Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction

Genome-wide DNA methylation markers associated with metabolic liver cancer

Background and Aims

Metabolic liver disease is the fastest rising cause of hepatocellular carcinoma (HCC) worldwide, but the underlying molecular processes that drive HCC development in the setting of metabolic perturbations are unclear. We investigated the role of aberrant DNA methylation in metabolic HCC development in a multicenter international study.

Methods

We used a case-control design, frequency-matched on age, sex, and study site. Genome-wide profiling of peripheral blood leukocyte DNA was performed using the 850k EPIC array. Cell type proportions were estimated from the methylation data. The study samples were split 80% and 20% for training and validation. Differential methylation analysis was performed with adjustment for cell type, and we generated area under the receiver-operating curves (ROC-AUC).

Results

We enrolled 272 metabolic HCC patients and 316 control patients with metabolic liver disease from six sites. Fifty-five differentially methylated CpGs were identified; 33 hypermethylated and 22 hypomethylated in cases versus controls. The panel of 55 CpGs discriminated between cases and controls with AUC=0.79 (95%CI=0.71-0.87), sensitivity=0.77 (95%CI=0.66-0.89), and specificity=0.74 (95%CI=0.64-0.85). The 55-CpG classifier panel performed better than a base model that comprised age, sex, race, and diabetes mellitus (AUC=0.65, 95%CI=0.55-0.75, sensitivity=0.62 (95%CI=0.49-0.75) and specificity=0.64 (95%CI=0.52-0.75). A multifactorial model that combined the 55 CpGs with age, sex, race, and diabetes, yielded AUC=0.78 (95%CI=0.70-0.86), sensitivity=0.81 (95%CI=0.71-0.92), and specificity=0.67 (95%CI=0.55-0.78).

Conclusions

A panel of 55 blood leukocyte DNA methylation markers differentiates patients with metabolic HCC from control patients with benign metabolic liver disease, with a slightly higher sensitivity when combined with demographic and clinical information.

Role of SYT11 in human pan-cancer using comprehensive approaches

BACKGROUND

Synaptotagmin 11 (SYT11) plays a pivotal role in neuronal vesicular trafficking and exocytosis. However, no independent prognostic studies have focused on various cancers. In this study, we aimed to summarize the clinical significance and molecular landscape of SYT11 in various tumor types.

METHODS

Using several available public databases, we investigated abnormal SYT11 expression in different tumor types and its potential clinical association with prognosis, methylation profiling, immune infiltration, gene enrichment analysis, and protein-protein interaction analysis, and identified common pathways.

RESULTS

TCGA and Genotype-Tissue Expression (GTEx) showed that SYT11 was widely expressed across tumor and corresponding normal tissues. Survival analysis showed that SYT11 expression correlated with the prognosis of seven cancer types. Additionally, SYT11 mRNA expression was not affected by promoter methylation, but regulated by certain miRNAs and associated with cancer patient prognosis. In vitro experiments further verified a negative correlation between the expression of SYT11 and miR-19a-3p in human colorectal, lung, and renal cancer cell lines. Moreover, aberrant SYT11 expression was significantly associated with immune infiltration. Pathway enrichment analysis revealed that the biological and molecular processes of SYT11 were related to clathrin-mediated endocytosis, Rho GTPase signaling, and cell motility-related functions.

CONCLUSIONS

Our results provide a clear understanding of the role of SYT11 in various cancer types and suggest that SYT11 may be of prognostic and clinical significance.

Benefit and harm of waiting time in liver transplantation for HCC

Liver transplantation is the most successful treatment for limited-stage HCC. The waiting time for liver transplantation (LT) can be a critical factor affecting the oncological prognosis and outcome of patients with HCC. Efficient strategies to optimize waiting time are essential to maximize the benefits of LT and to reduce the harm of delay in transplantation. The ever-increasing demand for donor livers emphasizes the need to improve the organization of the waiting list for transplantation and to optimize organ availability for patients with and without HCC. Current progress in innovations to expand the donor pool includes the implementation of living donor LT and the use of grafts from extended donors. By expanding selection criteria, an increased number of patients are eligible for transplantation, which necessitates criteria to prevent futile transplantations. Thus, the selection criteria for LT have evolved to include not only tumor characteristics but biomarkers as well. Enhancing our understanding of HCC tumor biology through the analysis of subtypes and molecular genetics holds significant promise in advancing the personalized approach for patients. In this review, the effect of waiting time duration on outcome in patients with HCC enlisted for LT is discussed.

Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.

Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?

DNA methylation of cytosine-guanine sites (CpGs) is associated with type 1 diabetes (T1D). The sequence of methylated and non-methylated sites in a specific genetic region constitutes its methyl-haplotype. The aim of the present study was to identify insulin gene promoter (IGP) methyl-haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl-haplotypes. A total of 40 individuals (20 T1D) participated. The IGP region from peripheral whole blood DNA of 40 participants (20 T1D) was sequenced using next-generation sequencing, sequences were read using FASTQ files and methylation status was calculated by python-based pipeline for targeted deep bisulfite sequenced amplicons (ampliMethProfiler). Methylation profile at 10 CpG sites proximal to transcription start site of the IGP was recorded and coded as 0 for unmethylation or 1 for methylation. A single read could result in '1111111111' methyl-haplotype (all methylated), '000000000' methyl-haplotype (all unmethylated) or any other combination. Principal component analysis was applied to the generated methyl-haplotypes for dimensionality reduction, and the first three principal components were employed as features with five different classifiers (random forest, decision tree, logistic regression, Naive Bayes, support vector machine). Naive Bayes was the best-performing classifier, with 0.9 accuracy. Predictive models were evaluated using receiver operating characteristics (AUC 0.96). Methyl-haplotypes '1111111111', '1111111011', '1110111111', '1111101111' and '1110101111' were revealed to be the most significantly associated with T1D according to the dimensionality reduction method. Methylation-based biomarkers such as IGP methyl-haplotypes could serve to identify individuals at high risk for T1D.

Is Cell-Free DNA Testing in Hepatocellular Carcinoma Ready for Prime Time?

Revamping the current biomarker landscape of hepatocellular carcinoma (HCC) with cell-free DNA (cfDNA) could improve overall outcomes. The use of commercially available cfDNA testing (also known as liquid biopsy) is limited by the low prevalence of targetable mutations and does not have any prognostic or predictive value. Thus, current cfDNA testing cannot be relied upon for perioperative risk stratification (POR), including early detection of recurrence, long-term surveillance, predicting outcomes, and treatment response. Prior evidence on cfDNA mutation profiling (non-specific detection or gene panel testing) suggests that it can be a reliable tool for POR and prognostication, but it still requires significant improvements. cfDNA methylation changes or epigenetic markers have not been explored extensively, but early studies have shown potential for it to be a prognostic biomarker tool. The predictive value of cfDNA (mutations and EM) to assist treatment selection (systemic therapy, immune-checkpoint inhibitor vs. tyrosine kinase inhibitor) and to monitor response to systemic and locoregional therapies should be a future area of focus. We highlighted the unmet needs in the HCC management and the current role of cfDNA testing in HCC in addressing them.