Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer

Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer Rongrong Peng, Yu Li, Bo Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656418/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Breast cancer (BRCA) remains the leading cause of cancer-related mortality in women globally. Recent studies implicate dysregulated copper homeostasis in tumorigenesis, with cuproptosis—a copper-dependent cell death mechanism—emerging as a potential therapeutic target. Here, we systematically analyzed the prognostic value of cuproptosis-related genes (CRGs) in BRCA using multi-omics data from TCGA and GEO cohorts. Weighted gene co-expression network analysis (WGCNA) identified four hub genes ( CCDC24 , TMEM65 , XPOT , NUDCD1 ) that form a prognostic signature. High-risk scores derived from this signature correlated with poor survival, distinct tumor microenvironment (TME) features, and increased TP53 mutation frequency. Functional enrichment revealed associations with immune evasion and metabolic pathways. Single-cell analysis of BRCA tissues identified diverse cell populations and differential CRG expression (CCDC24, TMEM65, XPOT, NUDCD1), linking cuproptosis to tumor metabolism, immune dynamics, and therapeutic potential. Our findings establish cuproptosis as a critical regulator of BRCA progression and propose a novel prognostic tool for clinical stratification. BRCA cuproptosis prognostic signature TME Single-cell analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer, a malignancy originating from ductal epithelial cells, remains the most frequently diagnosed cancer and leading cause of cancer-related mortality among women worldwide, accounting for 24.5% of new cancer cases and 15.0% of cancer deaths in 2020 [ 1 ] . While therapeutic advances have improved outcomes, molecular heterogeneity and resistance mechanisms continue to challenge prognosis prediction and treatment efficacy. Emerging evidence implicates dysregulated transition metal homeostasis in tumorigenesis, with copper emerging as a critical yet understudied mediator of oncogenic processes. As an essential cofactor for mitochondrial respiration, redox regulation, and iron metabolism, copper homeostasis is tightly controlled; deviations from physiological levels are associated with neurodegenerative, metabolic, and hematologic disorders [ 2 ] . Notably, recent studies reveal that supraphysiological copper accumulation induces a novel regulated cell death modality—cuproptosis—characterized by mitochondrial copper overload, aggregation of lipoylated TCA cycle enzymes, and irreversible proteotoxic stress [ 3 ] . Genome-wide CRISPR-Cas9 screens have delineated a core molecular framework for cuproptosis regulation, identifying seven positive regulators (FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, and PDHB) and three suppressors (MTF1, GLS, and CDKN2A). Clinically, elevated serum copper levels correlate with poor breast cancer prognosis The latest research has revealed a previously unknown mechanism of cell death regulation, which has been named cuproptosis. Cuproptosis is a process that primarily takes place in cells that are actively engaged in respiration and the TCA cycle. It encourages the combination of copper and fatty acylating components, which causes fatty acylating protein to aggregate, lose iron-containing sulfur cluster protein, induce HSP70, start intracellular toxic oxidative stress, and ultimately cause cell death [ 4 ] , while preclinical models demonstrate cuproptosis induction suppresses tumor growth across malignancies [ 5 – 7 ] , suggesting its dual role as a prognostic biomarker and therapeutic vulnerability. However, the functional landscape of cuproptosis-related genes (CRGs) in breast cancer progression, their interplay with tumor microenvironment (TME) dynamics, and clinical translation potential remain unexplored. Here, we perform the first systematic integration of multi-omics data from TCGA and GEO cohorts to interrogate CRG dysregulation in breast cancer. Employing weighted gene co-expression network analysis (WGCNA) and machine learning-driven prognostic modeling, we identify a four-gene signature (CCDC24, TMEM65, XPOT, and NUDCD1) that stratifies patients into distinct risk cohorts. Through comprehensive molecular profiling, we further elucidate how high-risk tumors exhibit TP53 mutation enrichment, immunosuppressive TME remodeling, and metabolic reprogramming linked to immune evasion. Our findings establish cuproptosis as a pivotal regulator of breast cancer progression and provide a clinically actionable framework for risk stratification and therapeutic targeting. Materials and methods BRCA data source and preprocessing Clinical data and gene expression data of BRCA (log2(FPKM + 1)) were obtained from The Cancer Genome Atlas of America database (TCGA, https://cancergenome.nih.gov/ ) through the R package TCGA biolinks. A total of 1059 female breast cancer samples and 99 normal breast samples with expression and survival information were selected. The prediction model was validated using the GSE20685 dataset, which contains 327 cancer samples. Gene expression profiles and clinical data were downloaded from the GEO database: https://www.ncbi.nlm.nih.gov/geo . According to the relevant platform annotation file, the probes were transformed into gene symbols. When a single probe represented more than one gene, the probe was eliminated, and the median value was determined when more than one probe represented the same symbol. CRGs used for analysis: 10 CRGs were obtained from the literature [ 3 ] (PMID: 35298263). Too little clinical information was available to analyze the data obtained from the TCGA. The PAM50 and immune phenotypes information we used is available in Appendix 2 of PMID: 29628290, and the information on whether it is triple negative was obtained from cBioPortal ( http://www.cbioportal.org/datasets ). The hallmark pathway gene set (msigdb.v7.4.symbols.gmt) was downloaded from: https://www.gsea-msigdb.org/gsea/msigdb/ . Landscape of genetic expression and variation of CRGs in breast cancer Analysis of the expression differences of CRGs between BRCA and paired normal tissues (rank-sum test) and demonstration of differences in expression between clinical feature groups, using the rank-sum test for significance between two groups and the Kruskal-Wallis test for significance between more than two groups. R package maftools was used to show the mutation of the entire CRGs; ggplot2 was used to show the copy number variation (CNV) of the CDGs in the training cohort data using Fisher's exact test; and R package RCircos was used to show the chromosome distribution of CRGs (circos plot). Weighted gene co-expression network construction The WGCNA R package was used to build the co-expression network [ 8 ] . The main steps in the network construction procedure were as follows: (1) define the similarity matrix; (2) choose the weighting coefficient, and convert the similarity matrix to an adjacency matrix; (3) convert the adjacency matrix to a topological overlap matrix (TOM); (4) to obtain the hierarchical clustering tree, perform hierarchical clustering for TOM-based dissimilarity (dissTOM); (5) identify the modules from the hierarchical clustering tree using the dynamic tree cut method; (6) compute the module eigengene (ME) of each module. ME denotes the module's total expression level. The average distance between the MEs of all modules was determined using the 1-Pearson correlation coefficient, obtained using the Pearson correlation coefficients between the MEs of all modules. The MEs of all modules were clustered using the average-linkage hierarchical clustering method, which relies on a minimum size (gene group) of 30. The modules with the highest levels of similarity were then combined to create the co-expression network. The module eigengene (ME) represents the first principal component of the module and is used to describe the expression pattern of the module in each sample. The Pearson correlation coefficients for all modules' MEs and clinical data were computed to see whether modules were connected to clinical traits. In this study, we used the network Screening function based on GS (representing the correlation between the gene and a given clinical trait) and MM (representing the correlation between the gene and a given module) in the WGCNA package to directly identify hub genes [ 8 ] which permitted the identification of modules that were significantly related to the trait (P < 0.05). Functional enrichment analyses The ClusterProfiler R package was used for the hub genes' gene ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, respectively, to determine the hub genes' biological functions and signaling pathways [ 9 , 10 ] . The default values were used as the parameters in the ClusterProfiler R package. P < 0.05 were chosen as the cutoffs for identifying the KEGG pathways and GO functions of hub genes, respectively. Identification of hub genes We used univariate Cox analysis of overall survival (OS) to select the potential CRGs prognostic genes by R “survival” filtered by P < 0.05. The coefficients of the regressed variables were compressed by the R package glmnet using the LASSO algorithm so that some regression coefficients were strictly equal to zero to minimize the risk of overfitting, resulting in an interpretable model. Patient signature scores were then calculated based on the gene expression levels and their corresponding regression coefficients. Score = \(\:\sum\:_{i=0}^{n}{\beta\:}\text{i}\:\text{*}\:{\chi\:}\text{i}\) βi: weighting factor for each gene; χi: expression levels of each gene Predictive ability of the Prognostic Signature of CRGs The prognosis-related CRGs after LASSO regression analysis were divided into high and low groups according to the median expression level, and then KM survival analysis of CRGs was performed using the R package survival and survminer. Univariable and multivariable analyses were performed on the clinical information from the training and validation cohort using the R package coxph, with a threshold of P < 0.05, and forest plots were drawn, to see whether the signature was significant in the single- and multi-factor analyses. Cuproptosis signature variation analysis and functional annotation The simple nucleotide variation (SNV) and copy number variation (CNV) were based on the training cohort data. Using the R package maftools, samples with high and low groups of CDG signatures were compared at the mutation level using the Fisher test, and mutation results were presented using waterfall plots. The R package ggplot2 pair for CDGs signature was used to map the distribution of CNVs in the genome. We downloaded the HALLMARK gene set “msigdb.v7.4.symbols.gmt” from the MSigDB database ( https://www.gsea-msigdb.org/gsea/index.jsp ) to run the GSVA analysis. Estimate the score of the sample in each pathway. Then use the R package pheatmap to plot the distribution of enrichment scores for different hallmark paths in different groups. CDG signature tumor microenvironment infiltration characteristics Stromal and immune cells in tumor tissues were estimated by the pearson method using R package estimation, and the Stromal Score and Immune Score were calculated respectively, and finally, an ESTIMATE Score was generated by combining these two scores. The immune infiltration scores were then calculated by ssGSEA, cibersort, and xcell methods, and the differences in immune infiltration scores between high and low signature groups were plotted using the R package ggplot2, and the test for significance of differences was a rank sum test. Single-cell RNA-seq analysis to reveal the expression of key genes across different cell types We employed single-cell RNA-seq analysis to dissect the breast cancer microenvironment and explore key gene expression patterns. Data processing and dimensionality reduction were performed using Seurat, including quality control, normalization (LogNormalize), and identification of highly variable genes. PCA was applied for initial dimensionality reduction, followed by UMAP and kNN clustering to ensure biological consistency. Cell composition was analyzed by calculating proportions and visualizing distributions via bar plots. Key genes ( CCDC24 , TMEM65 , XPOT , NUDCD1 ) were analyzed across cell types and visualized using DotPlot with a blue-white-red gradient. UMAP (DimPlot) and bar plots (ggplot2) were used for clustering and cell proportion visualization, respectively. This approach reveals the potential roles of key genes in the tumor microenvironment. Results Differential Expression and Genetic Alterations of Cuproptosis-Related Genes (CRGs) in BRCA Analysis of 10 cuproptosis-related genes (CRGs) in 1,059 breast cancer (BRCA) samples from the TCGA dataset revealed significant transcriptional dysregulation compared to 99 normal breast tissues ( P < 0.001, Wilcoxon rank-sum test). Among these, seven CRGs ( DLD , LIAS , PDHA1 , FDX1 , LIPT1 , MTF1 , and GLS ) were upregulated in tumors, while CDKN2A and PDHB were downregulated. Notably, DLAT expression showed no significant difference between tumor and normal tissues (Fig. 1 A). Our findings indicate that dysregulation of copper metabolism-related genes (CRGs) may play a pivotal role in the pathogenesis of breast cancer (BRCA). Breast cancer is molecularly classified into four major subtypes—Luminal A, Luminal B, HER2-enriched, and triple-negative breast cancer (TNBC)—based on immunohistochemical profiles and comprehensive genetic analyses. TNBC, representing 15–20% of invasive breast carcinomas, is characterized by its high molecular heterogeneity, aggressive behavior, and increased risk of recurrence [ 11 , 12 ] . To investigate the potential involvement of CRGs in breast cancer biology, we stratified our cohort into TNBC and non-TNBC subgroups for comparative analysis. Differential expression analysis revealed that six CRGs (DLD, GLS, PDHA1, FDX1, CDKN2A, and DLAT) were significantly upregulated in TNBC compared to non-TNBC, while three genes (LIAS, LIPT1, and LIPT2) showed reduced expression (Fig. 1 B). Further examination of PAM50 molecular phenotypes demonstrated significant differential expression of ten CRGs across subtypes ( Supplementary Fig. 1A ). Notably, with the exception of MTF1, nine CRGs exhibited distinct expression patterns among the five PAM50 classifications ( Supplementary Fig. 1B ). These findings collectively demonstrate substantial variations in CRG expression patterns between normal and malignant tissues, as well as across different molecular subtypes, suggesting that transcriptional dysregulation of copper metabolism pathways may contribute to breast cancer initiation and progression. Genomic analysis revealed frequent copy number variations (CNVs) and somatic mutations in CRGs (Fig. 1 C), with specific chromosomal loci showing recurrent alterations (Fig. 1 D). Quantitative assessment of CNV frequencies demonstrated that MTF1 was predominantly affected by amplification events, while FDX1, DLAT, and PDHB showed higher rates of deletion events compared to other CRGs ( Supplementary Fig. 1C ). These genomic alterations may contribute to the observed expression patterns and functional consequences of CRG dysregulation in breast cancer. Network analysis reveals CRG-associated modules in breast cancer To construct a robust gene co-expression network, we first performed scale-free topology analysis to determine the optimal soft-thresholding power (β). Figure 2 A presents the scale-free topology model fit (left panel) and mean connectivity (right panel) across a range of soft-threshold powers, enabling the selection of the most appropriate β value for network construction. We next integrated clinical trait data with CRG expression profiles to identify clinically relevant modules. Hierarchical clustering analysis of module eigengenes (MEs) revealed distinct gene co-expression patterns, with closely related modules subsequently merged to optimize network topology ( Supplementary Fig. 2A ). Module-trait relationship analysis, quantified using Pearson correlation coefficients, demonstrated significant associations between specific modules and clinical phenotypes ( Supplementary Fig. 2B ). Notably, the pink, yellow, and midnight blue modules showed strong correlations with CRG expression patterns (Fig. 2 B). Through rigorous screening using dual criteria (module membership [MM] > 0.6 and gene significance [GS] > 0.4), we identified 108 CRGs as key network hubs (Fig. 2 C and Supplementary Fig. 2C ). These criteria ensured the selection of genes that were both highly connected within their respective modules and strongly associated with CRG-related phenotypes. Construction of the prognostic signature of CRGs in BRCA To establish a robust prognostic model, we utilized the 108 cuproptosis-associated genes identified through WGCNA as candidate predictors. Initial screening via univariate Cox regression analysis identified 23 potential prognostic factors. Patients were stratified into high- and low-expression groups based on median expression values of these factors, with subsequent survival analysis revealing significant differences between groups ( Supplementary Fig. 3A ). Among the top six most significant prognostic genes (ranked by p-value), elevated expression of CCDC24 was associated with improved survival, whereas lower expression levels of CCT6A, CDCA7, TMEM65, SLC7A5, and CCNE1 correlated with better clinical outcomes. To refine the model and mitigate overfitting, we performed LASSO regression analysis, which identified four core prognostic genes: CCDC24, TMEM65, XPOT, and NUDCD1 (Figs. 3 A, 3 B). These genes were incorporated into a multivariate risk score model, weighted by their respective LASSO coefficients: Risk Score = (-0.051 × CCDC24) + (6.355e-5 × TMEM65) + (0.056 × XPOT) + (0.115 × NUDCD1) Notably, the negative coefficient for CCDC24 suggests its potential role as a protective factor, consistent with its association with improved survival in the univariate analysis. This risk score model provides a quantitative framework for assessing patient prognosis based on cuproptosis-related gene expression profiles ( Supplementary Figs. 3A ). Predictive ability of the Prognostic Signature of CRGs To evaluate the clinical utility of our risk score model, we stratified breast cancer patients from both the TCGA (training cohort) and GEO (validation cohort) datasets into high- and low-risk groups based on the median risk score ( Supplementary Figs. 3B ). Survival analysis revealed that patients in the low-risk group exhibited significantly better overall survival compared to their high-risk counterparts in both cohorts (Figs. 3 C and 3 E; Supplementary Figs. 3B ), demonstrating the robust prognostic value of the cuproptosis-related gene signature. The predictive accuracy of the risk score model was further validated using time-dependent receiver operating characteristic (ROC) curve analysis. In the training cohort, the model achieved area under the curve (AUC) values of 0.595, 0.619, and 0.597 for 1-year, 3-year, and 5-year overall survival predictions, respectively (Fig. 3 D). The validation cohort showed even stronger predictive performance, with corresponding AUC values of 0.738, 0.655, and 0.641 (Fig. 3 F). Gene expression analysis across risk groups revealed distinct patterns among the core prognostic genes. CCDC24, which carries a negative coefficient in the risk score model, was significantly upregulated in the low-risk group. Conversely, TMEM65, XPOT, and NUDCD1—all positively weighted in the model—showed higher expression levels in the high-risk group, consistent with their association with poorer clinical outcomes. Validation and functional analysis of the CRG prognostic signature To assess the independence of the cuproptosis-related gene (CRG) prognostic signature, we performed univariate and multivariate Cox regression analyses. While the signature showed a trend toward significance as an independent prognostic factor (P = 0.055, Fig. 4 A), further validation in the training cohort confirmed its prognostic value across PAM50 subtypes (Fig. 4 B). Stratification analysis revealed significant associations between risk scores and clinical characteristics. Notably, patients aged < 60 years exhibited higher risk scores compared to older individuals (Fig. 4 C). Substantial variations in risk scores were observed across molecular subtypes, with Basal-like tumors demonstrating significantly higher risk scores among PAM50 subtypes. Similarly, the C4 immunotype was associated with elevated risk scores. Triple-negative breast cancer (TNBC) cases showed consistently higher risk scores across all three immunohistochemical markers (ER, PR, and HER-2). Functional enrichment analysis reveals distinct biological pathways in risk groups To elucidate the biological functions of the 108 tumor differentiation-associated genes, we performed comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses ( Supplementary Fig. 4A ). GO analysis revealed distinct functional categories: Biological Processes (BP): Genes were predominantly associated with chromosomal segregation and organelle fission. Cellular Components (CC): Significant enrichment was observed in condensed chromosomes and chromosomal regions. Molecular Functions (MF): Genes were strongly linked to serine kinase activity and single-stranded DNA binding. KEGG pathway analysis identified cell cycle regulation and oocyte meiosis as the most significantly enriched pathways. Quantitative pathway activity scoring ( Supplementary Fig. 4B ) demonstrated distinct metabolic profiles between risk groups. The high-risk group showed reduced association with xenobiotic metabolism and adipogenesis pathways compared to the low-risk group. Conversely, spermatogenesis and MTORC1 signaling pathways were significantly enriched in the high-risk group. Furthermore, stratification analysis revealed significant differences in molecular subtypes, immunophenotypes, and triple-negative status between high- and low-risk groups, suggesting distinct biological characteristics underlying the risk stratification. To elucidate the biological underpinnings of the CRG risk scores, we conducted functional enrichment analysis using GO and KEGG databases. Mutation profiling revealed distinct patterns between risk groups, with TP53 and PIK3CA emerging as the most frequently mutated genes ( Supplementary Fig. 5A ). Notably, TP53 mutations were predominantly missense variants. Comparative genomic analysis demonstrated that the high-risk group exhibited significantly higher rates of deletion mutations and copy number alterations compared to the low-risk group ( Supplementary Fig. 5B ). Immune microenvironment characteristics and therapeutic response in high-risk tumors The potential impact of copper metabolism-related genes (CRGs) on immune cell recruitment within the tumor microenvironment (TME) and its subsequent influence on BRCA prognosis remains poorly understood. To address this, we employed single-sample gene set enrichment analysis (ssGSEA) to quantify the enrichment scores of 28 distinct immune cell populations across risk-stratified groups, thereby investigating the role of the CRG signature in shaping the BRCA TME. Complementing this analysis, we utilized the ESTIMATE algorithm to assess tumor purity and TME composition, including stromal, immune, and ESTIMATE scores. Comparative analysis revealed significant differences in TME characteristics between risk groups (Fig. 5 A). Notably, the high-risk subgroup exhibited markedly reduced stromal scores compared to the low-risk subgroup, suggesting distinct stromal remodeling patterns associated with CRG expression profiles. Further characterization of immune cell infiltration using the CIBERSORT algorithm demonstrated significant correlations between CRG risk scores and specific immune populations (Fig. 5 B). High-risk CRG scores showed negative associations with anti-tumor immune cells, including CD56 dim natural killer cells and plasmacytoid dendritic cells. Conversely, positive correlations were observed with pro-tumorigenic immune populations, such as activated CD4 T cells and activated dendritic cells. These findings suggest that CRG expression patterns may influence BRCA progression through modulation of immune cell composition within the TME. Correlation with drug sensitivity We continued to explore the correlation between CRG risk scores and drug sensitivity, highlighting that the high-risk group exhibits significant resistance to specific drugs (e.g., AZD2014, GNE-317), as evidenced by higher IC 50 values. These findings suggest that CRG risk scores may serve as a predictive biomarker for drug resistance (Fig. 5 C). Figure 5 D presented a box plot comparing drug sensitivity across high- and low-risk groups, revealing distinct differences in IC 50 values for various compounds. Key observations include differential responses to drugs such as AZD5991 and UMI-77, with the high-risk group demonstrating significant resistance. These findings reinforce the potential utility of CRG-related risk scores in predicting treatment efficacy and guiding personalized therapeutic strategies for breast cancer patients. Single-Cell Transcriptomic Analysis Reveals Tumor Microenvironment Heterogeneity and Cuproptosis-Related Gene Expression in Breast Cancer We further analyzed TME of BRCA tissues using single-cell transcriptomic data and investigated the distribution of CRGs and their expression relationships across different cell types. UMAP dimensionality reduction analysis revealed that BRCA tissues contain diverse cell populations, such as tumor cells, T cells, fibroblasts, and endothelial cells, with each cell type exhibiting distinct functional patterns within the TME (Fig. 6 A). Further analysis of cell proportions indicated that cancer cells dominate, while the infiltration of immune cells highlighted their potential roles in immune evasion and anti-tumor immune responses (Fig. 6 B). The expression of cuproptosis-related genes CCDC24 , TMEM65 , XPOT , and NUDCD1 varied across different cell populations, which may be closely associated with metabolic reprogramming of tumors and changes in the immune microenvironment (Fig. 6 C, 6 D, 6 E, 6 F). The differential expression of these genes across cell populations may be closely linked to tumor metabolic reprogramming and immune microenvironment alterations. By analyzing marker genes, the characteristic expression patterns of different cell populations were further validated (Fig. 6 G). For example, EPCAM and KRT8 were highly expressed in cancer cells, CD3D and CD8A marked T cells, and MS4A1 was predominantly found in B cells. These results not only provide new insights into tumor heterogeneity in BRCA tissues but also offer data support for cuproptosis as a potential therapeutic target. Discussion In this study, we employed weighted gene co-expression network analysis (WGCNA) to investigate cuproptosis-related gene (CRG) signatures in breast cancer (BRCA) and evaluated their prognostic potential. WGCNA offers distinct advantages over conventional analytical approaches by identifying co-expression modules with strong correlations to clinical features, providing biologically meaningful insights with high reproducibility. This method enables the identification of functionally related gene clusters within modules, allowing for the discovery of physiologically relevant modules and hub genes that may serve as potential diagnostic or therapeutic biomarkers. To our knowledge, this represents the first comprehensive investigation into the relationship between CRGs and BRCA pathogenesis. Our analysis revealed significant differential expression patterns of CRGs between tumor and normal tissues, suggesting a potential role for cuproptosis in BRCA prognosis. Furthermore, we developed a novel CRG-based prognostic scoring system with significant predictive value for BRCA outcomes. Our genomic analysis of 10 BRCA-related CRGs demonstrated significant expression alterations, with 7 genes showing marked downregulation in tumor tissues, while DLAT expression remained unchanged. CRG expression patterns varied significantly across different clinical subgroups, particularly among PAM50 genotypes. Using WGCNA, we identified four key prognostic genes and constructed a CRG-based prognostic signature for BRCA. This signature demonstrated robust predictive capacity, with lower CRG scores correlating with improved survival outcomes in both training and validation cohorts. Comparative analysis revealed significant differences in overall survival, clinical characteristics, mutational profiles, and tumor microenvironment (TME) features between high-risk and low-risk CRG score groups. Multivariate Cox regression analysis confirmed the CRG signature as an independent prognostic factor for BRCA. Notably, the basal subtype exhibited the highest CRG scores, consistent with its established poor prognosis, while Luminal A showed the lowest scores [ 11 , 12 ] . The WGCNA-derived gene modules demonstrated strong biological relevance, with co-expressed genes showing functional relationships. This approach identified physiologically significant modules and hub genes with potential as diagnostic or therapeutic biomarkers. Our analysis also revealed distinct immune infiltration patterns and tumor signaling pathways between the two CRG-based subgroups. The tumor microenvironment, comprising tumor cells and their surrounding stromal components including lymphocytes, tumor-infiltrating immune cells, and vascular systems, plays a crucial role in tumor progression and therapeutic resistance [ 13 – 15 ] . Our study identified significant differences in the abundance of 23 tumor-infiltrating immune cells (TIICs) and TME characteristics between the high-risk and low-risk groups. Notably, the high-risk subgroup showed significantly lower stromal scores compared to the low-risk subgroup, suggesting a potential role for cuproptosis in modulating TME characteristics and immune cell infiltration in BRCA [ 16 – 18 ] . Gene set enrichment analysis (GSEA) revealed significant enrichment of antigen processing and presentation pathways in the high cuproptosis score group. Comparative analysis of somatic mutations and copy number variations (CNVs) between groups showed a significantly higher TP53 mutation frequency in the high cuproptosis score group. This finding aligns with previous studies demonstrating TP53's role in immune signaling modulation and its high mutation frequency in basal subtype BRCA [ 19 – 22 ] . We are currently lacking in vitro and in vivo experimental validation of the oncological functions of the key cuproptosis-related genes associated with breast cancer prognosis. However, we have clarified their roles in different cell types through single-cell transcriptomic analysis. We will further investigate the impact of these key genes on breast cancer development in in vitro and in vivo experiments in subsequent series of studies. Our findings suggest that cuproptosis may represent a novel therapeutic target and prognostic marker in breast cancer. The identification of CRG-based signatures and their association with TME characteristics opens new avenues for breast cancer detection and treatment strategies. These results warrant further investigation into the molecular mechanisms underlying cuproptosis in BRCA pathogenesis and its potential clinical applications. Declarations Acknowledgments Not applicable. Conflict of Interest Statement No conflict of interest was reported in this study. Funding Jiangxi Provincial Health Commission Youth Project (202510933). Availability of data and materials This study utilizes publicly available datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). As both databases consist of anonymized, de-identified patient data, patient involvement was not required, and informed consent was not necessary in compliance with the ethical guidelines governing the use of these resources. Author contributions R.R.P. and Y.L. developed the concept and design of this study. R.R.P. and Y.L. analyzed and interpreted the data. R.R.P. and Y.L. wrote the manuscript. Y.L. designed and illustrated the figures. B.X. conducted a critical review of the manuscript and provided constructive feedback and suggestions for revisions. B.X. provided guidance and supervision throughout the writing process. 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Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif FigureS5.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 15 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 31 May, 2025 First submitted to journal 31 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6656418","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472451748,"identity":"2b608ff9-69e4-4205-87ff-6cf21b0305be","order_by":0,"name":"Rongrong Peng","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Peng","suffix":""},{"id":472451749,"identity":"fd899410-9fc8-42d1-beef-81c729c02a8b","order_by":1,"name":"Yu Li","email":"","orcid":"","institution":"PingXiang people’s hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Li","suffix":""},{"id":472451750,"identity":"290fb1d0-7a7f-4dd7-8aa9-578f918bc74f","order_by":2,"name":"Bo Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACAxCRUGEjx8befIAELQ/OpBnz8RxLIF4L48O2Q4nzJHIUiNNizt5jJpHAdiC9jSGHgeFHxTbCWix7jqVJJPDcyW1jOHuAsefMbSIcdiP5mESCxLPcNsa+BGbGNqK0JLZJJBgcTmdj5jEgVgvIloTDCWxsxGoB+iXZIuFAmmEbD1vCQaL8Agwxw5s//9nIy89/fPDBjwoitKCAAySqHwWjYBSMglGACwAAOjo8lUC/jXQAAAAASUVORK5CYII=","orcid":"","institution":"Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-05-13 14:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6656418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6656418/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84914870,"identity":"24accfce-8783-4ff8-9ade-721c704970fe","added_by":"auto","created_at":"2025-06-18 18:00:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLandscape of genetic variation of CRGs in BRCA. \u003c/strong\u003e(A) The expression of 10 CRGs between normal and BRCA tissues; (B) Differential expression of key genes in triple-negative breast cancer; (C) Mutation frequency of 10 CRGs; (D) The position of CRGs CNV changes on 23 chromosomes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/c5eb1a3b9bc3f44020867682.png"},{"id":84914872,"identity":"589345f0-ed18-407a-9c42-27d42525743e","added_by":"auto","created_at":"2025-06-18 18:00:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key modules associated with cuproptosis via gene co-expression network analysis. \u003c/strong\u003e(A) Analysis of the scale-free index for various soft-threshold powers (β). Analysis of the mean connectivity for various soft-threshold powers; (B) Heatmap of the correlation between the ME and clinical traits of BRCA; (C) Module Membership in pink.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/41943ae1b2477078e5c3b40d.png"},{"id":84915860,"identity":"35b6fe69-3c79-4c75-8bc0-db21aac6c3a6","added_by":"auto","created_at":"2025-06-18 18:16:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and Validation of a Prognostic Model Using Machine Learning.\u003c/strong\u003e (A) LASSO regression of the 23 genes possessing prognostic value; The least absolute shrinkage and selection operator (LASSO) regression was performed with the minimum criteria; (B) LASSO regression coefficients of key prognostic genes; (C) The patient survival status and CRG score distribution in the training cohort from TCGA; (D) The AUC value of 1 year, 3 years, and 5 years after diagnosis in the training cohort; (E) The patient survival status and CRG score distribution in the validation cohort from GEO and the AUC value of 1 year, 3 years, and 5 years after diagnosis in the validation cohort (F); (G) and (H) Expression of key genes in low-risk and high-risk groups in the training and validation cohorts.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/57dec6bdcc969da05243cb38.png"},{"id":84915546,"identity":"6248f8a5-ea82-47c3-9acc-4e99b5e77cb9","added_by":"auto","created_at":"2025-06-18 18:08:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive Power of Risk Scores for Clinical Subtypes and Molecular Features. \u003c/strong\u003e(A) The forest plot of univariate and multivariate Cox regression analysis for the training and validation cohort (B); (C) The risk score in different group divided by clinical characteristics.\u003c/p\u003e\n\u003cp\u003eTo elucidate the biological underpinnings of the CRG risk scores, we conducted functional enrichment analysis using GO and KEGG databases. Mutation profiling revealed distinct patterns between risk groups, with TP53 and PIK3CA emerging as the most frequently mutated genes (\u003cstrong\u003eSupplementary Figure 5A\u003c/strong\u003e). Notably, TP53 mutations were predominantly missense variants. Comparative genomic analysis demonstrated that the high-risk group exhibited significantly higher rates of deletion mutations and copy number alterations compared to the low-risk group (\u003cstrong\u003eSupplementary Figure 5B\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/27d2ed695d2a8c6520a78195.png"},{"id":84914873,"identity":"bd8c46a4-0db7-4f43-9a05-151e0b96cd06","added_by":"auto","created_at":"2025-06-18 18:00:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune evasion and therapeutic vulnerability.\u003c/strong\u003e (A-B) Differences in Stromal/Immune Scores; (C-D) Prediction of Targeted Drug Sensitivity.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/2024bdd0b7bf57086e5529d6.png"},{"id":84915547,"identity":"8958241b-d956-4d28-b065-f30050286a59","added_by":"auto","created_at":"2025-06-18 18:08:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":154585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell analysis of BRCA tissues identified diverse cell populations and differential CRG expression. \u003c/strong\u003e(A) UMAP of cell clusters; (B) Cell type proportion; (C-F) CCDC24, XPOT, TMEM65, and NUDCD1 expression in different cell types; (G) Marker genes expression in different cell types.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/ddbd8f1fd19d188e14c47fa0.png"},{"id":84916315,"identity":"592888a6-303a-4032-b70f-0597b0769e7c","added_by":"auto","created_at":"2025-06-18 18:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2328863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/ab1e129c-1e7c-429f-9024-0fbf7cc53ee5.pdf"},{"id":84915550,"identity":"27c7a45a-a4b3-414b-90d0-149147137f4c","added_by":"auto","created_at":"2025-06-18 18:08:50","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6336652,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/63fbcc81a46766002f780bbc.tif"},{"id":84914880,"identity":"49a97198-6029-4e08-a887-44eb4724dbe3","added_by":"auto","created_at":"2025-06-18 18:00:50","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5169554,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/7268704a38061917aad110c8.tif"},{"id":84914902,"identity":"fb8addbb-2158-484b-a540-4921f4bc08ba","added_by":"auto","created_at":"2025-06-18 18:00:51","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4755878,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/836ea5564eeb882f66e050d3.tif"},{"id":84914901,"identity":"2648534f-1116-4bf3-bd71-fb10d607ec6b","added_by":"auto","created_at":"2025-06-18 18:00:51","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12844994,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/4632805a40aecffe2519a1f3.tif"},{"id":84914876,"identity":"48cad6a7-22bb-456f-b2c0-df6ec79dbe21","added_by":"auto","created_at":"2025-06-18 18:00:50","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1133872,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6656418/v1/19ce73fae58ebb9d2a9ef7f1.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer, a malignancy originating from ductal epithelial cells, remains the most frequently diagnosed cancer and leading cause of cancer-related mortality among women worldwide, accounting for 24.5% of new cancer cases and 15.0% of cancer deaths in 2020\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. While therapeutic advances have improved outcomes, molecular heterogeneity and resistance mechanisms continue to challenge prognosis prediction and treatment efficacy. Emerging evidence implicates dysregulated transition metal homeostasis in tumorigenesis, with copper emerging as a critical yet understudied mediator of oncogenic processes. As an essential cofactor for mitochondrial respiration, redox regulation, and iron metabolism, copper homeostasis is tightly controlled; deviations from physiological levels are associated with neurodegenerative, metabolic, and hematologic disorders\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, recent studies reveal that supraphysiological copper accumulation induces a novel regulated cell death modality\u0026mdash;cuproptosis\u0026mdash;characterized by mitochondrial copper overload, aggregation of lipoylated TCA cycle enzymes, and irreversible proteotoxic stress\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Genome-wide CRISPR-Cas9 screens have delineated a core molecular framework for cuproptosis regulation, identifying seven positive regulators (FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, and PDHB) and three suppressors (MTF1, GLS, and CDKN2A). Clinically, elevated serum copper levels correlate with poor breast cancer prognosis\u003c/p\u003e \u003cp\u003eThe latest research has revealed a previously unknown mechanism of cell death regulation, which has been named cuproptosis. Cuproptosis is a process that primarily takes place in cells that are actively engaged in respiration and the TCA cycle. It encourages the combination of copper and fatty acylating components, which causes fatty acylating protein to aggregate, lose iron-containing sulfur cluster protein, induce HSP70, start intracellular toxic oxidative stress, and ultimately cause cell death\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, while preclinical models demonstrate cuproptosis induction suppresses tumor growth across malignancies\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, suggesting its dual role as a prognostic biomarker and therapeutic vulnerability. However, the functional landscape of cuproptosis-related genes (CRGs) in breast cancer progression, their interplay with tumor microenvironment (TME) dynamics, and clinical translation potential remain unexplored.\u003c/p\u003e \u003cp\u003eHere, we perform the first systematic integration of multi-omics data from TCGA and GEO cohorts to interrogate CRG dysregulation in breast cancer. Employing weighted gene co-expression network analysis (WGCNA) and machine learning-driven prognostic modeling, we identify a four-gene signature (CCDC24, TMEM65, XPOT, and NUDCD1) that stratifies patients into distinct risk cohorts. Through comprehensive molecular profiling, we further elucidate how high-risk tumors exhibit TP53 mutation enrichment, immunosuppressive TME remodeling, and metabolic reprogramming linked to immune evasion. Our findings establish cuproptosis as a pivotal regulator of breast cancer progression and provide a clinically actionable framework for risk stratification and therapeutic targeting.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBRCA data source and preprocessing\u003c/h2\u003e \u003cp\u003eClinical data and gene expression data of BRCA (log2(FPKM\u0026thinsp;+\u0026thinsp;1)) were obtained from The Cancer Genome Atlas of America database (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) through the R package TCGA biolinks. A total of 1059 female breast cancer samples and 99 normal breast samples with expression and survival information were selected.\u003c/p\u003e \u003cp\u003eThe prediction model was validated using the GSE20685 dataset, which contains 327 cancer samples. Gene expression profiles and clinical data were downloaded from the GEO database: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. According to the relevant platform annotation file, the probes were transformed into gene symbols. When a single probe represented more than one gene, the probe was eliminated, and the median value was determined when more than one probe represented the same symbol.\u003c/p\u003e \u003cp\u003eCRGs used for analysis: 10 CRGs were obtained from the literature\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e (PMID: 35298263). Too little clinical information was available to analyze the data obtained from the TCGA. The PAM50 and immune phenotypes information we used is available in Appendix 2 of PMID: 29628290, and the information on whether it is triple negative was obtained from cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org/datasets\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org/datasets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The hallmark pathway gene set (msigdb.v7.4.symbols.gmt) was downloaded from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLandscape of genetic expression and variation of CRGs in breast cancer\u003c/h3\u003e\n\u003cp\u003eAnalysis of the expression differences of CRGs between BRCA and paired normal tissues (rank-sum test) and demonstration of differences in expression between clinical feature groups, using the rank-sum test for significance between two groups and the Kruskal-Wallis test for significance between more than two groups. R package maftools was used to show the mutation of the entire CRGs; ggplot2 was used to show the copy number variation (CNV) of the CDGs in the training cohort data using Fisher's exact test; and R package RCircos was used to show the chromosome distribution of CRGs (circos plot).\u003c/p\u003e\n\u003ch3\u003eWeighted gene co-expression network construction\u003c/h3\u003e\n\u003cp\u003eThe WGCNA R package was used to build the co-expression network\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The main steps in the network construction procedure were as follows: (1) define the similarity matrix; (2) choose the weighting coefficient, and convert the similarity matrix to an adjacency matrix; (3) convert the adjacency matrix to a topological overlap matrix (TOM); (4) to obtain the hierarchical clustering tree, perform hierarchical clustering for TOM-based dissimilarity (dissTOM); (5) identify the modules from the hierarchical clustering tree using the dynamic tree cut method; (6) compute the module eigengene (ME) of each module. ME denotes the module's total expression level. The average distance between the MEs of all modules was determined using the 1-Pearson correlation coefficient, obtained using the Pearson correlation coefficients between the MEs of all modules. The MEs of all modules were clustered using the average-linkage hierarchical clustering method, which relies on a minimum size (gene group) of 30. The modules with the highest levels of similarity were then combined to create the co-expression network. The module eigengene (ME) represents the first principal component of the module and is used to describe the expression pattern of the module in each sample. The Pearson correlation coefficients for all modules' MEs and clinical data were computed to see whether modules were connected to clinical traits. In this study, we used the network Screening function based on GS (representing the correlation between the gene and a given clinical trait) and MM (representing the correlation between the gene and a given module) in the WGCNA package to directly identify hub genes\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e which permitted the identification of modules that were significantly related to the trait (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analyses\u003c/h3\u003e\n\u003cp\u003eThe ClusterProfiler R package was used for the hub genes' gene ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, respectively, to determine the hub genes' biological functions and signaling pathways\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The default values were used as the parameters in the ClusterProfiler R package. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were chosen as the cutoffs for identifying the KEGG pathways and GO functions of hub genes, respectively.\u003c/p\u003e\n\u003ch3\u003eIdentification of hub genes\u003c/h3\u003e\n\u003cp\u003eWe used univariate Cox analysis of overall survival (OS) to select the potential CRGs prognostic genes by R \u0026ldquo;survival\u0026rdquo; filtered by P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The coefficients of the regressed variables were compressed by the R package glmnet using the LASSO algorithm so that some regression coefficients were strictly equal to zero to minimize the risk of overfitting, resulting in an interpretable model. Patient signature scores were then calculated based on the gene expression levels and their corresponding regression coefficients.\u003c/p\u003e \u003cp\u003eScore =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=0}^{n}{\\beta\\:}\\text{i}\\:\\text{*}\\:{\\chi\\:}\\text{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eβi: weighting factor for each gene; χi: expression levels of each gene\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredictive ability of the Prognostic Signature of CRGs\u003c/h2\u003e \u003cp\u003eThe prognosis-related CRGs after LASSO regression analysis were divided into high and low groups according to the median expression level, and then KM survival analysis of CRGs was performed using the R package survival and survminer. Univariable and multivariable analyses were performed on the clinical information from the training and validation cohort using the R package coxph, with a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and forest plots were drawn, to see whether the signature was significant in the single- and multi-factor analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCuproptosis signature variation analysis and functional annotation\u003c/h3\u003e\n\u003cp\u003eThe simple nucleotide variation (SNV) and copy number variation (CNV) were based on the training cohort data. Using the R package maftools, samples with high and low groups of CDG signatures were compared at the mutation level using the Fisher test, and mutation results were presented using waterfall plots. The R package ggplot2 pair for CDGs signature was used to map the distribution of CNVs in the genome.\u003c/p\u003e \u003cp\u003eWe downloaded the HALLMARK gene set \u0026ldquo;msigdb.v7.4.symbols.gmt\u0026rdquo; from the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to run the GSVA analysis. Estimate the score of the sample in each pathway. Then use the R package pheatmap to plot the distribution of enrichment scores for different hallmark paths in different groups.\u003c/p\u003e\n\u003ch3\u003eCDG signature tumor microenvironment infiltration characteristics\u003c/h3\u003e\n\u003cp\u003eStromal and immune cells in tumor tissues were estimated by the pearson method using R package estimation, and the Stromal Score and Immune Score were calculated respectively, and finally, an ESTIMATE Score was generated by combining these two scores. The immune infiltration scores were then calculated by ssGSEA, cibersort, and xcell methods, and the differences in immune infiltration scores between high and low signature groups were plotted using the R package ggplot2, and the test for significance of differences was a rank sum test.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell RNA-seq analysis to reveal the expression of key genes across different cell types\u003c/h2\u003e \u003cp\u003eWe employed single-cell RNA-seq analysis to dissect the breast cancer microenvironment and explore key gene expression patterns. Data processing and dimensionality reduction were performed using Seurat, including quality control, normalization (LogNormalize), and identification of highly variable genes. PCA was applied for initial dimensionality reduction, followed by UMAP and kNN clustering to ensure biological consistency. Cell composition was analyzed by calculating proportions and visualizing distributions via bar plots. Key genes (\u003cem\u003eCCDC24\u003c/em\u003e, \u003cem\u003eTMEM65\u003c/em\u003e, \u003cem\u003eXPOT\u003c/em\u003e, \u003cem\u003eNUDCD1\u003c/em\u003e) were analyzed across cell types and visualized using DotPlot with a blue-white-red gradient. UMAP (DimPlot) and bar plots (ggplot2) were used for clustering and cell proportion visualization, respectively. This approach reveals the potential roles of key genes in the tumor microenvironment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expression and Genetic Alterations of Cuproptosis-Related Genes (CRGs) in BRCA\u003c/h2\u003e \u003cp\u003eAnalysis of 10 cuproptosis-related genes (CRGs) in 1,059 breast cancer (BRCA) samples from the TCGA dataset revealed significant transcriptional dysregulation compared to 99 normal breast tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Wilcoxon rank-sum test). Among these, seven CRGs (\u003cem\u003eDLD\u003c/em\u003e, \u003cem\u003eLIAS\u003c/em\u003e, \u003cem\u003ePDHA1\u003c/em\u003e, \u003cem\u003eFDX1\u003c/em\u003e, \u003cem\u003eLIPT1\u003c/em\u003e, \u003cem\u003eMTF1\u003c/em\u003e, and \u003cem\u003eGLS\u003c/em\u003e) were upregulated in tumors, while \u003cem\u003eCDKN2A\u003c/em\u003e and \u003cem\u003ePDHB\u003c/em\u003e were downregulated. Notably, \u003cem\u003eDLAT\u003c/em\u003e expression showed no significant difference between tumor and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eOur findings indicate that dysregulation of copper metabolism-related genes (CRGs) may play a pivotal role in the pathogenesis of breast cancer (BRCA). Breast cancer is molecularly classified into four major subtypes\u0026mdash;Luminal A, Luminal B, HER2-enriched, and triple-negative breast cancer (TNBC)\u0026mdash;based on immunohistochemical profiles and comprehensive genetic analyses. TNBC, representing 15\u0026ndash;20% of invasive breast carcinomas, is characterized by its high molecular heterogeneity, aggressive behavior, and increased risk of recurrence\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo investigate the potential involvement of CRGs in breast cancer biology, we stratified our cohort into TNBC and non-TNBC subgroups for comparative analysis. Differential expression analysis revealed that six CRGs (DLD, GLS, PDHA1, FDX1, CDKN2A, and DLAT) were significantly upregulated in TNBC compared to non-TNBC, while three genes (LIAS, LIPT1, and LIPT2) showed reduced expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Further examination of PAM50 molecular phenotypes demonstrated significant differential expression of ten CRGs across subtypes (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). Notably, with the exception of MTF1, nine CRGs exhibited distinct expression patterns among the five PAM50 classifications (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). These findings collectively demonstrate substantial variations in CRG expression patterns between normal and malignant tissues, as well as across different molecular subtypes, suggesting that transcriptional dysregulation of copper metabolism pathways may contribute to breast cancer initiation and progression.\u003c/p\u003e \u003cp\u003eGenomic analysis revealed frequent copy number variations (CNVs) and somatic mutations in CRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), with specific chromosomal loci showing recurrent alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Quantitative assessment of CNV frequencies demonstrated that MTF1 was predominantly affected by amplification events, while FDX1, DLAT, and PDHB showed higher rates of deletion events compared to other CRGs (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C\u003c/b\u003e). These genomic alterations may contribute to the observed expression patterns and functional consequences of CRG dysregulation in breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNetwork analysis reveals CRG-associated modules in breast cancer\u003c/h2\u003e \u003cp\u003eTo construct a robust gene co-expression network, we first performed scale-free topology analysis to determine the optimal soft-thresholding power (β). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA presents the scale-free topology model fit (left panel) and mean connectivity (right panel) across a range of soft-threshold powers, enabling the selection of the most appropriate β value for network construction.\u003c/p\u003e \u003cp\u003eWe next integrated clinical trait data with CRG expression profiles to identify clinically relevant modules. Hierarchical clustering analysis of module eigengenes (MEs) revealed distinct gene co-expression patterns, with closely related modules subsequently merged to optimize network topology (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). Module-trait relationship analysis, quantified using Pearson correlation coefficients, demonstrated significant associations between specific modules and clinical phenotypes (\u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e). Notably, the pink, yellow, and midnight blue modules showed strong correlations with CRG expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThrough rigorous screening using dual criteria (module membership [MM]\u0026thinsp;\u0026gt;\u0026thinsp;0.6 and gene significance [GS]\u0026thinsp;\u0026gt;\u0026thinsp;0.4), we identified 108 CRGs as key network hubs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e). These criteria ensured the selection of genes that were both highly connected within their respective modules and strongly associated with CRG-related phenotypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the prognostic signature of CRGs in BRCA\u003c/h2\u003e \u003cp\u003eTo establish a robust prognostic model, we utilized the 108 cuproptosis-associated genes identified through WGCNA as candidate predictors. Initial screening via univariate Cox regression analysis identified 23 potential prognostic factors. Patients were stratified into high- and low-expression groups based on median expression values of these factors, with subsequent survival analysis revealing significant differences between groups (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e). Among the top six most significant prognostic genes (ranked by p-value), elevated expression of CCDC24 was associated with improved survival, whereas lower expression levels of CCT6A, CDCA7, TMEM65, SLC7A5, and CCNE1 correlated with better clinical outcomes.\u003c/p\u003e \u003cp\u003eTo refine the model and mitigate overfitting, we performed LASSO regression analysis, which identified four core prognostic genes: CCDC24, TMEM65, XPOT, and NUDCD1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These genes were incorporated into a multivariate risk score model, weighted by their respective LASSO coefficients:\u003c/p\u003e \u003cp\u003eRisk Score = (-0.051 \u0026times; CCDC24) + (6.355e-5 \u0026times; TMEM65) + (0.056 \u0026times; XPOT) + (0.115 \u0026times; NUDCD1)\u003c/p\u003e \u003cp\u003eNotably, the negative coefficient for CCDC24 suggests its potential role as a protective factor, consistent with its association with improved survival in the univariate analysis. This risk score model provides a quantitative framework for assessing patient prognosis based on cuproptosis-related gene expression profiles (\u003cb\u003eSupplementary Figs.\u0026nbsp;3A\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePredictive ability of the Prognostic Signature of CRGs\u003c/h2\u003e \u003cp\u003eTo evaluate the clinical utility of our risk score model, we stratified breast cancer patients from both the TCGA (training cohort) and GEO (validation cohort) datasets into high- and low-risk groups based on the median risk score (\u003cb\u003eSupplementary Figs.\u0026nbsp;3B\u003c/b\u003e). Survival analysis revealed that patients in the low-risk group exhibited significantly better overall survival compared to their high-risk counterparts in both cohorts (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE; \u003cb\u003eSupplementary Figs.\u0026nbsp;3B\u003c/b\u003e), demonstrating the robust prognostic value of the cuproptosis-related gene signature.\u003c/p\u003e \u003cp\u003eThe predictive accuracy of the risk score model was further validated using time-dependent receiver operating characteristic (ROC) curve analysis. In the training cohort, the model achieved area under the curve (AUC) values of 0.595, 0.619, and 0.597 for 1-year, 3-year, and 5-year overall survival predictions, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The validation cohort showed even stronger predictive performance, with corresponding AUC values of 0.738, 0.655, and 0.641 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eGene expression analysis across risk groups revealed distinct patterns among the core prognostic genes. CCDC24, which carries a negative coefficient in the risk score model, was significantly upregulated in the low-risk group. Conversely, TMEM65, XPOT, and NUDCD1\u0026mdash;all positively weighted in the model\u0026mdash;showed higher expression levels in the high-risk group, consistent with their association with poorer clinical outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation and functional analysis of the CRG prognostic signature\u003c/h2\u003e \u003cp\u003eTo assess the independence of the cuproptosis-related gene (CRG) prognostic signature, we performed univariate and multivariate Cox regression analyses. While the signature showed a trend toward significance as an independent prognostic factor (P\u0026thinsp;=\u0026thinsp;0.055, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), further validation in the training cohort confirmed its prognostic value across PAM50 subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eStratification analysis revealed significant associations between risk scores and clinical characteristics. Notably, patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years exhibited higher risk scores compared to older individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Substantial variations in risk scores were observed across molecular subtypes, with Basal-like tumors demonstrating significantly higher risk scores among PAM50 subtypes. Similarly, the C4 immunotype was associated with elevated risk scores. Triple-negative breast cancer (TNBC) cases showed consistently higher risk scores across all three immunohistochemical markers (ER, PR, and HER-2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis reveals distinct biological pathways in risk groups\u003c/h2\u003e \u003cp\u003eTo elucidate the biological functions of the 108 tumor differentiation-associated genes, we performed comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (\u003cb\u003eSupplementary Fig.\u0026nbsp;4A\u003c/b\u003e). GO analysis revealed distinct functional categories:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBiological Processes (BP): Genes were predominantly associated with chromosomal segregation and organelle fission.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCellular Components (CC): Significant enrichment was observed in condensed chromosomes and chromosomal regions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMolecular Functions (MF): Genes were strongly linked to serine kinase activity and single-stranded DNA binding.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eKEGG pathway analysis identified cell cycle regulation and oocyte meiosis as the most significantly enriched pathways. Quantitative pathway activity scoring (\u003cb\u003eSupplementary Fig.\u0026nbsp;4B\u003c/b\u003e) demonstrated distinct metabolic profiles between risk groups. The high-risk group showed reduced association with xenobiotic metabolism and adipogenesis pathways compared to the low-risk group. Conversely, spermatogenesis and MTORC1 signaling pathways were significantly enriched in the high-risk group.\u003c/p\u003e \u003cp\u003eFurthermore, stratification analysis revealed significant differences in molecular subtypes, immunophenotypes, and triple-negative status between high- and low-risk groups, suggesting distinct biological characteristics underlying the risk stratification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the biological underpinnings of the CRG risk scores, we conducted functional enrichment analysis using GO and KEGG databases. Mutation profiling revealed distinct patterns between risk groups, with TP53 and PIK3CA emerging as the most frequently mutated genes (\u003cb\u003eSupplementary Fig.\u0026nbsp;5A\u003c/b\u003e). Notably, TP53 mutations were predominantly missense variants. Comparative genomic analysis demonstrated that the high-risk group exhibited significantly higher rates of deletion mutations and copy number alterations compared to the low-risk group (\u003cb\u003eSupplementary Fig.\u0026nbsp;5B\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmune microenvironment characteristics and therapeutic response in high-risk tumors\u003c/h2\u003e \u003cp\u003eThe potential impact of copper metabolism-related genes (CRGs) on immune cell recruitment within the tumor microenvironment (TME) and its subsequent influence on BRCA prognosis remains poorly understood. To address this, we employed single-sample gene set enrichment analysis (ssGSEA) to quantify the enrichment scores of 28 distinct immune cell populations across risk-stratified groups, thereby investigating the role of the CRG signature in shaping the BRCA TME.\u003c/p\u003e \u003cp\u003eComplementing this analysis, we utilized the ESTIMATE algorithm to assess tumor purity and TME composition, including stromal, immune, and ESTIMATE scores. Comparative analysis revealed significant differences in TME characteristics between risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, the high-risk subgroup exhibited markedly reduced stromal scores compared to the low-risk subgroup, suggesting distinct stromal remodeling patterns associated with CRG expression profiles.\u003c/p\u003e \u003cp\u003eFurther characterization of immune cell infiltration using the CIBERSORT algorithm demonstrated significant correlations between CRG risk scores and specific immune populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). High-risk CRG scores showed negative associations with anti-tumor immune cells, including CD56 dim natural killer cells and plasmacytoid dendritic cells. Conversely, positive correlations were observed with pro-tumorigenic immune populations, such as activated CD4 T cells and activated dendritic cells. These findings suggest that CRG expression patterns may influence BRCA progression through modulation of immune cell composition within the TME.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with drug sensitivity\u003c/h2\u003e \u003cp\u003eWe continued to explore the correlation between CRG risk scores and drug sensitivity, highlighting that the high-risk group exhibits significant resistance to specific drugs (e.g., AZD2014, GNE-317), as evidenced by higher IC\u003csub\u003e50\u003c/sub\u003e values. These findings suggest that CRG risk scores may serve as a predictive biomarker for drug resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD presented a box plot comparing drug sensitivity across high- and low-risk groups, revealing distinct differences in IC\u003csub\u003e50\u003c/sub\u003e values for various compounds. Key observations include differential responses to drugs such as AZD5991 and UMI-77, with the high-risk group demonstrating significant resistance. These findings reinforce the potential utility of CRG-related risk scores in predicting treatment efficacy and guiding personalized therapeutic strategies for breast cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell Transcriptomic Analysis Reveals Tumor Microenvironment Heterogeneity and Cuproptosis-Related Gene Expression in Breast Cancer\u003c/h2\u003e \u003cp\u003eWe further analyzed TME of BRCA tissues using single-cell transcriptomic data and investigated the distribution of CRGs and their expression relationships across different cell types. UMAP dimensionality reduction analysis revealed that BRCA tissues contain diverse cell populations, such as tumor cells, T cells, fibroblasts, and endothelial cells, with each cell type exhibiting distinct functional patterns within the TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Further analysis of cell proportions indicated that cancer cells dominate, while the infiltration of immune cells highlighted their potential roles in immune evasion and anti-tumor immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The expression of cuproptosis-related genes \u003cem\u003eCCDC24\u003c/em\u003e, \u003cem\u003eTMEM65\u003c/em\u003e, \u003cem\u003eXPOT\u003c/em\u003e, and \u003cem\u003eNUDCD1\u003c/em\u003e varied across different cell populations, which may be closely associated with metabolic reprogramming of tumors and changes in the immune microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The differential expression of these genes across cell populations may be closely linked to tumor metabolic reprogramming and immune microenvironment alterations. By analyzing marker genes, the characteristic expression patterns of different cell populations were further validated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). For example, \u003cem\u003eEPCAM\u003c/em\u003e and \u003cem\u003eKRT8\u003c/em\u003e were highly expressed in cancer cells, \u003cem\u003eCD3D\u003c/em\u003e and \u003cem\u003eCD8A\u003c/em\u003e marked T cells, and \u003cem\u003eMS4A1\u003c/em\u003e was predominantly found in B cells. These results not only provide new insights into tumor heterogeneity in BRCA tissues but also offer data support for cuproptosis as a potential therapeutic target.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we employed weighted gene co-expression network analysis (WGCNA) to investigate cuproptosis-related gene (CRG) signatures in breast cancer (BRCA) and evaluated their prognostic potential. WGCNA offers distinct advantages over conventional analytical approaches by identifying co-expression modules with strong correlations to clinical features, providing biologically meaningful insights with high reproducibility. This method enables the identification of functionally related gene clusters within modules, allowing for the discovery of physiologically relevant modules and hub genes that may serve as potential diagnostic or therapeutic biomarkers.\u003c/p\u003e \u003cp\u003eTo our knowledge, this represents the first comprehensive investigation into the relationship between CRGs and BRCA pathogenesis. Our analysis revealed significant differential expression patterns of CRGs between tumor and normal tissues, suggesting a potential role for cuproptosis in BRCA prognosis. Furthermore, we developed a novel CRG-based prognostic scoring system with significant predictive value for BRCA outcomes.\u003c/p\u003e \u003cp\u003eOur genomic analysis of 10 BRCA-related CRGs demonstrated significant expression alterations, with 7 genes showing marked downregulation in tumor tissues, while DLAT expression remained unchanged. CRG expression patterns varied significantly across different clinical subgroups, particularly among PAM50 genotypes. Using WGCNA, we identified four key prognostic genes and constructed a CRG-based prognostic signature for BRCA. This signature demonstrated robust predictive capacity, with lower CRG scores correlating with improved survival outcomes in both training and validation cohorts. Comparative analysis revealed significant differences in overall survival, clinical characteristics, mutational profiles, and tumor microenvironment (TME) features between high-risk and low-risk CRG score groups. Multivariate Cox regression analysis confirmed the CRG signature as an independent prognostic factor for BRCA. Notably, the basal subtype exhibited the highest CRG scores, consistent with its established poor prognosis, while Luminal A showed the lowest scores\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The WGCNA-derived gene modules demonstrated strong biological relevance, with co-expressed genes showing functional relationships. This approach identified physiologically significant modules and hub genes with potential as diagnostic or therapeutic biomarkers. Our analysis also revealed distinct immune infiltration patterns and tumor signaling pathways between the two CRG-based subgroups.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment, comprising tumor cells and their surrounding stromal components including lymphocytes, tumor-infiltrating immune cells, and vascular systems, plays a crucial role in tumor progression and therapeutic resistance\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Our study identified significant differences in the abundance of 23 tumor-infiltrating immune cells (TIICs) and TME characteristics between the high-risk and low-risk groups. Notably, the high-risk subgroup showed significantly lower stromal scores compared to the low-risk subgroup, suggesting a potential role for cuproptosis in modulating TME characteristics and immune cell infiltration in BRCA\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA) revealed significant enrichment of antigen processing and presentation pathways in the high cuproptosis score group. Comparative analysis of somatic mutations and copy number variations (CNVs) between groups showed a significantly higher TP53 mutation frequency in the high cuproptosis score group. This finding aligns with previous studies demonstrating TP53's role in immune signaling modulation and its high mutation frequency in basal subtype BRCA\u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe are currently lacking \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experimental validation of the oncological functions of the key cuproptosis-related genes associated with breast cancer prognosis. However, we have clarified their roles in different cell types through single-cell transcriptomic analysis. We will further investigate the impact of these key genes on breast cancer development in \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments in subsequent series of studies.\u003c/p\u003e \u003cp\u003eOur findings suggest that cuproptosis may represent a novel therapeutic target and prognostic marker in breast cancer. The identification of CRG-based signatures and their association with TME characteristics opens new avenues for breast cancer detection and treatment strategies. These results warrant further investigation into the molecular mechanisms underlying cuproptosis in BRCA pathogenesis and its potential clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest was reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiangxi Provincial Health Commission Youth Project (202510933).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilizes publicly available datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). As both databases consist of anonymized, de-identified patient data, patient involvement was not required, and informed consent was not necessary in compliance with the ethical guidelines governing the use of these resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.R.P. and Y.L. developed the concept and design of this study. R.R.P. and Y.L. analyzed and interpreted the data. R.R.P. and Y.L. wrote the manuscript. Y.L. designed and illustrated the figures. B.X. conducted a critical review of the manuscript and provided constructive feedback and suggestions for revisions. B.X. provided guidance and supervision throughout the writing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent\u0026nbsp;and ethics approval for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCAO W, CHEN H D, YU Y W, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020[J]. Chin Med J (Engl). 2021;134(7):783\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN J, JIANG Y, SHI H, et al. The molecular mechanisms of copper metabolism and its roles in human diseases[J]. Pflugers Arch. 2020;472(10):1415\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTSVETKOV P, COY S. Copper induces cell death by targeting lipoylated TCA cycle proteins[J]. Science. 2022;375(6586):1254\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDUAN F, LI J, HUANG J, et al. Establishment and Validation of Prognostic Nomograms Based on Serum Copper Level for Patients With Early-Stage Triple-Negative Breast Cancer[J]. Front Cell Dev Biol. 2021;9:770115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBIAN Z, FAN R. XIE L. A Novel Cuproptosis-Related Prognostic Gene Signature and Validation of Differential Expression in Clear Cell Renal Cell Carcinoma[J]. Genes (Basel), 2022, 13(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHAN J, HU Y, LIU S et al. A Newly Established Cuproptosis-Associated Long Non-Coding RNA Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Soft Tissue Sarcoma[J]. J Oncol, 2022, 2022: 8489387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLV H, LIU X, ZENG X, et al. Comprehensive Analysis of Cuproptosis-Related Genes in Immune Infiltration and Prognosis in Melanoma[J]. Front Pharmacol. 2022;13:930041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLANGFELDER P. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinformatics. 2008;9:559.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUANG DA W, SHERMAN B T, LEMPICKI RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J]. Nat Protoc. 2009;4(1):44\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYU G, WANG L G, HAN Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters[J]. Omics. 2012;16(5):284\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKUMAR P. An overview of triple-negative breast cancer[J]. Arch Gynecol Obstet. 2016;293(2):247\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOCCO S, PIEZZO M, CALABRESE A et al. Biomarkers in Triple-Negative Breast Cancer: State-of-the-Art and Future Perspectives[J]. Int J Mol Sci, 2020, 21(13).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBRADY D C, CROWE M S, GREENBERG D N, et al. Copper Chelation Inhibits BRAF(V600E)-Driven Melanomagenesis and Counters Resistance to BRAF(V600E) and MEK1/2 Inhibitors[J]. Cancer Res. 2017;77(22):6240\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDAVIS C I, GU X, KIEFER R M, et al. Altered copper homeostasis underlies sensitivity of hepatocellular carcinoma to copper chelation[J]. Metallomics. 2020;12(12):1995\u0026ndash;2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRAMCHANDANI D, BERISA M, TAVAREZ D A, et al. 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Loss of p53 triggers WNT-dependent systemic inflammation to drive breast cancer metastasis[J]. Nature. 2019;572(7770):538\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGHOSH M, SAHA S, BETTKE J, et al. Mutant p53 suppresses innate immune signaling to promote tumorigenesis[J]. Cancer Cell. 2021;39(4):494\u0026ndash;e508495.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHAH SP, GOYA ROTHA. The clonal and mutational evolution spectrum of primary triple-negative breast cancers[J]. Nature. 2012;486(7403):395\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComprehensive molecular portraits. of human breast tumours[J]. Nature. 2012;490(7418):61\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BRCA, cuproptosis, prognostic signature, TME, Single-cell analysis","lastPublishedDoi":"10.21203/rs.3.rs-6656418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6656418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer (BRCA) remains the leading cause of cancer-related mortality in women globally. Recent studies implicate dysregulated copper homeostasis in tumorigenesis, with cuproptosis\u0026mdash;a copper-dependent cell death mechanism\u0026mdash;emerging as a potential therapeutic target. Here, we systematically analyzed the prognostic value of cuproptosis-related genes (CRGs) in BRCA using multi-omics data from TCGA and GEO cohorts. Weighted gene co-expression network analysis (WGCNA) identified four hub genes (\u003cem\u003eCCDC24\u003c/em\u003e, \u003cem\u003eTMEM65\u003c/em\u003e, \u003cem\u003eXPOT\u003c/em\u003e, \u003cem\u003eNUDCD1\u003c/em\u003e) that form a prognostic signature. High-risk scores derived from this signature correlated with poor survival, distinct tumor microenvironment (TME) features, and increased \u003cem\u003eTP53\u003c/em\u003e mutation frequency. Functional enrichment revealed associations with immune evasion and metabolic pathways. Single-cell analysis of BRCA tissues identified diverse cell populations and differential CRG expression (CCDC24, TMEM65, XPOT, NUDCD1), linking cuproptosis to tumor metabolism, immune dynamics, and therapeutic potential. Our findings establish cuproptosis as a critical regulator of BRCA progression and propose a novel prognostic tool for clinical stratification.\u003c/p\u003e","manuscriptTitle":"Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 18:00:45","doi":"10.21203/rs.3.rs-6656418/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-30T09:48:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T02:24:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284069048972557250512164841802239446749","date":"2025-06-26T15:24:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T02:03:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T14:11:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193769604195213761344984466910292688548","date":"2025-06-18T01:54:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191372165949661887495893810493526330947","date":"2025-06-17T09:07:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138283180231424721834550506947769716626","date":"2025-06-17T08:58:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T01:58:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T08:38:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-31T05:05:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-05-31T05:01:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32a27bc1-fcd2-4089-98de-0be6e957a0c5","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-28T06:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 18:00:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6656418","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6656418","identity":"rs-6656418","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}