Differential gene expression analysis unravels key genes associated with breast cancer proliferation

Abstract Background: Improved understanding of breast cancer (BC) complex molecular pathways is required to predict prognosis and develop new therapeutic strategies. The purpose of this study is to apply an integrated approach that combine both clinical and bioinformatic data to reveal novel regulators of proliferation.Methods and Results: Whole slide images generated from haematoxylin and eosin-stained sections of The Cancer Genome Atlas (TCGA) BC database alongside their transcriptomic and clinical data, were used to identify differentially expressed genes (DEG) associated with cell division determined using mitotic scores. DEGs enriched in the cell cycle pathway were utilised to predict protein-protein interaction (PPI) network. Ten hub genes (ORC6, SMC1B, CDKN2A, CDC25B, E2F1, E2F2, SKP2, ORC1, PTTG1, CDC25A) were identified using CytoHubba, a plugin of Cytoscape. The results were validated using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. High expression of the ten hub genes was significantly correlated with worse survival. ORC 6, SKP2, and CDC25B were predictors of survival independent of mitotic score or Ki67. ORC6 and SKP2 protein expression level in tumor tissue were significantly higher than in normal breast tissue.Conclusions: ORC6, SKP2 and CDC25B play important role in BC proliferation and surpassed both Ki67 and mitotic score in multivariate analysis, thus can serve as potential prognostic markers and therapeutic targets.