Predicting prognosis and clinical features of the tumor microenvironment based on ferroptosis score in patients with breast cancer

Abstract

Ferroptosis genes have recently been reported to be involved in regulating the development of cancer, but their potential role in breast cancer (BRCA) is not fully understood. The purpose of this study is to systematically study the mechanism of ferroptosis in BRCA and its relationship with this cancer’s prognosis, cell infiltration, gene mutation, and other clinical features. In this study, The Cancer Genome Atlas breast cancer (TCGA-BRCA) database (UCSC Xena) was used to mine the ferroptosis genes related to BRCA patients, and the genes with prognostic value were screened by Cox regression analysis, which were then used to construct a prognostic model for scoring prognostic molecular risk. The relationships between ferroptosis score and prognosis, molecular typing, and clinical characteristics of BRCA were also analyzed. A total of 176 ferroptosis genes related to BRCA were retrieved from the database, 22 of which were found to be significantly related to BRCA prognosis after screening by single-factor Cox regression analysis (p < 0.01). Unsupervised clustering of samples was performed using factoextra, and two subgroups (ferroptosis cluster A and ferroptosis cluster B) with significant differences in prognosis were identified. Subsequently, single-factor Cox regression analysis and random forest dimensionality reduction were used to screen characteristic genes to construct a ferroptosis score model, which included a high ferroptosis score group and a low ferroptosis score group. The results showed that there were significant differences in ferroptosis scores between the ferroptosis cluster A and B groups. The prognosis of patients with low ferroptosis scores was poor, and the overall survival (OS) rate of patients with high ferroptosis scores was significantly higher, indicating that the prognosis of the sample can be well characterized based on calculated ferroptosis scores. Ferroptosis scores differed significantly according to patient age, TP53 and PIK3CA gene mutations, different PAM50 molecular types, and clinical stages. Ferroptosis activation plays a non-negligible role in tumor occurrence and development. Evaluating the ferroptosis score within BRCA will help advance our understanding of the infiltrating properties of cells in the tumor microenvironment and may guide more effective immunotherapy strategies.

Correlation between Ferroptosis-Related Gene Signature and Immune Landscape, Prognosis in Breast Cancer

“…Long survival times have been achieved in BC, as the improvement of the comprehensive treatment of local recurrence or distant metastasis has become the main challenge for clinicians. Previous study has built a ferroptosis score model and showed a good predictive value for OS in BS patients [ 39 ]. In this study, a novel prognostic model for DFS combined with 6 FRGs was also established with the TCGA-BRCA training cohort and revealed a good predictive value of recurrence in internal and external validation cohorts.…”

Novel Ferroptosis-related gene signature for predicting the prognosis of breast cancer

“…Calibration curve con rmed its high predictive e cacy for 3-, and 5-year survival time. Recently, an 8 ferroptosis-genes prognostic model has been conducted for BRCA prognosis (24).Therefore, the ferroptosis score may be very helpful for us to predict the clinical outcome of patients. Cancer is a genetic disease associated with the accumulation of mutations (25).…”

“…At present, most chemotherapeutic drugs play a role by inducing tumor cell apoptosis. However, if the apoptosis of tumor cells fails, there will be chemotherapy resistance (24). Chemoresistance is a major obstacle to curing cancer (32).…”

Identification of Markers for Diagnosis and Treatment of Diabetic Kidney Disease Based on the Ferroptosis and Immune

“…Besides, principal component analysis (PCA) was performed to extract principal component 1 as the characteristic fraction. The ferroptosis score index for each sample was then calculated using a method similar to the gene expression rating as follows: ferroptosis score = ∑PC1B − ∑PC1A, where PC1A represents the first component of feature A, PC1B represents the first component of feature B, and the scores of ferroptosis modification patterns and genotyping were detected [ 28 ]. Use R software “ggalluvial” package to draw Sankey.…”