MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction

Exploring the Role of Ferroptosis-Related Circular RNAs in Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular event associated with high mortality and significant morbidity. Recent studies have highlighted the emerging role of ferroptosis, a novel form of regulated cell death, in the pathogenesis of SAH. Circular RNAs (circRNAs), have been found to play essential roles in various cellular processes, including gene regulation and disease pathogenesis. The expression profile of circRNAs in neural tissues, particularly in the brain, suggests their critical role in synaptic function and neurogenesis. Moreover, the interplay between circRNAs and ferroptosis-related pathways, such as iron metabolism and lipid peroxidation, is explored in the context of SAH. Understanding the functional roles of specific circRNAs in the context of SAH may provide potential therapeutic targets to attenuate ferroptosis-associated brain injury. Furthermore, the potential of circRNAs as diagnostic biomarkers for SAH severity, prognosis, and treatment response is discussed. Overall, this review highlights the significance of studying the intricate interplay between circRNAs and ferroptosis in the context of SAH. Unraveling the mechanisms by which circRNAs modulate ferroptotic cell death may pave the way for the development of novel therapeutic strategies and diagnostic approaches for SAH management, ultimately improving patient outcomes and quality of life.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

CircRNAs: A Promising Star for Treatment and Prognosis in Oral Squamous Cell Carcinoma

CircRNAs are a class of endogenous long non-coding RNAs with a single-stranded circular structure. Most circRNAs are relatively stable, highly conserved, and specifically expressed in tissue during the cell and developmental stages. Many circRNAs have been discovered in OSCC. OSCC is one of the most severe and frequent forms of head and neck cancer today, with a poor prognosis and low overall survival rate. Due to its prevalence, OSCC is a global health concern, characterized by genetic and epigenomic changes. However, the mechanism remains vague. With the advancement of biotechnology, a large number of circRNAs have been discovered in mammalian cells. CircRNAs are dysregulated in OSCC tissues and thus associated with the clinicopathological characteristics and prognosis of OSCC patients. Research studies have demonstrated that circRNAs can serve as biomarkers for OSCC diagnosis and treatment. Here, we summarized the properties, functions, and biogenesis of circRNAs, focusing on the progress of current research on circRNAs in OSCC.

Circular RNAs in ferroptosis: regulation mechanism and potential clinical application in disease

Ferroptosis, an iron-dependent non-apoptotic form of cell death, is reportedly involved in the pathogenesis of various diseases, particularly tumors, organ injury, and degenerative pathologies. Several signaling molecules and pathways have been found to be involved in the regulation of ferroptosis, including polyunsaturated fatty acid peroxidation, glutathione/glutathione peroxidase 4, the cysteine/glutamate antiporter system Xc-, ferroptosis suppressor protein 1/ubiquinone, and iron metabolism. An increasing amount of evidence suggests that circular RNAs (circRNAs), which have a stable circular structure, play important regulatory roles in the ferroptosis pathways that contribute to disease progression. Hence, ferroptosis-inhibiting and ferroptosis-stimulating circRNAs have potential as novel diagnostic markers or therapeutic targets for cancers, infarctions, organ injuries, and diabetes complications linked to ferroptosis. In this review, we summarize the roles that circRNAs play in the molecular mechanisms and regulatory networks of ferroptosis and their potential clinical applications in ferroptosis-related diseases. This review furthers our understanding of the roles of ferroptosis-related circRNAs and provides new perspectives on ferroptosis regulation and new directions for the diagnosis, treatment, and prognosis of ferroptosis-related diseases.

Benchmarking of computational methods for predicting circRNA-disease associations

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.