Prioritizing Cancer lncRNA Modulators via Integrated lncRNA-mRNA Network and Somatic Mutation Data

Background: Long noncoding RNAs (LncRNAs) represent a large category of functional RNA molecules that play a significant role in human cancers. lncRNAs can be gene modulators to affect the biological process of multiple cancers. Methods: Here, we developed a computational framework that uses the lncRNA-mRNA network and mutations in individual genes of 9 cancers from TCGA to prioritize cancer lncRNA modulators. Our method screened risky cancer lncRNA regulators based on integrated multiple lncRNA functional networks and 3 calculation methods. Results: Validation analyses revealed that our method was more effective than prioritization based on a single lncRNA network. This method showed high predictive performance, and the highest ROC score was 0.836 in breast cancer. It's worth noting that we found that 5 lncRNAs scores were abnormally high, and these lncRNAs appeared in 9 cancers. By consulting the literature, these 5 lncRNAs were experimentally supported lncRNAs. Analyses of prioritizing lncRNAs reveal that these lncRNAs are enriched in various cancer-related biological processes and pathways. Conclusion: Together, these results demonstrated the ability of this method to identify candidate lncRNA molecules and improved insights into the pathogenesis of cancer.

Automated summary

This study developed a computational framework to prioritize cancer lncRNA modulators and demonstrated its effectiveness through validation analyses. The method showed high predictive performance and identified several experimentally supported high-risk lncRNA regulators.