Advancing cancer driver gene identification through an integrative network and pathway approach

The framework of the MCSdriver: Step 1: the input expression and mutation data were preprocessed within the functional interaction network to construct a bipartite graph with mutated and dysregulated expression genes. Step 2: mutual exclusivity scores were computed for each pair of genes within the bipartite graph. Step 3: bidirectional random walk with restart was employed on the weighted bipartite graph to quantity the impact of mutation genes and dysregulated expression genes. Step 4: patient similarity scores were calculated by combining the coverage score and transition probability score after bidirectional random walk. Step 5: the patient predictive rating lists were generated according to the combination of propagation scores and similarity scores. [Display omitted] Cancer is a complex genetic disease characterized by the accumulation of various mutations, with driver genes playing a crucial role in cancer initiation and progression. Distinguishing driver genes from passenger mutations is essential for understanding cancer biology and discovering therapeutic targets. However, the majority of existing methods ignore the mutational heterogeneity and commonalities among patients, which hinders the identification of driver genes more effectively. This study introduces MCSdriver, a novel computational model that integrates network and pathway information to prioritize the identification of cancer driver genes. MCSdriver employs a bidirectional random walk algorithm to quantify the mutual exclusivity and functional relationships between mutated genes within patient cohorts. It calculates similarity scores based on a mutual exclusivity-weighted network and pathway coverage patterns, accounting for patient-specific heterogeneity and molecular profile similarity. This approach enhances the accuracy and quality of driver gene identification. MCSdriver demonstrates superior performance in identifying cancer driver genes across four cancer types from The Cancer Genome Atlas, showing a higher F-score, Recall and Precision compared to existing ranking list-based and module-based models. The MCSdriver model not only outperforms other models in identifying known cancer driver genes but also effectively identifies novel driver genes involved in cancer-related biological processes. The model’s consideration of patient-specific heterogeneity and similarity in molecular profiles significantly enhances the accuracy and quality of driver gene identification. Validation t