Protein-RNA interactions are fundamentally important in understanding cellular processes. In particular, non-coding RNA-protein interactions play an important role to facilitate biological functions in signalling, transcriptional regulation, and even the progression of complex diseases. However, experimental determination of protein-RNA interactions remains time-consuming and labour-intensive. Here, we develop a novel extended naïve-Bayes-classifier for de novo prediction of protein-RNA interactions, only using protein and RNA sequence information. Specifically, we first collect a set of known protein-RNA interactions as gold-standard positives and extract sequence-based features to represent each protein-RNA pair. To fill the gap between high dimensional features and scarcity of gold-standard positives, we select effective features by cutting a likelihood ratio score, which not only reduces the computational complexity but also allows transparent feature integration during prediction. An extended naïve Bayes classifier is then constructed using these effective features to train a protein-RNA interaction prediction model. Numerical experiments show that our method can achieve the prediction accuracy of 0.77 even though only a small number of protein-RNA interaction data are available. In particular, we demonstrate that the extended naïve-Bayes-classifier is superior to the naïve-Bayes-classifier by fully considering the dependences among features. Importantly, we conduct ncRNA pull-down experiments to validate the predicted novel protein-RNA interactions and identify the interacting proteins of sbRNA CeN72 in C. elegans, which further demonstrates the effectiveness of our method.

Intervertebral disc degeneration (IDD) is a common cause of low back pain, which inflicts more global disability than any other condition. Although IDD was deemed to be a natural process that comes with ageing, a growing body of evidence suggested that both genetic and environmental factors could modify the development of IDD. In this connection, aberrant function of nucleus pulposus cells has been implicated in IDD pathogenesis. Circular RNAs are a novel class of endogenous non‐coding RNAs that play crucial regulatory roles in diverse cellular processes. Recently, deregulation of circRNAs in nucleus pulposus cells was found to functionally participate in IDD development. In this review, we summarize the current knowledge regarding the deregulation of circRNAs in IDD in relation to their actions on nucleus pulposus cell functions, including cell proliferation, apoptosis and extracellular matrix synthesis/degradation. The potential clinical utilities of circRNAs as therapeutic targets for the management of IDD are also discussed.

Osteosarcoma, a neoplasm thought to be derived from the bone-forming mesenchymal stem cells, is the most common primary bone malignancy, predominantly involving metaphyseal regions of the long bones (eg, proximal end of tibia or humerus and distal end of femur) which are the most rapidly growing parts of the skeleton in children and adolescents. 1-5 The incidence of osteosarcoma follows a bimodal age distribution with ages between 10 and 30 primarily affected. 6-10 The second peak appears in the elderly in which osteosarcoma very often comes as a later cancer secondary to radiation exposure or is associated with Paget disease (a disorder of bone remodelling resulting in structural weakening). 11-16 With the advent of neoadjuvant and adjuvant chemotherapy with cisplatin, doxorubicin and high-dose methotrexate and the advances in surgery, the 5-year survival rate of patients with osteosarcoma has improved from ~20% before the 1980s to currently ~ 70%. 17-22 Nevertheless, half of the patients still do not survive for longer than 10 year. 23-25 Thus, it is

Germline variants in tumor suppressor genes (TSGs) can result in RNA mis-splicing and predisposition to cancer. However, identification of variants that impact splicing remains a challenge, contributing to a substantial proportion of patients with suspected hereditary cancer syndromes remaining without a molecular diagnosis. To address this, we used capture RNAsequencing (RNA-seq) to generate a splicing profile of 18 TSGs (APC,