The initial expression alterations occurring to transcription factors during the formation of breast cancer: Evidence from bioinformatics.

Abstract

BackgroundBreast cancer (BC) is the leading malignancy among women worldwide.AimThis work aimed to present a comprehensively bioinformatic analysis of gene expression profiles and to identify the hub genes during BC tumorigenesis, providing potential biomarkers and targets for the diagnosis and therapy of BC.Materials & MethodsIn this study, multiple public databases, bioinformatics approaches, and online analytical tools were employed and the real‐time reverse transcription polymerase chain reaction was implemented.ResultsFirst, we identified 10, 107, and 3869 differentially expressed genes (DEGs) from three gene expression datasets (GSE9574, GSE15852, and GSE42568, covering normal, para‐cancerous, and BC samples, respectively), and investigated different biological functions and pathways involved. Then, we screened out 8, 16, and 29 module genes from these DEGs, respectively. Next, 10 candidate genes were determined through expression and survival analyses. We noted that seven candidate genes JUN, FOS, FOSB, EGR1, ZFP36, CFD, and PPARG were downregulated in BC compared to normal tissues and lower expressed in aggressive types of BC (basal, HER2+, and luminal B), TP53 mutation group, younger patients, higher stage BC, and lymph node metastasis BC, while CD27, PSMB9, and SELL were upregulated. The present study discovered that the expression levels of these candidate genes were correlated with the infiltration of immune cells (CD8+ T cell, macrophage, natural killer [NK] cell, and cancer‐associated fibroblast) in BC, as well as biomarkers of immune cells and immune checkpoints. We also revealed that promoter methylation, amplification, and deep deletion might contribute to the abnormal expressions of candidate genes. Moreover, we illustrated downstream‐targeted genes of JUN, FOS, FOSB, EGR1, and ZFP36 and demonstrated that these targeted genes were involved in “positive regulation of cell death”, “pathways in cancer”, “PI3K‐Akt signaling pathway”, and so on.Discussion & ConclusionWe presented differential gene expression profiles among normal, para‐cancerous, and BC tissues and further identified candidate genes that might contribute to tumorigenesis and progression of BC, as potential diagnostic and prognostic targets for BC patients.