Identifying Cancer Driver Genes through Sample-Specific Weighted Differential Networks

Cancer has long been a focal point for researchers in the field of bioinformatics. The occurrence of cancer is influenced by multiple factors, including genetics, immunity, infections, and genetic mutations, all of which can contribute to the onset and progression of the disease. Notably, mutations in cancer driver genes play a crucial role in these processes, making the accurate and efficient identification of cancer driver genes particularly important in cancer treatment. This paper introduces a Sample-Specific Weighted Differential Network (SWCDN) to reconstruct a complete network for each sample that incorporates differences among reference sample populations and conducts comparative experiments. The results indicate that our method outperforms the previous SWEET method in identifying cancer driver genes. Additionally, we compared the constructed single-sample networks with human gene interaction networks. The findings demonstrate that the networks built using the SWCDN method are closer to the true networks compared to those constructed previously, further validating the reliability of the SWCDN method. Overall, the experimental results suggest that the networks constructed by the SWCDN method can serve as effective network biomarkers for predicting diseases in individual patients.