Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis - Yin, Xin; Liu, Jiaxiang; Wang, Xin; Yang, Tianshu; Li, Gen; Shang, Yaxin; Teng, Xu; Yu, Hefen; Wang, Shuang; Huang, Wei (Frontiers in Oncology, 2021)
Breast cancer is the most frequently diagnosed cancer and is the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription whose dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed to construct a prognostic risk model and verify its validity. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. As reported by multiple databases, COPS5, HDAC2, and NONO were highly expressed in human breast cancer cell lines and clinical breast cancer samples. Immune infiltration analysis revealed that resting dendritic and mast cells were higher in the low-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis and provide a framework for the co-expression of TF modules and immune infiltration of breast cancer.