MTPrior: A Multi-Task Hierarchical Graph Embedding Framework for Prioritizing Hepatocellular Carcinoma-Associated Genes and Long Noncoding RNAs

Hepatocellular carcinoma (HCC) represents a highly prevalent liver cancer, posing a substantial global health challenge. The prioritization of both coding genes and noncoding RNAs, such as long noncoding RNAs (lncRNAs), is paramount in unraveling the mechanisms of HCC and advancing diagnostics, prognostics and therapeutic strategies. The development of computational models for prioritizing cancer-associated RNAs plays a pivotal role in reducing reliance on costly and time-consuming experimental methodologies. However, most existing approaches focus on a single factor, such as genes, lncRNAs, or microRNAs (miRNAs), neglecting the interactions between coding genes and noncoding RNAs as well as their combined influence. Models capable of prioritizing multiple RNA types while accounting for these interactions remain scarce. In this study, we introduce MTPrior, a multi-task graph embedding prioritization model. Our approach is designed to achieve multi-task prioritization by constructing an adaptable framework that accommodates diverse tasks and refines the network structure tailored to specific tasks. It meticulously considers interactions between coding and noncoding RNAs, navigating efficient biological pathways to discover the most pertinent results. By analyzing extensive datasets from HCC patients, alongside a comprehensive inventory of genes and lncRNAs, we have developed a model that proficiently prioritizes and identifies the most relevant genes and lncRNAs associated with HCC, thereby streamlining research efforts towards key candidates for further investigation. Furthermore, an ablation study underscores the effectiveness of each component within our proposed method. The convincing results demonstrate that MTPrior outperforms other state-of-the-art methods in predicting disease-related genes and lncRNAs, highlighting its efficiency and advantages.