A bioinformatics approach to decode core genes and molecular pathways shared by breast cancer and endometrial cancer

Abstract

Endometrial cancer (EC) arises from the lining of the uterus, and is a common female genital tract malignancy of post-menopausal women. Breast cancer (BC) is one of the risk factors for EC, but the molecular pathways and core genes shared by these two diseases have not yet been elucidated. In this study, we sought to identify the common molecular pathways and prognostic hub proteins in EC and BC that can be used for the prediction of the progression. We employed the statistical method Limma to perform differential analysis of transcriptomes of EC and BC downloaded from the Gene Expression Omnibus. The gene functional annotations were performed through gene ontology (GO) and pathway enrichment analysis. Hub proteins were identified from the protein-protein interactions (PPI) network analysis using the STRING database, and survival analysis was done on the hub proteins to assess the prognostic values using SurvExpress. We analyzed the EC and BC transcriptomics datasets individually, and 57 common differentially expressed genes were identified, shared by both EC and BC. GO and pathway analysis showed that 57 commonly dysregulated genes were involved in several altered molecular pathways, including protein digestion and absorption, cysteine and methionine metabolism, ECM-receptor interaction, and drug metabolism. We have detected key hub proteins (CDC20, EZH2, TOP2A, SPTBN1) based on a topological analysis of the PPI network which play vital roles in the progression and regulation of EC and BC. The survival analysis of the hub genes demonstrated that they were significantly associated with worse survival outcomes in EC and BC. In the present study, we have identified novel pathways shared by EC and BC at the molecular level, and also identified possible gene expression links between EC and BC. Keywords: Endometrial cancer, Breast cancer, Differentially expressed genes, Molecular pathways, Protein–protein interaction, Survival analysis