids, they can activate signalling cascades and thus change the phenotype of recipient cells at local and systemic levels. Their substantial role as modulators of various physiological and pathological processes is acknowledged. Importantly, more and more evidence arises that EVs play a pivotal role in many stages of carcinogenesis. Via EV-mediated communication, tumour cells can manipulate cells from host immune system or from the tumour microenvironment, and, ultimately, they promote tumour progression and modulate host immunity towards tumour's favour. Additionally, the role of EVs in modulating resistance to pharmacological and radiological therapy of many cancer types has become evident lately. Our understanding of EV biology and their role in cancer promotion and drug resistance has evolved considerably in recent years. In this review, we specifically discuss the current knowledge on the association between EVs and gastrointestinal (GI) and liver cancers, including their potential for diagnosis and treatment.