An iron metabolism and immune related gene signature for the prediction of clinical outcome and molecular characteristics of triple-negative breast cancer

Abstract

Background: An imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand.

Methods: We constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA).

Results: Compared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups.

Conclusions: The IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.

Keywords: Ferroptosis; Gene signature; Immune infiltration; Iron metabolism; Triple-negative breast cancer.

Fig. 1

Study flow chart. Firstly, 2087 TNBC-DEGs and 56 IMRGs were identified. Next, a Pearson correlation analysis was performed between these two datasets and then we acquired 1244 TNBC-IMRGs. Subsequently, this dataset was integrated with the BRCA prognostic genes to obtain 30 candidate prognostic TNBC-IMRGs. Through LASSO analysis, a five-IMRGs signature was constructed. Survival analysis and ROC curve were performed to identify the prognostic value. Differences of molecular characteristics were evaluated between high- and low- risk groups. Finally, a combined model was constructed by combining the gene signature with the clinical variables

Fig. 2

Thirty (30) candidate prognostic TNBC-IMRGs. a Volcano plot of TNBC-DEGs (|log₂ fold change|> 1 and P < 0.05). Significantly upregulated and downregulated genes are depicted as red and blue dots, respectively. b Heatmap of expression profiles of 30 TNBC-IMRGs between TNBC and normal samples. c Correlation heatmap of 30 TNBC-IMRGs in expression levels

Fig. 3

Identification and validation of TNBC-IMRGs signature. a The optimal lambda resulted in five nonzero coefficients. b Partial likelihood deviation curve was plotted. c Distribution of the risk score, survival status, and expression profiles of five genes in the TCGA training set. d Kaplan–Meier survival analysis of the high-risk group and low-risk group. e Time-dependent ROC curves at 1-, 3-, and 5-year of the TNBC-IMRGs signature for the training cohort. f, i Distribution of the risk score, survival status, and expression profiles of five genes in GSE2603 and GSE21653 validation set. g-h, j-k Kaplan–Meier survival analysis and ROC curves for the TNBC-IMRGs signature in the GSE2603 and GSE21653. Survival status: 1: dead, 0: alive. Low: low-risk group, High: high-risk group. Time: days. AUC: area under the curve

Fig. 4

Stratified prognostic analysis of the TNBC-IMRGs signature. a-h The survival differences between high- and low- risk groups stratified by clinical variables: a, b age (≤ 55 and > 55), c, d node (N0 and N1-3), e, f tumor (T1-2 and T3-4), g, h stage (stage I-II and stage III-IV). Time: days

Fig. 5

Functional enrichment analysis. a Volcano plot of DEGs between high- and low-risk groups (|log₂ fold change|> 1 and P < 0.05). Significantly upregulated and downregulated genes are depicted as red and blue dots, respectively. b-c GO and KEGG enrichment analysis. d-f GSEA analysis for high- and low-risk groups. NES: normalize enrichment score. g Comparison of the differential expression of ferroptosis regulators in high- and low-risk groups

Fig. 6

Immune infiltration analysis. a-c The correlation between risk score and immune related-scores with ESTIMATE algorithm. d Heatmap of infiltrating immune cells based on TIMER, QUANTISEQ, MCPCOUNTER, XCELL, EPIC, and CIBERSORT (* higher in low-risk group, *P < 0.05, **P < 0.01, ***P < 0.001; # higher in high-risk group, #P < 0.05, ###P < 0.001). e Scatter plot of the correlation between risk score and CD4 + T cell. f Enrichment scores of immune cells evaluated by ssGSEA. aDC: activated DC, iDC: immature DC, pDC: plasmacytoid DC, TFH: T follicular helper, Tgd: T gamma delta. g Differential expression analysis of immune checkpoint genes between high- and low-risk groups

Fig. 7

A predictive nomogram of the combined model was established. a Univariate and multivariate Cox regression analysis of the correlation between OS and various clinical variables including risk score. b-c Kaplan–Meier survival analysis (b) and ROC curves (c) in the clinical model. d The nomogram of the combined model for predicting the OS of patients with TNBC at 1-, 3-, and 5-year survival (Node stage and Tumor stage are categorical variables, Risk Score is a numeric variable). e Calibration plots of the nomogram at 1-, 3-, and 5-year survival. f-g Kaplan–Meier survival analysis (f) and ROC curves (g) in combined model. h Decision curve analysis of clinical model, gene signature and combined model. Low: low-risk group, High: high-risk group. Time: days. AUC: area under the curve

Fig. 8

Validation of the combined model. a-d Kaplan–Meier survival analysis (a, c) and ROC curves (b, d) of the combined model in the validation sets GSE2603 and GSE21653