MODCAN: driver gene identification based on multi-omics features and differential co-association networks for tumor subtypes

BACKGROUND: Despite the identification of some pan-cancer driver genes through international collaborative initiatives, the discovery of key cancer driver genes remains a formidable challenge. This limitation continues to hinder progress in critical areas, such as early diagnosis, prognostic evaluation, and precision medicine. Consequently, the accurate identification of key driver genes for specific cancer types has become a central focus of bioinformatics research. RESULTS: We present MODCAN, a novel semi-supervised algorithm based on multi-omics features and differential co-association networks. MODCAN facilitates the meaningful stratification of tumor samples into distinct subtypes, enabling a comprehensive exploration of multi-omics features that reflect the inherent heterogeneity of tumors. By constructing differential co-association networks, MODCAN reveals the unique genetic interactions characteristic of each subtype, thereby facilitating the complementary integration of information. CONCLUSIONS: When applied to ten cancer datasets from TCGA, MODCAN significantly outperforms both existing supervised and unsupervised learning algorithms, exhibiting superior performance in terms of precision, recall, and AUPR. Furthermore, MODCAN demonstrates substantial advantages in predicting potential tumor-specific driver genes. Notably, these genes not only exhibit strong specificity for their respective cancers, but also reveal tumor heterogeneity across distinct subtypes.