Insights into Breast Cancer Prognosis: A Differential Regulatory Network Approach to Identify Key Transcription Factor Biomarkers

Abstract

Abstract Background. Breast cancer (BC) is the most common and aggressive type of cancer in females, and exploring the mechanisms of disease progression is playing a crucial role in the development of potential therapeutics. Recently, systems biology approaches such as network strategies have been successfully applied to reveal the interaction mechanisms between genes. The main objective of the current study was to investigate potential biomarkers for BC patients at different stages by constructing differential regulatory networks (DRNs). Method. In the present study, clinical information and RNA-seq data from patients with BC were obtained from The Cancer Genome Atlas (TCGA). According to the clinical staging information, the gene expression data of TCGA-BRCA was divided into different stages (stages I–IV) and analyzed separately. The differentially co-expressed genes and links (DCGL) package in R was used to identify differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) in different stages (I–IV) of BC patients compared to normal samples. A q < 0.25 was considered the cut-off criterion. Besides, differentially-regulated genes (DRGs) and differentially-regulated links (DRLs) were identified by DCGs, DCLs, and TF-to-target knowledge. Stage-specific gene regulatory networks (GRNs) were further analyzed with Cytoscape to explore the core TFs. Afterward, Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of the core TFs. Cancer-related pathway analysis of candidate hub TF was done through the GSCALite database. Finally, the relationship between candidate transcription factors expression and tumor-infiltrating lymphocytes was analyzed using TCGA-BRCA data and the TIMER database. Results. From DRNs of stages I–IV, 29 unique core TFs were screened. Survival analysis indicated that the expression of KLF12, FOS, BACH2 EPAS1, PPARA, and MRPL36 had significant effects on the survival of breast cancer patients (P < 0.05). Hub genes were responsible for the infiltration levels of immunocytes. Based on the GSCALite database, these six TFs are significantly related to multiple signaling pathways, including RAS/MAPK, EMT, PI3K/AKT, and TSC/mTOR. These pathways play vital roles in oncogenesis, suggesting that these candidate hub TFs may participate in BC progression. Conclusion. Our findings suggest these six TFs might play important roles in the pathogenesis of BC and could be used as therapeutic targets for BC. However, further studies at the molecular level are required to confirm these observations.