Determination of target genes for classified molecular subtypes of triple-negative breast cancer form microarray gene expression profiling: An integrative in silico approach

Abstract

ABSTRACT Background: Highly heterogeneous triple-negative breast cancer (TNBC) has tough clinical features, which were gradually solving and improving in diagnosis by the molecular subtyping of TNBC. Aim: Presently, this study was focused on analyzing the genetic makeup of TNBC subtypes. Settings and Design: This study explored the MicroArray expression profiling of differentially expressed genes in molecular subtypes BL1, BL2, IM, luminal androgen receptor, M, and mesenchymal stem-like of TNBC by analyzing the Gene Expression Omnibus dataset GSE167213. Various gene ontologies-based protein–protein interaction (PPI) networks were subtyped TNBC genes. The effect of genetic alteration on TNBC cases was also interpreted. Materials and Methods: The MicroArray gene expression profiling was done through R programming and subjected to functional annotation through the database for annotation, visualization, and integrated discovery. The PPI networking of functionally associated genes was interpreted by STRING. The survival analysis was done through cBioPortal. Statistical Analysis Used: The t-test was used through R programming to generate the P values for a test of the significance of expressed genes. Results: A total of 54,613 significant probes were analyzed in the TNBC MicroArray dataset. The functional PPI networks of BL1, BL2, and IM upregulated genes showed significant associations. The survival analysis of differentially expressed genes showed the significant prognostic effect of 32 upregulated genes of different subtypes on TNBC cases with genetic alterations, whereas the remaining genes showed no significant effects. Conclusion: The output of the present study provided significant target gene panels for different TNBC subtypes, which would add an informative genetic value to TNBC diagnosis.