A genome-wide cell-free DNA methylation analysis identifies an episignature associated with metastatic luminal B breast cancer

Multi-omics profiling reveals key factors involved in Ewing sarcoma metastasis

Ewing sarcoma (EWS) is the second most common bone tumor affecting children and young adults, with dismal outcomes for patients with metastasis at diagnosis. Mechanisms leading to metastasis remain poorly understood. To deepen our knowledge on EWS progression, we have profiled tumors and metastases from a spontaneous metastasis mouse model using a multi-omics approach. Combining transcriptomics, proteomics, and methylomics analyses, we identified signaling cascades and candidate genes enriched in metastases that could be modulating aggressiveness in EWS. Phenotypical validation of two of these candidates, cyclic AMP-responsive element-binding protein 1 (CREB1) and lipoxygenase homology domain-containing protein 1 (LOXHD1), showed an association with migration and clonogenic abilities. Moreover, previously described CREB1 downstream targets were present amongst the metastatic-enriched results. Combining the different omics datasets, we identified FYVE, RhoGEF, and PH domain-containing protein 4 (FGD4) as a CREB1 target interconnecting the different EWS biological layers (RNA, protein and methylation status) and whose high expression is associated with worse clinical outcome. Further studies will provide insight into EWS metastasis mechanisms and ultimately improve survival rates for EWS patients.

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.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Epigenetic reprogramming in cancer: From diagnosis to treatment

Disruption of the epigenetic program of gene expression is a hallmark of cancer that initiates and propagates tumorigenesis. Altered DNA methylation, histone modifications and ncRNAs expression are a feature of cancer cells. The dynamic epigenetic changes during oncogenic transformation are related to tumor heterogeneity, unlimited self-renewal and multi-lineage differentiation. This stem cell-like state or the aberrant reprogramming of cancer stem cells is the major challenge in treatment and drug resistance. Given the reversible nature of epigenetic modifications, the ability to restore the cancer epigenome through the inhibition of the epigenetic modifiers is a promising therapy for cancer treatment, either as a monotherapy or in combination with other anticancer therapies, including immunotherapies. Herein, we highlighted the main epigenetic alterations, their potential as a biomarker for early diagnosis and the epigenetic therapies approved for cancer treatment.

Epigenetics of Thymic Epithelial Tumors

Thymic epithelial tumors (TETs) arise from the epithelial cells of the thymus and consist in the 1% of all adult malignancies, despite the fact that they are the most common lesions of the anterior mediastinum. TETs can be divided mainly into thymomas, thymic carcinomas, and the rarest ad aggressive neuroendocrine forms. Despite the surgical resection is quite resolving, the diagnosis of TETs is complicated by the absence of symptoms and the clinical presentation aggravated by several paraneoplastic disorders, including myasthenia gravis. Thus, the heterogeneity of TETs prompts the search for molecular biomarkers that could be helpful for tumor characterization and clinical outcomes prediction. With these aims, several researchers investigated the epigenetic profiles of TETs. In this manuscript, we narratively review the works investigating the deregulation of epigenetic mechanisms in TETs, highlighting the need for further studies combining genetic, epigenetic, and expression data to better characterize the different molecular subtypes and identify, for each of them, the most relevant epigenetic biomarkers of clinical utility.