Exploring the Influence of Gene Networks on Driver Gene Classification

Abstract

Cancer is a complex disease caused by mutations in the genome of cells. Genetic mutations can be divided into driver mutations, which are significant for the initiation and progression of cancer, and passenger mutations, which have a neutral effect. In recent years, computational methods have been developed to identify driver genes. Some of these methods use data from gene networks to classify the genes. However, the impact of different gene networks on the performance of these methods remains unexplored. This article aims to analyze the influence of genetic networks in driver gene classification. We analyzed driver gene classification methods that use gene networks as input data, using different cancer mutation datasets and distinct gene networks. Computational methods show significant variation in their results when different gene networks are employed. The results highlight the need to carefully interpret driver gene classification and emphasize the importance of using different gene networks. These findings underline the necessity of developing more robust computational approaches that account for network variability, ensuring greater reliability in driver gene identification and its applications in cancer research.