Following the implementation of breast screening programs, the occurrence of ductal carcinoma in situ (DCIS) as an early type of neoplasia has increased. Although the prognosis is promising, 20%–50% of DCIS patients will progress to invasive ductal carcinoma (IDC) if not treated. It is essential to look for promising biomarkers for predicting DCIS prognosis. The Gene Expression Omnibus (GEO) database was used to explore the expression of genes that differed between DCIS and normal tissue in this investigation. Enrichment analysis was performed to characterize the biological role and intrinsic process pathway. The Cancer Genome Atlas Breast Cancer Dataset was used to categorize the hub genes, and the results were confirmed using the Cytoscape plugin CytoHubba and MCODE. The prognostic ability of the core gene signature was determined through time‐dependent receiver operating characteristic (ROC), Kaplan–Meier survival curve, Oncomine databases, and UALCAN databases. In addition, the prognostic value of core genes was verified in proliferation assays. We identified 217 common differentially expressed genes (DEGs) in the present study, with 101 upregulated and 138 downregulated genes. The top genes were obtained from the PPI network (protein–protein interaction). A unique six‐gene signature (containing GAPDH, CDH2, BIRC5, NEK2, IDH2, and MELK) was developed for DCIS prognostic prediction. Centered on the Cancer Genome Atlas (TCGA) cohort, the ROC curve showed strong results in prognosis prediction. The six core gene signatures is often overexpressed in DCIS, with a weak prognosis. Furthermore, when breast cancer cells are transfected with small interfering RNAs, downregulation of core gene expression substantially inhibits cell proliferation, revealing a high potential for employing core genes in DCIS prognosis. In conclusion, the current investigation verified the six core genes signatures for prospective DCIS biomarkers, which may aid clinical decision‐making for individual care.
Ferroptosis is distinct from classic apoptotic cell death characterized by the accumulation of reactive oxygen species (ROS) and lipid peroxides on the cell membrane. Increasing findings have demonstrated that ferroptosis plays an important role in cancer development, but the exploration of ferroptosis in breast cancer is limited. In our study, we aimed to establish a ferroptosis activation-related model based on the differentially expressed genes between a group exhibiting high ferroptosis activation and a group exhibiting low ferroptosis activation. By using machine learning to establish the model, we verified the accuracy and efficiency of our model in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) set and gene expression omnibus (GEO) dataset. Additionally, our research innovatively utilized single-cell RNA sequencing data to systematically reveal the microenvironment in the high and low FeAS groups, which demonstrated differences between the two groups from comprehensive aspects, including the activation condition of transcription factors, cell pseudotime features, cell communication, immune infiltration, chemotherapy efficiency, and potential drug resistance. In conclusion, different ferroptosis activation levels play a vital role in influencing the outcome of breast cancer patients and altering the tumor microenvironment in different molecular aspects. By analyzing differences in ferroptosis activation levels, our risk model is characterized by a good prognostic capacity in assessing the outcome of breast cancer patients, and the risk score can be used to prompt clinical treatment to prevent potential drug resistance. By identifying the different tumor microenvironment landscapes between the high- and low-risk groups, our risk model provides molecular insight into ferroptosis in breast cancer patients.
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