m⁷GDisAI: N7-methylguanosine (m⁷G) sites and diseases associations inference based on heterogeneous network

Background: Recent studies have confirmed that N7-methylguanosine (m⁷G) modification plays an important role in regulating various biological processes and has associations with multiple diseases. Wet-lab experiments are cost and time ineffective for the identification of disease-associated m⁷G sites. To date, tens of thousands of m⁷G sites have been identified by high-throughput sequencing approaches and the information is publicly available in bioinformatics databases, which can be leveraged to predict potential disease-associated m⁷G sites using a computational perspective. Thus, computational methods for m⁷G-disease association prediction are urgently needed, but none are currently available at present. Results: To fill this gap, we collected association information between m⁷G sites and diseases, genomic information of m⁷G sites, and phenotypic information of diseases from different databases to build an m⁷G-disease association dataset. To infer potential disease-associated m⁷G sites, we then proposed a heterogeneous network-based model, m⁷G Sites and Diseases Associations Inference (m⁷GDisAI) model. m⁷GDisAI predicts the potential disease-associated m⁷G sites by applying a matrix decomposition method on heterogeneous networks which integrate comprehensive similarity information of m⁷G sites and diseases. To evaluate the prediction performance, 10 runs of tenfold cross validation were first conducted, and m⁷GDisAI got the highest AUC of 0.740(± 0.0024). Then global and local leave-one-out cross validation (LOOCV) experiments were implemented to evaluate the model’s accuracy in global and local situations respectively. AUC of 0.769 was achieved in global LOOCV, while 0.635 in local LOOCV. A case study was finally conducted to identify the most promising ovarian cancer-related m⁷G sites for further functional analysis. Gene Ontology (GO) enrichment analysis was performed to explore the complex associations between host gene of m⁷G sites and GO terms. The results showed that m⁷GDisAI identified disease-associated m⁷G sites and their host genes are consistently related to the pathogenesis of ovarian cancer, which may provide some clues for pathogenesis of diseases. Conclusion: The m⁷GDisAI web server can be accessed at http://180.208.58.66/m7GDisAI/, which provides a user-friendly interface to query disease associated m⁷G. The list of top 20 m⁷G sites predicted to be associted with 177 diseases can be achieved. Furthermore, detailed information about specific m⁷G sites and diseases are also shown.