Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis

Background: Breast cancer stem cells (BCSCs) play a role in the high rates of resistance, recurrence, and metastasis. The precise biomarkers of BCSCs can assist effectively in identifying cancer, assessing prognosis, diagnosing, and monitoring therapy. The aim of this study was to give a complete analysis for predicting specific biomarkers of BCSCs. Methods: We aggregated profile datasets in this work to shed light on the underlying critical genes and pathways of BCSCs. We obtained two expression profiling by array datasets (GSE7513 and GSE7515) from the Gene Expression Omnibus (GEO) database to identify biomarkers in BCSCs. Enrichr was used to do functional analysis, including gene ontology (GO) and reactome pathway. Furthermore, the protein-protein interaction (PPI) of these differential expression genes (DEGs) was visualized using Cytoscape with the search tool for the retrieval of interacting genes (STRING). The hub genes in the PPI network were chosen for further investigation. Results: We identified 65 up-regulated and 190 down- regulated DEGs and the GO enrichment analysis revealed that these DEGs were enriched in biological process related to tumorigenesis and stemness, including alter the extracellular matrix’s physicochemical properties, cytoskeletal reorganisation, adhesion, motility, migration, growth, and survival. The Reactome analysis indicated that these DEGs were also involved in modulating function of ECM, regulation cancer metabolism and angiogenesis, tumor growth, proliferation, and metastasis. Conclusion: Our bioinformatic study revealed that FYN, INADL, OCLN, F11R, and TOP2A were potential biomarker panel of BCSCs from secretome.