Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Roles of Post-Translational Modifications of Transcription Factors Involved in Breast Cancer Hypoxia

BC is the most commonly diagnosed cancer and the second leading cause of cancer death among women worldwide. Cellular stress is a condition that leads to disrupted homeostasis by extrinsic and intrinsic factors. Among other stressors, hypoxia is a driving force for breast cancer (BC) progression and a general hallmark of solid tumors. Thus, intratumoral hypoxia is an important determinant of invasion, metastasis, treatment failure, prognosis, and patient mortality. Acquisition of the epithelial-mesenchymal transition (EMT) phenotype is also a consequence of tumor hypoxia. The cellular response to hypoxia is mainly regulated by the hypoxia signaling pathway, governed by hypoxia-inducible factors (HIFs), mainly HIF1α. HIFs are a family of transcription factors (TFs), which induce the expression of target genes involved in cell survival and proliferation, metabolic reprogramming, angiogenesis, resisting apoptosis, invasion, and metastasis. HIF1α cooperates with a large number of other TFs. In this review, we focused on the crosstalk and cooperation between HIF1α and other TFs involved in the cellular response to hypoxia in BC. We identified a cluster of TFs, proposed as the HIF1α-TF interactome, that orchestrates the transcription of target genes involved in hypoxia, due to their post-translational modifications (PTMs), including phosphorylation/dephosphorylation, ubiquitination/deubiquitination, SUMOylation, hydroxylation, acetylation, S-nitrosylation, and palmitoylation. PTMs of these HIF1α-related TFs drive their stability and activity, degradation and turnover, and the bidirectional translocation between the cytoplasm or plasma membrane and nucleus of BC cells, as well as the transcription/activation of proteins encoded by oncogenes or inactivation of tumor suppressor target genes. Consequently, PTMs of TFs in the HIF1α interactome are crucial regulatory mechanisms that drive the cellular response to oxygen deprivation in BC cells.

RNA-binding proteins in breast cancer: Biological implications and therapeutic opportunities

RNA-binding proteins (RBPs) refer to a class of proteins that participate in alternative splicing, RNA stability, polyadenylation, localization and translation of RNAs, thus regulating gene expression in post-transcriptional manner. Dysregulation of RNA-RBP interaction contributes to various diseases, including cancer. In breast cancer, disorders in RBP expression and function influence the biological characteristics of tumor cells. Targeting RBPs has fostered the development of innovative therapies for breast cancer. However, the RBP-related mechanisms in breast cancer are not completely clear. In this review, we summarize the regulatory mechanisms of RBPs and their signaling crosstalk in breast cancer. Specifically, we emphasize the potential of certain RBPs as prognostic factors due to their effects on proliferation, invasion, apoptosis, and therapy resistance of breast cancer cells. Most importantly, we present a comprehensive overview of the latest RBP-related therapeutic strategies and novel therapeutic targets that have proven to be useful in the treatment of breast cancer.

Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis

Breast cancer (BRCA) has an undesirable prognosis and is the second most common cancer among women after lung cancer. A novel mechanism of programmed cell death called cuproptosis is linked to the development and spread of tumor cells. However, the function of cuproptosis in BRCA remains unknown. To this date, no studies have used machine learning methods to screen for characteristic genes to explore the role of cuproptosis-related genes (CRGs) in breast cancer. Therefore, 14 cuproptosis-related characteristic genes (CRCGs) were discovered by the feature selection of 39 differentially expressed CRGs using the three machine learning methods LASSO, SVM-RFE, and random forest. Through the PPI network and immune infiltration analysis, we found that PRNP was the key CRCG. The miRTarBase, TargetScan, and miRDB databases were then used to identify hsa-miR-192-5p and hsa-miR-215-5p as the upstream miRNA of PRNP, and the upstream lncRNA, CARMN, was identified by the StarBase database. Thus, the mRNA PRNP/miRNA hsa-miR-192-5p and hsa-miR-215-5p/lncRNA CARMN ceRNA network was constructed. This ceRNA network, which has not been studied before, is extremely innovative. Furthermore, four cuproptosis-related lncRNAs (CRLs) were screened in TCGA-BRCA by univariate Cox, LASSO, and multivariate Cox regression analysis. The risk model was constructed by using these four CRLs, and the risk score = C9orf163 * (1.8365) + PHC2-AS1 * (-2.2985) + AC087741.1 * (-0.9504) + AL109824.1 * (0.6016). The ROC curve and C-index demonstrated the superior predictive capacity of the risk model, and the ROC curve demonstrated that the AUC of 1-, 3-, and 5-year OS in all samples was 0.721, 0.695, and 0.633, respectively. Finally, 50 prospective sensitive medicines were screened with the pRRophetic R package, among which 17-AAG may be a therapeutic agent for high-risk patients, while the other 49 medicines may be suitable for the treatment of low-risk patients. In conclusion, our study constructs a new ceRNA network and a novel risk model, which offer a theoretical foundation for the treatment of BRCA and will aid in improving the prognosis of BRCA.