Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

Abstract

Background: Invasive ductal carcinoma (IDC) is the most common type of breast cancer (BC) worldwide. Nowadays, due to its heterogeneity and high capacity for metastasis, it is necessary to discover novel diagnostic and prognostic biomarkers. Therefore, this study aimed to identify novel candidate prognostic genes for IDC using an integrated bioinformatics approach. Methods: Three expression profile data sets were obtained from GEO (GSE29044, GSE3229, and GSE21422), from which differentially expressed genes (DEGs) were extracted for comparative transcriptome analysis of experimental groups (IDC versus control). Next, STRING was utilized to construct a protein interaction network with the shared DEGs, and MCODE and cytoHubba were used to identify the hub genes, which were then characterized using functional enrichment analysis in DAVID and KEGG. Finally, using the Kaplan-Meier tracer database, we determined the correlation between the expression of hub genes and overall survival in BC. Results: We identified seven hub genes (Kinesin-like protein KIF23 [KIF23], abnormal spindle-like microcephaly [ASPM]-associated protein [ASPMAP], Aurora kinase A [AURKA], Rac GTPase-activating protein 1 [RACGAP1], centromere protein F [CENPF], hyaluronan-mediated motility receptor [HMMR], and protein regulator of cytokinesis 1 [PRC1]), which were abundant in microtubule binding and tubulin binding, pathways linked to fundamental cellular structures including the mitotic spindle, spindle, microtubule, and spindle pole. The role of these genes in the pathophysiology of IDC is not yet well characterized; however, they have been associated with other common types of BC, modulating pathways such as Wnt/β-catenin, the epithelial-to-mesenchymal transition (EMT) process, chromosomal instability (CIN), PI3K/AKT/mTOR, and BRCA1 and BRCA2, playing an important role in its progression and being associated with a poor prognosis, thus representing a way to improve our understanding of the process of tumorigenesis and the underlying molecular events of IDC. Conclusions: Genes identified may lead to the discovery of new prognostic targets for IDC.