DyNRW: Time-Series Dynamical Networks for Identifying HCC-Related Genes

Hepatocellular carcinoma (HCC) is a multifactorial and highly complex disease. Gaining a comprehensive understanding of the genetic factors associated with HCC is crucial for unraveling its intricate pathogenesis and identifying potential biomarkers. Recent advances suggest that biological network-based strategies are useful in prioritizing genes associated with diseases. However, existing network models predominantly rely on static biological networks, which to some extent hinder the modeling of dynamic biological processes and restrict the predictive capacity of disease genes. Therefore, we proposed the Dynamic Networks Random Walk (DyNRW) method to identify HCC-related genes. DyNRW overcomes the limitations of previous static network-based methods and models the dynamic regulatory relationships between genes in the biological system by constructing a time-series dynamic network based on the progression of HCC. To construct a more comprehensive biological network model, DyNRW presents an effective background-temporal multi-layer network framework to combine both static and dynamic network information. DyNRW extends the random-walk process to the multi-layer network, enabling the extraction of gene scores associated with HCC. According to the experimental results, DyNRW demonstrates better performance and stability compared to other state-of-the-art algorithms, and yields a set of promising candidate genes, many of which are confirmed by further biological validation.