DNA methylation-mediated epigenetic regulation of oncogenic RPS2 as a novel therapeutic target and biomarker in hepatocellular carcinoma

PRMT3-Mediated H4R3me2a Promotes Primary Age-Related Tauopathy by Driving Tau Hyperphosphorylation in Neuron

Primary age-related tauopathy (PART) and Alzheimer's disease (AD) both exhibit 3R/4R hyperphosphorylated tau-positive neurofibrillary tangles (NFTs) within the hippocampal-entorhinal system. Notably, PART patients show a higher degree of tau hyperphosphorylation in the entorhinal cortex (EC) than AD, yet the molecular mechanisms driving Aβ-independent tau hyperphosphorylation in PART remain poorly understood. Herein, through transcriptomic profiling of postmortem EC tissues and in vitro and in vivo functional validation, the present study identifies protein arginine methyltransferase 3 (PRMT3) as a critical driver of tau hyperphosphorylation. Mechanistically, PRMT3-mediated tau hyperphosphorylation is dependent on asymmetric dimethylation of histone H4 at arginine 3 (H4R3me2a), which upregulates miR-448. Elevated miR-448 specifically targets and suppresses IGF1R, leading to downstream GSK3β activation and subsequent tau hyperphosphorylation through PI3K/AKT/GSK3β signaling. Treatment with SGC707, a selective PRMT3 inhibitor, effectively reduces tau hyperphosphorylation and demonstrates therapeutic promise for PART and potentially other tauopathies. Collectively, this study defines the PRMT3/H4R3me2a/miR-448 axis as a critical regulatory pathway in tau hyperphosphorylation within PART, underscoring the potential of PRMT3 inhibition as a targeted therapeutic strategy for tauopathies.

Comprehensive review and updated analysis of DNA methylation in hepatocellular carcinoma: From basic research to clinical application

Hepatocellular carcinoma (HCC) is a primary malignant tumour, ranking second in global mortality rates and posing significant health threats. Epigenetic alterations, particularly DNA methylation, have emerged as pivotal factors associated with HCC diagnosis, therapy, prognosis and malignant progression. However, a comprehensive analysis of the DNA methylation mechanism driving HCC progression and its potential as a therapeutic biomarker remains lacking. This review attempts to comprehensively summarise various aspects of DNA methylation, such as its mechanism, detection methods and biomarkers aiding in HCC diagnosis, treatment and prognostic assessment of HCC. It also explores the role of DNA methylation in regulating HCC's malignant progression and sorafenib resistance, alongside elaborating the therapeutic effects of DNA methyltransferase inhibitors on HCC. A detailed examination of these aspects underscores the significant research on DNA methylation in tumour cells to elucidate malignant progression mechanisms, identify diagnostic markers and develop new tumour-specific inhibitors for HCC. KEY POINTS: A comprehensive summary of various aspects of DNA methylation, such as its mechanism, detection methods and biomarkers aiding in diagnosis and treatment. The role of DNA methylation in regulating hepatocellular carcinoma's (HCC) malignant progression and sorafenib resistance, alongside elaborating therapeutic effects of DNA methyltransferase inhibitors. Deep research on DNA methylation is critical for discovering novel tumour-specific inhibitors for HCC.

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.