
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00244-4
10.1016/j.tranon.2024.102117
102117
Original Research
Excavating regulated cell death signatures to predict prognosis, tumor microenvironment and therapeutic response in HR+/HER2- breast cancer
Mao Shuangshuang a
Zhao Yuanyuan b
Xiong Huihua a
Gong Chen chengong@tjh.tjmu.edu.cn
a⁎
a Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
b Department of Organ Transplantation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
⁎ Corresponding author. chengong@tjh.tjmu.edu.cn
05 9 2024
12 2024
05 9 2024
50 1021174 6 2024
25 7 2024
2 9 2024
© 2024 The Authors. Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• By integrative analyzing 12 kinds of RCD (Regulated Cell Death) patterns, we constructed a novel prognostic RCD scoring system (CDScore) based on six RCD genes (LEF1, SLC7A11, SFRP1, IGFBP6, CXCL2, STXBP1) to predict the prognosis of HR+/HER2- breast cancer patients.

• The CDScore model were closely associated with tumor microenvironments and immune checkpoints.

• HR+/HER2- breast cancer patients in CDScore high group were resistant to standard chemotherapy (docetaxel, epirubicin, gemcitabine and vinorelbine) and target therapy (palbociclib and niraparib).

• A nomogram was established based on CDScore, age and TNM stage to predict patient overall survival, with an area under the ROC curve of 0.89, 0.82 and 0.8 in predicting 1-year, 3-year and 5-year overall survival rates, respectively.

Regulated cell death (RCD) has been documented to have great potentials for discovering novel biomarkers and therapeutic targets in malignancies. But its role and clinical value in HR+/HER2- breast cancer, the most common subtype of breast cancer, are obscure. In this study, we comprehensively explored 12 types of RCD patterns and found extensive mutations and dysregulations of RCD genes in HR+/HER2- breast cancer. A prognostic RCD scoring system (CDScore) based on six critical genes (LEF1, SLC7A11, SFRP1, IGFBP6, CXCL2, STXBP1) was constructed, in which a high CDScore predicts poor prognosis. The expressions and prognostic value of LEF1 and SFRP1were also validated in our tissue microarrays. The nomogram established basing on CDScore, age and TNM stage performed satisfactory in predicting overall survival, with an area under the ROC curve of 0.89, 0.82 and 0.8 in predicting 1-year, 3-year and 5-year overall survival rates, respectively. Furthermore, CDScore was identified to be correlated with tumor microenvironments and immune checkpoints by excavation of bulk and single-cell sequencing data. Patients in CDScore high group might be resistant to standard chemotherapy and target therapy. Our results underlined the potential effects and importance of RCD in HR+/HER2- breast cancer and provided novel biomarkers and therapeutic targets for HR+/HER2- breast cancer patients.

Graphical abstract

Image, graphical abstract

Keywords

Regulated cell death
Prognosis
Drug response
HR+/HER2- breast cancer
==== Body
pmcIntroduction

Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer and has been the leading cause of cancer death for women worldwide [1]. According to the mutation status of hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2), breast cancer is classified into different molecular subtypes which showed disparate biological characteristics, therapeutic options, and clinical prognosis [2]. Among diverse subtypes, HR-positive and HER2-negative (HR+/HER2-) breast cancer accounts for about 70 % of all breast cancer cases and is a heterogeneous subgroup [3]. Although most of the HR+/HER2- breast cancers were diagnosed at an early stage and showed a considerable overall survival and prognosis, some patients inevitably suffered from relapsed cancers ultimately leading to death. There have been many factors utilized to predict the prognosis and guide the therapies of early-stage HR+/HER2- breast cancer patients [4,5]. However, a more reliable prediction model with better sensitivity and specificity is still demanded in the clinic. Another fraction of patients diagnosed with metastatic HR+/HER2- breast cancer was considered to be incurable and they are most likely to have drug resistance and disease progression [6]. Thus, the furtherly understood molecular mechanisms and novel drug targets are still needed to improve the prognosis of HR+/HER2- breast cancer patients.

Increasing evidences have indicated that regulated cell death (RCD) is the key hallmark of tumorigenesis and might contribute to the establishment of novel therapeutic strategies [7,8]. Different from accident cell death (ACD), RCD is an autonomous and well-organized death of cells which is tightly regulated by a series of genes and signaling cascades [9,10]. With the deepening of researches on RCD, there are about twelve kinds of RCD (including apoptosis, autophagy-dependent cell death, alkaliptosis, cuproptosis, entotic cell death, ferroptosis, lysosome-dependent cell death, necroptosis, netotic cell death, oxeiptosis, parthanatos and pyroptosis) identified and revealed to play crucial roles in various cancers [11]. Apoptosis is well-known as a critical intracellular process which is characterized by cell shrinkage, chromatin condensation, DNA fragmentation and apoptotic body formation to maintain the homeostasis of cellular environment [12]. Autophagy is a phagocytic activity which destroys damaging proteins or organelles via multistep lysosomal fusion and degradation [13] and have been revealed to participate into drug resistance in cancer [[14], [15], [16]]. Alkaliptosis is a novel form of RCD which was firstly identified in 2018 and was revealed to be driven by intracellular alkalinization [17]. Cuproptosis is a copper dependent cell death which has just been illuminated in recent years and might be closely related to mitochondrial respiration and lipoic acid pathway [18]. Entotic cell death, characterized by the occurrence of cell-in-cell structures, is an activity of cell cannibalism in which one cell engulfs and eliminates adjacent cells [19]. Ferroptosis is a new form of iron-dependent cell death and has been a hot issue in cancer researches in recent years [20]. Lysosome-dependent cell death is a hydrolytic enzyme-mediated RCD subtype which is characterized by lysosomal rupture [21,22]. Necroptosis, driven by receptor-interacting serine/threonine kinase protein 1 (RIPK1) through its kinase function, is a programmed form of necrosis and shows similar morphological features to necrosis [23]. Netotic cell death is triggered by the release of neutrophil extracellular traps (NETs) which are net-like DNA-protein complexes secreted by cells when suffering infection or injury [24]. Oxeiptosis induced by the reactive oxygen species (ROS) is mediated by the cascade of KEAP1-PGAM5-AIFM1 pathway which is different from AIFM1-mediated other RCDs such as apoptosis [25]. Parthanatos is a PARP-1-dependent RCD that is activated by the hyperactivation of PARP1 in response to oxidative stress-induced DNA damage and chromatolysis [26]. Pyroptosis is a GSDMD-dependent RCD which is correlated with the inflammatory response and plays a critical role in inflammation and immunity [27].

With the achievement made in RCD researches in recent years, various crucial signaling pathways of RCD have been revealed to be involved in the development and progression of many types of malignant tumors [28]. Meanwhile, the exploration of novel potential therapeutic strategies targeting the key modulators of RCD has become a promising therapeutic avenue, which might make a huge progress in improving the prognosis of cancer patients [29]. To provide novel clues of RCD in tumors, the complicated networks among different RCD subtypes and their comprehensive functions have been explored in several tumors basing on the gene expression profile datasets. In non-small cell lung cancer, a new classification system and RCD scoring model were constructed basing on RCD-related genes to predict the response of chemotherapy and immunotherapy [30]. In triple-negative breast cancer, a cell death index calculated basing on 12 RCD genes was established to predict patient prognosis and drug sensitivity, which might provide novel therapeutic options in the future management of triple-negative breast cancer patients [31]. However, the comprehensive profiles as well as the particular functions of RCD in HR+/HER2- breast cancer are unknown.

In our study, we summarized the mutation and dysregulation of RCD related genes in HR+/HER2- breast cancer. We also explored the prognostic value of RCD related genes and constructed a RCD score (CDScore) to predict the prognosis and therapeutic effects of HR+/HER2- breast cancer patients. The nomogram established according to the CDScore and clinical features performed satisfactory in predicting patient overall survival. Our results not only helped to better understand the underlying mechanisms of RCD in HR+/HER2- breast cancer, but also validated a novel predicting system, which provide potential therapeutic targets and might help to guide the clinical strategies in the management of HR+/HER2- breast cancer patients in the future.

Materials and methods

Data acquisition and procession

Publicly available datasets of breast cancer patients who fulfilled the following criteria were included in this study: (1) pathologically diagnosed with breast cancer; (2) molecular subtypes were confirmed with estrogen or progesterone receptor positivity and HER2 negativity; (3) the complete prognostic data including overall survival data is available. Following the above criteria, the normalized and log2 transformed FPKM of 590 HR+/HER2- breast cancer patients together with the raw count data of 60 HR+/HER2- breast cancer tissues and 60 paired adjacent normal tissues in TCGA dataset were downloaded from the UCSC database (https://xena.ucsc.edu/). The Mutect2 somatic mutation data, copy number variation (CNV) information, and clinical features in TCGA cohort were also obtained from the UCSC database. The log2 transformed mRNA levels and clinical information of 956 HR+/HER2- breast cancer patients in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were downloaded from the cBioPortal online website (https://www.cbioportal.org/). We also acquired the log2 converted CHIP-Seq data and clinical information of 2449 HR+/HER2- breast cancer patients with the accession number of GSE96058 from GEO database (https://www.ncbi.nlm.nih.gov/geo/), and acquired the log2 converted chip-seq data of 87 HR+/HER2- breast cancer tissues and 87 paired adjacent normal tissues with the accession number of GSE70947. Single cell RNA sequencing data of 11 HR+ breast cancer was obtained from GEO database with the accession number of GSE176078. The detailed clinical information of the above datasets was summarized in Supplementary Table 1.

We collected RCD-related genes for the above mentioned 12 kinds of RCD patterns by integrating relevant literatures, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and manual selection. As a result, a total of 1880 RCD-related genes were obtained, which include 753 apoptosis genes, 614 autophagy-dependent cell death genes, 7 alkaliptosis genes, 27 cuproptosis genes, 15 entotic cell death genes, 487 ferroptosis genes, 220 lysosome-dependent cell death genes, 121 necroptosis genes, 8 netotic cell death genes, 5 oxeiptosis genes, 9 parthanatos genes, and 146 pyroptosis genes. The detailed RCD genes were listed in Supplementary Table 2.

Evaluation of the dysregulation and mutation profile of RCD genes

The “maftools” R package was used to evaluate the somatic mutation profile of RCD-related genes in HR+/HER2- breast cancer in TCGA cohort. “DESeq2” and “limma” packages were used to search differentially expressed RCD genes (DEGs) in HR+/HER2- breast cancer tissues compared with adjacent normal tissues in TCGA and GSE70947 cohorts, respectively. Genes with the adjusted p value < 0.05 and |log2FoldChange| > 1 were considered as statistically significant. GO and KEGG enrichment analyses were performed on the common DEGs in both TCGA and GSE70947 cohorts to excavate the potential biological functions and molecular pathways.

Construction of the CDScore model

To explore the prognostic value of RCD-related genes in HR+/HER2- breast cancer patients, univariate cox regression analysis was first performed. Genes with p value < 0.05 in univariate cox regression analysis were considered as survival associated genes and were further incorporated into the LASSO cox regression analysis. The “glmnet” R package was used to conduct the LASSO cox regression analysis and the “lambda. min” value was used to select the most suitable model. Finally, the CDScore was calculated for each patient by the formula: CDScore = ∑αi*Ai, where Ai indicated the expression of selected RCD genes and αi indicated the corresponding risk coefficient of each gene obtained from the LASSO cox regression analysis. The “pheatmap” was used to show the expression profiles of CDScore genes and the clinical features of CDScore signature. Gene set variation analysis (GSVA) was performed using hallmark gene set “h.all.v7.4.symbols.gmt” download from MSigDB datasets to identify the potential cancer hallmarks affected by CDScore. Kaplan–Meier curves and receiver operator characteristic (ROC) curves were plotted by “survival”, “survminer” and “survivalROC” package to evaluate the prognostic value of CDScore.

Immunohistochemistry (IHC) and scoring

The expression of two critical CDScore genes were further evaluated in tissue microarrays which contained a total of 77 breast cancer tissues. The clinical information of 77 breast cancer patients were summarized in Supplementary Table 3. Anti‐LEF1 (14972-1-AP, Proteintech) and anti-SFRP1 (26460-1-AP, Proteintech) antibodies were used according to the manufacturer's instructions. The expression scores of IHC were evaluated by two pathologists independently. The staining intensity was graded into 0 (negative), 1 (low), 2 (moderate), or 3 (high), while the proportion of staining cells was graded into 0 (negative), 1 (<10 %), 2 (10‐50 %), 3 (51‐80 %), or 4 (>80 %). IHC scores were generated by multiplying the intensity and proportion scores. For LEF1, IHC score < 6 was considered as low expression and IHC score ≥6 was considered as high expression. For SFRP1, IHC score < 3 was considered as low expression and IHC score ≥3 was considered as high expression.

Establishment of clustering pattern based on the expression of CDScore genes

To determine the clustering patterns and subtypes of HR+/HER2- breast cancer, unsupervised clustering analysis was conducted using “ConsensusClusterplus” package based on the expression levels of CDScore genes. ConsensusClusterPlus implements the Consensus Clustering algorithm to provide quantitative stability evidence for determining a cluster count and cluster membership in an unsupervised analysis [32]. This function takes a numerical data matrix of items as columns and rows as features and clustering the data into 2 to maxK clusters. The parameter of “maxK” is set as 6 to indicate 6 as the maximum cluster number. The parameter of “reps” is 1000 to indicate that it was repeated 1000 times to increase the stability of clusters.

Construction of the nomogram based on CDScore and clinical features

The CDScore together with clinical features (including age, ER and PR status, menopause status and TNM stage) was incorporated into the multivariate cox regression analysis to figure out the independent prognostic factors in HR+/HER2- breast cancer patients. Factors with p value < 0.05 were considered as independent prognostic factors and were used to construct the nomogram using the “rms” package. The calibration curve was plotted to evaluate the accuracy of the nomogram model. Kaplan–Meier curves and ROC curves were plotted to evaluate the prognostic value of the nomogram model.

Immune microenvironment and drug sensitivity analysis

Single-sample gene-set enrichment analysis (ssGSEA) was performed to calculate the proportion of infiltrated immune cells in HR+/HER2- breast cancer patients. The “Seurat” R package was used to excavate the cell clusters basing on single-cell RNA sequencing data. The “oncoPredict” R package was used to predict the drug sensitivity of more than 200 kinds of drugs in HR+/HER2- breast cancer patients. Spearman correlation analysis was conducted to evaluate the correlations between CDScore and infiltrated immune cells or between CDScore and drug sensitivities. All statistical analyses in this study were performed on RStudio 2023.03.1+446.

Results

The mutation and expression landscape of RCD genes in HR+/HER2- breast cancer

We first evaluated the mutation landscape of RCD-related genes in HR+/HER2- breast cancer in TCGA cohort. As a result, there were about 74.67 % of HR+/HER2- breast cancer patients had somatic mutations in RCD genes and the top 20 mutated genes were shown in Fig. 1A, with PIK3CA possessing the highest mutation rate of 38 % followed by TP53, CDH1 and GATA3, which showed a mutation rate of 17 %, 15 % and 13 %, respectively (Fig. 1A). Genetic interaction analysis found that most of the mutations of RCD genes are co-occurrence with other mutations except for the mutations of GATA3 and CDH1, which were mutually exclusive with the mutations of PIK3CA and TP53, respectively (Fig. 1B). The mutation of PRKDC co-occurred the most with gene mutations in BIRC6, DYNC1H1, VPS13C and PIK3CA (Fig. 1B), which underlined the interactions of RCD genes between each other. The CNV status of RCD genes was also explored in TCGA cohort and the top 20 gain of CNV and loss of CNV genes were displayed in Fig. 1C and Fig. 1D, respectively. The results showed widespread copy number variations of RCD genes in HR+/HER2- breast cancer patients, with CCND1 possessing the highest frequency of copy number gain while NNMT possessing the highest frequency of copy number loss (Fig. 1C-D).Fig. 1 The mutation and expression landscape of RCD genes in HR+/HER2- breast cancer. A. The mutation landscape of top 20 mutated RCD genes in HR+/HER2- breast cancer in TCGA cohort. B. The interactions of gene mutations in TCGA cohort. C. The RCD genes of top 20 gain of CNV in HR+/HER2- breast cancer in TCGA cohort, with the red dot indicating the frequencies of gain of CNV while green dot indicating the frequencies of loss of CNV. D. The RCD genes of top 20 loss of CNV in HR+/HER2- breast cancer in TCGA cohort, with the red dot indicating the frequencies of gain of CNV while green dot indicating the frequencies of loss of CNV. E. The volcano plot and heatmap of differentially expressed RCD genes in HR+/HER2- breast cancer in TCGA cohort. F. The volcano plot and heatmap of differentially expressed RCD genes in GSE70947 cohort. G. The Venn diagram of common upregulated and downregulated RCD genes in both TCGA and GSE70947 cohort. H. GO enrichment results basing on the common dysregulated RCD genes. I. KEGG enrichment results basing on the common dysregulated RCD genes (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

Fig 1

Next, we conducted differential expression analysis in both TCGA and GSE70947 cohort to figure out the dysregulations of RCD-related genes, which contained 60 and 87 paired HR+/HER2- breast cancer and adjacent normal tissues respectively. As a result, a total of 331 and 203 dysregulated RCD genes were identified in TCGA and GSE70947 cohort, respectively, and the differentially expressed genes were listed in Supplementary Tables 4-5. The volcano plot and heatmap of differentially expressed genes were shown in Fig. 1E-F. Altogether, there were 53 upregulated genes and 88 downregulated genes commonly identified in both TCGA and GSE70947 cohort (Fig. 1G). GO and KEGG enrichment analysis revealed that multiple cancer-related pathways were affected by the common dysregulated RCD genes, including TNF and TGF-beta signaling pathways, signaling pathways regulating pluripotency of stem cells, PPAR, PI3K-AKT and MAPK signaling pathways and so on (Fig. 1H-I). Besides, endocrine resistance and EGFR tyrosine kinase inhibitor resistance were also enriched by the dysregulated RCD genes (Fig. 1I), which suggesting that RCD genes might participate into drug resistance in HR+/HER- breast cancer patients.

Construction of the prognostic CDScore signatures in HR+/HER2- breast cancer

To construct the prognostic CDScore signatures based on survival associated RCD genes, we collected the gene expression and survival information of 590 HR+/HER- breast cancer patients in TCGA cohort as the training dataset to perform univariate cox regression followed by LASSO cox regression analysis. Univariate cox regression analysis based on the above 141 differentially expressed RCD genes identified a total of 13 survival associated genes, among which STXBP1 and SLC7A11 predicted poor overall survival with HR > 1, while the other 11 genes predicted better overall survival with HR < 1 (Fig. 2A). Then, the 13 survival associated genes were further screened by LASSO cox regression analysis and 6 critical RCD genes were finally screened out to construct the prognostic CDScore signature (Fig. 2B-C). Based on the coefficients obtained from LASSO cox regression analysis, the CDScore was calculated according to the formula: CDScore = (-0.1308*LEF1) + (0.1543* SLC7A11) + (–0.0889*SFRP1) + (–0.0972*IGFBP6) + (-0.1669*CXCL2) + (0.3198*STXBP1) (Supplementary Table 6). Among the above 6 CDScore genes, SFRP1 and IGFBP6 were regulators of apoptosis, SLC7A11 and CXCL2 were regulators of ferroptosis, STXBP1 was a regulator of lysosome dependent cell death, while LEF1 played a role in both necroptosis and apoptosis. The detailed expression review of 6 CDScore genes and the clinical correlations of CDScore in TCGA cohort were shown in Fig. 2D. We found that the CDScore in HR+/HER- breast cancer patients with elder age, post-menopause or dead status was higher than those with younger age, per-menopause or alive status (Fig. 2E). The CDScore was also associated with TNM stage, the CDScore in patients with later stages was higher than those with earlier stages (Fig. 2E). Further exploration of the expressions of 6 CDScore genes revealed that the expressions of LEF1 and SLC7A11 were upregulated while the expressions of SFRP1, IGFBP6, CXCL2 and STXBP1 were downregulated in HR+/HER- breast cancer compared with adjacent normal tissues (Fig. 2F-G). Kaplan–Meier curves of 6 CDScore genes in HR+/HER- breast cancer patients were also displayed in Supplementary Fig. 1.Fig. 2 Construction of the CDScore signatures in HR+/HER2- breast cancer. A. The forest plot of hazard ratios of survival associated RCD genes in the univariate cox regression analysis. B. Process of RCD gene selection in LASSO cox regression model. C. Partial likelihood deviance in every lambda of LASSO model. D. The heatmap of CDScore genes and clinical features in HR+/HER2- breast cancer in TCGA cohort. E. The CDScore in different clinical groups of HR+/HER2- breast cancer patients. F-G. The expression levels of CDScore genes in HR+/HER2- breast cancer and adjacent normal tissues in TCGA (F) and GSE70947 (G) cohort.

Fig 2

Next, we further validated the expressions and prognostic value of CDScore genes in our own tissues. We chose two critical CDScore genes, LEF1 and SFRP1, to perform IHC in our tissue microarrays which contained a total of 77 breast cancer tissues. The representative IHC images of LEF1 and SFRP1 were shown in Supplementary Fig. 2A and the number of patients with high or low expression of LEF1 and SFRP1 in different subgroups was summarized in Supplementary Table 3. As a result, there were more breast cancer patients showed high expression of LEF1 and low expression of SFRP1 in N1-N3 or III-IV stages compared with N0 or I-II stages (Supplementary Fig. 2B-2E), which implied that the expressions of LEF1 and SFRP1 were correlated with lymph node metastasis and TNM stage. However, the expressions of both LEF1 and SFRP1 in different molecular subtypes were not significantly different (Supplementary Fig. 2F-2G). Survival analysis showed that in the HR+/HER2- molecular subtype, patients with high expression of LEF1 and SFRP1 showed better overall survival than those with low expression of LEF1 and SFRP1 (Supplementary Fig. 2H-2I), although the p value was not statistically significant, which might due to the small sample size. The prognostic value of LEF1 and SFRP1 in HR+/HER2- breast cancer need to be further validated in more patients.

Prognostic value of CDScore signatures in HR+/HER2- breast cancer

We evaluated the prognostic value of CDScore in both the training cohort and two external validation cohorts. The survival rates were obviously lower in HR+/HER- breast cancer patients with high CDScore than those with low CDScore in TCGA, METABRIC and GSE96058 cohorts (Fig. 3A-C). Kaplan–Meier curves revealed that the overall survival of patients in CDScore high group were significantly poorer than whom in CDScore low group (Fig. 3D-F). Moreover, the disease specific survival and recurrence free survival of patients in CDScore high group were also significantly poorer than whom in CDScore low group (Fig. 3G-H). The ROC curves showed that the CDScore performed well in predicting the overall survival, disease specific survival, and recurrence free survival in HR+/HER2- breast cancer patients (Fig. 3I).Fig. 3 The prognostic value of CDScore in HR+/HER2- breast cancer patients. A-C. The distributions of CDScore according to the survival status and overall survival years in TCGA (A), METABRIC (B) and GSE96058 (C) cohorts. D-F. Kaplan–Meier curves of the overall survival in low and high CDScore groups in TCGA (D), METABRIC (E) and GSE96058 (F) cohorts. G. Kaplan–Meier curves of the disease specific survival in low and high CDScore groups in TCGA cohort. H. Kaplan–Meier curves of the recurrence free survival in low and high CDScore groups in METABRIC cohort. I. ROC curves of CDScore in predicting the overall survival, disease specific survival and recurrence free survival in TCGA, METABRIC and GSE96058 cohorts.

Fig 3

To excavate the potential cancer-associated pathways affected by the CDScore, we performed GSVA analysis using hallmark gene set “h.all.v7.4.symbols.gmt” to compare cancer hallmarks in different CDScore groups. As a result, most of the cancer hallmarks were affected by the CDScore in both the training and validation cohorts, with G2M checkpoint, E2F targets, PI3K-ATK-mTOR pathway, glycolysis, MYC targets, DNA repair and oxidative phosphorylation pathways were enriched in CDScore high group, while hypoxia, epithelial mesenchymal transition, angiogenesis, p53 pathway, apoptosis, inflammatory response and interferon response were enriched in CDScore low group (Supplementary Fig. 3A-3C). All above implied that RCD genes in CDScore are likely to involve in various cancer hallmark pathways to affect the prognosis of HR+/HER2- breast cancer patients.

Clustering subtypes of HR+/HER2- breast cancer basing on CDScore genes

To identify the potential subtypes defined by CDScore genes in HR+/HER2- breast cancer, we performed unsupervised clustering analysis of the expression levels of CDScore genes in TCGA, METABRIC and GSE96058 cohorts. As shown in Fig. 4A-B, all patients in the above three cohorts were clearly divided into two clusters. Kaplan–Meier curves showed that the overall survival of patients in two clusters was significantly different with a longer overall survival in the Cluster 1 patients comparing to those Cluster 2 patients (Fig. 4C). Further evaluation of the distributions of patients in different clusters and different CDScore groups found that most of the patients in cluster 1 belonged to low CDScore group while most of the patients in cluster 2 belonged to high CDScore group (Fig. 4D), which verified that the prognosis of patients was better in low CDScore group than in high CDScore group (Fig. 3D-F).Fig. 4 Clustering patterns of HR+/HER2- breast cancer basing on CDScore genes. A. All HR+/HER2- breast cancer patients in TCGA, METABRIC and GSE96058 cohorts were clearly divided into two clusters basing on the expression levels of CDScore genes. B. Cumulative distribution function (CDF) plots of each k value in the unsupervised clustering analysis in the above three cohorts. C. Kaplan–Meier curves of the overall survival of patients in two clusters in the three cohorts. D. The alluvial diagrams showed the correlations among different clusters, patient survival status and CDScore groups in the three cohorts.

Fig 4

Construction of the nomogram model for HR+/HER2- breast cancer

To identify whether the CDScore was an independent prognostic factor in HR+/HER2- breast cancer, CDScore and critical clinical features were incorporated to conduct multivariate cox regression analysis. As expected, the results showed that CDScore was an independent prognostic factor in HR+/HER2- breast cancer in TCGA (HR = 3.02, p < 0.0001, Fig. 5A), METABRIC (HR = 1.63, p = 0.0144, Fig. 5B) and GSE96058 (HR = 1.40, p = 0.0018, Fig. 5C) cohorts. Considering that patient age and stage were also independent prognostic factors in the three cohorts except for GSE96058 which lacks the stage information (Fig. 5A-C), we constructed the nomogram model based on CDScore, patient age and stage information to predict the 1-year, 3-year and 5-year overall survival of HR+/HER2- breast cancer patients in TCGA cohort (Fig. 5D). The calibration curve showed satisfactory accuracies of the nomogram model in predicting 1-year, 3-year and 5-year overall survival rates (Fig. 5E). The overall survival of patients in low and high nomogram score groups was significantly different, where patients in high nomogram score group having a poorer prognosis than whom in low score group (Fig. 5F). The area under curve (AUC) of ROC in the nomogram model for predicting 1-year, 3-year and 5-year overall survival rates in TCGA cohort was 0.89, 0.82 and 0.8, respectively (Fig. 5G). Besides, we used the METABRIC cohort as an external validation dataset to verify the prognostic value of the nomogram model. Similar to the results in TCGA cohort, the overall survival of patients in high nomogram score group was significantly poorer comparing to those in low nomogram score group in METABRIC cohort (Fig. 5H). The ROC curves also showed that the nomogram model performed well in predicting the overall survival in HR+/HER2- breast cancer patients in METABRIC cohort (Fig. 5I).Fig. 5 Construction of the nomogram model in HR+/HER2- breast cancer. A-C. The forest plot of HR of CDScore and clinical features in the multivariate cox regression analysis in TCGA (A), METABRIC (B) and GSE96058 (C) cohorts. D. The nomogram model constructed basing on CDScore, age and stage in TCGA cohort. E. The calibration plot of nomogram model in predicting 1-year, 3-year and 5-year overall survival rates in TCGA cohort. F. Kaplan–Meier curves of the overall survival in low and high nomogram score groups in TCGA cohort. G. ROC curves of the nomogram model in predicting 1-year, 3-year and 5-year overall survival rates in TCGA cohort. H. Kaplan–Meier curves of the overall survival in low and high nomogram score groups in METABRIC cohort. I. ROC curves of the nomogram model in predicting 1-year, 3-year and 5-year overall survival rates in METABRIC cohort.

Fig 5

Tumor microenvironment affected by CDScore signature

To explore the tumor microenvironment, ssGSEA was performed to evaluate the infiltrated immune cells in HR+/HER2- breast cancer in TCGA cohort. Spearman correlation analysis was conduct to determine the correlations between infiltrated immune cells and CDScore signatures. As shown in Fig. 6A, most of the immune cells were negatively correlated with the CDScore but positively correlated with most of the CDScore genes except for gene STXBP1. Further excavation of the subtypes of immune cells found that, compared with CDScore high group, there were more immune killer cells infiltrated in CDScore low group, including activated B cells, activated CD8 T cells, CD56 bright natural killer cells, central memory CD4 T cells, effector memory CD8 T cells, mast cells, natural killer T cells and type 1 T helper cells (Fig. 6B). Meanwhile, the expression levels of immune check points (including PD-1, PD-L1, CTLA4 and other immune check points) were also higher in CDScore low group compared with that in CDScore high group (Fig. 6C). To confirm the above results, we performed the same analysis in METABRIC and GSE96058 cohorts and found similar results in both validation databases (Supplementary Fig. 4A-4F). These results underlined the potential efficiency of immunotherapy in CDScore low group of HR+/HER2- breast cancer patients.Fig. 6 Tumor microenvironment affected by CDScore signature. A. The correlations between infiltrated immune cells and CDScore signatures in TCGA cohort, with the right-side bar indicating the correlation coefficient. B. The boxplot of the proportions of infiltrated immune cells in CDScore low and high group in TCGA cohort. C. The relative expression levels of immune checkpoints in CDScore low and high group in TCGA cohort. D. The distributions of all major cell subtypes in ER+ breast cancer patients basing on single-cell RNA sequencing data. E. The doughnut plot showed the proportions of each major cell subtypes annotated by Seurat algorithm. F. The proportions of each major cell subtype in each ER+ breast cancer patient. G. The levels of CDScore in each major cell subtype in ER+ breast cancer.

Fig 6

Next, we further excavated the CDScore distributions in HR+/HER2- breast cancer from single-cell RNA sequencing data (GSE176078). As shown in Fig. 6D, 9 major subtypes of cells were annotated including cancer epithelial cells, normal epithelial cells, endothelial cells, cancer-associated fibroblasts (CAFs), perivascular-like cells (PVLs), plasmablasts, myeloid cells, T cells and B cells. Cancer epithelial cells accounted for the most part of cells followed by infiltrated T cells, while B cells were the least detected cells (Fig. 6E), Fig. 6F showed the detailed proportions of cells in each patient. Then, we evaluated the distributions of CDScore in each cell types and found that, the CDSocre was higher in Cancer epithelial cells and T cells while lower in normal epithelial cells and CAFs (Fig. 6G).

Potential of CDScore signature in predicting drug sensitivities

Nowadays, the chemotherapy, endocrine therapy and target therapy were still the standard strategies in treating HR+/HER2- breast cancer, thus, we further calculated the half maximal inhibitory concentration (IC50) of clinical drugs by “oncoPredict” to investigate the potential value of CDScore in predicting drug response. The results showed that CDScore was significantly positively correlated with the response of most of clinical drugs except for doramapimod and fulvestrant (Fig. 7A). Concentration on the classic chemotherapy drugs used in HR+/HER2- breast cancer, we found that the IC50 of docetaxel, epirubicin, gemcitabine and vinorelbine are significantly higher in CDScore high group compared with that in CDScore low group (Fig. 7B). As for the targeted drugs, the IC50 of palbociclib and niraparib are also significantly higher in CDScore high group compared with that in CDScore low group (Fig. 7C). These results implied that HR+/HER2- breast cancer patients in CDScore high group might be resistant to standard chemotherapy and target therapy.Fig. 7 Potential of CDScore signature in predicting drug sensitivities. A. The correlations between the IC50 of drugs and CDScore signatures, with the right-side bar indicating the correlation coefficient. B. The boxplot of the IC50 of classic chemotherapy drugs in CDScore low and high group and their correlations with CDScore. C. The boxplot of the IC50 of targeted drugs in CDScore low and high group and their correlations with CDScore.

Fig 7

Discussion

HR+/HER2- breast cancer is the most common molecular subtype of breast cancer. Although most HR+/HER2- breast cancer patients obtained a satisfactory prognosis, some patients still suffered drug resistance, disease progression and recurrence which ultimately cause the death. Therefore, there is still an urgent need to excavate improved predictive models and potential targets to improve the prognosis of HR+/HER2- breast cancer patients. In our study, we focused on 12 subtypes of RCD patterns to excavate their underlying effects in HR+/HER2- breast cancer for the first time. We comprehensively profiled the mutations and dysregulations of 12 subtypes of RCD-related genes and constructed a novel survival-associated CDScore basing on 6 critical RCD genes. The nomogram was also established based on CDScore and clinical features to predict the prognosis of HR+/HER2- breast cancer patients. Moreover, the tumor microenvironment and drug sensitivities were also found to be closely correlated with the CDScore, which implied the potential value of CDScore in guiding the clinical decisions in HR+/HER2- breast cancer patients.

RCD is a process of regulated cell death which was mediated by various modulators and molecular signaling pathways. It is well known that RCD has been widely confirmed to involve in tumor development and progression and considered as the potential target for exploring novel therapeutic strategies [11]. In our study, we identified the widespread mutations and dysregulations of RCD-related genes in HR+/HER2- breast cancer. Most of the cancer hallmark pathways including TNF and TGF-beta signaling pathways, signaling pathways regulating pluripotency of stem cells, PPAR, PI3K-AKT and MAPK signaling pathways, endocrine resistance and EGFR tyrosine kinase inhibitor resistance were revealed to be affected by the dysregulated RCD genes, which highlighted the fundamental roles of RCD patterns in HR+/HER2- breast cancer. The novel CDScore signature was created based on 6 critical survival-associated RCD genes including SFRP1, IGFBP6, SLC7A11, CXCL2, STXBP1 and LEF1. SFRP1, as an extracellular protein, is revealed to be involved in several tumor processes including apoptosis by modulating the canonical WNT pathway [33]. The inhibition of SFRP1 was found to promote breast cancer metastasis via activating the WNT pathway [34]. The expression of SFRP1 decreased during breast cancer progression and the lack of SFRP1 was determined to promote the aggressiveness of early breast cancer [35]. Consistent with the literatures above, in our study, we found that the expression level of SFRP1 in HR+/HER2- breast cancer is significantly lower than that in adjacent normal tissues, and the lower expression of SFRP1 predicted poor overall survival of HR+/HER2- breast cancer patients. IGFBP6 functioned mainly through inhibiting IGF-II actions and was an inducer of cell apoptosis, the downregulation of IGFBP6 was found in many malignancies [36]. In HR+/HER2- breast cancer, we also found that IGFBP6 was downregulated and was a favorable prognostic factor in predicting the prognosis of breast cancer patients. SLC7A11 functioned as a cystine/glutamate antiporter to contribute to the biosynthesis of glutathione via mediating the import of cystine in exchange for the export of intracellular glutamate [37]. Glutathione could suppress the ferroptosis of cells via reducing lipid hydroperoxides, which were the inducer of ferroptosis. Diverse researches have revealed that the overexpression of SLC7A11 promoted tumor growth via suppressing ferroptosis and SLC7A11 has emerged as a promising drug target in cancer therapy [38]. The overexpression of SLC7A11 and its oncogenic role via inhibiting ferroptosis have been reported in breast cancer [39]. Our study also validated the overexpression of SLC7A11 in HR+/HER2- breast cancer. Besides, we also found that the upregulation of SLC7A11 predicted poor survival of HR+/HER2- breast cancer patients. CXCL2 has also been regarded as a ferroptosis-associated gene, the use of ferroptosis inhibitor could inhibit the expression of CXCL2 while the ferroptosis inducer could increase its expression [40]. Although the mechanisms of CXCL2 in ferroptosis have not been fully understood, the dysregulations and prognostic value of CXCL2 as a ferroptosis modulator have been reported in multiple malignancies [41,42]. The downregulation of CXCL2 and its positive correlation with patient survival in HR+/HER2- breast cancer patients have been found in our study, but the detailed role of CXCL2 in breast cancer remains to be explored. STXBP1 participated into the process of exocytosis by interacting with GTP-binding proteins and was a regulator of lysosome-dependent cell death [43]. The effects of STXBP1 in malignancies were rarely explored previously. We revealed that the expression of STXBP1 was reduced and the decreased STXBP1 was correlated with better prognosis in HR+/HER2- breast cancer. The specific function of STXBP1 in breast cancer needs to be determined in the future. LEF1, one of the TCF/LEF transcription factors activated by WNT signaling pathway, has been revealed to suppress cell apoptosis and could be an attractive drug target in many tumors [44]. Besides, LEF1 has also been considered as a necroptosis-related gene and has been identified to be closely correlated with patient prognosis in several cancers including breast cancer [45]. In this study, we also verified the increased expression of LEF1 and found that low expression of LEF1 indicated poor survival in HR+/HER2- breast cancer. LEF1 has also been reported to mediated docetaxel resistance in breast cancer, inhibition of LEF1 could restore the sensitivity of cells to docetaxel [46]. The functions of LEF1 in breast cancer were still rarely studied and need to be further excavated.

The progression and recurrence of malignancies usually resulted from a minority of survived tumor cells which escaped from the surveillance of immune system and the cytotoxicity of therapeutic drugs [47]. Therefore, we explored the correlations between the constructed CDScore signature and tumor microenvironment or drug resistance in HR+/HER2- breast cancer. Surprisingly, we found that the CDScore was negatively correlated with most of the immune killer cells, indicating that the immune microenvironment of HR+/HER2- breast cancer with low CDScore showed an immune-inflamed phenotype which might respond better to immune checkpoint inhibitors [48]. Meanwhile, the upregulated expressions of several immune checkpoints in CDScore low group also implied that HR+/HER2- breast cancer patients with low CDScore might response better to immune checkpoint inhibitors. Nowadays, most clinical researches of immunotherapy were conducted in triple-negative breast cancer because of its specific immune microenvironment and the lack of effective therapeutic strategies, and several immune checkpoint inhibitors have been approved to be used in treating triple-negative breast cancer in combination with chemotherapy or targeted therapy [49,50]. Our results provided a novel horizon for the researches of immunotherapy in HR+/HER2- breast cancer in the future. Chemotherapy, endocrine therapy and targeted therapy were three sharp swords in treating HR+/HER2- breast cancer [3]. By analyzing the correlations of CDScore signature and IC50 of therapeutic drugs, we found that CDScore was positively correlated with the IC50 of several common drugs including docetaxel, epirubicin, gemcitabine, vinorelbine, palbociclib and niraparib, while was negatively correlated with the IC50 of fulvestrant, which indicated that the CDScore genes might mediated drug resistance in HR+/HER2- breast cancer. Our results also provide clues for the decision-making in choosing therapeutic strategies for HR+/HER2- breast cancer patients.

Although our study comprehensively profiled the landscape of 12 types of RCD patterns in HR+/HER2- breast cancer and constructed a novel prognostic CDScore model which showed excellent performance in both training and validation datasets, there are still some limitations. Firstly, the CDScore and nomogram model were constructed and validated in publicly available datasets, there is a lack of multicenter and large-scale clinical trials to verify the clinical utility of our model. Secondly, we just chose two critical RCD genes (LEF1, SFRP1) to conduct IHC validation in our own tissue microarrays, the expressions and prognostic value of other RCD genes still need to be validated in a larger cohort. Lastly, the biological functions and potential pathways affected by RCD genes such as LEF1 and SFRP1 have not been investigated in cell and animal experiments. We will continue to excavate the important roles of RCD genes and further verify the performance of our model in lager samples in our future studies.

Conclusions

In summary, our study was the first to evaluate the profile of 12 subtypes of RCD patterns in HR+/HER2- breast cancer. We not only summarized the dysregulations of RCD genes at the genomic and transcriptomic levels, but also validated the prognostic value of two critical genes in our own tissue microarrays. A novel prognostic RCD scoring system (CDScore) based on six RCD genes (LEF1, SLC7A11, SFRP1, IGFBP6, CXCL2, STXBP1) was constructed to predict the prognosis of HR+/HER2- breast cancer patients and was revealed to be closely correlated with tumor microenvironment and drug response. We also constructed a nomogram model based on CDScore and clinical features, which showed potential value in predicting the overall survival of HR+/HER2- breast cancer patients. Our results provided novel biomarkers and therapeutic targets and might help to guide the clinical strategies in the management of HR+/HER2- breast cancer patients in the future.

Ethics approval and consent to participate

All procedures followed were in accordance with the ethical standards of the medical ethics committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study.

Availability of data and materials

The data that support the findings of this study are openly available in TCGA datasets (https://portal.gdc.cancer.gov/), METABRIC datasets (https://ega-archive.org/dacs/EGAC00001000484) and GEO datasets (https://www.ncbi.nlm.nih.gov/geo/). The original R scripts are available from the corresponding author upon reasonable request.

Funding

This work was supported by the National Natural Science Foundation of China (82303350).

CRediT authorship contribution statement

Shuangshuang Mao: Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Yuanyuan Zhao: Validation, Supervision, Software, Resources, Data curation. Huihua Xiong: Validation, Project administration, Methodology, Investigation, Data curation. Chen Gong: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, image 1

Image, image 2

Image, image 3

Image, image 4

Image, application 5

Image, application 6

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102117.
==== Refs
References

1 Bray F. Laversanne M. Sung H. Ferlay J. Siegel R.L. Soerjomataram I. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J. Clin. 74 3 2024 229 263 38572751
2 Thuc Nguyen T.M. Dinh Le R. Nguyen C.V. Breast cancer molecular subtype and relationship with clinicopathological profiles among Vietnamese women: a retrospective study Pathol. Res. Pract. 250 2023 154819
3 Burstein HJ. Systemic therapy for estrogen receptor-positive, HER2-negative breast cancer N. Engl. J. Med. 383 26 2020 2557 2570 33369357
4 Walsh E.M. Smith K.L. Stearns V. Management of hormone receptor-positive, HER2-negative early breast cancer Semin. Oncol. 47 4 2020 187 200 32546323
5 Miglietta F. Dieci M.V. Giarratano T. Torri V. Giuliano M. Zustovich F. Association of tumor-infiltrating lymphocytes with recurrence score in hormone receptor-positive/HER2-negative breast cancer: analysis of four prospective studies Eur. J. Cancer 195 2023 113399
6 Başaran G.A. Twelves C. Diéras V. Cortés J. Awada A. Ongoing unmet needs in treating estrogen receptor-positive/HER2-negative metastatic breast cancer Cancer Treat. Rev. 63 2018 144 155 29329006
7 Jin X. Jin W. Tong L. Zhao J. Zhang L. Lin N. Therapeutic strategies of targeting non-apoptotic regulated cell death (RCD) with small-molecule compounds in cancer Acta Pharm. Sin. B 14 7 2024 2815 2853 39027232
8 Fu B. Lou Y. Wu P. Lu X. Xu C. Emerging role of necroptosis, pyroptosis, and ferroptosis in breast cancer: new dawn for overcoming therapy resistance Neoplasia 55 2024 101017
9 Tang D. Kang R. Berghe T.V. Vandenabeele P. Kroemer G. The molecular machinery of regulated cell death Cell Res. 29 5 2019 347 364 30948788
10 D’Amico M. De Amicis F. Challenges of regulated cell death: implications for therapy resistance in cancer Cells 13 13 2024 1083 38994937
11 Peng F. Liao M. Qin R. Zhu S. Peng C. Fu L. Regulated cell death (RCD) in cancer: key pathways and targeted therapies Signal. Transduct. Target. Ther. 7 1 2022 286 35963853
12 Nagata S. Apoptosis and clearance of apoptotic cells Annu. Rev. Immunol. 36 2018 489 517 29400998
13 Wu N. Zheng W. Zhou Y. Tian Y. Tang M. Feng X. Autophagy in aging-related diseases and cancer: principles, regulatory mechanisms and therapeutic potential Ageing Res. Rev. 100 2024 102428
14 Ashrafizadeh M. Zhang W. Zou R. Sethi G. Klionsky D.J. Zhang X. A bioinformatics analysis, pre-clinical and clinical conception of autophagy in pancreatic cancer: complexity and simplicity in crosstalk Pharmacol. Res. 194 2023 106822
15 Yang Y. Liu L. Tian Y. Gu M. Wang Y. Ashrafizadeh M. Autophagy-driven regulation of cisplatin response in human cancers: exploring molecular and cell death dynamics Cancer Lett. 587 2024 216659
16 Qin Y. Ashrafizadeh M. Mongiardini V. Grimaldi B. Crea F. Rietdorf K. Autophagy and cancer drug resistance in dialogue: pre-clinical and clinical evidence Cancer Lett. 570 2023 216307
17 Chen F. Tang H. Lin J. Xiang L. Lu Y. Kang R. Macropinocytosis inhibits alkaliptosis in pancreatic cancer cells through fatty acid uptake Carcinogenesis 10 2024 45
18 Xie J. Yang Y. Gao Y. He J. Cuproptosis: mechanisms and links with cancers Mol. Cancer 22 1 2023 46 36882769
19 Overholtzer M. Mailleux A.A. Mouneimne G. Normand G. Schnitt S.J. King R.W. A nonapoptotic cell death process, entosis, that occurs by cell-in-cell invasion Cell 131 5 2007 966 979 18045538
20 Zhong F. Zhang X. Wang Z. Li X. Huang B. Kong G. The therapeutic and biomarker significance of ferroptosis in chronic myeloid leukemia Front. Immunol. 15 2024 1402669
21 Aits S. Jäättelä M. Lysosomal cell death at a glance J. Cell Sci. 126 Pt 9 2013 1905 1912 23720375
22 Wu Y. Yang J. Xu G. Chen X. Qu X. Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma BMC Genomics 25 1 2024 379 38632516
23 Pasparakis M. Vandenabeele P. Necroptosis and its role in inflammation Nature 517 7534 2015 311 320 25592536
24 Brinkmann V. Reichard U. Goosmann C. Fauler B. Uhlemann Y. Weiss D.S. Neutrophil extracellular traps kill bacteria Science 303 5663 2004 1532 1535 15001782
25 Holze C. Michaudel C. Mackowiak C. Haas D.A. Benda C. Hubel P. Oxeiptosis, a ROS-induced caspase-independent apoptosis-like cell-death pathway Nat. Immunol. 19 2 2018 130 140 29255269
26 Fatokun A.A. Dawson V.L. Dawson TM. Parthanatos: mitochondrial-linked mechanisms and therapeutic opportunities Br. J. Pharmacol. 171 8 2014 2000 2016 24684389
27 Yu P. Zhang X. Liu N. Tang L. Peng C. Chen X. Pyroptosis: mechanisms and diseases Signal. Transduct. Target. Ther. 6 1 2021 128 33776057
28 Koren E. Fuchs Y. Modes of regulated cell death in cancer Cancer Discov. 11 2 2021 245 265 33462123
29 Tong X. Tang R. Xiao M. Xu J. Wang W. Zhang B. Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research J. Hematol. Oncol. 15 1 2022 174 36482419
30 Su W. Hong T. Feng B. Yang Z. Lei G. A unique regulated cell death-related classification regarding prognosis and immune landscapes in non-small cell lung cancer Front. Immunol. 14 2023 1075848
31 Zou Y. Xie J. Zheng S. Liu W. Tang Y. Tian W. Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery Int. J. Surg. 107 2022 106936
32 Wilkerson M.D. Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking Bioinformatics 26 12 2010 1572 1573 20427518
33 Losada-García A. Salido-Guadarrama I. Cortes-Ramirez S.A. Cruz-Burgos M. Morales-Pacheco M. Vazquez-Santillan K. SFRP1 induces a stem cell phenotype in prostate cancer cells Front. Cell Dev. Biol. 11 2023 1096923
34 Ren L. Chen H. Song J. Chen X. Lin C. Zhang X. MiR-454-3p-mediated Wnt/β-catenin signaling antagonists suppression promotes breast cancer metastasis Theranostics 9 2 2019 449 465 30809286
35 Clemenceau A. Lacouture A. Bherer J. Ouellette G. Michaud A. Audet-Walsh É. Role of secreted frizzled-related protein 1 in early breast carcinogenesis and breast cancer aggressiveness Cancers. (Basel) 15 8 2023
36 Bach L.A. Fu P. Yang Z. Insulin-like growth factor-binding protein-6 and cancer Clin. Sci. (Lond.) 124 4 2013 215 229 23126425
37 Koppula P. Zhang Y. Zhuang L. Gan B. Amino acid transporter SLC7A11/xCT at the crossroads of regulating redox homeostasis and nutrient dependency of cancer Cancer Commun. (Lond) 38 1 2018 12 29764521
38 Koppula P. Zhuang L. Gan B. Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy Protein Cell 12 8 2021 599 620 33000412
39 Yadav P. Sharma P. Sundaram S. Venkatraman G. Bera A.K. Karunagaran D. SLC7A11/xCT is a target of miR-5096 and its restoration partially rescues miR-5096-mediated ferroptosis and anti-tumor effects in human breast cancer cells Cancer Lett. 522 2021 211 224 34571083
40 Jin R. Yang R. Cui C. Zhang H. Cai J. Geng B. Ferroptosis due to cystathionine γ lyase/hydrogen sulfide downregulation under high hydrostatic pressure exacerbates VSMC dysfunction Front. Cell Dev. Biol. 10 2022 829316
41 Pan B. Li Y. Xu Z. Miao Y. Yin H. Kong Y. Identifying a novel ferroptosis-related prognostic score for predicting prognosis in chronic lymphocytic leukemia Front. Immunol. 13 2022 962000
42 Wang H. Yang C. Jiang Y. Hu H. Fang J. Yang F. A novel ferroptosis-related gene signature for clinically predicting recurrence after hepatectomy of hepatocellular carcinoma patients Am. J. Cancer Res. 12 5 2022 1995 2011 35693077
43 Gulyás-Kovács A. de Wit H. Milosevic I. Kochubey O. Toonen R. Klingauf J. Munc18-1: sequential interactions with the fusion machinery stimulate vesicle docking and priming J. Neurosci. 27 32 2007 8676 8686 17687045
44 Zhang Z. Min L. Li H. Chen L. Zhao Y. Liu S. Asporin represses gastric cancer apoptosis via activating LEF1-mediated gene transcription independent of β-catenin Oncogene 40 27 2021 4552 4566 34127813
45 Hu T. Zhao X. Zhao Y. Cheng J. Xiong J. Lu C. Identification and verification of necroptosis-related gene signature and associated regulatory axis in breast cancer Front. Genet. 13 2022 842218
46 Prieto-Vila M. Shimomura I. Kogure A. Usuba W. Takahashi R.U. Ochiya T. Quercetin inhibits lef1 and resensitizes docetaxel-resistant breast cancer cells Molecules 25 11 2020
47 Wu Q. You L. Nepovimova E. Heger Z. Wu W. Kuca K. Hypoxia-inducible factors: master regulators of hypoxic tumor immune escape J. Hematol. Oncol. 15 1 2022 77 35659268
48 Gerard C.L. Delyon J. Wicky A. Homicsko K. Cuendet M.A. Michielin O. Turning tumors from cold to inflamed to improve immunotherapy response Cancer Treat. Rev. 101 2021 102227
49 Keenan T.E. Tolaney SM. Role of immunotherapy in triple-negative breast cancer J. Natl. Compr. Canc. Netw. 18 4 2020 479 489 32259782
50 Syrnioti A. Petousis S. Newman L.A. Margioula-Siarkou C. Papamitsou T. Dinas K. Triple negative breast cancer: molecular subtype-specific immune landscapes with therapeutic implications Cancers. (Basel) 16 11 2024
