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. 2022 Jun 2:12:896927.
doi: 10.3389/fonc.2022.896927. eCollection 2022.

Development and Verification of a Prognostic Ferroptosis-Related Gene Model in Triple-Negative Breast Cancer

Song Wu  1 , Ruilin Pan  2 , Jibu Lu  2 , Xiaoling Wu  3 , Jingdong Xie  1 , Hailin Tang  1 , Xing Li  1
Affiliations

Development and Verification of a Prognostic Ferroptosis-Related Gene Model in Triple-Negative Breast Cancer

Song Wu et al. Front Oncol. .

Abstract

Triple-negative breast cancer (TNBC) is the subtype with the worst prognosis of breast cancer. Ferroptosis, a novel iron-dependent programmed cell death, has an increasingly important role in tumorigenesis and development. However, there is still a lack of research on the relationship between ferroptosis-related genes and the prognosis of TNBC. In this study, we obtained the gene expression profile of TNBC patients and matched clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis was used to screen out ferroptosis-related genes associated with TNBC prognosis. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was employed to establish a prognostic prediction model. A 15-ferroptosis-related gene prognostic prediction model was developed, which classified patients into low-risk (LR) or high-risk (HR) groups. Kaplan-Meier analysis results showed that the prognosis of the LR group was better. The receiver operating characteristic (ROC) curve also confirmed the satisfactory predictive ability of this model. Evaluation of the immune microenvironment of TNBC patients in the HR and LR group suggested these 15 ferroptosis-related genes might affect the prognosis of TNBC by regulating the tumor microenvironment. Our prognostic model can provide a theoretical basis for accurate prognosis prediction of TNBC in clinical practice.

Keywords: ferroptosis; mRNA signature; prognostic model; triple-negative breast cancer (TNBC); tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Establishment of a prognostic ferroptosis-related gene pattern. (A) Correlation analysis of the ferroptosis-related genes. (B) Forest plots interpreting the univariate Cox analysis results between ferroptosis-related genes expression and OS. (C) Variables filtering based on Lasso regression. (D) Each curve represents the change trajectory of each ferroptosis-related genes coefficient. (E) Corplot showing the pairwise correlation analysis of these 15 ferroptosis-related genes. (F) The prognostic regression model established by LASSO regression analysis.
Figure 2
Figure 2
Kaplan–Meier curve, Time-dependent ROC curve, Risk score analysis and Survival status analysis of the 15-ferroptosis-related gene signature. (A) Risk score analysis for the 15-ferroptosis-related gene signature. (B) Kaplan–Meier curve of 15-ferroptosis-related gene signature in the TCGA cohort. (C) Survival status analysis for the 15-ferroptosis-related gene signature. (D–F) The time-dependent ROC curve of 1-, 3-, 5-year OS of the 15-ferroptosis-related gene signature in the TCGA cohort.
Figure 3
Figure 3
Validation of the expression levels of the 15 ferroptosis‐related genes in clinical specimens. Student’s t-test (two-tailed) was used for the comparison analyses. *P < 0.05, **P < 0.01, ***P < 0.001 ****P < 0.0001 respectively.
Figure 4
Figure 4
Different immune microenvironment between HR and LR TNBC patients. (A) The fraction of stromal and immune cells in HR and LR groups. (B) The Vioplot shows that the abundances of T cells CD4 memory activated, macrophages M1 (P < 0.01), and mast cells resting(P < 0.05) were significantly different between HR and LR.
Figure 5
Figure 5
Gene set enrichment analysis (GSEA) in the TCGA cohort. (A–E) Signaling pathways significantly enriched in HR group. (F–H) 3 non-ferroptosis-related pathways enriched in LR group.
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