Global Level of Plasma DNA Methylation is Associated with Overall Survival in Patients with Hepatocellular Carcinoma

The Current Status of the Liver Liquid Biopsy in MASH Related HCC: Overview and Future Directions

Metabolic dysfunction-associated steatohepatitis (MASH) is one of the major risk factors for chronic liver disease and hepatocellular carcinoma (HCC). The incidence of MASH in Western countries continues to rise, driving HCC as the third cause of cancer-related death worldwide. HCC has become a major global health challenge, partly from the obesity epidemic promoting metabolic cellular disturbances but also from the paucity of biomarkers for its early detection. Over 50% of HCC cases are clinically present at a late stage, where curative measures are no longer beneficial. Currently, there is a paucity of both specific and sensitive biological markers for the early-stage detection of HCC. The search for biological markers in the diagnosis of early HCC in high-risk populations is intense. We described the potential role of surrogates for a liver biopsy in the screening and monitoring of patients at risk for nesting HCC.

Liquid Biopsy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most deadly neoplasms with a poor prognosis. Due to the significant tumor heterogeneity of HCC, alpha-fetoprotein (AFP) or liver biopsy has not yet met the clinical needs in terms of early diagnosis or determining prognosis. In recent years, liquid biopsy techniques that analyze tumor by-products released into the circulation have shown great potential. Its ability to monitor tumors in real time and respond to their global characteristics is expected to improve the management of HCC patients clinically. This review discusses some of the findings of a liquid biopsy in terms of diagnosis and prognosis of HCC.

HCC biomarkers - state of the old and outlook to future promising biomarkers and their potential in everyday clinical practice

Hepatocellular carcinoma (HCC) is one of the most common and deadly tumors worldwide. Management of HCC depends on reliable biomarkers for screening, diagnosis, and monitoring of the disease, as well as predicting response towards therapy and safety. To date, imaging has been the established standard technique in the diagnosis and follow-up of HCC. However, imaging techniques have their limitations, especially in the early detection of HCC. Therefore, there is an urgent need for reliable, non/minimal invasive biomarkers. To date, alpha-fetoprotein (AFP) is the only serum biomarker used in clinical practice for the management of HCC. However, AFP is of relatively rather low quality in terms of specificity and sensitivity. Liquid biopsies as a source for biomarkers have become the focus of clinical research. Our review highlights alternative biomarkers derived from liquid biopsies, including circulating tumor cells, proteins, circulating nucleic acids, and exosomes, and their potential for clinical application. Using defined combinations of different biomarkers will open new perspectives for diagnosing, treating, and monitoring HCC.

Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma

Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.

The regulatory role of N6 -methyladenosine modification in the interaction between host and microbes

N⁶ -methyladenosine (m⁶ A) is the most prevalent posttranscriptional modification in eukaryotic mRNAs. Dynamic and reversible m⁶ A modification regulates gene expression to control cellular processes and diverse biological functions. Growing evidence indicated that m⁶ A modification is involved in the homeostasis of host and microbes (mostly viruses and bacteria). Disturbance of m⁶ A modification affects the life cycles of viruses and bacteria, however, these microbes could in turn change host m⁶ A modification leading to human disease including autoimmune diseases and cancer. Thus, we raise the concept that m⁶ A could be a "messenger" molecule to participate in the interactions between host and microbes. In this review, we summarize the regulatory mechanisms of m⁶ A modification on viruses and commensal microbiota, highlight the roles of m⁶ A methylation in the interaction of host and microbes, and finally discuss drugs development targeting m⁶ A modification. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Dietary intake of one-carbon metabolism-related nutrients and hepatocellular carcinoma survival in the Guangdong Liver Cancer Cohort

Dietary intake of one-carbon metabolism-related nutrients has been linked to cancer-related outcomes, but their effects on hepatocellular carcinoma (HCC) mortality are still unknown. The objective was to assess whether pre-diagnostic...

Environmental exposure and clinical correlates of hepatocellular carcinoma in New York City: a case only study

In the U.S., Hepatocellular carcinoma (HCC) incidence rates have increased. We aimed to determine whether environmental exposure plays a role in the high incidence of HCC observed in New York City. We conducted a hospital-based case only study to examine the prevalence of aflatoxin B1 (AFB1)- and polycyclic aromatic hydrocarbon (PAH)-albumin adducts and the distribution of adducts by different characteristics of HCC patients. Blood samples were collected from 155 HCC patients for biomarker analyses. We observed that about 46% and 49% of cases had detectable AFB1- and PAH-albumin adducts, respectively. There were significant differences between AFB1-albumin adducts and selected factors such as HCV infection status (p = 0.04), diabetes (p = 0.03) and Barcelona Clinic Liver Cancer stage (p = 0.02). Cases with detectable PAH-albumin adducts had a smoking history compared with those with nondetectable levels (p = 0.04). The level of AFB1-albumin adducts was positively correlated with plasma bilirubin (r_s = 0.32, p < 0.0001) and adiponectin concentrations (r_s = 0.28, p = 0.0005). The level of aflatoxin B1-albumin adducts was negatively associated with blood albumin concentration (r_s = - 0.28, p = 0.0009) and plasma DNA LINE-1 methylation (r_s = - 0.16, p = 0.04). Our study provides additional evidence that environmental exposures including to aflatoxin might drive the high incidence of HCC observed in the New York City.

Liquid Biopsy in Hepatocellular Carcinoma: Where Are We Now?

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer related death worldwide. Diagnostic, prognostic, and predictive biomarkers are urgently needed in order to improve patient survival. Indeed, the most widely used biomarkers, such as alpha-fetoprotein (AFP), have limited accuracy as both diagnostic and prognostic tests. Liver biopsy provides an insight on the biology of the tumor, but it is an invasive procedure, not routinely used, and not representative of the whole neoplasia due to the demonstrated intra-tumoral heterogeneity. In recent years, liquid biopsy, defined as the molecular analysis of cancer by-products, released by the tumor in the bloodstream, emerged as an appealing source of new biomarkers. Several studies focused on evaluating extracellular vesicles, circulating tumor cells, cell-free DNA and non-coding RNA as novel reliable biomarkers. In this review, we aimed to provide a comprehensive overview on the most relevant available evidence on novel circulating biomarkers for early diagnosis, prognostic stratification, and therapeutic monitoring. Liquid biopsy seems to be a very promising instrument and, in the near future, some of these new non-invasive tools will probably change the clinical management of HCC patients.

The Role of Liquid Biopsy in Hepatocellular Carcinoma Prognostication

Simple Summary

Hepatocellular carcinoma (HCC) is one of the deadliest cancer. Clinical guidelines for the management of HCC endorse algorithms deriving from clinical variables whose performances to prognosticate HCC is limited. Liquid biopsy is the molecular analysis of tumor by-products released into the bloodstream. It offers minimally-invasive access to circulating analytes like DNA, RNA, exosomes and cells. This technology demonstrated promising results for various applications in cancers, including prognostication. This review aimed to provide a comprehensive overview of the contribution of liquid biopsy in HCC prognostication. The results suggested that liquid biopsy may be a polyvalent and valuable tool to prognosticate HCC.

Abstract

Showing a steadily increasing cancer-related mortality, the epidemiological evolution of hepatocellular carcinoma (HCC) is concerning. Numerous strategies have attempted to prognosticate HCC but their performance is modest; this is partially due to the heterogeneous biology of this cancer. Current clinical guidelines endorse classifications and scores that use clinical variables, such as the Barcelona Clinic Liver Cancer (BCLC) classification. These algorithms are unlikely to fully recapitulate the genomic complexity of HCC. Integrating molecular readouts on a patient-basis, following a precision-medicine perspective, might be an option to refine prognostic systems. The limited access to HCC tissue samples is an important limitation to these approaches but it could be partially circumvented by using liquid biopsy. This concept consists of the molecular analysis of products derived from a solid tumor and released into biological fluids, mostly into the bloodstream. It offers an easy and minimally-invasive access to DNA, RNA, extracellular vesicles and cells that can be analyzed with next-generation sequencing (NGS) technologies. This review aims to investigate the potential contributions of liquid biopsy in HCC prognostication. The results identified prognostic values for each of the components of liquid biopsy, suggesting that this technology may help refine HCC prognostication.

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.

Association between dietary patterns and prognosis of hepatocellular carcinoma in the Guangdong liver cancer cohort study

AIM

Adherence to dietary recommendations has been linked to a reduced risk of developing hepatocellular carcinoma (HCC) and dying of chronic liver disease. However, its role in the prognosis of HCC is still unclear. We prospectively investigated the association of two dietary quality indices, the Chinese Healthy Eating Index (CHEI) and the Healthy Eating Index-2015 (HEI-2015), with all-cause and HCC-specific mortality in a large prospective cohort of HCC survivors.

METHODS

We included 887 patients with newly diagnosed, previously untreated HCC enrolled in the Guangdong Liver Cancer Cohort (GLCC) between September 2013 and April 2017 in the analysis. CHEI and HEI-2015 scores were calculated based on the dietary intake in the year before diagnosis of HCC. Cox proportional hazards regression models were used to estimate multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for each index.

RESULTS

During a median follow-up of 797 days, 389 deaths were identified, including 347 from HCC. Higher CHEI scores, reflecting favorable adherence to the 2016 Dietary Guidelines for Chinese, were associated with a lower risk of all-cause mortality (T₃ vs. T₁ : HR = 0.75, 95% CI: 0.58-0.98) and HCC-specific mortality (T₃ vs. T₁ : HR = 0.74, 95% CI: 0.56-0.98). Non-significant, inverse associations of HEI-2015 score with all-cause mortality (T₃ vs. T₁ : HR = 0.86, 95% CI: 0.67-1.11) and HCC-specific mortality (T₃ vs. T₁ : HR = 0.93, 95% CI: 0.71-1.21) were suggested.

CONCLUSIONS

Our findings suggest that better adherence to the 2016 Dietary Guidelines for Chinese may reduce the risk of all-cause and HCC-specific mortality in patients with HCC.

Transcriptome-Wide 5-Methylcytosine Functional Profiling of Long Non-Coding RNA in Hepatocellular Carcinoma

Background

Growing evidence indicates that methylation status is associated with the pathogenesis of numerous types of cancers. Among these, hepatocellular carcinoma (HCC) is a deadly disease threatening global human health. Although 5-methylcytosine (m5C) has been identified as an important regulatory modification, its distribution in solid tumors, including HCC, remains unclear. The present study aimed to explore the distribution of m5C in HCC.

Materials and Methods

Six pairs of human HCC tissues and adjacent non-tumor tissues were collected to analyze the transcriptome-wide m5C methylation of long non-coding RNA (lncRNA). RNA MeRIP-seq was performed to identify m5C peaks on lncRNA and differences in m5C distribution between HCC and adjacent tissues. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses were explored to predict the possible roles of m5C.

Results

Using m5C peak sequencing, we observed that a sequence motif was necessary for m5C methylation in HCC lncRNA. Unsupervised hierarchical cluster analysis confirmed that lncRNA m5C methylation occurred more frequently in HCC than adjacent non-tumor tissues. RNA sequencing data demonstrated that more genes were up-regulated by methylation in HCC, while methylation down-regulated more genes in adjacent non-tumor tissues. GO and KEGG pathway analyses revealed that genes having a significant correlation with m5C sites in lncRNA were involved in HCC signaling pathways.

Conclusion

Our results revealed the substantially different amounts and distributions of m5C in HCC compared to adjacent non-tumor tissue. We further predicted the cellular functions in HCC that m5C may participate in to provide evidence implicating m5C lncRNA epigenetic regulation in the tumorigenesis and progression in HCC.

Association of Hepatic Global DNA Methylation and Serum One-Carbon Metabolites with Histological Severity in Patients with NAFLD

OBJECTIVE

Clinical relevance of global DNA methylation and one-carbon metabolite levels with histological severity remains uncertain in patients with nonalcoholic fatty liver disease (NAFLD). This study aimed to evaluate hepatic global DNA methylation and serum one-carbon metabolite concentrations in patients with NAFLD and the possible associations of these parameters with liver histology.

METHODS

Liver biopsies from 18 control participants and 47 patients with NAFLD were evaluated.

RESULTS

The hepatic global DNA methylation level was significantly lower in the NAFLD group than in the control group among participants with overweight. Participants with moderate inflammation and mild fibrosis had significantly lower levels of global DNA methylation than those without these characteristics. Participants with borderline nonalcoholic steatohepatitis had significantly lower global DNA methylation levels than controls. The hepatic global DNA methylation level tended to decrease with the increasing hepatic inflammation grade and disease progression. The NAFLD group had a significantly higher serum homocysteine concentration than the control group among participants with overweight. This level tended to increase with increasing hepatic steatosis grade and disease progression.

CONCLUSIONS

Patients with NAFLD exhibited lower hepatic levels of global DNA methylation and elevated serum homocysteine concentrations, which are associated with the histological severity of NAFLD.

Ultra-low level detection of hepatocellular carcinoma global methylation using a AuNP modified carbon fiber microelectrode

Hepatocellular carcinoma (HCC) is one of the most common cancerous diseases, with a low 5 year survival rate. Global hypomethylation drives genomic instability, which is regarded as one biomarker for early diagnosis. Long interspersed nucleotide element-1 (LINE-1) makes up around 17% of the genome, and could be regarded as a surrogate marker for global DNA methylation. In this work, a gold nanoparticle (AuNP) modified carbon fiber microelectrode (CFME) with a diameter of 7 μm was applied for the first time to detect the methylation level of LINE-1, by distinguishing adsorption affinities between different DNA bases and AuNPs. Several parameters, including AuNP electrodeposition time, sample adsorption time, and DNA concentration have been analyzed and optimized. The detection limit of our assay was 0.1 nM with only 2 μL sample solution. And the CFME had an excellent sensitivity of 10% methylation change and had the capacity to distinguish only one methylated CpG site. The global DNA methylation level of real samples including cell lines and clinical tissues was tested. Higher signals of HCC cell lines and cancer tissues were observed respectively, compared with normal hepatic cell lines and normal tissues. This work provides a promising approach for HCC early diagnosis and prognosis.

Using a AuNP modified carbon fiber microelectrode to detect hepatocellular carcinoma global methylation with an ultra-low concentration of DNA samples.

Serum folate concentrations at diagnosis are associated with hepatocellular carcinoma survival in the Guangdong Liver Cancer Cohort study

Existing data on folate status and hepatocellular carcinoma (HCC) prognosis are scarce. We prospectively examined whether serum folate concentrations at diagnosis were associated with liver cancer-specific survival (LCSS) and overall survival (OS) among 982 patients with newly diagnosed, previously untreated HCC, who were enrolled in the Guangdong Liver Cancer Cohort (GLCC) study between September 2013 and February 2017. Serum folate concentrations were measured using chemiluminescent microparticle immunoassay. Cox proportional hazards models were performed to estimate hazard ratios (HR) and 95 % CI by sex-specific quartile of serum folate. Compared with patients in the third quartile of serum folate, patients in the lowest quartile had significantly inferior LCSS (HR = 1·48; 95 % CI 1·05, 2·09) and OS (HR = 1·43; 95 % CI 1·03, 1·99) after adjustment for non-clinical and clinical prognostic factors. The associations were not significantly modified by sex, age at diagnosis, alcohol drinking status and Barcelona Clinic Liver Cancer (BCLC) stage. However, there were statistically significant interactions on both multiplicative and additive scale between serum folate and C-reactive protein (CRP) levels or smoking status and the associations of lower serum folate with worse LCSS and OS were only evident among patients with CRP > 3·0 mg/l or current smokers. An inverse association with LCSS were also observed among patients with liver damage score ≥3. These results suggest that lower serum folate concentrations at diagnosis are independently associated with worse HCC survival, most prominently among patients with systemic inflammation and current smokers. A future trial of folate supplementation seems to be promising in HCC patients with lower folate status.

Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning

Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC samples and 50 adjacent normal samples in the TCGA database. We screened 47,099 differentially methylated sites using Cox regression as well as SVM-RFE and FW-SVM algorithms, and constructed a model using three risk categories to predict the overall survival based on 134 methylation sites. The model showed a 10-fold cross-validation score of 0.95 and satisfactory predictive power, and correctly classified 26 of 33 samples in testing set obtained by stratified sampling from high, intermediate and low risk groups.