Genome-wide perturbations of A-to-I RNA editing dysregulated circular RNAs promoting the development of cervical cancer.

Cervical cancer, the second most common female malignant tumor, seriously threatens women's health and lives. Despite the availability of the HPV vaccine, effective treatment options for cervical cancer are still lacking. New research perspectives now clarify that RNA editing dysregulation and changes in circRNA expression are jointly involved in disease pathogenesis, so molecular changes associated with circRNA and RNA editing may provide clues for the development of new therapeutic strategies for cervical cancer. In this study, we designed a series of pipelines to identify and analyze dysregulated RNA editing events in circRNAs. Our findings indicate a decrease in A-to-I RNA editing levels in cervical cancer compared to normal tissues, and editing may influence the back-splicing process of circRNAs through structural modifications of Alu elements. Moreover, our research reveals that RNA editing could modulate circRNA biogenesis by influencing RNA binding protein (RBP) binding on a transcriptome-wide scale, as well as influence the expression and coding potential of circRNAs. Importantly, we identified three RNA editing sites that could serve as potential biomarkers. In summary, our study presents a comprehensive landscape of RNA editing perturbations in circRNAs, providing new insights into the complex relationship between RNA editing and circRNA dysregulation in cervical cancer.

In-depth characterization and identification of translatable lncRNAs.

Long non-coding RNAs (LncRNAs) are non-protein coding transcripts more than 200 nucleotides in length. Deep sequencing technologies have unveiled lncRNAs can harbor translatable short open reading frames (sORFs). Yet the regulatory mechanisms governing lncRNA translation events remain poorly understood. Here, we exhaustively detected the sequence, functional element, and structure features relevant to lncRNA translation in human. Extensive identification and analysis reveal that translatable lncRNAs contain richer protein-coding related sequence features, cap-dependent and cap-independent translation initiation mechanisms, and more stable secondary structures, as compared to untranslatable lncRNAs. These findings strongly support lncRNAs serve as a repository for the production of new small peptides. Based on the feature fusion affecting translation and the extreme gradient boosting (XGBoost) algorithm, we developed the first computational tool that dedicated for predicting translatable lncRNAs, named TransLncPred. Benchmark experimental results show that our method outperforms several state-of-the-art RNA coding potential prediction tools on the same training and testing datasets. The 100-time 10-fold cross-validation tests also demonstrate that regulatory element-derived features, especially N7-methylguanosine (m7G) and internal ribosome entry site (IRES), contribute to the improvement in predictive performance.

pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties.

BACKGROUND: Protein histidine phosphorylation (pHis) plays critical roles in prokaryotic signal transduction pathways and various eukaryotic cellular processes. It is estimated to account for 6-10% of the phosphoproteome, however only hundreds of pHis sites have been discovered to date. Due to the inherent disadvantages of experimental methods, it is an urgent task for developing efficient computational approaches to identify pHis sites. RESULTS: Here, we present a novel tool, pHisPred, for accurately identifying pHis sites from protein sequences. We manually collected the largest number of experimental validated pHis sites to build benchmark datasets. Using randomized tenfold CV, the weighted SVM-RBF model shows the best performance than other four commonly used classification models (LR, KNN, RF, and MLP). From ten thousands of features, 140 and 150 most informative features were individually selected out for eukaryotic and prokaryotic models. The average AUC and F1-score values of pHisPred were (0.81, 0.40) and (0.78, 0.46) for tenfold CV on the eukaryotic and prokaryotic training datasets, respectively. In addition, pHisPred significantly outperforms other tools on testing datasets, in particular on the eukaryotic one. CONCLUSION: We implemented a python program of pHisPred, which is freely available for non-commercial use at https://github.com/xiaofengsong/pHisPred . Moreover, users can use it to train new models with their own data.

The circular RNA expression profile in ovarian serous cystadenocarcinoma reveals a complex circRNA-miRNA regulatory network.

BACKGROUND: Ovarian serous cystadenocarcinoma is one of the most serious gynecological malignancies. Circular RNA (circRNA) is a type of noncoding RNA with a covalently closed continuous loop structure. Abnormal circRNA expression might be associated with tumorigenesis because of its complex biological mechanisms by, for example, functioning as a microRNA (miRNA) sponge. However, the circRNA expression profile in ovarian serous cystadenocarcinoma and their associations with other RNAs have not yet been characterized. The main purpose of this study was to reveal the circRNA expression profile in ovarian serous cystadenocarcinoma. METHODS: We collected six specimens from three patients with ovarian serous cystadenocarcinoma and adjacent normal tissues. After RNA sequencing, we analyzed the expression of circRNAs with relevant mRNAs and miRNAs to characterize potential function. RESULTS: 15,092 unique circRNAs were identified in six specimens. Approximately 46% of these circRNAs were not recorded in public databases. We then reported 353 differentially expressed circRNAs with oncogenes and tumor-suppressor genes. Furthermore, a conjoint analysis with relevant mRNAs revealed consistent changes between circRNAs and their homologous mRNAs. Overall, construction of a circRNA-miRNA network suggested that 4 special circRNAs could be used as potential biomarkers. CONCLUSIONS: Our study revealed the circRNA expression profile in the tissues of patients with ovarian serous cystadenocarcinoma. The differential expression of circRNAs was thought to be associated with ovarian serous cystadenocarcinoma in the enrichment analysis, and co-expression analysis with relevant mRNAs and miRNAs illustrated the latent regulatory network. We also constructed a complex circRNA-miRNA interaction network and then demonstrated the potential function of certain circRNAs to aid future diagnosis and treatment.