Application of bioinformatics and data science in studies of cancer epigenetics

The dysregulation at the epigenetic level, such as DNA methylation, histone modifications, and changes in noncoding RNA, plays an important role in many serious human pathologies, including cancers. Epigenetics modulates the expression of tumor-related genes without any changes in the DNA sequences, thus understanding the epigenetic profile is a promising way to clarify the underlying mechanism of cancers as well as other diseases. Specific techniques like chromatin immunoprecipitation followed by sequencing (ChIP-seq), whole-genome bisulfite sequencing (WGBS), or mass spectrometry (MS) were developed to obtain epigenetic data. This leads to the need for robust tools to analyze and interpret these high-throughput data. With the development of bioinformatics tools, several complex interactions between DNA methylation and chromatin modification were clarified to help researchers control gene expression. Moreover, data science, and information technology, especially machine learning and deep learning, have revolutionized the study of epigenetics in cancer, providing powerful tools to integrate both genetic and epigenetic data, as well as clinical/para-clinical characteristics to enhance the accuracy of cancer diagnosis and treatment strategies. In this study, we will provide an overview of epigenetics' role in cancer, and more importantly, an update on the application of bioinformatics and data science in the epigenetic field for cancers. Integrating epigenetic data with other data like omics and clinical data with the help of bioinformatics and data science is an emerging direction for deciphering the epigenetic landscape of cancer and identifying potential therapeutic targets.