A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer

Abstract

Background: Ferroptosis, a regulated cell death which is driven by the iron-dependent peroxidation of lipids, plays an important role in cancer. However, studies about ferroptosis-related Long non-coding RNAs (lncRNAs) in breast cancer (BC) are limited. Besides, the prognostic role of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer remain unclear. This study aimed to explore the potential prognostic value of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer. Methods: RNA-sequencing data of female breast cancer patients were downloaded from TCGA database. 937 patients were randomly separated into training or validation cohort in 2:1 ratio. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 239 reported ferroptosis-related genes. A ferroptosis-related lncRNAs signature was constructed with univariate and multivariate Cox regression analyses in the training cohort, and its prognostic value was further tested in the validation cohort. Results: An 8-ferroptosis-related-lncRNAs signature was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.791 at 1 year, 0.778 at 2 years, 0.722 at 5 years in the validation cohort. Further analysis demonstrated that immune-related pathways were significantly enriched in the high-risk group. Analysis of the immune cell infiltration landscape showed that breast cancer in the high-risk group tended be immunologically "cold". Conclusion: We identified a novel ferroptosis-related lncRNA signature which could precisely predict the prognosis of breast cancer patients. Ferroptosis-related lncRNAs may have a potential role in the process of anti-tumor immunity and serve as therapeutic targets for breast cancer.

Keywords: breast cancer; ferroptosis; lncRNA; prognostic signature; tumor immune microenvironment.

FIGURE 1

The flow chart of our study.

FIGURE 2

Identification of prognostic differentially expressed ferroptosis-related lncRNAs in breast cancer patient. (A) Venn diagram to identify the common lncRNAs of differentially expressed lncRNAs, ferroptosis-related lncRNAs and prognostic lncRNAs. (B) The 11 overlapping lncRNAs were differently expressed in normal and tumor tissue. (C) Forest plots showing the results of the univariate Cox regression analysis between overlapping lncRNAs and overall survival. (D) lncRNA-mRNA co-expression network of candidate lncRNAs and ferroptosis-related genes.

FIGURE 3

Prognostic analysis of the ferroptosis-related lncRNAs signature model in the training cohort and validation cohort. (A) The distribution of the risk scores in the training cohort. (B) The distribution of the risk scores in the validation cohort. (C) The distributions of overall survival status, overall survival and risk score in the training cohort. (D) The distributions of overall survival status, overall survival and risk score in the validation cohort. (E) Kaplan-Meier curves for the overall survival of patients in the high- and low-risk groups in the training cohort. (F) Kaplan-Meier curves for the overall survival of patients in the high- and low-risk groups in the validation cohort. (G) AUC of time-dependent ROC curves verified the prognostic accuracy of the risk score in the training cohort. (H) AUC of time-dependent ROC curves verified the prognostic accuracy of the risk score in the validation cohort.

FIGURE 4

Prognostic value of the 8-ferroptosis-related-lncRNAs signature in patients receiving different treatment regimens. (A) Kaplan-Meier curves for the overall survival of high- and low-risk patients receiving chemotherapy. (B) Kaplan-Meier curves for the overall survival of high- and low-risk patients receiving anthracycline chemotherapy. (C) Kaplan-Meier curves for the overall survival of high- and low-risk patients receiving endocrinotherapy. (D) Kaplan-Meier curves for the overall survival of high- and low-risk patients receiving cyclophosphamide chemotherapy. (E) Kaplan-Meier curves for the overall survival of high- and low-risk patients receiving anti-HER2 therapy. (F) Kaplan-Meier curves for the overall survival of high- and low-risk patients receiving paclitaxel chemotherapy.

FIGURE 5

Independent prognostic value of the ferroptosis-related-lncRNAs signature. Results of the univariate Cox regression analysis and multivariate Cox regression analysis regarding OS in the training cohort (A,C) and the validation cohort (B,D). AUC of ROC curves compared the prognostic accuracy of the risk score and other prognostic factors in the training cohort and validation cohort (E,F).

FIGURE 6

Construction of the ferroptosis-related lncRNA–mRNA co-expression network (A). Diagram of the ferroptosis-related lncRNA–mRNA network (B). The Sankey diagram shows the connection degree between the ferroptosis-related lncRNAs and ferroptosis-related genes.

FIGURE 7

Gene set enrichment analysis (GSEA) of high-risk group and low-risk group based on the ferroptosis-related lncRNAs prognostic signature. (A) GSEA results show significant enrichment of antioxidant pathways and cell proliferation pathways in the high-risk breast cancer patients. (B) GSEA results show significant enrichment of oxidative damage-related pathways and immunoregulatory pathways in the low-risk breast cancer patients.

FIGURE 8

Results of GO and KEGG analyses. GO analysis showed differentially expressed genes between high- and low-risk groups were obviously enriched in immune-related biological processes (A), immune-related cell components (B), and immune-related molecular functions (C). KEGG analysis showed differentially expressed genes were enriched in immune-related pathway (D).

FIGURE 9

The immune cell infiltration landscape in breast cancer. (A) Barplot of the tumor-infiltrating cell proportions. (B) Heatmap of the tumor-infiltrating cell proportions. (C) Correlation matrix of immune cell proportions. (D) Violin plot showed the different proportions of tumor-infiltrating cells between high-risk group and low-risk group. (E) The expression levels of immune checkpoint molecules in high-risk group and low-risk group.

FIGURE 10

The overall survival and immune cell infiltration landscape in different immunohistochemical subtypes of breast cancer. (A) Kaplan-Meier curves for the overall survival of patients in the high- and low-risk groups in different immunohistochemical subtypes of breast cancer. (B–D) Violin plot showed the different proportions of tumor-infiltrating cells between high-risk group and low-risk group in luminal breast cancer, HR-Her2+ breast cancer or TNBC. (E–G) The expression levels of immune checkpoint molecules in high-risk group and low-risk group in luminal breast cancer, HR-Her2+ breast cancer or TNBC. (*p < 0.05, **p < 0.01, and ***p < 0.001).