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. 2025 Nov 4;16:2021. doi: 10.1007/s12672-025-03884-3

Cuproptosis scoring system identifies novel molecular subtypes and modulates biological behavior in breast cancer

Rongyang Zou 1,#, Qiong Wu 1,#, Wanjun Zhao 2,✉
PMCID: PMC12586749  PMID: 41186622

Abstract

Background

Cuproptosis, a regulated mechanism of cell death mediated by copper, significantly influences tumor progression, prognosis, and antitumor immunity. Although cuproptosis-related genes (CRGs) exhibit therapeutic potential in oncology, their mechanistic role in the pathogenesis of breast cancer (BRCA) remains incompletely characterized.

Methods

Gene expression analysis was conducted utilizing UCSC-XENA data alongside GSE20685 from the Gene Expression Omnibus (GEO) database. The expression and mutation profiles of BRCA samples were examined using the R software package. Drawing from existing literature on CRGs, a total of 52 CRGs were identified for analysis. Patients were categorized into four distinct CRG subtypes through unsupervised consensus clustering. The MsigDB database facilitated the comparison of pathway differences among the four subtypes, while the distribution of samples across these subtypes and the variations in immune infiltration were also assessed. Genes exhibiting Cox p-values < 0.0001 were selected for further validation. Based on the identified genes, the CRG score (principal component 1 + principal component 2) was computed, and the relationships between classification, CRG score, and prognosis were analyzed. Furthermore, the GSCA database was employed to investigate the expression, mutation, copy number variation, and methylation differences between high and low CRG score groups, alongside their correlations with drug sensitivity and clinical characteristics.

Results

In total, 25 CRGs in BRCA were enrolled to identify four distinct subtypes. The expression levels of these CRGs varied among the four subtypes, further elucidating the relationship between clinical characteristics (such as type, stage, TNM classification, metastasis, age, full state and full time) and gene expression. Functional and multi-omic analyses of immune infiltration demonstrated significant variability in immune cell infiltration across various subtypes, including B cells and myeloid-derived suppressor cells (MDSCs). Subsequently, principal component analysis (PCA) classified BRCA patients into high and low scoring groups. The analysis revealed that patients in the high-scoring group experienced significantly improved long-term clinical outcomes compared to those in the low-scoring group. Furthermore, survival status, M stage, and recurrence of metastasis indicated that deceased patients, those in M1 stage, and metastatic patients exhibited lower scores. The expression of immune checkpoints in the high CRGs score group was found to be low, alongside a low tumor mutation burden (TMB) and microsatellite instability (MSI), with few gene mutations observed. This suggests that individuals in the low CRGs group may have a greater opportunity to benefit from immunotherapy.

Conclusion

Through our comprehensive analysis of CRGs, we have elucidated their extensive regulatory effects on immune response, clinical features, and prognosis. The cuproptosis subtype effectively differentiates various clinical and molecular characteristics of BRCA. Additionally, the cuproptosis score emerges as a promising biomarker for predicting BRCA prognosis and assessing the benefits of immunotherapy and chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03884-3.

Keywords: Cuproptosis, Breast cancer, Subtypes, Immunotherapy, ICI treatment

Introduction

Breast cancer (BRCA) is the most prevalent cancer among female patients, significantly impacting women’s health worldwide. Statistics indicate that 1.1 out of every 200,000 women die from BRCA annually [1]. Due to its high invasiveness, heterogeneity, and recurrence, despite advancements in chemotherapy and targeted therapies that have improved prognosis, some patients continue to be unable to benefit from these treatments. This situation warrants further in-depth research.

Copper is the second most essential trace element in the human body, significantly influencing the development and function of blood, the central nervous system, the immune system, hair, skin, skeletal tissues, and internal organs such as the brain, liver, and heart. It is involved in numerous biological processes, including the activation of hemoglobin, promotion of iron absorption, and formation of phospholipids and nervous tissue. An imbalance in copper homeostasis can lead to Wilson’s disease and Menkes disease [2]. A substantial body of research has confirmed the presence of copper in tumor tissues and serum across various cancers, demonstrating its close association with tumor occurrence, invasion, and metastasis, including in BRCA-related cancers, gastric cancer, bladder cancer, and head and neck squamous carcinoma [3–7]. A substantial body of research has confirmed the presence of copper in tumor tissues and serum across various cancers, demonstrating its close association with tumor occurrence, invasion, and metastasis, including in BRCA-related cancers, gastric cancer, bladder cancer, and head and neck squamous carcinoma [8]. Clinical trials have shown that cancer metastasis can be inhibited through copper consumption or the disruption of copper homeostasis [9–11]. Furthermore, copper complexes can mediate mitochondrial damage and oxidative stress via reactive oxygen species (ROS) production, inducing apoptosis in HeLa cells [12]. Cobine PA et al. discovered that copper directly interacts with the fatty acylation component of the tricarboxylic acid (TCA) cycle, resulting in the aggregation of fatty proteins and subsequent loss of iron-sulfur cluster proteins, which leads to proteotoxic stress and ultimately cell death. This mechanism is distinct from the known modes of controlled cell death, such as apoptosis, necroptosis, and ferroptosis, and is therefore termed ‘copper death‘ [13].

Recent studies have revealed the significant potential of cuproptosis-related genes in predicting prognosis and immunotherapy sensitivity across various tumor types. Researchers have constructed a cuproptosis-related prognostic model specifically for pancreatic cancer, demonstrating that patients classified in the high-risk group exhibited poorer outcomes (P = 0.00557). This risk model achieved an AUC value significantly exceeding 0.7, indicating enhanced accuracy in predicting the prognosis of pancreatic cancer patients. Furthermore, significant positive correlations were observed between the mRNA expression levels of several cuproptosis genes (including DLAT, DLD, GLS, LIPT1, MTF1, and PDHB) and the infiltration levels of B cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells [14]. Similar phenomena and consistent conclusions have also been observed in other solid tumors, including hepatocellular carcinoma, bladder cancer, gastric cancer, and glioma [15–18]. Guo D et al. demonstrated that the downregulation of IAPP mediates copper accumulation, which enhances the viability and function of CD8 + T cells through the NF-κB pathway, thereby exerting an anti-breast cancer effect [19]. Current research on cuproptosis genes and tumor immunotherapy predominantly employs methods such as the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to assess the responsiveness to immunotherapy. Notably, no studies have yet analyzed the association between the differential expression of cuproptosis-related genes and clinicopathological characteristics, as well as prognosis, by collecting local clinical data. While CRGs demonstrate prognostic utility across various malignancies, their role in BRCA remains undefined. This study investigates the CRG-mediated regulation of BRCA biology.

Materials and methods

Data sources and preprocessing

All the data utilized in this study is publicly accessible, primarily sourced from UCSC Xena (https://xenabrowser.net/datapages/) and the GSE20685 dataset downloaded from the Gene Expression Omnibus (GEO) database. Two manuscripts related to cuproptosis were merged and deduplicated, resulting in a dataset comprising 52 CRGs [20, 21]. We performed dataset consolidation and removed batch effects using the R packages ‘limma’ and ‘sva’. The GSE20685 dataset contains 327 tumor samples, while TCGA-BRCA comprises 1097 tumor samples and 113 normal samples. After merging, we obtained a total of 16,928 genes and 1,424 tumor samples.

Consensus clustering analysis

Univariate Cox regression analysis was performed to identify prognostic CRGs for BRCA. Based on the expression levels of these prognostic CRGs, consensus clustering analysis was conducted using the R software package “ConsensusClusterPlus” to delineate cuproptosis-related molecular subtypes. The Kaplan-Meier (KM) analysis was employed to assess the prognosis among the four identified groups. A heat map was utilized to illustrate the correlation between the clusters and CRGs, as well as clinicopathological features. Differences in clinicopathological characteristics, including age, race, tumor location, T, N, M, and tumor staging, were analyzed using the chi-square test. Furthermore, the relationship between subtypes, pathways, and immune infiltration was examined.

Survival analysis

The KM curve was utilized to compare the survival outcomes between high- and low-copper-related gene scoring groups, stratified by the risk score associated with copper-related genes. This analysis was conducted using the R packages “survival”, “survminer”, and “ggplot2” (R version 4.2.0). Genes were deemed statistically significant at the p < 0.05 threshold.

Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis

Using the GSVA R package, we conducted a Gene Set Variation Analysis (GSVA) to compare biological pathway differences among molecular subtypes. An adjusted p-value of less than 0.05 was employed as the criterion for determining statistically significant differences in pathway enrichment across the various subtypes, utilizing the ‘limma’ package. Additionally, the R package ‘clusterProfiler’ was utilized to perform GO and KEGG analyses.

Immunotherapy datasets

Three immunotherapeutic cohorts were utilized to validate the prediction of immunotherapy efficacy based on subtypes: ICB.Riaz2017_Nivolumab_Melanoma_Naïve (GSE91061); IMvigor210CoreBiologies, Urinary Epithelial Cancer, immunotherapy; and a comparison between nivolumab (anti-PD-1) and the mTOR inhibitor everolimus in the Checkmate study [22].

Development of a CRGs score prognostic system

We employed the Principal Component Analysis (PCA) algorithm to develop a scoring system, termed the CRGs Score, which is based on differentially expressed necroptosis regulators across the clusters in BRCA. This score is calculated using the formula: CRGs Score = ∑(PC1 + PC2). To assess immune scores, matrix scores, and their combined total, we utilized the ESTIMATE package, enabling a comparison of differences among various subtypes. Additionally, we applied the ssGSEA function from the GSVA package to compute the scores for immune cell infiltration, facilitating an analysis of the disparities in immune cell infiltration across different subtypes.

Chemotherapy response and small-molecule drugs

The response to chemotherapeutic drugs was predicted using the Genomics of Drug Sensitivity in Cancer (GDSC) database [23]. The Half Maximal Inhibitory Concentration (IC50) was calculated using the ‘pRRophetic’ package. The Connectivity Map (cMap) Database, which integrates biological applications related to disease, gene expression, and small-molecule drugs, can predict compounds that may induce or reverse tumor biological processes by comparing upregulated and downregulated genes across the four subtypes.

Statistical analysis

Data analysis was conducted using R version 4.2.0. The function removeBatchEffect was employed to adjust for batch effects in the data (Sup-Fig. 1). Comparative methods to assess differences between groups included Student’s t-test, Wilcoxon test, Kruskal-Wallis test, and Log-Rank test for survival analysis. Statistical significance was indicated by asterisks representing p-values (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).

Results

Expression and prognostic effects of CRGs in BRCA

Correlation analysis is looking at the correlation between gene expression. Cox regression analysis indicates that LIPT1, ATP7B, DAXX, MT1M, and MAP1LC3A are protective factors, while DLST, AOC1, DLAT, BACE1, and SLC31A1 are prognostic risk factors, with p < 0.05 (Fig. 1A and Supplementary Table 1). Both Cox regression and KM analyses revealed that the aforementioned genes were significantly associated with prognosis. Specifically, high expression levels of ATP7B, DAXX, LIPT1, MAP1LC3A, and MT1M correlate with a favorable prognosis (Fig. 1B). Conversely, elevated levels of BACE1, DLAT, DLST, and SLC31A1 are significantly associated with poor prognostic outcomes.

Fig. 1.

Fig. 1

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The interaction network and prognosis of CRGs. (A) Correlation analysis among cuproptosis-related genes, connection indicated a correlation between genes and p < 0.05 (sup Table 1). (B) Cox regression analysis and KM survival analysis simultaneously significant CRGs

Subtypes of CRGs have been identified in BRCA

An unsupervised cluster analysis was conducted on 25 CRGs (p < 0.05) derived from the KM survival analysis using ConsusClusterPlus. The CRGs were categorized into four distinct subtypes: subtype A (n = 371), subtype B (n = 367), subtype C (n = 230), and subtype D (n = 452) (Fig. 2A). KM curves (p = 0.002) indicated significant differences in survival outcomes among these subtypes, with subtypes C and D demonstrating a markedly better prognosis compared to subtypes A and B (Fig. 2B). The expression levels of CRGs varied among the four subtypes, further elucidating their relationship with clinical characteristics such as type, stage, TNM classification, metastasis, age, and overall survival status (Fig. 2C, D). Notably, MT1M and AQP1 exhibited significantly higher expression in subtype C, while MT1G was found to have elevated expression in subtype B. Interestingly, the MT4 gene did not show significant differential expression between subtypes C and D within the TCGA cohort, although it did demonstrate differential expression in the GEO-derived subtypes. Overall, no significant differences in the age at onset were observed across the four subtypes. The HALLMARK, KEGG, and Reactome pathways were individually downloaded from the Msigdb database, and pathway scoring was performed using the R package GSVA. A comparative analysis of pathway differences among the four subtypes revealed distinct enrichment results for the HALLMARK pathway (Fig. 3), KEGG pathway (Sup-Fig. 2), and Reactome pathway (Sup-Fig. 3). The R package pheatmap was utilized to generate heatmaps, which illustrated varying degrees of aggregation in pairwise comparisons among the four subtypes.

Fig. 2.

Fig. 2

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Average-linked and K-means-based unsupervised clustering is used to identify cuproptosis subtypes. (A) Based on the 25 CRGs obtained from the above KM survival analysis (p < 0.05), the consensus matrix defining four clusters (κ = 4). (B) Survival analysis of four subtypes. (C) Differences of 25 CRGs among four subtypes, (*p < 0.05, **p < 0.01,***p < 0.001 and ns was the abbreviation of no significance). (D) Clinical features suggested relationship between gene expression of CRGs and subtyping

Fig. 3.

Fig. 3

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Investigations of cuproptosis and cancer-related signaling pathways. Download the HALLMARK pathway, KEGG pathway, and Reactome pathway from the Msigdb database. Among the 4 types, there were significant differences in hallmark pathway, KEGG pathway (SUP Fig. 1), reactome pathway (Sup-Fig. 3)

Immune infiltration

PCA reveals the distribution of samples across different subtypes. Notable differences exist among the four subtypes regarding immune score, matrix score, and their combined total (Fig. 4A, B). Our research utilized the ssGSEA function from the R package GSVA to calculate the scores of immune cell infiltration, revealing significant variability in immune cell infiltration across the different subtypes (Fig. 4C). According to our findings, neutrophils were most significantly represented in Subtype A. In Subtype B, the most prominent immune cells included CD8 T cells, CD4 T cells, gamma delta T cells, immature B cells, myeloid-derived suppressor cells (MDSC), regulatory T cells, and type 2 T helper cells. Subtype C exhibited a high prevalence of activated B cells, eosinophils, mast cells, natural killer (NK) cells, follicular helper T cells, and type 1 T helper cells. Finally, CD56 dim natural killer cells were found to be most enriched in Subtype D.

Fig. 4.

Fig. 4

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Immune infiltration of four subtypes. (A) PCA demonstrated different genotyping sample distributions. (B) Immune fraction, stromal fraction, and the sum of the two were assessed to compare differences across four subtypes. (C) The fraction of immune cell infiltration was calculated to compare differences in immune cell infiltration between the different subtypes (ns p > 0.05, *p < 0.05,**p < 0.01,***p < 0.001)

Functional and multinomic analysis

Differences between groups were analyzed for the genes of the four subtypes, resulting in the identification of 530 differential genes through the union of these genes (Fig. 5A). The R package ‘clusterProfiler’ was employed to conduct GO and KEGG analyses on the 530 differential genes. The GO analysis indicated that these differential genes were associated with functions such as transmembrane signaling, extracellular matrix binding, and chromosome regulation. The KEGG analysis revealed that these differential genes were implicated in various pathways, including cytokine-cytokine receptor interaction, the PPAR signaling pathway, complement and coagulation cascades, fluid shear stress and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, and the cell cycle, among others (Fig. 5B, C).

Fig. 5.

Fig. 5

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To screen differentially expressed genes in breast cancer and analyze their functions. (A) The union of differential genes was taken to obtain 530 differential genes. (B) Enrichment analysis was performed on 530 differential genes; GO annotations, demonstrating pathways annotated in biological process (BP), cellular component (CC), and molecular function (MF), respectively, indicate that differential genes are associated with these functions. (C) The result of KEGG

CRGs subtypes’ diversity, PCA analysis and comprehensive analysis

In this study, we employed the differential genes identified previously to perform single-factor regression analysis, selecting genes with Cox p < 0.0001, resulting in the identification of 13 genes for subsequent analysis (Fig. 6A, Sup-Table 2). Based on the prognostic genes identified, we categorized 1,440 BRCA patients into different molecular genetic categories through consensus clustering analysis. This analysis revealed four distinct subtypes: A (n = 332), B (n = 326), C (n = 340), and D (n = 442), as indicated by κ = 4, suggesting that BRCA instances can be separated into four groups (Fig. 6B). According to the KM curve analysis, patients with genetic subtype D exhibited the highest overall survival (OS), while those in genetic cluster C had the lowest OS, with p < 0.001 (Fig. 6C). Consistent with the expected cuproptosis patterns, we observed significant variations in the expression of CRGs across the four gene subtype groups (Fig. 6D). Furthermore, we investigated the relationship between the four genetic subtypes and the clinical characteristics of BRCA patients, discovering that the pathological types of gene clusters A and D were predominantly luminal A and B (Fig. 6E). Subsequently, PCA analysis stratified BRCA patients into high and low scoring groups. This analysis revealed that patients in the high-scoring group demonstrated a significant advantage in long-term survival outcomes compared to those in the low-scoring group (Fig. 6F). A LASSO Cox regression model was developed to predict a CRGs score for each patient based on the differentially expressed genes (DEGs) associated with subtypes. The results illustrated the relationship between the two classifications: score and survival status (Fig. 6G). Statistically significant differences in CRGs scores were observed among patients with different subtypes. The immunoassay demonstrated a correlation between scores and immune cell infiltration, where red indicates a positive correlation, blue indicates a negative correlation, and darker colors represent stronger correlations (* p < 0.05, Fig. 6H). Furthermore, survival status, M stage, and recurrence metastasis indicated that deceased patients, as well as those with M1 and metastatic conditions, exhibited lower scores (Fig. 7A-C). Additionally, significant differences in scores were noted among different types of breast cancer (BRCA) (Fig. 7D).

Fig. 6.

Fig. 6

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Differentially expressed genes (DEGs) are used to identify gene subtypes. (A) Univariate regression analysis was performed using the differential genes screened above, 13 genes were selected with Cox pvalue < 0.0001. (B) These genes were grouped into four subtypes as most appropriate. (C) Survival analysis indicated differences in prognosis of four subtypes. (D) The comparison of the expression levels of CRGs in four subtypes (ns p > 0.05, ***p < 0.001). (E) The Heatmap demonstrated the relationship between four subtypes and clinical data. (F) Using principal component analysis PCA, score (principal component 1 + principal component 2) was calculated based on the 13 genes, and survival analysis was performed between high and low scores. (G) The correlation in cuproptosis-related subtypes, scoring, and prognostic status. (H) The correlation of the score with immune cell infiltration was calculated, with red representing a positive correlation and blue representing a negative correlation, the darker the color, the stronger the correlation, *p < 0.05

Fig. 7.

Fig. 7

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Relationship between survival status (A), M satge (B), Recurrence Metastasis (C), and BRCA types (D) and score

Association between tumor microenvironment (TME), tumor mutation burden (TMB) and methylation and CRGs score

To explore the role of CRGs in the TME, we analyzed the expression of chemokines, interleukins, interferons, other cytokines, and their receptors across high and low CRG score groups. Significant differences were observed in the CLL family, IL family, TGF family, among others (Fig. 8A). Furthermore, we examined the correlation between CRG scores and 50 hallmark pathways, revealing that GSVA correction indicated positive correlations with ESTROGEN RESPONSE, BILE ACID METABOLISM, MYOGENESIS, and TGF-β SIGNALING, while negative correlations were found with MTORC1 SIGNALING, G2M CHECKPOINT, E2F TARGETS, and GLYCOLYSIS (Fig. 8B). Additionally, we investigated the relationship between immune checkpoints and CRG scores in 1,424 BRCA patients. The results demonstrated a decreasing trend in the expression of CD247, CTLA4, LAG3, PDCD1, and TIGIT as CRG scores increased (Fig. 8C). To provide further support, we analyzed TMB in high and low CRG score groups using the R maftools package. In the SCORE-HIGH group, alterations were identified in 371 (65.2%) of 569 samples, while the SCORE-LOW group exhibited alterations in 319 (77.62%) of 411 samples, with missense mutations constituting the majority of these changes (Fig. 9A, B). Moreover, a significant difference in gene mutations was observed between the high and low CRGs score groups (Fig. 9C). To further elucidate this, we examined the distribution, differential expression, and correlation of the mDNAsi score derived from methylation data, the mRNAsi score derived from expression data, Microsatellite Instability (MSI), TMB, and the CRGs score (Fig. 10A-D, Sup-Fig. 4A-E). The expression levels of the high and low score arrays exhibited significant differences and were negatively correlated with the CRGs score.

Fig. 8.

Fig. 8

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(A) Expression of high and low grouping chemokines and their receptors, ns p > 0.05, *p < 0.05,**p < 0.01,***p < 0.001. (B) Correlation between Score and 50 Hallmark Pathways. (C) The correlation between Score and immune checkpoint expression

Fig. 9.

Fig. 9

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Gene mutations in high and low score groups. (A) High score group. (B) Low score group (C) Differences in gene mutations between high and low score groups, *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 10.

Fig. 10

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Expression and correlation of mDNAsi score calculated from methylation data, mRNAsi score calculated from expression data, MSI, TMB and CRGs score. (A) Dryness index (mDNAsi score) calculated based on methylation data, distribution map of scores and mDNAsi score, differential expression map, correlation point map. (B) Dryness index (mRNAsi score) calculated based on expression data, distribution map of scores and mRNAsi score, differential expression map, correlation point map. (C) MSI, distribution map of scores and MSI score, differential expression map, correlation point map. (D) TMB, distribution map of scores and TMB score, differential expression map, correlation point map

Immunotherapy validation and drug sensitivity prediction of Non immunotherapy

The expression of immune checkpoints in the high CRGs score array was found to be low, with both TMB and MSI also exhibiting low levels, alongside a scarcity of gene mutations. This led to the hypothesis that the efficacy of immunotherapy in this population is limited. To validate this hypothesis, immunotherapy data were analyzed. OS results in melanoma revealed that the low-CRG-score cohort demonstrated a superior treatment response compared to the high-score group, with a p-value of 0.028 (Fig. 11A). Similar findings were observed in the OS of urothelial carcinoma, as well as in the OS and progression-free survival (PFS) of KIRC, with p-values of 0.018, 0.0037, and 0.0017 respectively (Fig. 11B, C). Furthermore, we utilized the pRRophetic package in R to predict the IC50 values for various anticancer drugs across each sample, comparing the differences between high and low CRGs score groups. A higher IC50 indicates reduced treatment sensitivity. We identified six sensitive and six insensitive samples (Fig. 12). Finally, we summarized the correlation between gene expression and the sensitivity of the top 30 CTRP and GDSC drugs across pan-cancer (Sup-Fig. 5). These results underscore the significant impact of CRGs on the efficacy of chemotherapeutic agents in cancer treatment.

Fig. 11.

Fig. 11

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Data validation of immunotherapy. (A) ICB.Riaz2017_Nivolumab_Melanoma_Naïve, GSE91061, left: survival analysis (OS); right: the relationship between high and low groups and the efficacy of immunotherapy. (B) IMvigor210CoreBiologies, Urothelial carcinoma, left: survival analysis (OS)༛right: the relationship between high and low groups and the efficacy of immunotherapy. (C) KIRC, nivolumab (anti-PD-1), left: survival analysis (OS)༛mid: survival analysis (PFS); right: the relationship between high and low groups and the efficacy of immunotherapy

Fig. 12.

Fig. 12

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Prediction of drug sensitivity in non immunotherapy. Predict the IC50 values of various anticancer drugs for each sample and compare the differences between high and low score groups. The higher the IC50, the less sensitive the treatment was

Discussion

Molecular biology research on tumors indicates that the identification of new predictive and therapeutic targets, based on the development of prognostic-related genes, has emerged as a prominent area in clinical treatment strategies for BRCA-related cancers. Recent studies have identified a novel form of cell death termed Cuproposis. Copper is known to play a critical role in cancer progression. Increasing evidence suggests that dysregulation of copper contributes to oncogenesis through mechanisms such as inflammatory signaling, extracellular matrix remodeling, and immune evasion [24–29]. Excessive copper accumulation in cells leads to mitochondrial protein aggregation, resulting in protein modification, DNA damage, and lipid peroxidation [30]. The discovery of cuproptosis significantly enhances our understanding of copper metabolic diseases and their underlying molecular mechanisms. This knowledge holds substantial value for the identification of potential pharmacological agents aimed at treating copper metabolic disorders. However, given the heterogeneity of BRCA, the classification of CRGs in BRCA offers a reliable foundation for therapeutic strategies. Consequently, we employed various bioinformatics analysis methods to investigate the theoretical framework of copper toxicity-related genes in BRCA typing, signaling mechanisms, immunotherapy, and the assessment of drug sensitivity in immunotherapy.

Our study identified four distinct molecular subtypes based on CRGs. The prognosis for subtype C and subtype D was significantly better than that for subtype A and subtype B. Cox regression analysis and KM analysis indicated that high expression levels of ATP7B, DAXX, LIPT1, MAP1LC3A, and MT1M were associated with a favorable prognosis. Conversely, elevated levels of BACE1, DLAT, DLST, and SLC31A1 were significantly correlated with poor prognosis. Additionally, immune infiltration within the microenvironment of BRCA played a crucial role in disease progression. Furthermore, we continued to investigate immune infiltration alongside functional and multi-omic analyses across the four subtypes. Significant variability in immune cell infiltration was observed between the different subtypes, including B cells and MDSC. GO analysis revealed that these differential genes are associated with functions such as transmembrane signaling, extracellular matrix binding, and chromosomal regulation. KEGG analysis indicated that these differential genes are involved in pathways such as cytokine-cytokine receptor interactions, the PPAR signaling pathway, complement and coagulation cascades, fluid shear stress and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, and the cell cycle. We further explored the relationship between the four genetic subtypes and the clinical characteristics of BRCA patients, revealing significant discrepancies. PCA categorized breast cancer patients into high and low score groups. Our findings indicated that survival status, M stage, and recurrence of metastasis were associated with lower scores; specifically, deceased patients, those with M1 stage, and metastatic patients exhibited lower scores. Notably, there were significant differences in gene mutations between the high and low CRGs score groups. The expression of immune checkpoints in the high CRGs score array was low, with both TMB and MSI also being low, indicating a scarcity of gene mutations. This suggests that individuals in the high CRGs group are less likely to benefit from immunotherapy. Furthermore, our multi-cohort validation across melanoma, renal cell carcinoma (RCC), and urothelial carcinoma confirmed the translational utility of the CRG score in predicting immune checkpoint inhibitor (ICI) response. Clinical prognostic results in melanoma, urothelial carcinoma, and KIRC demonstrated that the low score group significantly benefited from immunotherapy compared to the high score group. Additionally, the prediction of IC50 values for each sample across various anticancer drugs revealed that higher IC50 values corresponded to reduced treatment sensitivity.

Copper is not only a crucial component of numerous essential enzymes but also serves as a key regulator of cellular signal transduction pathways [31]. Numerous studies have demonstrated that, compared to healthy individuals, cancer patients exhibit significantly elevated average levels of serum and tumor copper, particularly in cases of BRCA, lung cancer, colorectal cancer, gastric cancer, gynecological malignancies, and bladder cancer [32–37]. The abnormal accumulation of copper within cells can selectively induce apoptosis in cancer cells [24]. Additionally, some studies indicate that copper plays a role in promoting cell proliferation, stimulating angiogenesis, and facilitating metastasis [38–42]. However, there is currently no direct evidence to verify which CRGs influence the biological behavior of tumor cells. Recent studies indicate that cuproptosis plays a vital role in human tumors. Zhu et al. investigated the reliability of CRGs in predicting prognosis and immune response in patients with low-grade glioma [43]. Based on five CRGs, Shao et al. established an effective application of CRGs to develop an improved prognostic model for Hepatocellular Carcinoma (HCC) [44]. Wei Y, et al. demonstrated the potential of CRGs in guiding the prognosis, treatment, and immunological characteristics of AML patients [45]. Li et al. identified two cuproptosis-related subtypes and investigated the characteristics of TME infiltration, as well as the development of a prognostic model in BRCA [46]. This study found a correlation between the clinical characteristics, gene expression, and subtypes of BRCA patients. Additionally, the differences in immune cell infiltration across various subtypes were inconsistent. The high-score array derived from PCA analysis exhibited characteristics of low expression of immune checkpoints, low MSI, and a limited number of gene mutations. Zeng et al. discovered a negative correlation between the risk index of cuproptosis-related lipid metabolism genes and the expression of most immune checkpoints, which aligns with our research findings [47]. It is speculated that patients in the low-score group may benefit from immunotherapy. Building on previous studies, we further analyzed additional genotyping studies of CRGs in BRCA using diverse databases and bioinformatics techniques, and validated our findings with a greater number of relevant differential genes. To enhance the reliability of the study, we integrated the efficacy of immunotherapy with the prediction of drug sensitivity for non-immunotherapy treatments, thereby jointly establishing the scientific basis for the clinical treatment of subtype studies.

This study, however, presents certain limitations. Firstly, the inherent heterogeneity and potential quality issues related to public database data—such as sequencing noise, missing sample information, and clinical annotation errors—may undermine the robustness and reproducibility of the analyses. Consequently, this could result in reduced statistical power in specific association analyses (e.g., differential expression, survival analysis) or the generation of false positives and negatives. Secondly, the exclusive reliance on bioinformatic analysis of public data prevents direct wet-lab validation of molecular mechanisms, causal relationships, or specific biological functions [48]. This limitation restricts our capacity to gain deeper insights into the biological principles underlying the findings (e.g., key genes, pathways, prognostic models) and hinders confirmation of their causal roles. To enhance the credibility of our findings, we employed multiple complementary bioinformatic approaches. Core findings were validated across various datasets; functional enrichment analysis provided insights into potential biological mechanisms; and other computational methods, such as network analysis and drug sensitivity prediction, offered corroborative evidence. Additionally, results were compared and discussed in the context of previously published experimental evidence. Nonetheless, the key discoveries generated by this study necessitate subsequent in vitro and in vivo experimental validation (e.g., gene knockout/overexpression, cellular functional assays, animal models) to confirm their functional roles and mechanisms. This represents a critical next step in translating computational findings into clinical or biological understanding. Furthermore, repeated mining of public datasets may lead to what is termed ‘salmon-colored ocean competition,’ which poses challenges for secondary analyses based solely on the same data sources, potentially hindering the generation of truly disruptive and original insights. Such studies may be limited to validating known findings, refining existing models, or applying established methods to specific subgroups. It is essential to position this study accurately: as a computational study based on public resources, its primary contributions lie in systematic analysis, resource integration, hypothesis generation, and the provision of a prioritized research agenda—not in replacing experimentally driven primary discoveries. Its objective is to inform and support subsequent original experimental and clinical investigations. Ultimately, achieving truly breakthrough innovations in the future will necessitate the close integration of computational findings with original experimental designs, the generation of novel data types (e.g., single-cell, spatial omics, and novel models), or prospective clinical studies.

Conclusion

This study demonstrates that CRGs are closely associated with the TME, clinicopathological characteristics, and prognosis of breast cancer, positioning them as potential prognostic tools for patient outcomes. Furthermore, it reveals that the cuproptosis subtype effectively differentiates between various temporal and molecular characteristics. The CRG score emerges as a promising biological phenotype for predicting the prognosis of BRCA and assessing the benefits of immunotherapy and chemotherapy. Additionally, we developed a cuproptosis scoring system that identifies novel molecular subtypes and influences the biological behavior of breast cancer. However, this study acknowledges certain limitations. To enhance the reliability of the research findings, further basic research and clinical investigations are warranted.

Supplementary Information

Supplementary Material 1. (120.2KB, docx)
Supplementary Material 2. (2.1MB, docx)

Acknowledgements

The data can be found in public, open access repositories. Data can be provided upon reasonable request. All data related to the study are included in the article or uploaded as supplementary information. The authors gratefully acknowledge the various databases that provided the data for this study.

Author contributions

Rongyang Zou and Qiong Wu are co-first authors of the article. Rongyang Zou and Wanjun Zhao designed the research methods and analyzed the data. Qiong Wu participated in data collection. Rongyang Zou and Qiong Wu drafted the manuscript. Wanjun Zhao and Rongyang Zou revised the manuscript.

Funding

The work was supported by the Qilu Research Fund Project (XQQL202417).

Data availability

The datasets analysed during the current study are available in UCSC-XENA (https://xenabrowser.net/datapages/) and the GSE20685 dataset downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The data can be found in public, open access repositories. Data can be provided upon reasonable request. All data related to the study are included in the article or uploaded as supplementary information.

Declarations

Ethics approval and consent to participate

This study involves a secondary analysis of public databases (e.g., UCSC-XENA, GEO). The datasets have been thoroughly anonymized by the data providers, ensuring they cannot be linked to individual identities. These datasets originate from prior projects that obtained informed consent from participants, permitting subsequent research. The data for this study were sourced from publicly available databases, and ethical approval was not needed. The study strictly adheres to database access policies and upholds the highest standards of data privacy and research ethics. UCSC-XENA and GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles.

Consent for publication

All authors approved the version to be released and agreed to be responsible for all aspects of the work.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rongyang Zou and Qiong Wu have contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Grubman A, White AR. Copper as a key regulator of cell signalling pathways. Expert Rev Mol Med. 2014;16. 10.1017/erm.2014.11. Published 2014 May 22. :e11. [DOI] [PubMed]

Supplementary Materials

Supplementary Material 1. (120.2KB, docx)
Supplementary Material 2. (2.1MB, docx)

Data Availability Statement

The datasets analysed during the current study are available in UCSC-XENA (https://xenabrowser.net/datapages/) and the GSE20685 dataset downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The data can be found in public, open access repositories. Data can be provided upon reasonable request. All data related to the study are included in the article or uploaded as supplementary information.

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