Driver Gene Expression Clustering Model for Prognostic Risk Estimation Using Cancer Genomic Data

Breast cancer (BRCA) and head and neck cancer (HNSC) represent significant global health challenges, underscoring the critical need for accurate prognosis in these patient populations. Tumor suppressor genes (TSGs) and oncogenes (OCGs) play pivotal roles in cancer progression, yet exhibit low mutation rates in affected individuals. Consequently, distinct omics patterns are necessary for estimating prognosis risk in patients harboring wild-type OCGs and TSGs. This study investigates mRNA expression of driver genes across TSG/OCG mutant subgroups and employs hierarchical clustering to identify mRNA expression patterns associated with higher prognosis risk. Data from both cancer cohorts were analyzed using agglomerative hierarchical clustering, revealing survival discrepancies between clusters in OCG/TSG-Wild subgroups. Our results emphasize the potential utilizing driver gene expression for prognostic risk estimation in BRCA and HNSC patients with wild-type OCGs and TSGs.