Non-canonical splice junction processing increases the diversity of RBFOX2 splicing isoforms

RNA splicing factor RBFOX2 is a key factor in the progression of cancer and cardiomyopathy

BACKGROUND

Alternative splicing of pre-mRNA is a fundamental regulatory process in multicellular eukaryotes, significantly contributing to the diversification of the human proteome. RNA-binding fox-1 homologue 2 (RBFOX2), a member of the evolutionarily conserved RBFOX family, has emerged as a critical splicing regulator, playing a pivotal role in the alternative splicing of pre-mRNA. This review provides a comprehensive analysis of RBFOX2, elucidating its splicing activity through direct and indirect binding mechanisms. RBFOX2 exerts substantial influence over the alternative splicing of numerous transcripts, thereby shaping essential cellular processes such as differentiation and development.

MAIN BODY OF THE ABSTRACT

Dysregulation of RBFOX2-mediated alternative splicing has been closely linked to a spectrum of cardiovascular diseases and malignant tumours, underscoring its potential as a therapeutic target. Despite significant progress, current research faces notable challenges. The complete structural characterisation of RBFOX2 remains elusive, limiting in-depth exploration beyond its RNA-recognition motif. Furthermore, the scarcity of studies focusing on RBFOX2-targeting drugs poses a hindrance to translating research findings into clinical applications.

CONCLUSION

This review critically assesses the existing body of knowledge on RBFOX2, highlighting research gaps and limitations. By delineating these areas, this analysis not only serves as a foundational reference for future studies but also provides strategic insights for bridging these gaps. Addressing these challenges will be instrumental in unlocking the full therapeutic potential of RBFOX2, paving the way for innovative and effective treatments in various diseases.

The role of alternative pre-mRNA splicing in cancer progression

Alternative pre-mRNA splicing is a critical mechanism that generates multiple mRNA from a single gene, thereby increasing the diversity of the proteome. Recent research has highlighted the significance of specific splicing isoforms in cellular processes, particularly in regulating cell numbers. In this review, we examine the current understanding of the role of alternative splicing in controlling cancer cell growth and discuss specific splicing factors and isoforms and their molecular mechanisms in cancer progression. These isoforms have been found to intricately control signaling pathways crucial for cell cycle progression, proliferation, and apoptosis. Furthermore, studies have elucidated the characteristics and functional importance of splicing factors that influence cell numbers. Abnormal expression of oncogenic splicing isoforms and splicing factors, as well as disruptions in splicing caused by genetic mutations, have been implicated in the development and progression of tumors. Collectively, these findings provide valuable insights into the complex interplay between alternative splicing and cell proliferation, thereby suggesting the potential of alternative splicing as a therapeutic target for cancer.

A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.