Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients

Abstract

Background: Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.

Methods: Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.

Results: We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).

Conclusion: Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Keywords: Breast cancer; Ferroptosis; Immune status; Prognostic signature.

Fig. 1

Overview of the process of this study

Fig. 2

Identification of the candidate ferroptosis-related genes in TCGA. a Venn diagram showed ferroptosis-related differentially expressed genes between tumor and adjacent normal tissue that were correlated with OS. b Tumor tissue contained eight upregulated genes and two downregulated genes. c Forest plots to show the results of the univariate Cox regression analysis between gene expression and OS. d The PPI network revealed the interactions among the candidate genes and excavate the hub genes

Fig. 3

The expression of candidate signatures in both BRCA tissue and normal tissue in HPA database. a-f The expression of six genes (ALOX15, CS, GCLC, EMC2, SQLE, G6PD) is higher in tumor tissue. g and h The expression of two genes (GPX4, ACSF2) isn’t significant difference

Fig. 4

Prognostic analysis of the 9-gene signature model in TCGA. a The distribution and median value of the risk scores. b The distributions of OS status, OS and risk scores. c and d PCA and t-SNE analysis plot. e Kaplan-Meier survival curves of OS of high-risk group and low-risk group. f AUC of time-dependent ROC curves verified the predictive power of the risk score

Fig. 5

Validation of the 9-gene signature model in GEO and ICGC. a The distribution and median value of the risk scores. b The distributions of OS status, OS and risk scores. c and d PCA and t-SNE analysis plot. e Kaplan-Meier survival curves of OS of high-risk group and low-risk group. f AUC of time-dependent ROC curves verified the predictive power of the risk score

Fig. 6

The results of the univariate and multivariate Cox regression analyses regarding significant survival-related clinicopathological parameters in TCGA

Fig. 7

Representative results of GO and KEGG analyses in TCGA. a and b The results of GO biological process enrichment, GO cellular component enrichment and GO molecular function enrichment of DEGs. c and d The results of KEGG pathways analysis of DEGs

Fig. 8

The results of the ssGSEA scores between different risk groups in TCGA. a The upper boxplots displayed the scores of 16 immune cells. b The under boxplots displayed the scores of 13 immune-related functions. Adjusted P values were showed as: ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001