Transcriptome profiling reveals cuproptosis heterogeneity in triple-negative breast cancer

Transcriptome profiling reveals cuproptosis heterogeneity in triple-negative 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptome profiling reveals cuproptosis heterogeneity in triple-negative breast cancer Xiao-Qing Song, Jun-Han Jiang, Xin-Yi Sui, Zhi-Ming Shao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3724433/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Breast cancer has various subtypes, among which triple-negative breast cancer (TNBC) stands out as the most malignant and heterogeneous form and has a poor prognosis. One area of focus is the phenomenon known as cuproptosis, which represents a recently identified and regulated type of cell death. Unlike previously recognized mechanisms, cuproptosis is characterized by its reliance on both copper and mitochondrial respiration. The relationship between TNBC and cuproptosis remains to be elucidated. Methods We obtained RNA-seq and clinical data form Fudan University Shanghai Cancer Center (FUSCC) and The Cancer Genome Atlas (TCGA) TNBC cohort. We conducted K-means cluster analysis of TNBC based on the expression of 13 cuproptosis regulators (DLAT, DLD, FDX1, LIAS, LIPT1, PDHA1, PDHB, ATP7B, ATP7A, DLST, SLC31A1, DBT and GCSH). Kaplan-Meier survival analysis was performed to determine the survival difference among different clusters. To examine the sensitivity to cuproptosis inducer, cell viability assays was performed in TNBC cell lines with different ATP7B protein expression. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct cuproptosis-associated risk score. Receiver operating characteristic (ROC) and calibration curves were used to evaluated the performance of risk score. Results We classified TNBC into four clusters. Compared with other clusters, cluster 1 exhibited high ATPase copper transporting beta (ATP7B) expression, low immune infiltrate and poor prognosis. Through subsequent experimental validation, we demonstrated ATP7B as a crucial cuproptosis regulator, TNBC cells with low ATP7B expression or ATP7B knockdown indicated drug-sensitive to cuproptosis inducer. In addition, we constructed and validated a cuproptosis-associated signature of 11 genes (CFAP44, HLA-C, PLA2G16, RDH10, GPC4, NEBL, NCAM2, MKL2, TRIQK, ATP7B and TMEM178). The 1-year, 3-year, and 5-year area under the ROC curve (AUC) in the training cohort were 0.69, 0.80, and 0.76, while in test cohort were 0.91, 0.85, and 0.82. The results suggested our cuproptosis-associated signature could accurately predict the progonosis for TNBC patients. Conclusion Our results provide new insights into heterogeneous phenotypes in cuproptosis for TNBC, and may inspire new approaches for TNBC treatment. TNBC Cuproptosis Heterogeneity ATP7B Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Triple-negative breast cancer (TNBC) accounts for approximately 15–20% of newly diagnosed breast cancer cases. It is distinguished by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression[ 1 , 2 ]. TNBC is an exceptionally aggressive and challenging clinical subtype within the diverse spectrum of breast cancer. This subtype poses significant difficulty to clinicians due to its unique characteristics and limited treatment options[ 3 , 4 ]. Previous studies have idicated that TNBC has a high degree of heterogeneity[ 5 , 6 ]. Previous studies have investigated the heterogeneity of TNBC from different perspectives. Recently, based on transcriptomic profiling, TNBC was classified into the luminal androgen receptor (LAR), immunomodulatory (IM), basal-like immune-suppressed (BLIS) and mesenchymal-like (MES) subtypes[ 7 ]. In another study, through a comprehensive analysis linking the TNBC metabolome to genomics, TNBC was classified into three distinct metabolomic subgroups[ 8 ]. Fan et al . found that TNBC exhibited heterogeneous phenotypes in ferroptosis-associated metabolic pathways. Along this line, further research demonstrated that the LAR subtype of TNBC was more sensitive to GPX4 inhibitors[ 9 ]. However, the interpretation and identification of TNBC heterogeneity is incompletely defined. Copper accumulation causes cuproptosis, which is a newly identified type of regulatory cell death[ 10 ]. Unlike other types of regulatory cell death, pyroptosis specifically affects mitochondrial lipoylated components of the tricarboxylic acid cycle (TCA). This process induces proteotoxic stress and ultimately results in cell death[ 10 , 11 ]. Recent studies have indicated that cuproptosis may function during cancer development, suggesting that it is expected to become a target for tumor treatment[ 12 ]. However, few studies have elucidated the relationship between cuproptosis and TNBC heterogeneity. Through transcriptome analysis in this study, we successfully categorized TNBC into four distinct clusters by evaluating the expression levels of cuproptosis-associated genes (CRGs). Furthermore, our investigation revealed ATP7B as a pivotal controller of cuproptosis. In addition, we created the CRG score to predict recurrence-free survival (RFS) for TNBC patients. Based on our findings, this study offers novel insights and valuable references for the advancement of treatment strategies targeting cuproptosis in TNBC. Methods Patient cohort In our research, we used a TNBC cohort obtained from our previous study[ 7 ]. This cohort comprised a total of 465 individuals who were diagnosed with TNBC[ 7 ]. Among the patients included in our study, 360 individuals had available RNA-seq data, and 279 patients had available WES data. The FUSCC Ethics Committee granted approval for our research, allowing us to obtain tissue samples. Prior to sample collection, written informed consent was collected from each patient, ensuring their understanding and agreement regarding the use of their data and tissue. TCGA TNBC RNA-Seq data were obtained from the website. https://tcga-data.nci.nih.gov/tcga/ . Expression-Based Unsupervised Clustering We were able to collect a signature consisting of 13 CRGs from previously published works[ 10 , 13 ]. To ascertain the ideal number of subtypes in mRNA data for TNBC, we employed k-means clustering as well as consensus clustering methods based on the expression of CRGs. This approach allowed us to accurately determine the most suitable TNBC subtypes. The “kmeans” function in R was employed for k-means clustering, while the “ConsensusClusterPlus” package in R was utilized for consensus clustering[ 14 ]. To assess the robustness of k-means clustering, we conducted consensus clustering with 1,000 iterations and 0.8 resampling. The optimal cluster number was determined using cumulative density function (CDF) analysis. The CDF curve, which spanned from 0 to 1, depicted the empirical cumulative distribution. Moreover, we calculated the proportionate increase in the area beneath the CDF curve. The cluster number was determined when additional increments in the cluster number (k) no longer yielded a substantial increase in the CDF area. Correlations between the clusters and clinical features and functional annotations To explore the potential clinical roles of the four cuproptosis-related clusters, we investigated the correlation between these subtypes and primary clinical and pathological parameters in patients with TNBC. These parameters included tumor size (T phase), lymph node involvement (N phase), mRNA subtype, and prognosis. To assess differences in relapse-free survival among the subtypes, we employed the Kaplan‒Meier survival analysis technique. We used the limma R package to analysis genes that were differentially expressed between cluster 1 and the remaining clusters. To be considered differentially expressed genes (DEGs), to meet the criteria for gene selection, the adjusted p value had to be below 0.05, and the fold change needed to exceed 0.5. Furthermore, Gene enrichment analysis and gene set enrichment analysis were conducted using ClusterProfiler package. The mutation profiles were visualized using the maftools R package. Quantification of immune cell populations in tumor tissues To estimate the relative subsets of RNA transcripts and identify cell types, we employed the CIBERSORT tool in absolute mode. This tool, accessible at https://cibersort.stanford.edu/ , facilitated the calculation of immune cell subset abundances across 22 different types within each sample[ 15 ]. Subsequently, we extracted and compared cell subsets known for their involvement in tumor cell elimination or tumor progression between cluster 1 and the remaining clusters. Evaluation of tumor infiltrating lymphocytes (TILs) According to our previous study[ 7 ], the evaluation of TILs was conducted on sections stained with hematoxylin and eosin. Stromal tumor-infiltrating lymphocytes (sTILs) are lymphocytes that reside within the stromal elements of tumor tissues, while intratumoral tumor-infiltrating lymphocytes (iTILs) are lymphocytes located within the epithelial components of tumor tissues. Two pathologists independently assessed each patient case according to the relevant guidelines. The scoring of sTILs and iTILs was performed separately for comprehensive evaluation. Creating and confirming the predictive CRG score To mitigate the potential for model overfitting, we employed the glmnet package in the R programming language for conducting least-squares regressions and selection operator regressions. Furthermore, we utilized a multivariate Cox regression with proportional hazards analysis to forecast the RFS of patients within the training dataset. To assess the accuracy of the CRG score, we generated receiver operating characteristic (ROC) curves and calibration plots using the survivalROC and rms R packages. Cell lines The TNBC cell lines were purchased from the American Type Culture Collection (ATCC, USA). Specific media were used for culturing the cell lines: DMEM supplemented with 10% fetal bovine serum (FBS) for MDA-MB-231, BT549, MDA-MB-468, MDA-MB-453, Hs578T, and HEK293T cells and RPMI 1640 supplemented with 10% FBS for HCC1806 cells. Regular testing with a Mycoplasma Detecting Kit (Vazyme) confirmed the absence of mycoplasma contamination in all cell lines. All cells were maintained under standard culture conditions, which included incubation at 37°C with 5% CO2. Furthermore, the authenticity of the cell lines was verified through STR profiling. Transfection and virus infection To generate lentiviruses, the pLKO.1 vector and packaging plasmids (psPAX2 and pMD2.G) were utilized in conjunction with HEK293T cells. An annealing oligonucleotide was synthesized (Sangon Biotech) to target ATP7B, which was then cloned and inserted into pLKO.1-Puro. After the completion of the experiment, the liquid portion, known as the supernatant, was gathered and passed through a syringe filter. Lentivirus was delivered to the target cells with a multiplicity of infection of 0.7, along with polybrene (6 mg/ml; Sigma‒Aldrich). Subsequently, puromycin was used to screen the TNBC cells and obtain stably transfected or infected cells. The knockdown efficiency of shRNAs was validated through qRT‒PCR. For the subsequent experiments, we chose the two shRNAs that exhibited the highest knockdown efficiency. The shRNA target sequences for ATP7B can be found in Supplementary Table 1. RNA isolation and qRT‒PCR To obtain cellular material, TRIzol reagent (Invitrogen) was employed, followed by reverse transcription into cDNA. Primers were designed using PrimerBank. Quantitative real-time PCR (qRT‒PCR). The qRT‒PCR data were normalized to the expression level of the ACTB gene (actin beta), which served as a reference gene. We calculated the relative gene expression values using the formula 2(-ΔCt). The primers specific to the target genes for amplification can be found in Supplementary Table 2. Western blotting Cellular proteins were collected utilizing RIPA lysis buffer and quantified via a BCA reagent kit (Solarbio). Subsequently, we performed SDS‒PAGE to separate the protein samples, followed by transferring them onto polyvinylidene difluoride membranes (Millipore). Incubation with specific primary antibodies was conducted, followed by incubation with secondary antibodies. Enhanced chemiluminescence (Pierce Biotechnology) was employed for detection. To acquire images, we utilized the Molecular Imager ChemiDoc XRS system equipped with Image Lab Software (Bio-Rad). In vitro cell viability assays To conduct cell viability assays, TNBC cells were cultured in 96-well plates at optimal densities per well using their respective growth medium. Following overnight incubation for adherence, the seeding densities were determined to ensure 75–80% confluence of each cell line at the end of the assay. Once confluence was achieved, to achieve the desired concentrations, specific inhibitors were added to 100 µl of fresh medium, replacing the growth medium accordingly. After 24 hours, Cell Counting Kit-8 (Yeasen, #40203ES92) was employed to evaluate cell viability. The absorbance at 450 nm was measured. The IC50 value, which indicates the concentration of the drug leading to a 50% decrease in cell viability, was calculated after cuproptosis inducer treatment for 24 hours. To perform colony formation survival assays, 5 × 10^3 cells were plated on 12-well plates and exposed to the specified drug treatments. After a 14-day period of incubation, the cells were treated with methanol for fixation and subsequently stained using a solution of crystal violet at a concentration of 0.5%. Subsequently, cell counting was conducted under a microscope to determine the number of colonies formed. Antibodies and chemical products The study employed a range of primary antibodies, including anti-ATP7B (Proteintech, cat no. 19786-1-AP) and anti-vinculin (Proteintech, cat no. 66305-1-Ig). Furthermore, secondary antibodies were obtained from Jackson ImmunoResearch, including horseradish peroxidase-conjugated anti-mouse (diluted at 1:5000) and anti-rabbit (diluted at 1:5000). The following chemical products were used: Elesclomol, a cuproptosis inducer (MedChemExpress, cat no. HY-12040), and copper (Sigma, cat no. 7440-50-8). Statistical analysis We used Student’s t test and LogRank test when appropriate to determine the statistical significance. Unless otherwise specified in the figure legend, the mean ± SD or the mean ± SEM. Three independent repeated experiments were performed for all cell-based in vitro experiments in triplicate. Statistical analyses were conducted using R software (R studio 3.5.2) or GraphPad Prism software (GraphPad Prism 9.0). A P value less than 0.05 was considered statistically significant, employing two-sided tests. R software can be obtained from the website. http://www.R-project.org . Results The expression prognostics values of CRGs in TNBC Thirteen previously defined CRGs were included in our study[ 10 ]. First, we analysed the expression of CRGs in TNBC and paracancerous tissues in the FUSCC TNBC and TCGA cohorts. The results of the FUSCC TNBC cohort indicated that the expression of DLAT, PDHA1 and SLC31A1 in TNBC tissues was upregulated. In contrast, the expression of DLD, FDX1, LIAS, LIPT1, ATP7B, ATP7A, DLST and DBT was downregulated in TNBC tissues. The expression of PDHB and GCSH showed a nonsignificant difference in TNBC tissues compared with paracancerous tissues ( Fig. 1 A ) . The results of the TCGA cohort showed that the expression of DLAT, ATP7B, SLC31A1 and GCSH in TNBC tissues was upregulated. In contrast, the expression of LIAS, LIPT1, PDHA1, PDHB, ATP7A, DLST and DBT in TNBC tissues was downregulated, and the expression of DLD and FDX1 showed a nonsignificant difference in TNBC tissues compared with paracancerous tissues (Fig. S1 A) . We then analysed the prognostic values of CRGs in TNBC. The results of the FUSCC TNBC cohort indicated that upregulation of LIPT1, ATP7B and ATP7A was correlated with poor prognosis of TNBC. The remaining CRGs showed no prognostic value in TNBC ( Fig. 1 B-N ) . The results of the TCGA cohort indicated that upregulation of DLST indicated poor prognosis of TNBC. In contrast, low expression of DLD, LIPT1, PDHA1, and GCSH indicated poor prognosis in TNBC, and the expression of the remaining CRGs showed no prognostic value in TNBC (Fig. S1 B-N) . These findings point to a potential involvement for CRGs in TNBC progression. Cuproptosis-based TNBC subtypes After conducting consensus clustering, we determined and chose four as the number of subtypes ( Fig. 2 A-B; Fig. S2 A). The PCA results demonstrated good discrimination between the four clusters ( Fig. 2 C ) . Next, we utilized unsupervised k-means clustering to classify 360 TNBC patients into four clusters based on the expression of 13 CRGs ( Fig. 2 D ) . Then, we investigated the prognostic value of these clusters. The results of survival analysis exhibited a substantial overall difference between the four clusters (P = 0.002) ( Fig. 2 E ). In addition, cluster 1 (n = 61) had the worst prognosis compared with the other subtypes (cluster 1 vs cluster 2, P = 0.02; cluster 1 vs cluster 3, P < 0.001, cluster 1 vs cluster 4, P = 0.03). Identification of molecular features for cluster 1 We aimed to elucidate the molecular characteristics of cluster 1 because it has a poor prognosis compared with the other clusters. The findings indicated that ATP7B exhibited higher expression in cluster 1 than in the remaining clusters ( Fig. 2 D ) , suggesting that ATP7B may have a functional role in TNBC progression. Furthermore, we conducted an analysis of gene expression and discovered 1529 genes that were differentially expressed (DEGs) between cluster 1 and the remaining clusters. The enrichment analysis of biological processes (BP) revealed that the DEGs were primarily enriched in mitotic nuclear division and nuclear division (Fig. S3A) . The enrichment analysis of cellular components (CC) revealed that the DEGs were primarily enriched in the extracellular matrix containing collagen and the immunoglobulin complex (Fig. S3B) . The analysis of molecular function (MF) revealed that the DEGs were highly concentrated in antigen binding and the structural constituents of the extracellular matrix (Fig. S3C) . According to the KEGG enrichment analysis, the DEGs play a role in both the PI3K-Akt signaling pathway and the cell cycle (Fig. S3D) . We next examined the immune microenvironment differences between cluster 1 and other clusters. Gene Set Enrichment Analysis (GSEA) revealed suppression of the inflammatory response and interferon alpha pathway cluster 1 and other clusters ( Fig. 3 A and B) . Furthermore, the occurrence of both iTILs and sTILs in cluster 1 surpassed the levels observed in the remaining clusters ( Fig. 3 C ) . Moreover, we calculated the composition ratio of 22 immune cell types. The findings indicated that cluster 1 had lower enrichment for immune-activated cells ( Fig. 3 D ) . In addition, expression profiling demonstrated that most immunostimulators and immunoinhibitors were significantly downregulated in cluster 1 ( Fig. 3 E and F) . These findings suggest that patients in other clusters may benefit more from immune checkpoint inhibitor therapy than patients in cluster 1. ATP7B plays an essential role in cuproptosis regulation Since cluster 1 is associated with poor prognosis and characterized by upregulation of ATP7B, we investigated the biological function of ATP7B in regulating TNBC cuproptosis. We first examined the basal expression of ATP7B protein in TNBC cell lines. The findings indicated that ATP7B exhibited low expression levels in MDA-MB-231, MDA-MB-453 and BT549 cells but was highly expressed in HCC1806, Hs578T and SUM159 cells ( Fig. 4 A ) . Next, we investigated whether ATP7B expression can affect sensitivity to cuproptosis inducers. The results showed that TNBC cells with low ATP7B expression were more sensitive to the cuproptosis inducer ( Fig. 4 B and C) . To further verify this conclusion, we constructed stable ATP7B-knockdown HCC1806 and SUM159 cells (Fig. S4A) . The results indicated that ATP7B knockdown increased sensitivity to the cuproptosis inducer (Fig. S4B) . Figures depicting CCK-8 and colony formation assays revealed that the depletion of ATP7B did not have a notable impact on cellular proliferation (Fig. S5A-D) . Taken together, our findings indicate that ATP7B is a crucial regulator of cuproptosis. Construction and validation of the cuproptosis-related risk score Our next objective was to develop a cuproptosis-associated risk score to forecast the prognosis of TNBC patients. Through univariate Cox analysis, we identified 17 prognosis-associated CRGs ( Fig. 5 A ) . Then, we used LASSO Cox regression analysis and identified 11 CRGs: CFAP44, HLA-C, PLA2G16, RDH10, GPC4, NEBL, NCAM2, MKL2, TRIQK, ATP7B and TMEM178 ( Fig. 5 B ) . These genes were utilized for the development of a predictive risk model. The risk score was calculated using the following formula: risk score = (CFAP44 expression * -0.44) + (HLA-C expression * -0.07) + (RDH10 expression * 0.08) + (PLA2G16 expression * 0.06) + (GPC4 expression * 0.09) + (NEBL expression * 0.10) + (NCAM2 expression * 0.01) + (MKL2 expression * 0.07) + (TRIQK expression * 0.12) + (ATP7B expression * 0.26) + (THEM178 expression * 0.26). After calculating the risk score for each patient, all the patients were divided into two groups based on whether their scores were above or below the median value. The risk score distribution between different risk groups in the training and validation sets is shown in Fig. 5 C and F. The prognostic outcome and survival time of patients in different risk groups in the training and validation sets are shown in Fig. 5 D and G. Next, we showed the relative expression of 11 CRGs in each patient in the training and validation sets ( Fig. 5 E and H). Afterwards, we examined the association between the risk score and prognosis. The results showed that patients who developed recurrence had higher risk scores ( Fig. 5 I ) . The analysis of survival indicated that the high-risk group had poorer recurrence-free survival (RFS) than the low-risk group (P < 0.05) ( Fig. 5 J ) . To enhance the accuracy for prediction, we integrated the risk score with the pathologic T and N stage and then created a nomogram ( Fig. 6 A ) . ROC curves and calibration plots were used to evaluate the precision of the model. The AUC values for the 1-year, 3-year, and 5-year AUC in the FUSCC TNBC cohort were 0.69, 0.80, and 0.76, respectively, while the AUC in TCGA cohort were 0.91, 0.85, and 0.82 ( Fig. 6 B and C) . The calibration graph also indicated that the forecast likelihood of the nomogram aligned with the real likelihood of RFS after 1, 3, and 5 years ( Fig. 6 D and E) . Taken together, our results indicated that the cuproptosis-associated risk score could accurately predict prognosis and is expected to be a predictor of prognosis in TNBC patients. Molecular features of the high- and low-risk groups We next examined the difference between the low- and high-risk groups. We conducted an analysis of differential expression for the prognostic genes identified in the high-risk and low-risk groups. A total of 301 genes with differential expression (p < 0.05, |log (FC)| ≥ 0.5) were found between the high-risk and low-risk groups. The enrichment analysis results of BP indicated that the DEGs were primarily enriched in immunoglobulin production and phagocytosis recognition pathways (Fig. S6A) . The results of the enrichment analysis for CC indicated that the DEGs were primarily enriched in immunoglobulin complex and immunoglobulin complex circulating pathways (Fig. S6B) . The results of the MF analysis indicated that the DEGs were primarily enriched in antigen binding and immunoglobulin receptor binding pathways (Fig. S6C) . According to the KEGG pathway enrichment analysis findings, the enrichment of DEGs was primarily observed in the pathways involving viral protein interactions with cytokines and cytokine receptor pathways (Fig. S6D) . In addition, we conducted additional research on the genetic characteristics within the high- and low-risk groups. The top five mutated genes in the low-risk group were TP53, TTN, PIK3CA, TNXB, and OBSCN, while in the high-risk group, the top five mutated genes were TP53, PI3KCA, TTN, MUC16, and KMT2C (Fig. S7A and B) . The combined findings indicated that the low- and high-risk groups possess unique molecular characteristics. Discussion In our study, transcriptome analysis revealed heterogeneous TNBC cuproptosis phenotypes. Using transcriptome data and drug response tests, we identified ATP7B as a key regulator of cuproptosis. In addition, we constructed a cuproptosis-associated prognostic model that could accurately predict the prognosis of TNBC patients. Human survival relies on copper, as it helps produce proteins and regulate cellular signaling pathways such as those involved in energy production[ 16 ]. To guarantee the best possible cellular functioning, it is crucial to maintain the appropriate balance of copper within the cell[ 17 ]. On the other hand, excess copper can result in the demise of cells. The discovery of a new form of cell death, known as 'cuproptosis', has revealed that protein lipoylation plays a role in the death of cells caused by copper[ 10 ]. Despite the identification of various crucial genes associated with cuproptosis[ 18 – 20 ], there is a lack of comprehensive understanding regarding the characteristics of cuproptosis in cancer. In addition, the heterogeneity of cuproptosis-related features correlated with the prognosis of TNBC has not been fully illustrated. We categorized TNBC into four groups and identified variation in cuproptosis within TNBC based on the expression of cuproptosis-associated genes. Next, we focused on cluster 1, as it was characterized by worse prognosis among all clusters. We found that the expression of ATP7B was higher in cluster 1 than in the other clusters. These results further suggested that ATP7B may serve as a key regulator of cuproptosis in TNBC. The ATP7B gene encodes copper-transporting P-type ATPase, which moves copper to the basement membrane of the extracellular matrix[ 21 ]. The mutation of ATP7B damages the nervous system, causing Wilson disease[ 22 , 23 ]. According to reports, ATP7B can attach to platinum medications and remove them from malignant cells, resulting in resistance to chemotherapy[ 24 ]. In our study, we found that ATP7B knockdown sensitized cells to the cuproptosis inducer. We considered ATP7B upregulation to be a decisive driver of the cuproptosis phenotype in TNBC, but a detailed mechanism needs further investigation. Tumor cells and stromal cells make up the tumor microenvironment (TME)[ 25 – 27 ]. A growing number of studies indicate that the TME has a significant impact on facilitating the advancement of tumors[ 28 – 30 ]. An in-depth investigation of the TME may help to identify novel mechanisms and have implications for immunotherapy. Immunotherapy has recently become a promising and appealing treatment option for tumors[ 31 , 32 ]. However, a significant number of individuals still do not respond to this form of treatment[ 33 – 35 ]. The impact of the tumor microenvironment on immune repertoires and functional status could explain the diverse immune therapeutic responses observed[ 36 ]. In our study, our results showed differences in characterizations of the TME between cluster 1 and other clusters. Compared with other clusters, cluster 1 displayed an immunosuppressed TME, with lower TIL infiltration and lower expression of immunostimulators and immune checkpoint molecules. Hence, individuals belonging to cluster 1 might experience restricted advantages from immunotherapy. The findings of our research suggested a connection between cuproptosis and the immune microenvironment of TNBC. Additionally, we proposed that identifying TNBC cuproptosis subtypes could help to identify individuals who have a higher probability of benefiting from immunotherapies. However, further evidence from clinical trials is needed to fully support this claim. In addition, we developed and validated the CRG score. Patients with low CRG risk scores and those with high CRG risk scores had significant differences in RFS. To enhance the predictive accuracy and increase clinical utility, we constructed a nomogram by incorporating clinicopathologic factors and CRG risk scores. Our nomogram provides new ideas for the treatment of TNBC. Our study has the following limitations. First, our study only focused on transcriptomic analyses. Multiomics data need to be integrated to develop the cuproptosis atlas and understand the mechanisms of cuproptosis heterogeneity in TNBC. Second, although we discovered that the differential expression of ATP7B could impact the sensitivity of TNBC cells to cuproptosis inducers, in vivo studies will be needed to validate our findings. In addition, additional work is needed to elucidate the upstream factors that govern ATP7B expression. Third, our nomogram needs to be validated in more external datasets. Conclusions In conclusion, our study first revealed cuproptosis heterogeneity in TNBC. Along this line, we identified ATP7B as a key regulator of TNBC cuproptosis. In addition, our nomogram, as a comprehensive model integrating CRG risk scores and clinicopathological factors, is effective in predicting the prognosis of TNBC. Our study may spark novel ideas for TNBC treatment. Abbreviations TNBC, triple-negative breast cancer; FUSCC, Fudan University Shanghai Cancer Center; TCGA, The Cancer Genome Atlas; ATP7B, ATPase copper transporting beta; DEGs, differential expressed genes; CRGs, cuproptosis-related genes; ER, estrogen receptor, PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; LAR, luminal androgen receptor; IM, immunomodulatory, BLIS, basal-like immune-suppressed; MES, mesenchymal-like; TCA, tricarboxylic acid cycle; RFS, recurrence-free survival; CDF, cumulative density function; TILs, tumor infiltrating lymphocytes; sTILs, stromal tumor-infiltrating lymphocytes; iTILs, intratumoral tumor infiltrating lymphocytes; ROC, receiver operating characteristic; AUC, area under the ROC curve; LASSO, least absolute shrinkage and selection; FBS, fetal bovine serum; CC, cellular components; MF, molecular function; BP, biological processes; GSEA, Gene Set Enrichment Analysis; TME, tumor microenvironment. Declarations Ethics approval and consent to participate FUSCC TNBC cohort was described in our previous study and approved from the FUSCC Ethics Committee[7]. Consent for publication Not applicable Availability of data and materials Publicly available datasets were analysed in this study. These data can be found here: the raw data were obtained from publicly available datasets (European Genome-phenome Archive with the study ID EGAS00001005061, TCGA), and our FUSCC datasets have been uploaded to the National Omics Data Encyclopedia (OEP000155). Competing interests The authors declare that they have no competing interests Funding This work was supported by grants from the National Key Research and Development Project of China (2020YFA0112304; to Z.S.) Authors' contributions ZMS designed the study. XQS analysed the data and performed the research. XQS, JHJ and XYS wrote the manuscript. All the authors have read and approved the final manuscript. 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Liu X, Luo B, Wu X, Tang Z: Cuproptosis and cuproptosis-related genes: Emerging potential therapeutic targets in breast cancer . Biochimica et biophysica acta Reviews on cancer 2023, 1878 (6):189013. Dreishpoon MB, Bick NR, Petrova B, Warui DM, Cameron A, Booker SJ, Kanarek N, Golub TR, Tsvetkov P: FDX1 regulates cellular protein lipoylation through direct binding to LIAS . The Journal of biological chemistry 2023, 299 (9):105046. Batzios S, Tal G, DiStasio AT, Peng Y, Charalambous C, Nicolaides P, Kamsteeg EJ, Korman SH, Mandel H, Steinbach PJ et al : Newly identified disorder of copper metabolism caused by variants in CTR1, a high-affinity copper transporter . Human molecular genetics 2022, 31 (24):4121-4130. Singh N, Kallollimath P, Shah MH, Kapoor S, Bhat VK, Viswanathan LG, Nagappa M, Bindu PS, Taly AB, Sinha S et al : Genetic analysis of ATP7B in 102 south Indian families with Wilson disease . PloS one 2019, 14 (5):e0215779. Collins CJ, Yi F, Dayuha R, Duong P, Horslen S, Camarata M, Coskun AK, Houwen RHJ, Pop TL, Zoller H et al : Direct Measurement of ATP7B Peptides Is Highly Effective in the Diagnosis of Wilson Disease . Gastroenterology 2021, 160 (7):2367-2382.e2361. Cai H, Cheng X, Wang XP: ATP7B gene therapy of autologous reprogrammed hepatocytes alleviates copper accumulation in a mouse model of Wilson's disease . Hepatology (Baltimore, Md) 2022, 76 (4):1046-1057. Yu Z, Cao W, Ren Y, Zhang Q, Liu J: ATPase copper transporter A, negatively regulated by miR-148a-3p, contributes to cisplatin resistance in breast cancer cells . Clinical and translational medicine 2020, 10 (1):57-73. Binnewies M, Mujal AM, Pollack JL, Combes AJ, Hardison EA, Barry KC, Tsui J, Ruhland MK, Kersten K, Abushawish MA et al : Unleashing Type-2 Dendritic Cells to Drive Protective Antitumor CD4(+) T Cell Immunity . Cell 2019, 177 (3):556-571.e516. Zhang Z, Zeng X, Wu Y, Liu Y, Zhang X, Song Z: Cuproptosis-Related Risk Score Predicts Prognosis and Characterizes the Tumor Microenvironment in Hepatocellular Carcinoma . Frontiers in immunology 2022, 13 :925618. Ruscetti M, Morris JPt, Mezzadra R, Russell J, Leibold J, Romesser PB, Simon J, Kulick A, Ho YJ, Fennell M et al : Senescence-Induced Vascular Remodeling Creates Therapeutic Vulnerabilities in Pancreas Cancer . Cell 2020, 181 (2):424-441.e421. Sathe A, Grimes SM, Lau BT, Chen J, Suarez C, Huang RJ, Poultsides G, Ji HP: Single-Cell Genomic Characterization Reveals the Cellular Reprogramming of the Gastric Tumor Microenvironment . Clinical cancer research : an official journal of the American Association for Cancer Research 2020, 26 (11):2640-2653. Galvani E, Mundra PA, Valpione S, Garcia-Martinez P, Smith M, Greenall J, Thakur R, Helmink B, Andrews MC, Boon L et al : Stroma remodeling and reduced cell division define durable response to PD-1 blockade in melanoma . Nature communications 2020, 11 (1):853. Huang KW, Hsu FF, Qiu JT, Chern GJ, Lee YA, Chang CC, Huang YT, Sung YC, Chiang CC, Huang RL et al : Highly efficient and tumor-selective nanoparticles for dual-targeted immunogene therapy against cancer . Science advances 2020, 6 (3):eaax5032. Zhao Z, Zheng L, Chen W, Weng W, Song J, Ji J: Delivery strategies of cancer immunotherapy: recent advances and future perspectives . Journal of hematology & oncology 2019, 12 (1):126. Dijkstra KK, Cattaneo CM, Weeber F, Chalabi M, van de Haar J, Fanchi LF, Slagter M, van der Velden DL, Kaing S, Kelderman S et al : Generation of Tumor-Reactive T Cells by Co-culture of Peripheral Blood Lymphocytes and Tumor Organoids . Cell 2018, 174 (6):1586-1598.e1512. Joshi SS, Badgwell BD: Current treatment and recent progress in gastric cancer . CA: a cancer journal for clinicians 2021, 71 (3):264-279. Kunk PR, Bauer TW, Slingluff CL, Rahma OE: From bench to bedside a comprehensive review of pancreatic cancer immunotherapy . Journal for immunotherapy of cancer 2016, 4 :14. Principe DR, Korc M, Kamath SD, Munshi HG, Rana A: Trials and tribulations of pancreatic cancer immunotherapy . Cancer letters 2021, 504 :1-14. Lakins MA, Ghorani E, Munir H, Martins CP, Shields JD: Cancer-associated fibroblasts induce antigen-specific deletion of CD8 (+) T Cells to protect tumour cells . Nature communications 2018, 9 (1):948. Additional Declarations No competing interests reported. Supplementary Files SupplementalTables.xlsx SupplementalFigures.pdf DatafileS1.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3724433","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":258283927,"identity":"224ce61f-d444-4f5e-96f4-dbfb6bec3cf7","order_by":0,"name":"Xiao-Qing Song","email":"","orcid":"","institution":"Fudan University, Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao-Qing","middleName":"","lastName":"Song","suffix":""},{"id":258283929,"identity":"2f1e7463-8525-4f89-892c-4d572f3c2721","order_by":1,"name":"Jun-Han Jiang","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun-Han","middleName":"","lastName":"Jiang","suffix":""},{"id":258283932,"identity":"5db834e5-126b-4a98-bbcf-499b1388dc76","order_by":2,"name":"Xin-Yi Sui","email":"","orcid":"","institution":"Fudan University, Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin-Yi","middleName":"","lastName":"Sui","suffix":""},{"id":258283933,"identity":"a5d0d75a-1985-47e8-864d-e232724a3934","order_by":3,"name":"Zhi-Ming Shao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACCcYGBgYDEIsHiCsk5ORJ1HLGwtiwgaAWOAuohbGtIpHhAAEd8rObG5huFNyxa+A/e/DDx3kSCYwNzA8f3cCjhXHOwQbmHINnyQ0SecmSM7dJ5LEzsBkb5+DRwiyRCNJyOJlBgsdAmnebRDFjAw+bND4tbHAt/GeMf/POAXIPENDCA9Vix8CQYybN20CEFgmolgQGiRwzyxnHJIwNmwn4RX5G+gPmnD+H7UEOu/Ghpk5Onr354WN8WoCA/QeQSNx/AMZnxq8cDuyJVDcKRsEoGAUjEQAApzJD41t4WXkAAAAASUVORK5CYII=","orcid":"","institution":"Fudan University, Shanghai Cancer Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhi-Ming","middleName":"","lastName":"Shao","suffix":""}],"badges":[],"createdAt":"2023-12-08 08:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3724433/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3724433/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48152054,"identity":"73b954dd-2e57-45e1-8d81-eb2a1d9b3c4c","added_by":"auto","created_at":"2023-12-13 19:47:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":871923,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression and prognostic analysis of the 13 cuproptosis regulators in the FUSCC TNBC cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eExpression of 13 cuproptosis regulators in TNBC tissues and normal tissues in the FUSCC TNBC cohort. \u003cstrong\u003eB-N. \u003c/strong\u003eRelapse-free survival analysis of cuproptosis regulators in the FUSCC TNBC cohort. *, P \u0026lt; 0.05, **, P \u0026lt; 0.01, ***, P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/801507a2adec4f33c5c1d7e2.png"},{"id":48150036,"identity":"e5fa95c9-7ad0-4094-8592-618d3155815f","added_by":"auto","created_at":"2023-12-13 19:31:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":724032,"visible":true,"origin":"","legend":"\u003cp\u003eTNBC exhibits cuproptosis heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B. \u003c/strong\u003eThe scree plot and complementary cumulative probability (CCDF) plot illustrate the proportional alteration in the area under the cumulative distribution function (CDF) curve, ranging from k = 2 to k = 5. \u003cstrong\u003eC.\u003c/strong\u003e A considerable transcriptome divergence between the four clusters is seen by PCA. \u003cstrong\u003eD.\u003c/strong\u003e k-means clustering of TNBC cuproptosis regulators based on four metabolic pathways calculated through the FUSCC TNBC cohort. \u003cstrong\u003eE.\u003c/strong\u003e Kaplan‒Meier curves of RFS between clusters in the FUSCC TNBC cohort.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/fc33a96c1cd0dc4424fee313.png"},{"id":48151086,"identity":"979950ce-60c2-426b-8fab-fba79fad145f","added_by":"auto","created_at":"2023-12-13 19:39:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":650485,"visible":true,"origin":"","legend":"\u003cp\u003eImmune landscape in cluster 1 and other clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B.\u003c/strong\u003eGSEA plot showing a downregulated inflammatory response (A) and interferon α-related pathway (B) in cluster 1 versus other clusters. \u003cstrong\u003eC.\u003c/strong\u003e iTIL and sTIL scores in cluster 1 and other clusters. \u003cstrong\u003eD.\u003c/strong\u003e Relative number of immunostimulatory and immunosuppressive cells in cluster 1 and other clusters. E-F. The expression of immunostimulatory molecules (E) and immune checkpoint genes (F) in cluster 1 and other clusters. *, P \u0026lt; 0.05, ***, P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/58baa81bb30079dd70e2954c.png"},{"id":48150034,"identity":"c40d6fbb-f673-4012-bc4a-9cd934a7aa9b","added_by":"auto","created_at":"2023-12-13 19:31:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1953202,"visible":true,"origin":"","legend":"\u003cp\u003eATP7B is a crucial regulatory factor of cuproptosis in TNBC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eThe protein expression of ATP7B in several TNBC cell lines. Full-length gels are presented in Supplementary Figure 8. \u003cstrong\u003eB.\u003c/strong\u003e TNBC cell lines with different ATP7B expression levels were subjected to incremental doses of specific cuproptosis inducers for 24 hours. The viability of the cells was evaluated using the CCK-8 assay. The results are presented as a percentage of cell viability against the concentration of cuproptosis inducers, which was the mean value derived from three independent measurements. The experiments were replicated three times, and the IC50 values were compared using Student’s t test. \u003cstrong\u003eC.\u003c/strong\u003e We investigated the effects of a cuproptosis inducer on TNBC cell lines with different ATP7B expression levels. The cells were treated with the indicated doses of cuproptosis inducer and then subjected to colony formation survival assays. The obtained results were quantitatively analysed. The data are presented as the mean ± SEM and were statistically compared using Student’s t test (n = 3). **, P \u0026lt; 0.01, ***, P \u0026lt; 0.001, ns, not significant.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/4f13fa370a0529c3e730c813.png"},{"id":48150029,"identity":"261404ff-4e83-4fa2-9db8-983de501fa89","added_by":"auto","created_at":"2023-12-13 19:31:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1146858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed CRGs and construction of a prognostic model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eSeventeen CRGs were associated with prognosis (p \u0026lt; 0.05) through the univariate regression analysis. \u003cstrong\u003eB.\u003c/strong\u003e The LASSO Cox regression model was utilized to plot partial likelihood deviations against the logarithm of λ. \u003cstrong\u003eC.\u003c/strong\u003eThe CRG risk score distribution of TNBC patients in the FUSCC TNBC cohort. The progression from low- to high-risk scores is shown by the color from green to red. \u003cstrong\u003eD.\u003c/strong\u003e Prognostic outcome and survival time of each TNBC patient in the FUSCC TNBC cohort. \u003cstrong\u003eE.\u003c/strong\u003e Heatmap showing the expression of 11 CRGs of TNBC patients in the FUSCC TNBC cohort. \u003cstrong\u003eF.\u003c/strong\u003e The CRG risk score distribution of TNBC patients in the TCGA TNBC cohort. The progression from low- to high-risk scores is shown by the color from green to red. \u003cstrong\u003eG.\u003c/strong\u003e Prognostic outcome and survival time of each TNBC patient in the TCGA TNBC cohort. \u003cstrong\u003eH.\u003c/strong\u003eHeatmap showing the expression of 11 CRGs of TNBC patients in the TCGA TNBC cohort. \u003cstrong\u003eI.\u003c/strong\u003e CRG risk score in the no recurrence and recurrence groups in the FUSCC TNBC (left) and TCGA TNBC cohorts (right). Unpaired t test. \u003cstrong\u003eJ.\u003c/strong\u003eSurvival analysis of TNBC patients in the high-risk and low-risk groups in the FUSCC TNBC (left) and TCGA TNBC cohorts (right). Log rank test.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/27bcf269fd9d0b719a638746.png"},{"id":48150030,"identity":"c842b6f9-8170-4514-92b3-7c516412ed92","added_by":"auto","created_at":"2023-12-13 19:31:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":332806,"visible":true,"origin":"","legend":"\u003cp\u003eInternal and external validation of the nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eNomogram combining the CRG risk score with clinical characteristics. \u003cstrong\u003eB-C.\u003c/strong\u003eThe time-ROC curves of the nomogram in the FUSCC TNBC (B) and TCGA TNBC cohorts (C). \u003cstrong\u003eD-E.\u003c/strong\u003e Calibration plots of the nomogram in the FUSCC TNBC (D) and TCGA TNBC cohorts (E).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/357b568416240f73088ae28b.png"},{"id":48310225,"identity":"3c9c8e86-eda9-4ffb-b5de-e9301f6e08e6","added_by":"auto","created_at":"2023-12-16 06:37:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4435139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/c3c79a3b-ef7c-44eb-af95-9a59ed5a528e.pdf"},{"id":48150031,"identity":"a19960d8-ba25-4e6e-a954-90cccd96fd58","added_by":"auto","created_at":"2023-12-13 19:31:08","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10421,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/904453d29c927a8b9103ba19.xlsx"},{"id":48150037,"identity":"b8e0916d-2336-4072-8ac4-d8e630f1a983","added_by":"auto","created_at":"2023-12-13 19:31:08","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1402918,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/7419bae931c4af83da0cb365.pdf"},{"id":48151084,"identity":"12aac16e-9504-4ea2-89d5-1aae6ee7cd07","added_by":"auto","created_at":"2023-12-13 19:39:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":84656,"visible":true,"origin":"","legend":"","description":"","filename":"DatafileS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3724433/v1/f0a00860920258c8d1d71739.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome profiling reveals cuproptosis heterogeneity in triple-negative breast cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eTriple-negative breast cancer (TNBC) accounts for approximately 15\u0026ndash;20% of newly diagnosed breast cancer cases. It is distinguished by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. TNBC is an exceptionally aggressive and challenging clinical subtype within the diverse spectrum of breast cancer. This subtype poses significant difficulty to clinicians due to its unique characteristics and limited treatment options[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have idicated that TNBC has a high degree of heterogeneity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Previous studies have investigated the heterogeneity of TNBC from different perspectives. Recently, based on transcriptomic profiling, TNBC was classified into the luminal androgen receptor (LAR), immunomodulatory (IM), basal-like immune-suppressed (BLIS) and mesenchymal-like (MES) subtypes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In another study, through a comprehensive analysis linking the TNBC metabolome to genomics, TNBC was classified into three distinct metabolomic subgroups[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Fan \u003cem\u003eet al\u003c/em\u003e. found that TNBC exhibited heterogeneous phenotypes in ferroptosis-associated metabolic pathways. Along this line, further research demonstrated that the LAR subtype of TNBC was more sensitive to GPX4 inhibitors[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the interpretation and identification of TNBC heterogeneity is incompletely defined.\u003c/p\u003e \u003cp\u003eCopper accumulation causes cuproptosis, which is a newly identified type of regulatory cell death[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Unlike other types of regulatory cell death, pyroptosis specifically affects mitochondrial lipoylated components of the tricarboxylic acid cycle (TCA). This process induces proteotoxic stress and ultimately results in cell death[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies have indicated that cuproptosis may function during cancer development, suggesting that it is expected to become a target for tumor treatment[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, few studies have elucidated the relationship between cuproptosis and TNBC heterogeneity.\u003c/p\u003e \u003cp\u003eThrough transcriptome analysis in this study, we successfully categorized TNBC into four distinct clusters by evaluating the expression levels of cuproptosis-associated genes (CRGs). Furthermore, our investigation revealed ATP7B as a pivotal controller of cuproptosis. In addition, we created the CRG score to predict recurrence-free survival (RFS) for TNBC patients. Based on our findings, this study offers novel insights and valuable references for the advancement of treatment strategies targeting cuproptosis in TNBC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient cohort\u003c/h2\u003e \u003cp\u003eIn our research, we used a TNBC cohort obtained from our previous study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This cohort comprised a total of 465 individuals who were diagnosed with TNBC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among the patients included in our study, 360 individuals had available RNA-seq data, and 279 patients had available WES data. The FUSCC Ethics Committee granted approval for our research, allowing us to obtain tissue samples. Prior to sample collection, written informed consent was collected from each patient, ensuring their understanding and agreement regarding the use of their data and tissue. TCGA TNBC RNA-Seq data were obtained from the website. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/tcga/\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/tcga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExpression-Based Unsupervised Clustering\u003c/h2\u003e \u003cp\u003eWe were able to collect a signature consisting of 13 CRGs from previously published works[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To ascertain the ideal number of subtypes in mRNA data for TNBC, we employed k-means clustering as well as consensus clustering methods based on the expression of CRGs. This approach allowed us to accurately determine the most suitable TNBC subtypes. The \u0026ldquo;kmeans\u0026rdquo; function in R was employed for k-means clustering, while the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package in R was utilized for consensus clustering[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To assess the robustness of k-means clustering, we conducted consensus clustering with 1,000 iterations and 0.8 resampling. The optimal cluster number was determined using cumulative density function (CDF) analysis. The CDF curve, which spanned from 0 to 1, depicted the empirical cumulative distribution. Moreover, we calculated the proportionate increase in the area beneath the CDF curve. The cluster number was determined when additional increments in the cluster number (k) no longer yielded a substantial increase in the CDF area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between the clusters and clinical features and functional annotations\u003c/h2\u003e \u003cp\u003eTo explore the potential clinical roles of the four cuproptosis-related clusters, we investigated the correlation between these subtypes and primary clinical and pathological parameters in patients with TNBC. These parameters included tumor size (T phase), lymph node involvement (N phase), mRNA subtype, and prognosis. To assess differences in relapse-free survival among the subtypes, we employed the Kaplan‒Meier survival analysis technique. We used the limma R package to analysis genes that were differentially expressed between cluster 1 and the remaining clusters. To be considered differentially expressed genes (DEGs), to meet the criteria for gene selection, the adjusted p value had to be below 0.05, and the fold change needed to exceed 0.5. Furthermore, Gene enrichment analysis and gene set enrichment analysis were conducted using ClusterProfiler package. The mutation profiles were visualized using the maftools R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of immune cell populations in tumor tissues\u003c/h2\u003e \u003cp\u003eTo estimate the relative subsets of RNA transcripts and identify cell types, we employed the CIBERSORT tool in absolute mode. This tool, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, facilitated the calculation of immune cell subset abundances across 22 different types within each sample[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Subsequently, we extracted and compared cell subsets known for their involvement in tumor cell elimination or tumor progression between cluster 1 and the remaining clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of tumor infiltrating lymphocytes (TILs)\u003c/h2\u003e \u003cp\u003eAccording to our previous study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the evaluation of TILs was conducted on sections stained with hematoxylin and eosin. Stromal tumor-infiltrating lymphocytes (sTILs) are lymphocytes that reside within the stromal elements of tumor tissues, while intratumoral tumor-infiltrating lymphocytes (iTILs) are lymphocytes located within the epithelial components of tumor tissues. Two pathologists independently assessed each patient case according to the relevant guidelines. The scoring of sTILs and iTILs was performed separately for comprehensive evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCreating and confirming the predictive CRG score\u003c/h2\u003e \u003cp\u003eTo mitigate the potential for model overfitting, we employed the glmnet package in the R programming language for conducting least-squares regressions and selection operator regressions. Furthermore, we utilized a multivariate Cox regression with proportional hazards analysis to forecast the RFS of patients within the training dataset. To assess the accuracy of the CRG score, we generated receiver operating characteristic (ROC) curves and calibration plots using the survivalROC and rms R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eThe TNBC cell lines were purchased from the American Type Culture Collection (ATCC, USA). Specific media were used for culturing the cell lines: DMEM supplemented with 10% fetal bovine serum (FBS) for MDA-MB-231, BT549, MDA-MB-468, MDA-MB-453, Hs578T, and HEK293T cells and RPMI 1640 supplemented with 10% FBS for HCC1806 cells. Regular testing with a Mycoplasma Detecting Kit (Vazyme) confirmed the absence of mycoplasma contamination in all cell lines. All cells were maintained under standard culture conditions, which included incubation at 37\u0026deg;C with 5% CO2. Furthermore, the authenticity of the cell lines was verified through STR profiling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTransfection and virus infection\u003c/h2\u003e \u003cp\u003eTo generate lentiviruses, the pLKO.1 vector and packaging plasmids (psPAX2 and pMD2.G) were utilized in conjunction with HEK293T cells. An annealing oligonucleotide was synthesized (Sangon Biotech) to target ATP7B, which was then cloned and inserted into pLKO.1-Puro. After the completion of the experiment, the liquid portion, known as the supernatant, was gathered and passed through a syringe filter. Lentivirus was delivered to the target cells with a multiplicity of infection of 0.7, along with polybrene (6 mg/ml; Sigma‒Aldrich). Subsequently, puromycin was used to screen the TNBC cells and obtain stably transfected or infected cells. The knockdown efficiency of shRNAs was validated through qRT‒PCR. For the subsequent experiments, we chose the two shRNAs that exhibited the highest knockdown efficiency. The shRNA target sequences for ATP7B can be found in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and qRT‒PCR\u003c/h2\u003e \u003cp\u003eTo obtain cellular material, TRIzol reagent (Invitrogen) was employed, followed by reverse transcription into cDNA. Primers were designed using PrimerBank. Quantitative real-time PCR (qRT‒PCR). The qRT‒PCR data were normalized to the expression level of the ACTB gene (actin beta), which served as a reference gene. We calculated the relative gene expression values using the formula 2(-ΔCt). The primers specific to the target genes for amplification can be found in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eCellular proteins were collected utilizing RIPA lysis buffer and quantified via a BCA reagent kit (Solarbio). Subsequently, we performed SDS‒PAGE to separate the protein samples, followed by transferring them onto polyvinylidene difluoride membranes (Millipore). Incubation with specific primary antibodies was conducted, followed by incubation with secondary antibodies. Enhanced chemiluminescence (Pierce Biotechnology) was employed for detection. To acquire images, we utilized the Molecular Imager ChemiDoc XRS system equipped with Image Lab Software (Bio-Rad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIn vitro cell viability assays\u003c/h2\u003e \u003cp\u003eTo conduct cell viability assays, TNBC cells were cultured in 96-well plates at optimal densities per well using their respective growth medium. Following overnight incubation for adherence, the seeding densities were determined to ensure 75\u0026ndash;80% confluence of each cell line at the end of the assay. Once confluence was achieved, to achieve the desired concentrations, specific inhibitors were added to 100 \u0026micro;l of fresh medium, replacing the growth medium accordingly. After 24 hours, Cell Counting Kit-8 (Yeasen, #40203ES92) was employed to evaluate cell viability. The absorbance at 450 nm was measured. The IC50 value, which indicates the concentration of the drug leading to a 50% decrease in cell viability, was calculated after cuproptosis inducer treatment for 24 hours.\u003c/p\u003e \u003cp\u003eTo perform colony formation survival assays, 5 \u0026times; 10^3 cells were plated on 12-well plates and exposed to the specified drug treatments. After a 14-day period of incubation, the cells were treated with methanol for fixation and subsequently stained using a solution of crystal violet at a concentration of 0.5%. Subsequently, cell counting was conducted under a microscope to determine the number of colonies formed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAntibodies and chemical products\u003c/h2\u003e \u003cp\u003eThe study employed a range of primary antibodies, including anti-ATP7B (Proteintech, cat no. 19786-1-AP) and anti-vinculin (Proteintech, cat no. 66305-1-Ig). Furthermore, secondary antibodies were obtained from Jackson ImmunoResearch, including horseradish peroxidase-conjugated anti-mouse (diluted at 1:5000) and anti-rabbit (diluted at 1:5000).\u003c/p\u003e \u003cp\u003eThe following chemical products were used: Elesclomol, a cuproptosis inducer (MedChemExpress, cat no. HY-12040), and copper (Sigma, cat no. 7440-50-8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used Student\u0026rsquo;s t test and LogRank test when appropriate to determine the statistical significance. Unless otherwise specified in the figure legend, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Three independent repeated experiments were performed for all cell-based in vitro experiments in triplicate. Statistical analyses were conducted using R software (R studio 3.5.2) or GraphPad Prism software (GraphPad Prism 9.0). A P value less than 0.05 was considered statistically significant, employing two-sided tests. R software can be obtained from the website. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe expression prognostics values of CRGs in TNBC\u003c/h2\u003e \u003cp\u003eThirteen previously defined CRGs were included in our study[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. First, we analysed the expression of CRGs in TNBC and paracancerous tissues in the FUSCC TNBC and TCGA cohorts. The results of the FUSCC TNBC cohort indicated that the expression of DLAT, PDHA1 and SLC31A1 in TNBC tissues was upregulated. In contrast, the expression of DLD, FDX1, LIAS, LIPT1, ATP7B, ATP7A, DLST and DBT was downregulated in TNBC tissues. The expression of PDHB and GCSH showed a nonsignificant difference in TNBC tissues compared with paracancerous tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The results of the TCGA cohort showed that the expression of DLAT, ATP7B, SLC31A1 and GCSH in TNBC tissues was upregulated. In contrast, the expression of LIAS, LIPT1, PDHA1, PDHB, ATP7A, DLST and DBT in TNBC tissues was downregulated, and the expression of DLD and FDX1 showed a nonsignificant difference in TNBC tissues compared with paracancerous tissues \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then analysed the prognostic values of CRGs in TNBC. The results of the FUSCC TNBC cohort indicated that upregulation of LIPT1, ATP7B and ATP7A was correlated with poor prognosis of TNBC. The remaining CRGs showed no prognostic value in TNBC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-N\u003cb\u003e)\u003c/b\u003e. The results of the TCGA cohort indicated that upregulation of DLST indicated poor prognosis of TNBC. In contrast, low expression of DLD, LIPT1, PDHA1, and GCSH indicated poor prognosis in TNBC, and the expression of the remaining CRGs showed no prognostic value in TNBC \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB-N)\u003c/b\u003e. These findings point to a potential involvement for CRGs in TNBC progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCuproptosis-based TNBC subtypes\u003c/h2\u003e \u003cp\u003eAfter conducting consensus clustering, we determined and chose four as the number of subtypes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B; \u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA).\u003c/b\u003e The PCA results demonstrated good discrimination between the four clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Next, we utilized unsupervised k-means clustering to classify 360 TNBC patients into four clusters based on the expression of 13 CRGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Then, we investigated the prognostic value of these clusters. The results of survival analysis exhibited a substantial overall difference between the four clusters (P\u0026thinsp;=\u0026thinsp;0.002) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e).\u003c/b\u003e In addition, cluster 1 (n\u0026thinsp;=\u0026thinsp;61) had the worst prognosis compared with the other subtypes (cluster 1 vs cluster 2, P\u0026thinsp;=\u0026thinsp;0.02; cluster 1 vs cluster 3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, cluster 1 vs cluster 4, P\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of molecular features for cluster 1\u003c/h2\u003e \u003cp\u003eWe aimed to elucidate the molecular characteristics of cluster 1 because it has a poor prognosis compared with the other clusters. The findings indicated that ATP7B exhibited higher expression in cluster 1 than in the remaining clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e, suggesting that ATP7B may have a functional role in TNBC progression. Furthermore, we conducted an analysis of gene expression and discovered 1529 genes that were differentially expressed (DEGs) between cluster 1 and the remaining clusters. The enrichment analysis of biological processes (BP) revealed that the DEGs were primarily enriched in mitotic nuclear division and nuclear division \u003cb\u003e(Fig. S3A)\u003c/b\u003e. The enrichment analysis of cellular components (CC) revealed that the DEGs were primarily enriched in the extracellular matrix containing collagen and the immunoglobulin complex \u003cb\u003e(Fig. S3B)\u003c/b\u003e. The analysis of molecular function (MF) revealed that the DEGs were highly concentrated in antigen binding and the structural constituents of the extracellular matrix \u003cb\u003e(Fig. S3C)\u003c/b\u003e. According to the KEGG enrichment analysis, the DEGs play a role in both the PI3K-Akt signaling pathway and the cell cycle \u003cb\u003e(Fig. S3D)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eWe next examined the immune microenvironment differences between cluster 1 and other clusters. Gene Set Enrichment Analysis (GSEA) revealed suppression of the inflammatory response and interferon alpha pathway cluster 1 and other clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand B)\u003c/b\u003e. Furthermore, the occurrence of both iTILs and sTILs in cluster 1 surpassed the levels observed in the remaining clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Moreover, we calculated the composition ratio of 22 immune cell types. The findings indicated that cluster 1 had lower enrichment for immune-activated cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. In addition, expression profiling demonstrated that most immunostimulators and immunoinhibitors were significantly downregulated in cluster 1 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE \u003cb\u003eand F)\u003c/b\u003e. These findings suggest that patients in other clusters may benefit more from immune checkpoint inhibitor therapy than patients in cluster 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eATP7B plays an essential role in cuproptosis regulation\u003c/h2\u003e \u003cp\u003eSince cluster 1 is associated with poor prognosis and characterized by upregulation of ATP7B, we investigated the biological function of ATP7B in regulating TNBC cuproptosis. We first examined the basal expression of ATP7B protein in TNBC cell lines. The findings indicated that ATP7B exhibited low expression levels in MDA-MB-231, MDA-MB-453 and BT549 cells but was highly expressed in HCC1806, Hs578T and SUM159 cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Next, we investigated whether ATP7B expression can affect sensitivity to cuproptosis inducers. The results showed that TNBC cells with low ATP7B expression were more sensitive to the cuproptosis inducer \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB \u003cb\u003eand C)\u003c/b\u003e. To further verify this conclusion, we constructed stable ATP7B-knockdown HCC1806 and SUM159 cells \u003cb\u003e(Fig. S4A)\u003c/b\u003e. The results indicated that ATP7B knockdown increased sensitivity to the cuproptosis inducer \u003cb\u003e(Fig. S4B)\u003c/b\u003e. Figures depicting CCK-8 and colony formation assays revealed that the depletion of ATP7B did not have a notable impact on cellular proliferation \u003cb\u003e(Fig. S5A-D)\u003c/b\u003e. Taken together, our findings indicate that ATP7B is a crucial regulator of cuproptosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the cuproptosis-related risk score\u003c/h2\u003e \u003cp\u003eOur next objective was to develop a cuproptosis-associated risk score to forecast the prognosis of TNBC patients. Through univariate Cox analysis, we identified 17 prognosis-associated CRGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Then, we used LASSO Cox regression analysis and identified 11 CRGs: CFAP44, HLA-C, PLA2G16, RDH10, GPC4, NEBL, NCAM2, MKL2, TRIQK, ATP7B and TMEM178 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. These genes were utilized for the development of a predictive risk model. The risk score was calculated using the following formula: risk score = (CFAP44 expression * -0.44) + (HLA-C expression * -0.07) + (RDH10 expression * 0.08) + (PLA2G16 expression * 0.06) + (GPC4 expression * 0.09) + (NEBL expression * 0.10) + (NCAM2 expression * 0.01) + (MKL2 expression * 0.07) + (TRIQK expression * 0.12) + (ATP7B expression * 0.26) + (THEM178 expression * 0.26). After calculating the risk score for each patient, all the patients were divided into two groups based on whether their scores were above or below the median value. The risk score distribution between different risk groups in the training and validation sets is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and F. The prognostic outcome and survival time of patients in different risk groups in the training and validation sets are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and G. Next, we showed the relative expression of 11 CRGs in each patient in the training and validation sets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE \u003cb\u003eand H).\u003c/b\u003e Afterwards, we examined the association between the risk score and prognosis. The results showed that patients who developed recurrence had higher risk scores \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI\u003cb\u003e)\u003c/b\u003e. The analysis of survival indicated that the high-risk group had poorer recurrence-free survival (RFS) than the low-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo enhance the accuracy for prediction, we integrated the risk score with the pathologic T and N stage and then created a nomogram \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. ROC curves and calibration plots were used to evaluate the precision of the model. The AUC values for the 1-year, 3-year, and 5-year AUC in the FUSCC TNBC cohort were 0.69, 0.80, and 0.76, respectively, while the AUC in TCGA cohort were 0.91, 0.85, and 0.82 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB \u003cb\u003eand C)\u003c/b\u003e. The calibration graph also indicated that the forecast likelihood of the nomogram aligned with the real likelihood of RFS after 1, 3, and 5 years \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD \u003cb\u003eand E)\u003c/b\u003e. Taken together, our results indicated that the cuproptosis-associated risk score could accurately predict prognosis and is expected to be a predictor of prognosis in TNBC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMolecular features of the high- and low-risk groups\u003c/h2\u003e \u003cp\u003eWe next examined the difference between the low- and high-risk groups. We conducted an analysis of differential expression for the prognostic genes identified in the high-risk and low-risk groups. A total of 301 genes with differential expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log (FC)| \u0026ge; 0.5) were found between the high-risk and low-risk groups. The enrichment analysis results of BP indicated that the DEGs were primarily enriched in immunoglobulin production and phagocytosis recognition pathways \u003cb\u003e(Fig. S6A)\u003c/b\u003e. The results of the enrichment analysis for CC indicated that the DEGs were primarily enriched in immunoglobulin complex and immunoglobulin complex circulating pathways \u003cb\u003e(Fig. S6B)\u003c/b\u003e. The results of the MF analysis indicated that the DEGs were primarily enriched in antigen binding and immunoglobulin receptor binding pathways \u003cb\u003e(Fig. S6C)\u003c/b\u003e. According to the KEGG pathway enrichment analysis findings, the enrichment of DEGs was primarily observed in the pathways involving viral protein interactions with cytokines and cytokine receptor pathways \u003cb\u003e(Fig. S6D)\u003c/b\u003e. In addition, we conducted additional research on the genetic characteristics within the high- and low-risk groups. The top five mutated genes in the low-risk group were TP53, TTN, PIK3CA, TNXB, and OBSCN, while in the high-risk group, the top five mutated genes were TP53, PI3KCA, TTN, MUC16, and KMT2C \u003cb\u003e(Fig. S7A and B)\u003c/b\u003e. The combined findings indicated that the low- and high-risk groups possess unique molecular characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, transcriptome analysis revealed heterogeneous TNBC cuproptosis phenotypes. Using transcriptome data and drug response tests, we identified ATP7B as a key regulator of cuproptosis. In addition, we constructed a cuproptosis-associated prognostic model that could accurately predict the prognosis of TNBC patients.\u003c/p\u003e \u003cp\u003eHuman survival relies on copper, as it helps produce proteins and regulate cellular signaling pathways such as those involved in energy production[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To guarantee the best possible cellular functioning, it is crucial to maintain the appropriate balance of copper within the cell[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. On the other hand, excess copper can result in the demise of cells. The discovery of a new form of cell death, known as 'cuproptosis', has revealed that protein lipoylation plays a role in the death of cells caused by copper[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite the identification of various crucial genes associated with cuproptosis[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], there is a lack of comprehensive understanding regarding the characteristics of cuproptosis in cancer. In addition, the heterogeneity of cuproptosis-related features correlated with the prognosis of TNBC has not been fully illustrated. We categorized TNBC into four groups and identified variation in cuproptosis within TNBC based on the expression of cuproptosis-associated genes.\u003c/p\u003e \u003cp\u003eNext, we focused on cluster 1, as it was characterized by worse prognosis among all clusters. We found that the expression of ATP7B was higher in cluster 1 than in the other clusters. These results further suggested that ATP7B may serve as a key regulator of cuproptosis in TNBC. The ATP7B gene encodes copper-transporting P-type ATPase, which moves copper to the basement membrane of the extracellular matrix[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The mutation of ATP7B damages the nervous system, causing Wilson disease[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. According to reports, ATP7B can attach to platinum medications and remove them from malignant cells, resulting in resistance to chemotherapy[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study, we found that ATP7B knockdown sensitized cells to the cuproptosis inducer. We considered ATP7B upregulation to be a decisive driver of the cuproptosis phenotype in TNBC, but a detailed mechanism needs further investigation.\u003c/p\u003e \u003cp\u003eTumor cells and stromal cells make up the tumor microenvironment (TME)[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A growing number of studies indicate that the TME has a significant impact on facilitating the advancement of tumors[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. An in-depth investigation of the TME may help to identify novel mechanisms and have implications for immunotherapy. Immunotherapy has recently become a promising and appealing treatment option for tumors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, a significant number of individuals still do not respond to this form of treatment[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The impact of the tumor microenvironment on immune repertoires and functional status could explain the diverse immune therapeutic responses observed[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In our study, our results showed differences in characterizations of the TME between cluster 1 and other clusters. Compared with other clusters, cluster 1 displayed an immunosuppressed TME, with lower TIL infiltration and lower expression of immunostimulators and immune checkpoint molecules. Hence, individuals belonging to cluster 1 might experience restricted advantages from immunotherapy. The findings of our research suggested a connection between cuproptosis and the immune microenvironment of TNBC. Additionally, we proposed that identifying TNBC cuproptosis subtypes could help to identify individuals who have a higher probability of benefiting from immunotherapies. However, further evidence from clinical trials is needed to fully support this claim. In addition, we developed and validated the CRG score. Patients with low CRG risk scores and those with high CRG risk scores had significant differences in RFS. To enhance the predictive accuracy and increase clinical utility, we constructed a nomogram by incorporating clinicopathologic factors and CRG risk scores. Our nomogram provides new ideas for the treatment of TNBC.\u003c/p\u003e \u003cp\u003eOur study has the following limitations. First, our study only focused on transcriptomic analyses. Multiomics data need to be integrated to develop the cuproptosis atlas and understand the mechanisms of cuproptosis heterogeneity in TNBC. Second, although we discovered that the differential expression of ATP7B could impact the sensitivity of TNBC cells to cuproptosis inducers, in vivo studies will be needed to validate our findings. In addition, additional work is needed to elucidate the upstream factors that govern ATP7B expression. Third, our nomogram needs to be validated in more external datasets.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study first revealed cuproptosis heterogeneity in TNBC. Along this line, we identified ATP7B as a key regulator of TNBC cuproptosis. In addition, our nomogram, as a comprehensive model integrating CRG risk scores and clinicopathological factors, is effective in predicting the prognosis of TNBC. Our study may spark novel ideas for TNBC treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTNBC, triple-negative breast cancer; FUSCC, Fudan University Shanghai Cancer Center; TCGA, The Cancer Genome Atlas; ATP7B, ATPase copper transporting beta; DEGs, differential expressed genes; CRGs, cuproptosis-related genes; ER, estrogen receptor, PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; LAR, luminal androgen receptor; IM, immunomodulatory, BLIS, basal-like immune-suppressed; MES, mesenchymal-like; TCA, tricarboxylic acid cycle; RFS, recurrence-free survival; CDF, cumulative density function; TILs, tumor infiltrating lymphocytes; sTILs, stromal tumor-infiltrating lymphocytes; iTILs, intratumoral tumor infiltrating lymphocytes; ROC, receiver operating characteristic; AUC, area under the ROC curve; LASSO, least absolute shrinkage and selection; FBS, fetal bovine serum; CC, cellular components; MF, molecular function; BP, biological processes; GSEA, Gene Set Enrichment Analysis; TME, tumor microenvironment.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFUSCC TNBC cohort was described in our previous study and approved from the FUSCC Ethics Committee[7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analysed in this study. These data can be found here: the raw data were obtained from publicly available datasets (European Genome-phenome Archive with the study ID EGAS00001005061, TCGA), and our FUSCC datasets have been uploaded to the National Omics Data Encyclopedia (OEP000155).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Key Research and Development Project of China (2020YFA0112304; to Z.S.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZMS designed the study. XQS analysed the data and performed the research. XQS, JHJ and XYS wrote the manuscript. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData contributions from the databases of the Fudan University Shanghai Cancer Center are appreciated by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZagorac I, Fernandez-Gaitero S, Penning R, Post H, Bueno MJ, Mouron S, Manso L, Morente MM, Alonso S, Serra V\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIn vivo phosphoproteomics reveals kinase activity profiles that predict treatment outcome in triple-negative breast cancer\u003c/strong\u003e. \u003cem\u003eNature communications \u003c/em\u003e2018, \u003cstrong\u003e9\u003c/strong\u003e(1):3501.\u003c/li\u003e\n\u003cli\u003eAdams S, Diamond JR, Hamilton E, 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\u003cstrong\u003e9\u003c/strong\u003e(1):948.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TNBC, Cuproptosis, Heterogeneity, ATP7B","lastPublishedDoi":"10.21203/rs.3.rs-3724433/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3724433/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eBreast cancer has various subtypes, among which triple-negative breast cancer (TNBC) stands out as the most malignant and heterogeneous form and has a poor prognosis. One area of focus is the phenomenon known as cuproptosis, which represents a recently identified and regulated type of cell death. Unlike previously recognized mechanisms, cuproptosis is characterized by its reliance on both copper and mitochondrial respiration. The relationship between TNBC and cuproptosis remains to be elucidated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We obtained RNA-seq and clinical data form Fudan University Shanghai Cancer Center (FUSCC) and The Cancer Genome Atlas (TCGA) TNBC cohort. We conducted K-means cluster analysis of TNBC based on the expression of 13 cuproptosis regulators (DLAT, DLD, FDX1, LIAS, LIPT1, PDHA1, PDHB, ATP7B, ATP7A, DLST, SLC31A1, DBT and GCSH). Kaplan-Meier survival analysis was performed to determine the survival difference among different clusters. To examine the sensitivity to cuproptosis inducer, cell viability assays was performed in TNBC cell lines with different ATP7B protein expression. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct cuproptosis-associated risk score. Receiver operating characteristic (ROC) and calibration curves were used to evaluated the performance of risk score.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe classified TNBC into four clusters. Compared with other clusters, cluster 1 exhibited high ATPase copper transporting beta (ATP7B) expression, low immune infiltrate and poor prognosis. Through subsequent experimental validation, we demonstrated ATP7B as a crucial cuproptosis regulator, TNBC cells with low ATP7B expression or ATP7B knockdown indicated drug-sensitive to cuproptosis inducer. In addition, we constructed and validated a cuproptosis-associated signature of 11 genes (CFAP44, HLA-C, PLA2G16, RDH10, GPC4, NEBL, NCAM2, MKL2, TRIQK, ATP7B and TMEM178). The 1-year, 3-year, and 5-year area under the ROC curve (AUC) in the training cohort were 0.69, 0.80, and 0.76, while in test cohort were 0.91, 0.85, and 0.82. The results suggested our cuproptosis-associated signature could accurately predict the progonosis for TNBC patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur results provide new insights into heterogeneous phenotypes in cuproptosis for TNBC, and may inspire new approaches for TNBC treatment.\u003c/p\u003e","manuscriptTitle":"Transcriptome profiling reveals cuproptosis heterogeneity in triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-13 19:31:03","doi":"10.21203/rs.3.rs-3724433/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da3b9920-e37e-4615-9a86-ecdeacd818c3","owner":[],"postedDate":"December 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-16T06:29:08+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-13 19:31:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3724433","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3724433","identity":"rs-3724433","version":["v1"]},"buildId":"oE6Zbj460LM0Up2FdVbMZ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}