Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine. 

New insight into circRNAs: characterization, strategies, and biomedical applications

CircRNAs in tumor immunity and immunotherapy: Perspectives from innate and adaptive immunity.

Circular RNA (circRNA) is a newly discovered endogenous non-coding RNA that plays important roles in the occurrence and development of various cancers. Current research indicates that circRNA can inhibit the function of miRNA by acting as an miRNA sponge, interacting with proteins, and being translated into proteins. Most current research focuses on the circRNA-miRNA interaction; however, few studies have investigated the interaction between circRNAs and RNA binding proteins (RBPs) in breast cancer. In this review, we systematically summarize the potential molecular mechanism of the circRNA-protein interaction in breast cancer. Specifically, we elaborate on the direct interaction between circRNAs and proteins in breast cancer, including the functions of circRNA as protein sponges, decoys, and scaffolds, thereby affecting the progression of breast cancer. We also discuss the indirect interaction between circRNAs and proteins in breast cancer in which RBPs, transcription factors and m6A modifying enzymes could in turn regulate the expression and formation of circRNA. Finally, we discuss the potential application of circRNA-protein interaction for treating breast cancer, providing a reference for further research in this field. 

Interactions between circRNA and protein in breast cancer.

Abstract Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported.

/pdf/computational-approaches-for-circrnas-prediction-and-in-1kr6kpd6.pdf

Computational approaches for circRNAs prediction and in silico characterization

Gastric cancer is one of the most common malignancies of the digestive system with high mortality rates. Recent studies have demonstrated that circRNAs are novel noncoding RNAs that play vital roles in the tumorigenesis and development of gastric cancer. Our study found a novel circRNA, namely, hsa_circ_0107595 (also called circABCA5), that is overexpressed in gastric cancer based on circRNA sequencing. qPCR demonstrated its overexpression in gastric cancer specimens. The overexpression or knockdown of circABCA5 in gastric cancer cell lines was achieved by lentiviral-mediated transfection. All MTS, EdU, Transwell and migration assays and xenograft experiments demonstrated that circABCA5 could promote gastric cancer proliferation, invasion, and migration in vitro and in vivo. Mechanistically, both RIP and RNA pulldown assays confirmed that circABCA5 could bind to the SPI1 protein, upregulate SPI1 expression, and promote its nuclear translocation. SPI1 could further promote the malignant phenotype of gastric cancer by activating IL6/JAK2/STAT3 signaling. In addition, EIF4A3 could directly bind to circABCA5, promoting its stability and expression. Our study reveals that circABCA5 plays a vital role in the diagnosis and prognosis of gastric cancer and may even be developed as a molecular target for the treatment of gastric cancer. 

EIF4A3 induced circABCA5 promotes the gastric cancer progression by SPI1 mediated IL6/JAK2/STAT3 signaling.

Circular ribonucleic acids (RNAs) (circRNAs) are formed by covalently linking the downstream splice donor and the upstream splice acceptor. One of the most important functions of circRNAs is mainly exerted through binding RNA-binding proteins (RBPs). However, there is no efficient algorithm for identifying genome-wide circRNA-RBP interactions. Here, we developed a unique algorithm, circRIP, for identifying circRNA-RBP interactions from RNA immunoprecipitation sequencing (RIP-Seq) data. A simulation test demonstrated the sensitivity and specificity of circRIP. By applying circRIP, we identified 95 IGF2BP3-binding circRNAs based on the IGF2BP3 RIP-Seq dataset. We further identified 2823 and 1333 circRNAs binding to >100 RBPs in K562 and HepG2 cell lines, respectively, based on enhanced cross-linking immunoprecipitation (eCLIP) data, demonstrating the significance to survey the potential interactions between circRNAs and RBPs. In this study, we provide an accurate and sensitive tool, circRIP (https://github.com/bioinfolabwhu/circRIP), to systematically identify RBP and circRNA interactions from RIP-Seq and eCLIP data, which can significantly benefit the research community for the functional exploration of circRNAs.

circRIP: an accurate tool for identifying circRNA-RBP interactions

The assessment of the cellular heterogeneity and abundance in bulk tissue samples is essential for characterising cellular and organismal states. Computational approaches to estimate cellular abundance from bulk RNA-Seq datasets have variable performances, often requiring benchmarking matrices to select the best performing methods for individual studies. However, such benchmarking investigations are difficult to perform and assess in typical applications because of the absence of gold standard/ground-truth cellular measurements. Here we describe Decosus, an R package that integrates seven methods and signatures for deconvoluting cell types from gene expression profiles (GEP). Benchmark analysis on a range of datasets with ground-truth measurements revealed that our integrated estimates consistently exhibited stable performances across datasets than individual methods and signatures. We further applied Decosus to characterise the immune compartment of skin samples in different settings, confirming the well-established Th1 and Th2 polarisation in psoriasis and atopic dermatitis, respectively. Secondly, we revealed immune system-related UV-induced changes in sun-exposed skin. Furthermore, a significant motivation in the design of Decosus is flexibility and the ability for the user to include new gene signatures, algorithms, and integration methods at run time.

/pdf/decosus-an-r-framework-for-universal-integration-of-cell-klawc3cw.pdf

Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods

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circRIP: an accurate tool for identifying circRNA-RBP interactions

Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods