Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Mitoepigenetics pathways and natural compounds: a dual approach to combatting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading liver cancer that significantly impacts global life expectancy and remains challenging to treat due to often late diagnoses. Despite advances in treatment, the prognosis is still poor, especially in advanced stages. Studies have pointed out that investigations into the molecular mechanisms underlying HCC, including mitochondrial dysfunction and epigenetic regulators, are potentially important targets for diagnosis and therapy. Mitoepigenetics, or the epigenetic modifications of mitochondrial DNA, have drawn wide attention for their role in HCC progression. Besides, molecular biomarkers such as mitochondrial DNA alterations and non-coding RNAs showed early diagnosis and prognosis potential. Additionally, natural compounds like alkaloids, resveratrol, curcumin, and flavonoids show promise in HCC show promise in modulating mitochondrial and epigenetic pathways involved in cancer-related processes. This review discusses how mitochondrial dysfunction and epigenetic modifications, especially mitoepigenetics, influence HCC and delves into the potential of natural products as new adjuvant treatments against HCC.

DNA methylation and gene expression profiling reveal potential association of retinol metabolism related genes with hepatocellular carcinoma development

Background

Aberrant DNA methylation patterns play a critical role in the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms associated with these aberrantly methylated genes remain unclear. This study aimed to comprehensively investigate the methylation-driven gene expression alterations in HCC using a multi-omics dataset.

Methods

Whole genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) techniques were used to assess the methylation and gene expression profiles of HCC tissues (HCCs) and normal adjacent tissues (NATs). The candidate genes' potential function was further investigated using single-cell RNA sequencing (scRNA seq) data.

Results

We observed widespread hypomethylation in HCCs compared to NATs. Methylation levels in distinct genomic regions exhibited significant differences between HCCs and NATs. We identified 247,632 differentially methylated regions (DMRs) and 4,926 differentially expressed genes (DEGs) between HCCs and NATs. Integrated analysis of DNA methylation and RNA-seq data identified 987 methylation-driven candidate genes, with 970 showing upregulation and 17 showing downregulation. Four genes involved in the retinol metabolic pathway, namely ADH1A, CYP2A6, CYP2C8, and CYP2C19, were identified as hyper-downregulated genes. Their expression levels could stratify HCCs into three subgroups with distinct survival outcomes, immune cell infiltration, and tumor microenvironments. Validation of these findings in an independent dataset yielded similar outcomes, confirming the high concordance and potential prognostic value of these genes. ScRNA seq data revealed the low expression of these genes in immune cells, emphasizing their role in promoting malignant cell proliferation and migration. In conclusion, this study provides insights into the molecular characteristics of HCC, revealing the involvement of retinol metabolism-related genes in the development and progression of HCC. These findings have implications for HCC diagnosis, prognosis prediction, and the development of therapeutic targets.

A high-throughput test enables specific detection of hepatocellular carcinoma

High-throughput tests for early cancer detection can revolutionize public health and reduce cancer morbidity and mortality. Here we show a DNA methylation signature for hepatocellular carcinoma (HCC) detection in liquid biopsies, distinct from normal tissues and blood profiles. We developed a classifier using four CpG sites, validated in TCGA HCC data. A single F12 gene CpG site effectively differentiates HCC samples from other blood samples, normal tissues, and non-HCC tumors in TCGA and GEO data repositories. The markers were validated in a separate plasma sample dataset from HCC patients and controls. We designed a high-throughput assay using next-generation sequencing and multiplexing techniques, analyzing plasma samples from 554 clinical study participants, including HCC patients, non-HCC cancers, chronic hepatitis B, and healthy controls. HCC detection sensitivity was 84.5% at 95% specificity and 0.94 AUC. Implementing this assay for high-risk individuals could significantly decrease HCC morbidity and mortality.

DNA methylation fingerprint of hepatocellular carcinoma from tissue and liquid biopsies

Hepatocellular carcinoma (HCC) is amongst the cancers with highest mortality rates and is the most common malignancy of the liver. Early detection is vital to provide the best treatment possible and liquid biopsies combined with analysis of circulating tumour DNA methylation show great promise as a non-invasive approach for early cancer diagnosis and monitoring with low false negative rates. To identify reliable diagnostic biomarkers of early HCC, we performed a systematic analysis of multiple hepatocellular studies and datasets comprising > 1500 genome-wide DNA methylation arrays, to define a methylation signature predictive of HCC in both tissue and cell-free DNA liquid biopsy samples. Our machine learning pipeline identified differentially methylated regions in HCC, some associated with transcriptional repression of genes related with cancer progression, that benchmarked positively against independent methylation signatures. Combining our signature of 38 DNA methylation regions, we derived a HCC detection score which confirmed the utility of our approach by identifying in an independent dataset 96% of HCC tissue samples with a precision of 98%, and most importantly successfully separated cfDNA of tumour samples from healthy controls. Notably, our risk score could identify cell-free DNA samples from patients with other tumours, including colorectal cancer. Taken together, we propose a comprehensive HCC DNA methylation fingerprint and an associated risk score for detection of HCC from tissue and liquid biopsies.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.