Pharmacological inhibition of DOT1L blocks estrogen receptor signaling in breast cancer.

Specific patterns of PIWI-interacting small noncoding RNA expression in dysplastic liver nodules and hepatocellular carcinoma

Estrogen receptors (ERα and ERβ) are ligand-activated transcription factors that play different roles in gene regulation and show both overlapping and specific tissue distribution patterns. ERβ, contrary to the oncogenic ERα, has been shown to act as an oncosuppressor in several instances. However, while the tumor-promoting actions of ERα are well-known, the exact role of ERβ in carcinogenesis and tumor progression is not yet fully understood. Indeed, to date, highly variable and even opposite effects have been ascribed to ERβ in cancer, including for example both proliferative and growth-inhibitory actions. Recently ERβ has been proposed as a potential target for cancer therapy, since it is expressed in a variety of breast cancers (BCs), including triple-negative ones (TNBCs). Because of the dependence of TNBCs on active cellular signaling, numerous studies have attempted to unravel the mechanism(s) behind ERβ-regulated gene expression programs but the scenario has not been fully revealed. We comprehensively reviewed the current state of knowledge concerning ERβ role in TNBC biology, focusing on the different signaling pathways and cellular processes regulated by this transcription factor, as they could be useful in identifying new diagnostic and therapeutic approaches for TNBC.

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

Metabolic reprogramming is a hallmark of cancer, with consistent rewiring of glucose, glutamine, and mitochondrial metabolism. While these metabolic alterations are adequate to meet the metabolic needs of cell growth and proliferation, the changes in critical metabolites have also consequences for the regulation of the cell differentiation state. Cancer evolution is characterized by progression towards a poorly differentiated, stem-like phenotype, and epigenetic modulation of the chromatin structure is an important prerequisite for the maintenance of an undifferentiated state by repression of lineage-specific genes. Epigenetic modifiers depend on intermediates of cellular metabolism both as substrates and as co-factors. Therefore, the metabolic reprogramming that occurs in cancer likely plays an important role in the process of the de-differentiation characteristic of the neoplastic process. Here, we review the epigenetic consequences of metabolic reprogramming in cancer, with particular focus on the role of mitochondrial intermediates and hypoxia in the regulation of cellular de-differentiation. We also discuss therapeutic implications.

Ovarian cancer (OC) shows the highest mortality rate among gynecological malignancies and, because of the absence of specific symptoms, it is frequently diagnosed at an advanced stage, mainly due to the lack of specific and early biomarkers, such as those based on cancer molecular signature identification. Indeed, although significant progress has been made toward improving the clinical outcome of other cancers, rates of mortality for OC are essentially unchanged since 1980, suggesting the need of new approaches to identify and characterize the molecular mechanisms underlying pathogenesis and progression of these malignancies. In addition, due to the low response rate and the high frequency of resistance to current treatments, emerging therapeutic strategies against OC focus on targeting single factors and pathways specifically involved in tumor growth and metastasis. To date, loss-of-function screenings are extensively applied to identify key drug targets in cancer, seeking for more effective, disease-tailored treatments to overcome lack of response or resistance to current therapies. We review here the information relative to essential genes and functional pathways recently discovered in OC, often strictly interconnected with each other and representing promising biomarkers and molecular targets to treat these malignancies.