Current research on circular RNAs and their potential clinical implications in breast cancer

CircEGFR reduces the sensitivity of pirarubicin and regulates the malignant progression of triple-negative breast cancer via the miR-1299/EGFR axis

Circular RNAs (circRNAs) have been found to be involved in cancer progression and chemotherapy sensitivity. However, the biological function of circRNAs in triple-negative breast cancer (TNBC) and its effect on the sensitivity to pirarubicin (THP) chemotherapy are still unclear. CircEGFR (hsa_circ_0080220) was screened and verified by bioinformatics analysis, proving it was highly expressed in TNBC cell lines, patient tissues, and plasma exosomes, and was associated with poor prognosis of patients. The expression level of circEGFR in patient tissue has potential diagnostic value to distinguish TNBC tissue from normal breast tissue. In vitro studies confirmed that overexpression of circEGFR promoted the proliferation, migration, invasion, and EMT of TNBC cells and decreased the sensitivity of THP treatment while silencing circEGFR showed the opposite effect. The circEGFR/miR-1299/EGFR pathway was cascaded and verified. CircEGFR regulated malignant progression of TNBC by regulating EGFR via sponging miR-1299. THP can inhibit the malignant phenotype of MDA-MB-231 cells by downregulating the expression of circEGFR. In vivo studies confirmed that overexpression of circEGFR can promote tumor growth and EMT and reduce tumor sensitivity to THP treatment. Silencing circEGFR inhibited the malignant progression of the tumor. These results revealed circEGFR is a promising biomarker for TNBC diagnosis, therapeutic and prognosis.

Circular RNAs: implications of signaling pathways and bioinformatics in human cancer

Circular RNAs (circRNAs) form a class of endogenous single-stranded RNA transcripts that are widely expressed in eukaryotic cells. These RNAs mediate post-transcriptional control of gene expression and have multiple functions in biological processes, such as transcriptional regulation and splicing. They serve predominantly as microRNA sponges, RNA-binding proteins, and templates for translation. More importantly, circRNAs are involved in cancer progression, and may serve as promising biomarkers for tumor diagnosis and therapy. Although traditional experimental methods are usually time-consuming and laborious, substantial progress has been made in exploring potential circRNA-disease associations by using computational models, summarized signaling pathway data, and other databases. Here, we review the biological characteristics and functions of circRNAs, including their roles in cancer. Specifically, we focus on the signaling pathways associated with carcinogenesis, and the status of circRNA-associated bioinformatics databases. Finally, we explore the potential roles of circRNAs as prognostic biomarkers in cancer.

EIF4A3-mediated circPRKCI expression promotes triple-negative breast cancer progression by regulating WBP2 and PI3K/AKT signaling pathway

Triple-negative breast cancer (TNBC) is known as a highly aggressive subtype of BC due to high rate of recurrence and metastasis, poor prognosis and lacking of effective targeted therapies. Circular RNAs (circRNAs) have been reported to participate in the progression of TNBC. In this study, we demonstrated that circPRKCI, derived from the PRKCI gene, was elevated in BC tissues and cell lines, especially in TNBC. The functional investigation showed that circPRKCI could significantly promote the proliferation and migration of TNBC in vivo and in vitro. In addition, circPRKCI regulated WBP2 and the phosphorylation of AKT via serving as miR-545-3p sponge. Of note, EIF4A3 could induce circPRKCI expression and nuclear export in TNBC cells. Taken together, EIF4A3-mediated circPRKCI could promote TNBC progression by regulating WBP2 and PI3K/AKT signaling pathway, providing a new avenue of therapy for TNBC.

Discovery of Novel Tubulin Polymerization Inhibitors by Utilizing 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

Tubulin is a potential therapeutic target for cancer. Compounds inhibit the polymerization of tubulin or promote the polymerization of tubulin to interfere with the mitotic process of cells, resulting in...

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.