Application of deep learning in cancer epigenetics through DNA methylation analysis

Epigenetic regulatory mechanism of macrophage polarization in diabetic wound healing (Review)

Diabetic wounds represent a significant complication of diabetes and present a substantial challenge to global public health. Macrophages are crucial effector cells that play a pivotal role in the pathogenesis of diabetic wounds, through their polarization into distinct functional phenotypes. The field of epigenetics has emerged as a rapidly advancing research area, as this phenomenon has the potential to markedly affect gene expression, cellular differentiation, tissue development and susceptibility to disease. Understanding epigenetic mechanisms is crucial to further exploring disease pathogenesis. A growing body of scientific evidence has highlighted the pivotal role of epigenetics in the regulation of macrophage phenotypes. Various epigenetic mechanisms, such as DNA methylation, histone modification and non‑coding RNAs, are involved in the modulation of macrophage phenotype differentiation in response to the various environmental stimuli present in diabetic wounds. The present review provided an overview of the various changes that take place in macrophage phenotypes and functions within diabetic wounds and discussed the emerging role of epigenetic modifications in terms of regulating macrophage plasticity in diabetic wounds. It is hoped that this synthesis of information will facilitate the elucidation of diabetic wound pathogenesis and the identification of potential therapeutic targets.

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.

Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns

This study investigates the ability to predict DNA methylation patterns in cervical cancer cells using decision-tree-based ensemble approaches and neural network-based models. The research findings suggest that a model based on random forest achieves a significant prediction accuracy of 91.35%. This projection was derived from comprehensive experimentation and a meticulous performance evaluation of the random forest model, employing a range of measures including Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, F1-score, Recall, and Precision. The results indicate that the random forest model exhibits superior performance compared to other tree-based models such as the Simple Decision Tree and XGBoost, as well as neural network-based models including Convolutional Neural Networks, Feed Forward Networks, and Wavelet Neural Networks. The findings indicate that using random forest-based techniques has great potential for future study and might be highly valuable in clinical applications, especially in improving diagnostic and treatment strategies based on epigenetic profiles.

The Epigenetic Hallmarks of Cancer

Cancer is a complex disease in which several molecular and cellular pathways converge to foster the tumoral phenotype. Notably, in the latest iteration of the cancer hallmarks, "nonmutational epigenetic reprogramming" was newly added. However, epigenetics, much like genetics, is a broad scientific area that deserves further attention due to its multiple roles in cancer initiation, progression, and adaptive nature. Herein, we present a detailed examination of the epigenetic hallmarks affected in human cancer, elucidating the pathways and genes involved, and dissecting the disrupted landscapes for DNA methylation, histone modifications, and chromatin architecture that define the disease. Significance: Cancer is a disease characterized by constant evolution, spanning from its initial premalignant stages to the advanced invasive and disseminated stages. It is a pathology that is able to adapt and survive amidst hostile cellular microenvironments and diverse treatments implemented by medical professionals. The more fixed setup of the genetic structure cannot fully provide transformed cells with the tools to survive but the rapid and plastic nature of epigenetic changes is ready for the task. This review summarizes the epigenetic hallmarks that define the ecological success of cancer cells in our bodies.

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.