The potential role of cuproptosis-related genes for therapy and immunoregulation in pan-cancer 

The potential role of cuproptosis-related genes for therapy and immunoregulation in pan-cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The potential role of cuproptosis-related genes for therapy and immunoregulation in pan-cancer Jianpeng Zhou, Chuanlei Wang, Jia Li, Guangyi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4403303/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 The primary drawbacks of current cancer therapies are lower selectivity for cancer cells, more side effects, and obscure resistance mechanisms. Novel approaches to overcome these drawbacks comprise the utilization of ionophores and metalliferous chelators to change the concentration of trace metal elements in cancer cells. As the concept of cuproptosis emerged, it might be a novel strategy to enhance the curative effects for resistant cancer cells potentially. FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, and SLC31A1 are the major regulators of cuproptosis. However, the expression landscape and clinical roles of these regulators remain to be addressed. This study explored the expression pattern and clinical role of these cuproptosis-related genes in pan-cancer by evaluating the association of tumor mutation burden, immune-related scores, cells in tumor microenvironment, and drug sensibility. The results displayed that the expressions of cuproptosis-related genes were significantly different in various cancer types, all cuproptosis-related gene upregulates significantly in LAML, ALL, PAAD, GBM, GBMLGG, LGG, and all significantly downregulated in cancers KIPP, WT, KIPAN, KIRC. Furthermore, the higher the level of cuproptosis-related genes expressed, the higher the survival in patients suffering from KIRC, and KIPAN increased. In addition, the expression of cuproptosis-related genes was negatively associated with immune-related scores, while SLC31A1 had a positive association with StromalScore, ImmuneScore, and EstimateScore in LAML. Importantly, the level of cuproptosis-related gene expressions is positively associated with CLP cells or Th2 cells, but negatively associated with NKT cells or Th1 cells. In summary, cuproptosis-related genes are disordered in various cancer types have prognostic value for different cancers, and also can evaluate the cells infiltrating in tumor microenvironment. Biological sciences/Cancer Biological sciences/Cell biology cuproptosis-related genes pan-cancer immunoregulation cuproptosis cancer therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Copper(Cu) is an essential cofactor in most organisms, which sustains at lower levels through active homeostasis mechanisms to hold back intracellular dissociative copper amassing regulated by concentration gradients. Thus, an overload of intracellular copper concentration leads to cell death. Recently, Todd R. Golub’s team demonstrated that the mechanisms of copper toxicity are different from other forms of cell death, such as apoptosis, ferroptosis, pyroptosis, and necrosis, and termed this previously uncharacterized cell death as cuproptosis[ 1 ], which is induced by cooper-dependent lipoylated proteins oligomerization and Fe-S cluster proteins instability. The study also suggests that cuproptosis might be regarded as a helpful and promising therapy against cancer. Previously, the level of Cu that was higher in a variety of tumor samples than in normal tissues has been reported. The connections between proliferation, angiogenesis, and metastasis of tumor and Cu accumulation also have been observed in a variety of cancer types. Furthermore, Cu levels have been found significantly altered in both serums and tumor tissues of patients with tumors such as oral, bladder, cervical, breast, ovarian, thyroid, pancreatic, prostate, gastric, and lung tumors[ 2 – 11 ]. Therefore, it is obvious that the dyshomeostasis of Cu affects cancer. Since the role of Cu in the genesis and progression of tumors is important, cuproptosis might be a novel method for holding back cancer development[ 12 ]. Conventional cancer therapies were mainly untargeted treatments to rapidly proliferating cells, which had large side effects. However, accurate target therapy with fewer side effects is supposed to be achieved in the future, and some compounds easily binding metal have been reported as promising in this field[ 13 ]. Thus, as a new method, cuproptosis would show an optimistic scenario in the tumor clinic. However, the correlations between cuproptosis-related gene expressions and relevant mechanisms in pan-cancer remain largely obscure. Therefore, this study mainly focuses on not only the systematical characteristics of the complex mechanisms that regulate tumor genesis and development by performing the analysis of extensive genetic alterations of cuproptosis-related genes but also the potential interaction of cuproptosis-related genes with tumor mutation burden (TMB), microsatellite instability score(MSI), neoantigen(NEO), cells in the tumor microenvironment(TME) and drug sensitivity. In addition, this study also aims to identify a potential biomarker by performing the correlations between cuproptosis-related genes and clinical characteristics. Material and Methods Cuproptosis-Related Genes Collection According to Todd R. Golub’s study, seven positive regulatory genes FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, and a Cu transporter encoding gene SLC31A1 were collected for the pan-cancer analysis[ 1 ]. Data resources The FANTOM5(60 tissue types)[ 14 ], Human Protein Atlas(HPA, 40 tissue types)[ 15 ], and GTEx database(37 tissue types)[ 16 ] were used to download the gene expression data of human tissues. The UCSC( https://xenabrowser.net/datapages/ ) was the data source for standardized universal omics data, which included gene expression RNAseq from HTSeq-FPKM GDC Hub, somatic mutation from MuTect2 Variant Aggregation and Masking, clinical phenotype from Curated clinical data by Pan-Cancer Atlas Hub, immune phenotype from immune subtype by Pan-Cancer Atlas Hub. The maftools R package was used to calculate the distribution of tumor mutation burden (TMB) according to somatic mutation data. The MSI score was obtained from the previous study[ 17 ]. The NEO score was obtained from the previous study[ 18 ]. To analyze cuproptosis-related genes expression, 34 cancer types were utilized, including adrenocortical carcinoma (ACC), acute lymphoblastic leukemia(ALL),bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma(CHOL), colon adenocarcinoma (COAD), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma(COADREAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), glioma(GBMLGG), head and neck squamous carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), Pan-kidney cohort(KIPAN),acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), Stomach and Esophageal carcinoma(STES) ,testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS) and High-Risk Wilms Tumor(WT). All abbreviations are shown in Supplementary Table 1. All statistical R packages are shown in Supplementary Table 2. Evaluation of Differential Cuproptosis-Related Genes Expression between Tumor and Normal Tissues The cuproptosis-related gene expression level was extracted from the RNAseq dataset. The differential expression genes(DEGs) between tumor and normal tissues across 34 cancer types were analyzed by ggpubr R package, with statistical significance (adjusted p-value < 0.05). The significance of gene expression alterations was identified by the Wilcox method. The p-value was adjusted with the Benjamini-Hochberg multiple testing correction. The heatmap of cupro ptosis-related genes was plotted by pheatmap R packages. Correlation Analysis of Cuproptosis-Related Genes Expression with TMB, MSI and NEO The corrplot R package was applied to evaluate the correlation between the cuproptosis-related gene expression and TMB, MSI, or NEO with the Spearman method (p < 0.05). The correlation radar plot was plotted by the ggradar R package, and the ggpubr R package was applied to plot boxplots. Survival Analysis of Cuproptosis-Related Gene Expressions The tumor samples were divided into high- and low-expression groups according to the median value of cuproptosis-related gene expression across 34 cancer types. The survminer R package was applied for overall survival analysis by the Kaplan-Meier method, for which the log-rank test was used, with a statistical significance of p < 0.05. The survival R package was used to perform Cox regression analysis for cuproptosis-related genes. The hazard ratio was calculated for the Cox proportional hazard regression models. Further, the differential expression of cuproptosis-related genes in different pathologic stages (including stages I, II, III, and IV) were analyzed across 34 cancer types. Correlation Analysis of Cuproptosis-related Genes Expression with Immune Microenvironment across 34 Cancer Types The presence of infiltrating stromal and immune cell scores was predicted by the ESTIMATE algorithm in the estimate R package that forecasted stromal and immune cells in tumor tissues with gene expression data. Based on ssGSEA analysis, the estimate algorithm generated ImmuneScore, StromalScore, and EstimateScore. These three scores represent the corresponding ratio of immune cells infiltrating in tumor tissues, stromal cells present in tumor tissues, and the sum of both, respectively. Furthermore, the higher score represents the larger ratio of the corresponding component in the tumor microenvironment. Correlation analysis of the cuproptosis-related gene expressions with ImmuneScore, StromalScore, or EstimateScore was performed by the Corrplot R package with the method of Spearman (p < 0.05). The Proportion of Cells in Microenvironment across 34 Cancer Types Based on xCell Method The xCell algorithm was employed to identify the proportion of cells in the microenvironment across 34 cancer types. The xCell R package was applied to discriminate 64 human cell phenotypes in the microenvironment based on the gene expression profile. The correlation analysis of the cuproptosis-related gene expressions with different cells was performed by the corrplot R package with the Spearman method (p < 0.05), including activated dendritic cell (aDC), adipocytes, astrocytes, B cells, basophils, CD4 + memory T cells, CD4 + naïve T cells, CD4 + T cells, CD4 + central memory T cells(CD4 + Tcm), CD4 + effector memory T cells(CD4 + Tem), CD8 + naïve T cells, CD8 + T cells, CD8 + T cells, CD8 + central memory T cells(CD8 + Tcm), CD8 + effector memory T cells(CD8 + Tem), conventional dendritic cells(cDC), chondrocytes, class switched memory B cells, common lymphoid progenitor cells(CLP), common myeloid progenitor(CMP), dendritic cells(DC), endothelial cells, eosinophils, epithelial cells, erythrocytes, fibroblasts, granulocyte-macrophage progenitor(GMP), hepatocytes, hematopoietic stem cells(HSC), immature dendritic cells(iDC), Keratinocytes, lymphatic(ly) endothelial cells, macrophages, M1 macrophages, M2 macrophages, mast cells, megakaryocytes, melanocytes, memory B cells, megakaryocyte-erythroid progenitor(MEP), mesangial cells, monocytes, multipotent progenitors(MPP), mesenchymal stem cells(MSC), microvascular(mv) endothelial cells, myocytes, naïve B cells, neurons, neutrophils, NK cells, natural killer T cells(NKT), osteoblasts, plasma dendritic cells(pDC), pericytes, plasma cells, platelets, preadipocytes, pro B cells, sebocytes, skeletal muscle cells, smooth muscle cells, gamma delta T cells, T helper 1 cells(Th1 cells), T helper 2 cells(Th2 cells), T regulator cells(Treg cells). Drug Sensitivity Analysis CellMiner database ( https://discover.nci.nih.gov/cellminer/ ) was used to explore transcript and drug patterns in the NCI-60 cell line set. The NCI-60 cell line panel was an anticancer drug efficacy screen by the Developmental Therapeutics Program (DTP) of the US National Cancer Institute (NCI). Thousands of compounds have been applied to the NCI-60. The sample of gene expression and drug sensitivity data were downloaded from the CellMiner database and then filtered drug sensitivity data after clinical laboratory verification and FDA standard certification. Next, the Spearman correlation test was performed for cuproptosis-related gene expression data combined with drug sensitivity data. Results The expression of cuproptosis-related genes in human tissues and cancers The expression of cupro ptosis-related genes was analyzed in human normal tissues and cancer samples from FANTOM5, HPA, and GTEx databases. All database data showed concordant results that cuproptosis-related genes were highly expressed in heart muscle tissue. and data from FANTOM5 and the HPA database showed that cuproptosis-related genes were highly expressed in the kidney (Fig. 1 A-C). In addition, cuproptosis-related is widely expressed in various cancer types (Fig. 1 D). Further, cuproptosis-related genes showed extensive alterations in tumor samples compared with adjacent samples across most of cancer types, such as ACC, ALL, BLCA, BRCA, CHOL, COAD, COADREAD, ESCA, GBM, GBMLGG, HNSC, KICH, KIPAN, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, STES, STAD, TGCT, THCA, UCS and WT (Fig. 1 E, Supplementary Figure S1 A-H). Most of the cuproptosis-related genes show significant upregulation compared with adjacent non-tumor tissues across different cancer types. As can be seen, all cuproptosis-related gene upregulates significantly in LAML, ALL, PAAD, GBM, GBMLGG, and LGG and all significantly downregulated in cancers KIPP, WT, KIPAN, and KIRC (Fig. 1 E). These results might indicate that different cuproptosis-related genes play different roles in various cancer types. The Potential Prognostic Value of Cuproptosis-Related Genes Expression in Different Cancer Types To evaluate the prognostic potential of cuproptosis-related genes across 34 cancer types, the Kaplan-Meier curve analysis based on TCGA, GTEx, and TARGET database was performed. Most of the genes showed significant correlations with the overall survival of patients. KIRC and KIPAN showed a lot of significant results. The higher the level of cuproptosis-related genes expressed, the higher the survival rate in patients suffering from KIRC, and KIPAN increased (Fig. 2 A-P). However, for other cancers, different cuproptosis-related genes played different roles. To be more specific, the higher the level of FDX1 expressed, the poorer survival in ALL, GBMLGG, LAML, and LGG (Figure S2 A-D). The increasing expression of LIAS has a significant correlation with poor survival in KICH, LAML, and THCA, while better survival in GBMLGG (Figure S2 E-H). The increasing expression of LIPT1 has a significant correlation with pool survival in GBMLGG, LIHC, and LAML, while better survival in BLCA, READ, and SKCM (Figure S2 I-N). The increasing expression of DLAT has a significant correlation with poor survival rate in BLCA, BRCA, GBMLGG, LGG, LIHC, and PAAD, but better survival in COAD, COADREAD, and READ (Figure S2 O-X). The increasing expression of DLD was highly correlated to poor survival rates in BRCA, GBMLGG, LGG, and LUAD, but better survival in COADREAD (Figure S3 A-G). The increasing expression of PDHA1 was highly correlated to poor survival in ESCA, LAML, LUAD, PRAD, and SKCM (Figure S3 H-M). The increasing expression of PDHB was highly correlated to poor survival in KICH, and LAML but better survival in KIRP (Figure S3 N-P). The increasing expression of SLC31A1 was highly correlated to poor survival in ACC, BLCA, BRCA, GBMLGG, LAML, and LGG (Figure S3 Q-V). Furtherly, to obtain hazard ratio (HR) across 34 cancer types for cuproptosis-related genes, the univariate Cox regression analysis was performed (Figure S4 ). These results further confirmed Kaplan-Meier curves of overall survival analysis, from which the indication that the same gene might be a risky factor (HR > 1) or protective factor (HR < 1) in different tumors. Taking SLC31A1 as an exaple, SLC31A1 played risky role in BLCA (HR = 1.28, p = 0.02), BRCA (HR = 1.31, p = 0.01), GBMLGG (HR = 3.25, p = 2.00E-21) and LGG (HR = 2.38, p = 6.70E-07) but played protective role in KIRC (HR = 0.68, p = 3.70E-06) and KIPAN (HR = 0.76, p = 1.20E-04). In addition, correlation analyses of pathologic stages (stages I, II, III, and IV) and cuproptosis-related genes were performed across 34 cancer types. The expression levels of a cuproprosis-related gene are significantly different among different stages in ACC, CESC, GBMLGG, KIRP, STAD, STES, and UCS. Detailly, the level of LIAS has a significant difference among different stages in UCS, and ACC, higher expression levels indicated increasing pathologic stage (Fig. 3 A). Lower expression of LIPT1 might suggest an increasing pathologic stage (Fig. 3 B). For the expression of DLAT among different stages, it showed a neutral difference in KIRP (Fig. 3 C). The lower expression of PDHA1 was, the lower stage of CESC, while no trend was shown in STAD and STES (Fig. 3 D). The level of SLC31A1 also has differences among different stages in cancers GBMLGG, and KIRP (Fig. 3 E). The Mutation Frequency of Cuproptosis-Related Genes The detailed mutation status of cuproptosis-related genes was displayed by the waterfall map from high to low percentage, LIAS, PDHA1, DLAT, DLD, LIPT1, SLC31A1, PDHB, FDX1 (Fig. 4 ). There were many types of mutations to be found, including frameshift variant, intron variant, missense variant, 3′ prime UTR variant, 5′ prime UTR variant, synonymous variant, downstream gene variant, splice region variant, splice donor variant, stop gained, inframe deletion, protein-altering variant, splice acceptor variant, start lost, stop lost, stop retained variant, and upstream gene variant. Correlation Analyses of the Cuproptosis-Related Gene Expressions and TMB or MSI or NEO The role of TMB (Tumor mutation burden) in tumors has been more and more important in recent years and it has been regarded as a novel biomarker in tumors. Correlation analysis of the expression of the cupro ptosis-related gene with TMB index was performed for various cancer types (Fig. 2 A). Cuproptosis-related genes have a significant correlation with TMB scores in most cancers. However, different cancers correlated differentially with cuproptosis-related genes. For example, SLC31A1 exhibited a positive correlation with TMB score in ACC, BRCA, COAD, COADREAD, and STAD but a negative correlation with KIPAN, and PRAD (Fig. 5 A, supplementary Figure S5 A-H). (Microsatellite instability) MSI has also attracted more and more attention in recent years. The correlation analyses of the cuproptosis-related gene expressions and MSI index were also performed in various tumor types (Fig. 5 B, supplementary Figure S6 A-H). Almost all cuproptosis-related genes have a significant correlation with MSI index in different tumor types. Neoantigens (NEO) are tumor-specific antigens derived from non-synonymous mutations and have become a very attractive target for tumor immunotherapy, They are highly expressed in tumor cells with strong immunogenicity and tumor heterogeneity. Therefore, to perform the correlation analysis of the Cuproptosis-related gene expressions and NEO was useful for clinical. The results showed that strong positive correlations exist between NEO and Cuproptosis-related genes in ACC and UCES, and negative correlations exist in COAD, COADREAD, LUAD, PCPG, and THCA. These results further confirmed that Cuproptosis-related genes may affect antitumor immunity using regulating TMB, MSI, and NEO (Fig. 5 C, supplementary Figure S7 A-H). Cuproptosis-Related Gene Expressions Is Associated with Cells Infiltrating in TME across various cancer types The immune microenvironment plays a significant role in cancer progression. Whether cuproptosis-related gene expressions correlated with the immune microenvironment was explored in various cancer types by analyzing the ImmuneScore, StromalScore, EstimateScore, and cells in the tumor microenvironment (TME). The study showed that there was a significant association of the same cuproptosis-related gene with immune-related scores in various cancers; especially, most cuproptosis-related genes had negative correlations with immune-related scores in different cancers (Fig. 6 A), while SLC31A1 had positive association with StromalScore, ImmuneScore and EstimateScore in LAML (Fig. 6 B-D).In addition, there were significant correlations between the expressions of cuproptosis-related genes and cells in TME(Figure S8 A-H), which suggested that cuproptosis might have a capacity to regulate the cells in TME. Furthermore, the study proved that the level of cuproptosis-related gene expressions is positively associated with CLP cells or Th2 cells, but negatively associated with NKT cells or Th1 cells (Fig. 7 ). Association of Cuproptosis-Related Gene Expressions with Drug Sensibility To explore the correlation between cuproptosis-related genes and drug sensibility, a correlation analysis was performed. The results showed that the drugs included MI-503, BY-87-2243, Crizotinib, RX-3117, tic10, and AT13387 have significantly positive association with cuproptosis-related genes (Fig. 8 A-B) (correlation coefficient > 0.43). Discussion As the new concept of cuproptosis emerged, both Cu ionophores and Cu chelators involved in cuproptosis would be conducive to overcoming the shortcomings of conventional anti-cancer agents. However, what cancer types can be treated with the mechanism of cuproptosis remains obscure. Therefore, this study gathered cupro ptosis-related genes including FDX1, LIAS, LIPT1, DLAT, DLD, PDHA1, PDHB, and SLC31A1 to perform a comprehensive analysis across pan-cancer. These genes were proved as positive hits in cuproptosis according to Todd R. Golub’s study[ 1 ]. Through this comprehensive analysis, some cancers that are suitable for treatment with the mechanism of cuproptosis can be screened out. The widespread alterations of cuproptosis-related gene expressions were shown in pan-cancer. All cuproptosis-related genes were upregulated in LAML, ALL, PAAD, GBM, GBMLGG, and LGG, while downregulated in KIRP, KIPAN, and KIRC. Further analysis of overall survival associated with cuproptosis-related genes, most of the dysregulated genes had significant associations with OS. Consistent with the gene differential expression results, all cuproptosis-related genes upregulated in LAML, ALL, PAAD, GBM, GBMLGG, and LGG have a poor prognosis, while genes upregulated in KIRC and KIPAN have a better prognosis. Other studies also have reported that high expression of SLC31A1 represents a poor prognosis[ 19 ]. In addition, the pathological stages of some cancers are also related to the cuproptosis-related genes. For example, the higher expression of DLAT indicated the more serious pathological stages in KIRP. Therefore, these highly concordant results might be important for subsequent treatment with the mechanism of cuproptosis. From the results of this study, a significant correlation between cuproptosis-related gene expressions and TMB, MSI, and NEO can be observed. TMB is an index for evaluating cancer mutation quantity, which is beneficial to forecast the survival of patients who experienced immunotherapy. Thus, genes significantly correlating with TMB would be meaningful for accurate therapy. In this study, cuproptosis-related genes highly correlated with TMB are supposed to be focused on, such as FDX1 in KIPAN, KIRC, LUAD, STAD, THCA, LIAS in BRCA, LIPT1 in BRCA, GBMLGG, LUSC, THCA, UCS, DLD in GBMLGG, DLAT in KIPAN, LUAD, STES, THCA, PDHB in ACC, BRCA, CHOL, COAD, ESCA, LUAD, STAD, SLC31A1 in ACC, BRCA, COAD, COADREAD, KIPAN, PRAD, STAD, STES, UCEC. The loss or gain of repetitive DNA tracts can generate MSI, and it has been regarded as a diagnostic phenotype for cancers. In this study, cuproptosis-related genes highly related to MSI should be focused on, such as FDX1 in ACC, GBMLGG, KIRC, LIPT1 in KIPAN, DLD in COAD, KIPAN, DLAT in STAD, PDHA1 in KIPAN, LUAD, LUSC, TGCT, PDHB in ACC, KIPAN, SLC31A1 in GBMLGG, THCA, UCS((|r|>0.2)). In addition, cuproptosis-related genes highly correlated with NEO should be focused on, such as FDX1 in TGCT, UCEC, LIPT1 in PCPG, DLD in ACC, DLAT in ACC, TGCT, PDHB in PCPG, SLC31A1 in CHOL, PCPG, UCEC. TMB, MSI, and NEO are novel clinical indexes, and cuproptosis-related genes highly correlated with them would be potential biomarkers for cancer-targeted therapy. Previously, there was no study reporting that cuproptosis-related genes prevent immune cells from lipoylation and cuproptosis. This study showed that most cuproptosis-related genes negatively correlated with ImmuneScore, which might indicate that cuproptosis-related genes involved in the evolution of various cancers by negatively adjusting the infiltrating of different immune cells. In addition, this study also demonstrated that all cuproptosis-related gene expressions had a significant positive association with the infiltration of CLP cells, and Th2 cells, while negative association with NKT cells and Th1 cells. These results suggested that cuproptosis-related gene expression played a key role in adjusting tumor immunity by influencing the cuproptosis of CLP cells, Th2 cells, NKT cells, and Th1 cells. Thus, CLP cells and Th2 cells have the potential to become the target of cancer therapies through cuproptosis. Finally, this study also performed the correlation analysis of cuproptosis-related genes and drug sensibility, MI-503, BY-87-2243, Crizotinib, RX-3117, tic10, AT13387 were found to have significant correlations with cuproptosis-related genes and the results might offer a potential novel strategy for the therapy of pan-cancer. Conclusions Cuproptosis-related genes altered extensively across various cancer types through pan-cancer systematical analysis. A comprehensive understanding of correlations between cuproptosis-related gene expression and MSI, TMB, NEO, clinical relevance, cells in TME, tumor stemness, and drug sensitivity is conducive to understanding the mechanism of Cuproptosis-related genes in various cancer types. As a consequence, the necessary and effective methods would be found to perform the individualized treatment. Declarations Conflict of Interest We declare that we have no conflict of interest. Author Contribution Jianpeng Zhou: Conceptualization; Data curation; Formal analysis; Software; Writing - original draftChuanlei Wang: Data curation; Formal analysis; SoftwareJia Li: Data curation; Formal analysis; Supervision; ValidationGuangyi Wang: Conceptualization; Writing- review & editing. All of the authors have read and approved the manuscript. Acknowledgement Not applicable Data Availability The datasets generated and analyzed during the present study are available from the corresponding author on reasonable request. References Tsvetkov, P. et al . Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 375, 1254–1261 (2022). Basu, S., Singh, M.K., Singh, T.B., Bhartiya, S.K., Singh, S.P., Shukla, V.K. Heavy and trace metals in carcinoma of the gallbladder. World J Surg. 37, 2641–2646 (2013). Ding, X. et al . Analysis of serum levels of 15 trace elements in breast cancer patients in Shandong, China. Environ Sci Pollut Res Int. 22, 7930–7935 (2015). Pavithra, V., Sathisha, T.G., Kasturi, K., Mallika, D.S., Amos, S.J., Ragunatha, S. Serum levels of metal ions in female patients with breast cancer. J Clin Diagn Res. 9, BC25-c27 (2015). Baltaci, A.K., Dundar, T.K., Aksoy, F., Mogulkoc, R. Changes in the Serum Levels of Trace Elements Before and After the Operation in Thyroid Cancer Patients. Biol Trace Elem Res. 175, 57–64 (2017). Stepien, M. et al . Pre-diagnostic copper and zinc biomarkers and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort. Carcinogenesis. 38, 699–707 (2017). Zhang, X., Yang, Q. Association between serum copper levels and lung cancer risk: A meta-analysis. J Int Med Res. 46, 4863–4873 (2018). Chen, F. et al . Serum copper and zinc levels and the risk of oral cancer: A new insight based on large-scale case-control study. Oral Dis. 25, 80–86 (2019). Aubert, L. et al . Copper bioavailability is a KRAS-specific vulnerability in colorectal cancer. Nat Commun. 11, 3701 (2020). Saleh, S.A.K., Adly, H.M., Abdelkhaliq, A.A., Nassir, A.M. Serum Levels of Selenium, Zinc, Copper, Manganese, and Iron in Prostate Cancer Patients. Curr Urol. 14, 44–49 (2020). Michniewicz, F. et al . Copper: An Intracellular Achilles' Heel Allowing the Targeting of Epigenetics, Kinase Pathways, and Cell Metabolism in Cancer Therapeutics. ChemMedChem. 16, 2315–2329 (2021). Shanbhag, V.C., Gudekar, N., Jasmer, K., Papageorgiou, C., Singh, K., Petris, M.J. Copper metabolism as a unique vulnerability in cancer. Biochim Biophys Acta Mol Cell Res. 1868, 118893 (2021). Steinbrueck, A. et al . Transition metal chelators, pro-chelators, and ionophores as small molecule cancer chemotherapeutic agents. Chem Soc Rev. 49, 3726–3747 (2020). Lizio, M. et al . Update of the FANTOM web resource: expansion to provide additional transcriptome atlases. Nucleic Acids Res. 47, D752-D758 (2019). Uhlen, M. et al . Proteomics. Tissue-based map of the human proteome. Science. 347, 1260419 (2015). Consortium, G.T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 369, 1318–1330 (2020). Bonneville, R. et al . Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol. 2017, (2017). Thorsson, V. et al . The Immune Landscape of Cancer. Immunity. 48, 812–830 e814 (2018). Deng, S.Z., Lai, M.F., Li, Y.P., Xu, C.H., Zhang, H.R., Kuang, J.G. Human marrow stromal cells secrete microRNA-375-containing exosomes to regulate glioma progression. Cancer Gene Ther. 27, 203–215 (2020). Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. Boxplot of cuproptosis-related genes differential expression between cancer and adjacent normal tissues. (A) FDX1 differential expression between cancer and adjacent normal tissue. (B) LIAS differential expression between cancer and adjacent normal tissue. (C) LIPT1 differential expression between cancer and adjacent normal tissue. (D) DLD differential expression between cancer and adjacent normal tissue. (E) DLAT differential expression between cancer and adjacent normal tissue. (F) PDHB differential expression between cancer and adjacent normal tissue. (G) PDHA1 differential expression between cancer and adjacent normal tissue. (H)SLC31A1 differential expression between cancer and adjacent normal tissue. FigureS2.tif Figure S2. The Kaplan-Meier curves of overall survival in various cancers for cuproptosis-related gene expression. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of FDX1 in (A) ALL, (B) GBMLGG, (C) LAML, and (D) LGG. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of LIAS in (E) KICH, (F) LAML, (G) THCA, (H) GBMLGG. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of LIPT1 in (I) BLCA, (J) GBMLGG, (K) LAML, (L) LIHC, (M)READ, (N) SKCM. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of DLAT in (O)BLCA, (P) BRCA, (Q) COAD, (R) COADREAD, (S) GBMLGG, (T) LAML, (U) LGG, (V) LIHC, (W) PAAD, (X) READ. FigureS3.tif Figure S3. The Kaplan-Meier curves of overall survival in various cancers for cuproptosis-related gene expression. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of DLD in (A) BRCA, (B) COADREAD, (C) GBMLGG, (D) KIRP, (E)LAML, (F) LGG, (G) LUAD. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of PDHA1 in (H) ESCA, (I) KIRP, (J) LAML, (K) LUAD, (L) PRAD, (M) SKCM. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of PDHB in (N) KICH, (O) KIRP, and (P) LAML. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of SLC31A1 in (Q) ACC, (R) BLCA, (S) BRCA, (T) GBMLGG, (U) LAML, (V) LGG. FigureS4.tif Figure S4. The forest plot of univariate Cox regression of cuproptosis-related genes for various cancers. FigureS5.tif Figure S5. The significant correlations between the cuproptosis-related gene expressions and TMB. The correlations between TMB and the expression of (A) FDX1, (B)LIAS, (C) LIPT1, (D) DLD, (E) DLAT, (F) PDHA1, (G)PDHB, (H) SLC31A1. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. FigureS6.tif Figure S6. The significant correlations between the cuproptosis-related gene expressions and MSI. The correlations between MSI and the expression of (A) FDX1, (B)LIAS, (C) LIPT1, (D) DLD, (E) DLAT, (F) PDHA1, (G)PDHB, (H) SLC31A1. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. FigureS7.tif Figure S7. The significant correlations between the cuproptosis-related gene expressions and NEO. The correlations between NEO and the expression of (A) FDX1, (B) LIAS, (C) LIPT1, (D)DLD, (E) DLAT, (F) PDHA1, (G) PDHB, (H) SLC31A1. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. FigureS8.tif Figure S8. Cuproptosis-related gene expressions are associated with cells infiltrating in TME of various cancer types. Correlations between cells infiltrating in TME of various cancer types and (A) FDX1, (B) LIAS, (C) LIPT1, (D) DLD, (E)DLAT, (F) PDHA1, (G) PDHB, (H) SLC31A1. SupplementaryTables.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4403303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":314755805,"identity":"09e326c7-8abc-467a-ada3-4f005caf6d7a","order_by":0,"name":"Jianpeng Zhou","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianpeng","middleName":"","lastName":"Zhou","suffix":""},{"id":314755806,"identity":"9059f806-84e0-4669-bfba-465e9b0a3e8d","order_by":1,"name":"Chuanlei Wang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Chuanlei","middleName":"","lastName":"Wang","suffix":""},{"id":314755807,"identity":"aeee8ec9-a12d-4f38-9c5d-f4438d88170a","order_by":2,"name":"Jia Li","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":314755808,"identity":"1461d144-b65e-425c-a409-fbadb366051d","order_by":3,"name":"Guangyi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYPACCTnGdhDNRoIWY8ZmErUwJDYwE6tFPiL52cOvbRbpzc08Bgwfyg4z8M9uwK/F8EaaubFsm0RuI1AL44xzhxkk7hwgoGVGgpm0JFQLM2/bYQYDiQRCWtK/gbSkM4K0/CVGi7xEjpnkxzaJBLAWRmK0GPC8KZNmOCdh2NjMVnCw51w6j8QNQra0p2+T/FFWJ2/Y3rzxwY8yazn+GYRsOcDAwMwLjA7DBgYGIJuBB796kC1AlYw//gAZBJWOglEwCkbBiAUA2fk9V6GkYjMAAAAASUVORK5CYII=","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Guangyi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-11 03:31:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4403303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4403303/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58760052,"identity":"74bce9b2-e620-4911-9c7b-4bb5bcda39ab","added_by":"auto","created_at":"2024-06-20 18:47:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":556651,"visible":true,"origin":"","legend":"\u003cp\u003eThe level of cuproptosis-related gene expressions in human normal tissues and tumor samples. The RNA-seq data of cuproptosis-related genes in human tissues from the \u003cstrong\u003e(A)\u003c/strong\u003eFANTOM5 database(60 tissues), \u003cstrong\u003e(B)\u003c/strong\u003e GTEx database(37 tissues), and \u003cstrong\u003e(C)\u003c/strong\u003eHPA database(40 tissues). \u003cstrong\u003e(D)\u003c/strong\u003e The RNA-seq data of cuproptosis-related genes in various cancers from UCSC. \u003cstrong\u003e(E)\u003c/strong\u003e The heatmap of widespread alterations of cuproptosis-related genes across 34 cancer types compared with adjacent or normal samples.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/807f386c420e17d1f7321fbd.png"},{"id":58760053,"identity":"08407a73-70d5-4bbd-864d-68d09fcebd19","added_by":"auto","created_at":"2024-06-20 18:47:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":396942,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall survival analysis for cuproptosis-related gene expression in KIRC and KIPAN by the Kaplan-Meier curves. The overall survival analysis for the expression(the median expression as a cut-off) of \u003cstrong\u003e(A) \u003c/strong\u003eFDX1, \u003cstrong\u003e(B)\u003c/strong\u003eLIPT1, \u003cstrong\u003e(C)\u003c/strong\u003eLIAS, \u003cstrong\u003e(D)\u003c/strong\u003eDLD, \u003cstrong\u003e(E) \u003c/strong\u003eDLAT, \u003cstrong\u003e(F)\u003c/strong\u003e PDHA1, \u003cstrong\u003e(G)\u003c/strong\u003e PDHB, \u003cstrong\u003e(H)\u003c/strong\u003eSLC31A1 in KIRC. The overall survival analysis for the expression(the median expression as a cut-off) of (I) FDX1, \u003cstrong\u003e(J) \u003c/strong\u003eLIAS, \u003cstrong\u003e(K)\u003c/strong\u003e DLD, \u003cstrong\u003e(L) \u003c/strong\u003eDLAT, \u003cstrong\u003e(M)\u003c/strong\u003e PDHA1, \u003cstrong\u003e(N)\u003c/strong\u003e PDHB, \u003cstrong\u003e(O)\u003c/strong\u003e SLC31A1, \u003cstrong\u003e(P)\u003c/strong\u003eLIPT1 in KIPAN.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/0c53b91e679ff11213f48c91.png"},{"id":58760054,"identity":"52f7de72-234e-4b96-bb69-f4034bfd8717","added_by":"auto","created_at":"2024-06-20 18:47:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202538,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference of cuproptosis-related gene expressions among different stages in various cancer types. \u003cstrong\u003e(A)\u003c/strong\u003e LIAS expressed among different stages UCS, ACC.\u003cstrong\u003e (B) \u003c/strong\u003eLIPT1 is expressed among different stages in STAD, and STES. \u003cstrong\u003e(C) \u003c/strong\u003eDLAT is expressed among different stages in KIRP. \u003cstrong\u003e(D)\u003c/strong\u003e PDHA1 is expressed among different stages in CESC, STAD, and STES.\u003cstrong\u003e (E) \u003c/strong\u003eSLC31A1 expressed among different stages in GBMLGG, KIRP.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/354f69902101806539545016.png"},{"id":58760058,"identity":"d2759766-2121-4de5-be31-8cd1b651d0a9","added_by":"auto","created_at":"2024-06-20 18:47:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109978,"visible":true,"origin":"","legend":"\u003cp\u003eThe mutation frequency of Cuproptosis-related genes\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/f59d0f50abc2ae5fba951457.png"},{"id":58760067,"identity":"65100139-3f8a-43f6-83af-b1c9379688cc","added_by":"auto","created_at":"2024-06-20 18:47:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":861108,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation analyses of the cuproptosis-related gene expressions and TMB, MSI, and NEO. \u003cstrong\u003e(A) \u003c/strong\u003eThe association of cuproptosis-related gene expressions with TMB.\u003cstrong\u003e (B) \u003c/strong\u003eThe association of cuproptosis-related gene expressions with MSI. \u003cstrong\u003e(C) \u003c/strong\u003eThe association of cuproptosis-related gene expressions with NEO.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/6ceb5062d7a02fac6d42b484.png"},{"id":58760068,"identity":"62e6b239-89b1-434d-9fe1-0b5e466c6623","added_by":"auto","created_at":"2024-06-20 18:47:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":850492,"visible":true,"origin":"","legend":"\u003cp\u003eCuproptosis-related gene expressions associated with ImmuneScore, EstimateScore, and StromalScore in different cancers. \u003cstrong\u003e(A)\u003c/strong\u003e Cuproptosis-related gene expressions associated with immune-related scores in different cancers. The correlations between SLC31A1 expression and \u003cstrong\u003e(B) \u003c/strong\u003eStromalScore, \u003cstrong\u003e(C) \u003c/strong\u003eImmuneScore, \u003cstrong\u003e(D) \u003c/strong\u003eEstimateScore in LAML.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/46623903ac8da2510f619bf1.png"},{"id":58760062,"identity":"788d3917-934e-421d-8659-6ba9b24f76ba","added_by":"auto","created_at":"2024-06-20 18:47:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1083875,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between cuproptosis-related gene expressions and cells in tumor microenvironment.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/e73e1ce4abc1db17e2270d46.png"},{"id":58761027,"identity":"19e00009-e9e2-4caa-a961-8bfbfcd38e79","added_by":"auto","created_at":"2024-06-20 18:55:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":370613,"visible":true,"origin":"","legend":"\u003cp\u003eCuproptosis-related genes associated with drug sensibility. \u003cstrong\u003e(A)\u003c/strong\u003e Cuproptosis-related genes correlated with drug sensibility. \u003cstrong\u003e(B) \u003c/strong\u003eThe difference between the level of cuproptosis-related gene expression and the IC50 of various drugs.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/250173d65e14e28f5dd7f4d5.png"},{"id":59994685,"identity":"00f292b7-ba39-4859-b512-ce70efb73a2a","added_by":"auto","created_at":"2024-07-10 09:15:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4052928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/a894752c-58ee-4c41-891e-d6c718ff27b8.pdf"},{"id":58760063,"identity":"fe30282c-abf3-41f8-91bd-0730318b6795","added_by":"auto","created_at":"2024-06-20 18:47:26","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46859608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e. Boxplot of cuproptosis-related genes differential expression between cancer and adjacent normal tissues. \u003cstrong\u003e(A)\u003c/strong\u003e FDX1 differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(B)\u003c/strong\u003e LIAS differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(C) \u003c/strong\u003eLIPT1 differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(D) \u003c/strong\u003eDLD differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(E)\u003c/strong\u003e DLAT differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(F)\u003c/strong\u003e PDHB differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(G)\u003c/strong\u003e PDHA1 differential expression between cancer and adjacent normal tissue. \u003cstrong\u003e(H)\u003c/strong\u003eSLC31A1 differential expression between cancer and adjacent normal tissue.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/30f179d9ac235d168bab1414.tif"},{"id":58760059,"identity":"922cb7a7-2d48-4c2f-ad08-458c076471c7","added_by":"auto","created_at":"2024-06-20 18:47:24","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17856628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003e. The Kaplan-Meier curves of overall survival in various cancers for cuproptosis-related gene expression. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of FDX1 in \u003cstrong\u003e(A) \u003c/strong\u003eALL, \u003cstrong\u003e(B) \u003c/strong\u003eGBMLGG, \u003cstrong\u003e(C) \u003c/strong\u003eLAML, and \u003cstrong\u003e(D)\u003c/strong\u003e LGG. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of LIAS in \u003cstrong\u003e(E) \u003c/strong\u003eKICH, \u003cstrong\u003e(F) \u003c/strong\u003eLAML, \u003cstrong\u003e(G) \u003c/strong\u003eTHCA, \u003cstrong\u003e(H)\u003c/strong\u003e GBMLGG. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of LIPT1 in \u003cstrong\u003e(I) \u003c/strong\u003eBLCA, \u003cstrong\u003e(J) \u003c/strong\u003eGBMLGG, \u003cstrong\u003e(K) \u003c/strong\u003eLAML, \u003cstrong\u003e(L)\u003c/strong\u003e LIHC, \u003cstrong\u003e(M)\u003c/strong\u003eREAD, \u003cstrong\u003e(N)\u003c/strong\u003e SKCM. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of DLAT in \u003cstrong\u003e(O)\u003c/strong\u003eBLCA, \u003cstrong\u003e(P) \u003c/strong\u003eBRCA, \u003cstrong\u003e(Q) \u003c/strong\u003eCOAD, \u003cstrong\u003e(R) \u003c/strong\u003eCOADREAD, \u003cstrong\u003e(S) \u003c/strong\u003eGBMLGG, \u003cstrong\u003e(T) \u003c/strong\u003eLAML, \u003cstrong\u003e(U) \u003c/strong\u003eLGG, \u003cstrong\u003e(V) \u003c/strong\u003eLIHC, \u003cstrong\u003e(W) \u003c/strong\u003ePAAD, \u003cstrong\u003e(X) \u003c/strong\u003eREAD.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/b645f7a877a8ea08802a49cd.tif"},{"id":58760065,"identity":"03f151ed-a81a-42b4-bde6-d8306b57e744","added_by":"auto","created_at":"2024-06-20 18:47:27","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17124072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e. The Kaplan-Meier curves of overall survival in various cancers for cuproptosis-related gene expression. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of DLD in \u003cstrong\u003e(A) \u003c/strong\u003eBRCA, \u003cstrong\u003e(B) \u003c/strong\u003eCOADREAD, \u003cstrong\u003e(C) \u003c/strong\u003eGBMLGG, \u003cstrong\u003e(D) \u003c/strong\u003eKIRP, \u003cstrong\u003e(E)\u003c/strong\u003eLAML, \u003cstrong\u003e(F) \u003c/strong\u003eLGG, \u003cstrong\u003e(G) \u003c/strong\u003eLUAD. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of PDHA1 in \u003cstrong\u003e(H) \u003c/strong\u003eESCA, \u003cstrong\u003e(I) \u003c/strong\u003eKIRP, \u003cstrong\u003e(J) \u003c/strong\u003eLAML, \u003cstrong\u003e(K)\u003c/strong\u003e LUAD, \u003cstrong\u003e(L)\u003c/strong\u003e PRAD, \u003cstrong\u003e(M)\u003c/strong\u003e SKCM. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of PDHB in \u003cstrong\u003e(N) \u003c/strong\u003eKICH, \u003cstrong\u003e(O) \u003c/strong\u003eKIRP, and \u003cstrong\u003e(P) \u003c/strong\u003eLAML. The Kaplan-Meier curves of overall survival for the expression(the median expression as a cut-off) of SLC31A1 in \u003cstrong\u003e(Q) \u003c/strong\u003eACC, \u003cstrong\u003e(R) \u003c/strong\u003eBLCA, \u003cstrong\u003e(S) \u003c/strong\u003eBRCA,\u003cstrong\u003e (T) \u003c/strong\u003eGBMLGG, \u003cstrong\u003e(U) \u003c/strong\u003eLAML, \u003cstrong\u003e(V) \u003c/strong\u003eLGG.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/89931f455085a80ab66b10c6.tif"},{"id":58760057,"identity":"e7b0b9e2-bf42-48cf-8828-3778758ec61f","added_by":"auto","created_at":"2024-06-20 18:47:24","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20707032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4.\u003c/strong\u003e The forest plot of univariate Cox regression of cuproptosis-related genes for various cancers.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/44a5853a9109653fbd0e0e78.tif"},{"id":58760056,"identity":"49cc8df7-57ca-4b53-9720-0779cec57b91","added_by":"auto","created_at":"2024-06-20 18:47:24","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":25298060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5.\u003c/strong\u003e The significant correlations between the cuproptosis-related gene expressions and TMB. The correlations between TMB and the expression of \u003cstrong\u003e(A)\u003c/strong\u003e FDX1, \u003cstrong\u003e(B)\u003c/strong\u003eLIAS, \u003cstrong\u003e(C) \u003c/strong\u003eLIPT1,\u003cstrong\u003e (D)\u003c/strong\u003e DLD,\u003cstrong\u003e (E) \u003c/strong\u003eDLAT, \u003cstrong\u003e(F) \u003c/strong\u003ePDHA1, \u003cstrong\u003e(G)\u003c/strong\u003ePDHB, \u003cstrong\u003e(H)\u003c/strong\u003e SLC31A1. * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001, **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/6482908ca1d0e9964050c794.tif"},{"id":58760061,"identity":"3ed236aa-6516-4743-84fb-02a1754ee6d2","added_by":"auto","created_at":"2024-06-20 18:47:25","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":25411900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6\u003c/strong\u003e. The significant correlations between the cuproptosis-related gene expressions and MSI. The correlations between MSI and the expression of\u003cstrong\u003e (A) \u003c/strong\u003eFDX1, \u003cstrong\u003e(B)\u003c/strong\u003eLIAS,\u003cstrong\u003e (C)\u003c/strong\u003e LIPT1, \u003cstrong\u003e(D)\u003c/strong\u003e DLD,\u003cstrong\u003e (E) \u003c/strong\u003eDLAT, \u003cstrong\u003e(F)\u003c/strong\u003e PDHA1, \u003cstrong\u003e(G)\u003c/strong\u003ePDHB, \u003cstrong\u003e(H)\u003c/strong\u003e SLC31A1. * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001, **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/d9545cad1de8607c8534ee33.tif"},{"id":58760064,"identity":"3abc36ed-7c76-46a9-88c0-143c7fd5ceb8","added_by":"auto","created_at":"2024-06-20 18:47:27","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":26538208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S7\u003c/strong\u003e. The significant correlations between the cuproptosis-related gene expressions and NEO. The correlations between NEO and the expression of \u003cstrong\u003e(A)\u003c/strong\u003e \u0026nbsp;FDX1, \u003cstrong\u003e(B)\u003c/strong\u003e LIAS,\u003cstrong\u003e (C)\u003c/strong\u003e LIPT1, \u003cstrong\u003e(D)\u003c/strong\u003eDLD, \u003cstrong\u003e(E)\u003c/strong\u003e DLAT, \u003cstrong\u003e(F) \u003c/strong\u003ePDHA1, \u003cstrong\u003e(G) \u003c/strong\u003ePDHB, \u003cstrong\u003e(H) \u003c/strong\u003eSLC31A1. * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001, **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"FigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/ac28a981a9771995ec165dec.tif"},{"id":58760066,"identity":"9006bc7a-ac73-4646-a107-5bfa163775b7","added_by":"auto","created_at":"2024-06-20 18:47:27","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":44544676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S8\u003c/strong\u003e. Cuproptosis-related gene expressions are associated with cells infiltrating in TME of various cancer types. Correlations between cells infiltrating in TME of various cancer types and \u003cstrong\u003e(A) \u003c/strong\u003eFDX1, \u003cstrong\u003e(B) \u003c/strong\u003eLIAS, \u003cstrong\u003e(C) \u003c/strong\u003eLIPT1, \u003cstrong\u003e(D) \u003c/strong\u003eDLD, \u003cstrong\u003e(E)\u003c/strong\u003eDLAT, \u003cstrong\u003e(F) \u003c/strong\u003ePDHA1, \u003cstrong\u003e(G) \u003c/strong\u003ePDHB, \u003cstrong\u003e(H) \u003c/strong\u003eSLC31A1.\u003c/p\u003e","description":"","filename":"FigureS8.tif","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/bfa2e184d4983455e79a15aa.tif"},{"id":58760060,"identity":"51ca1079-1645-4863-b1ef-cf741f7b15d5","added_by":"auto","created_at":"2024-06-20 18:47:25","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":38814,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4403303/v1/8d1952cb87e70f789a26ad3e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The potential role of cuproptosis-related genes for therapy and immunoregulation in pan-cancer ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCopper(Cu) is an essential cofactor in most organisms, which sustains at lower levels through active homeostasis mechanisms to hold back intracellular dissociative copper amassing regulated by concentration gradients. Thus, an overload of intracellular copper concentration leads to cell death. Recently, Todd R. Golub\u0026rsquo;s team demonstrated that the mechanisms of copper toxicity are different from other forms of cell death, such as apoptosis, ferroptosis, pyroptosis, and necrosis, and termed this previously uncharacterized cell death as cuproptosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which is induced by cooper-dependent lipoylated proteins oligomerization and Fe-S cluster proteins instability. The study also suggests that cuproptosis might be regarded as a helpful and promising therapy against cancer.\u003c/p\u003e \u003cp\u003ePreviously, the level of Cu that was higher in a variety of tumor samples than in normal tissues has been reported. The connections between proliferation, angiogenesis, and metastasis of tumor and Cu accumulation also have been observed in a variety of cancer types. Furthermore, Cu levels have been found significantly altered in both serums and tumor tissues of patients with tumors such as oral, bladder, cervical, breast, ovarian, thyroid, pancreatic, prostate, gastric, and lung tumors[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, it is obvious that the dyshomeostasis of Cu affects cancer. Since the role of Cu in the genesis and progression of tumors is important, cuproptosis might be a novel method for holding back cancer development[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conventional cancer therapies were mainly untargeted treatments to rapidly proliferating cells, which had large side effects. However, accurate target therapy with fewer side effects is supposed to be achieved in the future, and some compounds easily binding metal have been reported as promising in this field[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, as a new method, cuproptosis would show an optimistic scenario in the tumor clinic.\u003c/p\u003e \u003cp\u003eHowever, the correlations between cuproptosis-related gene expressions and relevant mechanisms in pan-cancer remain largely obscure. Therefore, this study mainly focuses on not only the systematical characteristics of the complex mechanisms that regulate tumor genesis and development by performing the analysis of extensive genetic alterations of cuproptosis-related genes but also the potential interaction of cuproptosis-related genes with tumor mutation burden (TMB), microsatellite instability score(MSI), neoantigen(NEO), cells in the tumor microenvironment(TME) and drug sensitivity. In addition, this study also aims to identify a potential biomarker by performing the correlations between cuproptosis-related genes and clinical characteristics.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCuproptosis-Related Genes Collection\u003c/h2\u003e \u003cp\u003eAccording to Todd R. Golub\u0026rsquo;s study, seven positive regulatory genes FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, and a Cu transporter encoding gene SLC31A1 were collected for the pan-cancer analysis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData resources\u003c/h2\u003e \u003cp\u003eThe FANTOM5(60 tissue types)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Human Protein Atlas(HPA, 40 tissue types)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and GTEx database(37 tissue types)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] were used to download the gene expression data of human tissues. The UCSC(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was the data source for standardized universal omics data, which included gene expression RNAseq from HTSeq-FPKM GDC Hub, somatic mutation from MuTect2 Variant Aggregation and Masking, clinical phenotype from Curated clinical data by Pan-Cancer Atlas Hub, immune phenotype from immune subtype by Pan-Cancer Atlas Hub. The maftools R package was used to calculate the distribution of tumor mutation burden (TMB) according to somatic mutation data. The MSI score was obtained from the previous study[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The NEO score was obtained from the previous study[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To analyze cuproptosis-related genes expression, 34 cancer types were utilized, including adrenocortical carcinoma (ACC), acute lymphoblastic leukemia(ALL),bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma(CHOL), colon adenocarcinoma (COAD), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma(COADREAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), glioma(GBMLGG), head and neck squamous carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), Pan-kidney cohort(KIPAN),acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), Stomach and Esophageal carcinoma(STES) ,testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS) and High-Risk Wilms Tumor(WT). All abbreviations are shown in Supplementary Table\u0026nbsp;1. All statistical R packages are shown in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Differential Cuproptosis-Related Genes Expression between Tumor and Normal Tissues\u003c/h2\u003e \u003cp\u003eThe cuproptosis-related gene expression level was extracted from the RNAseq dataset. The differential expression genes(DEGs) between tumor and normal tissues across 34 cancer types were analyzed by ggpubr R package, with statistical significance (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The significance of gene expression alterations was identified by the Wilcox method. The p-value was adjusted with the Benjamini-Hochberg multiple testing correction. The heatmap of cupro ptosis-related genes was plotted by pheatmap R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Cuproptosis-Related Genes Expression with TMB, MSI and NEO\u003c/h2\u003e \u003cp\u003eThe corrplot R package was applied to evaluate the correlation between the cuproptosis-related gene expression and TMB, MSI, or NEO with the Spearman method (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation radar plot was plotted by the ggradar R package, and the ggpubr R package was applied to plot boxplots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis of Cuproptosis-Related Gene Expressions\u003c/h2\u003e \u003cp\u003eThe tumor samples were divided into high- and low-expression groups according to the median value of cuproptosis-related gene expression across 34 cancer types. The survminer R package was applied for overall survival analysis by the Kaplan-Meier method, for which the log-rank test was used, with a statistical significance of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The survival R package was used to perform Cox regression analysis for cuproptosis-related genes. The hazard ratio was calculated for the Cox proportional hazard regression models. Further, the differential expression of cuproptosis-related genes in different pathologic stages (including stages I, II, III, and IV) were analyzed across 34 cancer types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Cuproptosis-related Genes Expression with Immune Microenvironment across 34 Cancer Types\u003c/h2\u003e \u003cp\u003eThe presence of infiltrating stromal and immune cell scores was predicted by the ESTIMATE algorithm in the estimate R package that forecasted stromal and immune cells in tumor tissues with gene expression data. Based on ssGSEA analysis, the estimate algorithm generated ImmuneScore, StromalScore, and EstimateScore. These three scores represent the corresponding ratio of immune cells infiltrating in tumor tissues, stromal cells present in tumor tissues, and the sum of both, respectively. Furthermore, the higher score represents the larger ratio of the corresponding component in the tumor microenvironment. Correlation analysis of the cuproptosis-related gene expressions with ImmuneScore, StromalScore, or EstimateScore was performed by the Corrplot R package with the method of Spearman (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe Proportion of Cells in Microenvironment across 34 Cancer Types Based on xCell Method\u003c/h2\u003e \u003cp\u003eThe xCell algorithm was employed to identify the proportion of cells in the microenvironment across 34 cancer types. The xCell R package was applied to discriminate 64 human cell phenotypes in the microenvironment based on the gene expression profile. The correlation analysis of the cuproptosis-related gene expressions with different cells was performed by the corrplot R package with the Spearman method (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including activated dendritic cell (aDC), adipocytes, astrocytes, B cells, basophils, CD4\u0026thinsp;+\u0026thinsp;memory T cells, CD4\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;central memory T cells(CD4\u0026thinsp;+\u0026thinsp;Tcm), CD4\u0026thinsp;+\u0026thinsp;effector memory T cells(CD4\u0026thinsp;+\u0026thinsp;Tem), CD8\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;central memory T cells(CD8\u0026thinsp;+\u0026thinsp;Tcm), CD8\u0026thinsp;+\u0026thinsp;effector memory T cells(CD8\u0026thinsp;+\u0026thinsp;Tem), conventional dendritic cells(cDC), chondrocytes, class switched memory B cells, common lymphoid progenitor cells(CLP), common myeloid progenitor(CMP), dendritic cells(DC), endothelial cells, eosinophils, epithelial cells, erythrocytes, fibroblasts, granulocyte-macrophage progenitor(GMP), hepatocytes, hematopoietic stem cells(HSC), immature dendritic cells(iDC), Keratinocytes, lymphatic(ly) endothelial cells, macrophages, M1 macrophages, M2 macrophages, mast cells, megakaryocytes, melanocytes, memory B cells, megakaryocyte-erythroid progenitor(MEP), mesangial cells, monocytes, multipotent progenitors(MPP), mesenchymal stem cells(MSC), microvascular(mv) endothelial cells, myocytes, na\u0026iuml;ve B cells, neurons, neutrophils, NK cells, natural killer T cells(NKT), osteoblasts, plasma dendritic cells(pDC), pericytes, plasma cells, platelets, preadipocytes, pro B cells, sebocytes, skeletal muscle cells, smooth muscle cells, gamma delta T cells, T helper 1 cells(Th1 cells), T helper 2 cells(Th2 cells), T regulator cells(Treg cells).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eCellMiner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/\u003c/span\u003e\u003cspan address=\"https://discover.nci.nih.gov/cellminer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to explore transcript and drug patterns in the NCI-60 cell line set. The NCI-60 cell line panel was an anticancer drug efficacy screen by the Developmental Therapeutics Program (DTP) of the US National Cancer Institute (NCI). Thousands of compounds have been applied to the NCI-60. The sample of gene expression and drug sensitivity data were downloaded from the CellMiner database and then filtered drug sensitivity data after clinical laboratory verification and FDA standard certification. Next, the Spearman correlation test was performed for cuproptosis-related gene expression data combined with drug sensitivity data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe expression of cuproptosis-related genes in human tissues and cancers\u003c/h2\u003e \u003cp\u003eThe expression of cupro ptosis-related genes was analyzed in human normal tissues and cancer samples from FANTOM5, HPA, and GTEx databases. All database data showed concordant results that cuproptosis-related genes were highly expressed in heart muscle tissue. and data from FANTOM5 and the HPA database showed that cuproptosis-related genes were highly expressed in the kidney (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). In addition, cuproptosis-related is widely expressed in various cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Further, cuproptosis-related genes showed extensive alterations in tumor samples compared with adjacent samples across most of cancer types, such as ACC, ALL, BLCA, BRCA, CHOL, COAD, COADREAD, ESCA, GBM, GBMLGG, HNSC, KICH, KIPAN, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, STES, STAD, TGCT, THCA, UCS and WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-H). Most of the cuproptosis-related genes show significant upregulation compared with adjacent non-tumor tissues across different cancer types. As can be seen, all cuproptosis-related gene upregulates significantly in LAML, ALL, PAAD, GBM, GBMLGG, and LGG and all significantly downregulated in cancers KIPP, WT, KIPAN, and KIRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These results might indicate that different cuproptosis-related genes play different roles in various cancer types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe Potential Prognostic Value of Cuproptosis-Related Genes Expression in Different Cancer Types\u003c/h2\u003e \u003cp\u003eTo evaluate the prognostic potential of cuproptosis-related genes across 34 cancer types, the Kaplan-Meier curve analysis based on TCGA, GTEx, and TARGET database was performed. Most of the genes showed significant correlations with the overall survival of patients. KIRC and KIPAN showed a lot of significant results. The higher the level of cuproptosis-related genes expressed, the higher the survival rate in patients suffering from KIRC, and KIPAN increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-P). However, for other cancers, different cuproptosis-related genes played different roles. To be more specific, the higher the level of FDX1 expressed, the poorer survival in ALL, GBMLGG, LAML, and LGG (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-D). The increasing expression of LIAS has a significant correlation with poor survival in KICH, LAML, and THCA, while better survival in GBMLGG (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE-H). The increasing expression of LIPT1 has a significant correlation with pool survival in GBMLGG, LIHC, and LAML, while better survival in BLCA, READ, and SKCM (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eI-N). The increasing expression of DLAT has a significant correlation with poor survival rate in BLCA, BRCA, GBMLGG, LGG, LIHC, and PAAD, but better survival in COAD, COADREAD, and READ (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eO-X). The increasing expression of DLD was highly correlated to poor survival rates in BRCA, GBMLGG, LGG, and LUAD, but better survival in COADREAD (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA-G). The increasing expression of PDHA1 was highly correlated to poor survival in ESCA, LAML, LUAD, PRAD, and SKCM (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eH-M). The increasing expression of PDHB was highly correlated to poor survival in KICH, and LAML but better survival in KIRP (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eN-P). The increasing expression of SLC31A1 was highly correlated to poor survival in ACC, BLCA, BRCA, GBMLGG, LAML, and LGG (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eQ-V).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurtherly, to obtain hazard ratio (HR) across 34 cancer types for cuproptosis-related genes, the univariate Cox regression analysis was performed (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). These results further confirmed Kaplan-Meier curves of overall survival analysis, from which the indication that the same gene might be a risky factor (HR\u0026thinsp;\u0026gt;\u0026thinsp;1) or protective factor (HR\u0026thinsp;\u0026lt;\u0026thinsp;1) in different tumors. Taking SLC31A1 as an exaple, SLC31A1 played risky role in BLCA (HR\u0026thinsp;=\u0026thinsp;1.28, p\u0026thinsp;=\u0026thinsp;0.02), BRCA (HR\u0026thinsp;=\u0026thinsp;1.31, p\u0026thinsp;=\u0026thinsp;0.01), GBMLGG (HR\u0026thinsp;=\u0026thinsp;3.25, p\u0026thinsp;=\u0026thinsp;2.00E-21) and LGG (HR\u0026thinsp;=\u0026thinsp;2.38, p\u0026thinsp;=\u0026thinsp;6.70E-07) but played protective role in KIRC (HR\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;3.70E-06) and KIPAN (HR\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;=\u0026thinsp;1.20E-04).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, correlation analyses of pathologic stages (stages I, II, III, and IV) and cuproptosis-related genes were performed across 34 cancer types. The expression levels of a cuproprosis-related gene are significantly different among different stages in ACC, CESC, GBMLGG, KIRP, STAD, STES, and UCS. Detailly, the level of LIAS has a significant difference among different stages in UCS, and ACC, higher expression levels indicated increasing pathologic stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Lower expression of LIPT1 might suggest an increasing pathologic stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For the expression of DLAT among different stages, it showed a neutral difference in KIRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The lower expression of PDHA1 was, the lower stage of CESC, while no trend was shown in STAD and STES (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The level of SLC31A1 also has differences among different stages in cancers GBMLGG, and KIRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe Mutation Frequency of Cuproptosis-Related Genes\u003c/h2\u003e \u003cp\u003eThe detailed mutation status of cuproptosis-related genes was displayed by the waterfall map from high to low percentage, LIAS, PDHA1, DLAT, DLD, LIPT1, SLC31A1, PDHB, FDX1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were many types of mutations to be found, including frameshift variant, intron variant, missense variant, 3\u0026prime; prime UTR variant, 5\u0026prime; prime UTR variant, synonymous variant, downstream gene variant, splice region variant, splice donor variant, stop gained, inframe deletion, protein-altering variant, splice acceptor variant, start lost, stop lost, stop retained variant, and upstream gene variant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analyses of the Cuproptosis-Related Gene Expressions and TMB or MSI or NEO\u003c/h2\u003e \u003cp\u003eThe role of TMB (Tumor mutation burden) in tumors has been more and more important in recent years and it has been regarded as a novel biomarker in tumors. Correlation analysis of the expression of the cupro ptosis-related gene with TMB index was performed for various cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Cuproptosis-related genes have a significant correlation with TMB scores in most cancers. However, different cancers correlated differentially with cuproptosis-related genes. For example, SLC31A1 exhibited a positive correlation with TMB score in ACC, BRCA, COAD, COADREAD, and STAD but a negative correlation with KIPAN, and PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, supplementary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Microsatellite instability) MSI has also attracted more and more attention in recent years. The correlation analyses of the cuproptosis-related gene expressions and MSI index were also performed in various tumor types (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, supplementary Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA-H). Almost all cuproptosis-related genes have a significant correlation with MSI index in different tumor types.\u003c/p\u003e \u003cp\u003eNeoantigens (NEO) are tumor-specific antigens derived from non-synonymous mutations and have become a very attractive target for tumor immunotherapy, They are highly expressed in tumor cells with strong immunogenicity and tumor heterogeneity. Therefore, to perform the correlation analysis of the Cuproptosis-related gene expressions and NEO was useful for clinical. The results showed that strong positive correlations exist between NEO and Cuproptosis-related genes in ACC and UCES, and negative correlations exist in COAD, COADREAD, LUAD, PCPG, and THCA. These results further confirmed that Cuproptosis-related genes may affect antitumor immunity using regulating TMB, MSI, and NEO (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, supplementary Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA-H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCuproptosis-Related Gene Expressions Is Associated with Cells Infiltrating in TME across various cancer types\u003c/h2\u003e \u003cp\u003eThe immune microenvironment plays a significant role in cancer progression. Whether cuproptosis-related gene expressions correlated with the immune microenvironment was explored in various cancer types by analyzing the ImmuneScore, StromalScore, EstimateScore, and cells in the tumor microenvironment (TME). The study showed that there was a significant association of the same cuproptosis-related gene with immune-related scores in various cancers; especially, most cuproptosis-related genes had negative correlations with immune-related scores in different cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), while SLC31A1 had positive association with StromalScore, ImmuneScore and EstimateScore in LAML (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-D).In addition, there were significant correlations between the expressions of cuproptosis-related genes and cells in TME(Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eA-H), which suggested that cuproptosis might have a capacity to regulate the cells in TME.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the study proved that the level of cuproptosis-related gene expressions is positively associated with CLP cells or Th2 cells, but negatively associated with NKT cells or Th1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of Cuproptosis-Related Gene Expressions with Drug Sensibility\u003c/h2\u003e \u003cp\u003eTo explore the correlation between cuproptosis-related genes and drug sensibility, a correlation analysis was performed. The results showed that the drugs included MI-503, BY-87-2243, Crizotinib, RX-3117, tic10, and AT13387 have significantly positive association with cuproptosis-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B) (correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.43).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the new concept of cuproptosis emerged, both Cu ionophores and Cu chelators involved in cuproptosis would be conducive to overcoming the shortcomings of conventional anti-cancer agents. However, what cancer types can be treated with the mechanism of cuproptosis remains obscure. Therefore, this study gathered cupro ptosis-related genes including FDX1, LIAS, LIPT1, DLAT, DLD, PDHA1, PDHB, and SLC31A1 to perform a comprehensive analysis across pan-cancer. These genes were proved as positive hits in cuproptosis according to Todd R. Golub\u0026rsquo;s study[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Through this comprehensive analysis, some cancers that are suitable for treatment with the mechanism of cuproptosis can be screened out.\u003c/p\u003e \u003cp\u003eThe widespread alterations of cuproptosis-related gene expressions were shown in pan-cancer. All cuproptosis-related genes were upregulated in LAML, ALL, PAAD, GBM, GBMLGG, and LGG, while downregulated in KIRP, KIPAN, and KIRC. Further analysis of overall survival associated with cuproptosis-related genes, most of the dysregulated genes had significant associations with OS. Consistent with the gene differential expression results, all cuproptosis-related genes upregulated in LAML, ALL, PAAD, GBM, GBMLGG, and LGG have a poor prognosis, while genes upregulated in KIRC and KIPAN have a better prognosis. Other studies also have reported that high expression of SLC31A1 represents a poor prognosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, the pathological stages of some cancers are also related to the cuproptosis-related genes. For example, the higher expression of DLAT indicated the more serious pathological stages in KIRP. Therefore, these highly concordant results might be important for subsequent treatment with the mechanism of cuproptosis.\u003c/p\u003e \u003cp\u003eFrom the results of this study, a significant correlation between cuproptosis-related gene expressions and TMB, MSI, and NEO can be observed. TMB is an index for evaluating cancer mutation quantity, which is beneficial to forecast the survival of patients who experienced immunotherapy. Thus, genes significantly correlating with TMB would be meaningful for accurate therapy. In this study, cuproptosis-related genes highly correlated with TMB are supposed to be focused on, such as FDX1 in KIPAN, KIRC, LUAD, STAD, THCA, LIAS in BRCA, LIPT1 in BRCA, GBMLGG, LUSC, THCA, UCS, DLD in GBMLGG, DLAT in KIPAN, LUAD, STES, THCA, PDHB in ACC, BRCA, CHOL, COAD, ESCA, LUAD, STAD, SLC31A1 in ACC, BRCA, COAD, COADREAD, KIPAN, PRAD, STAD, STES, UCEC. The loss or gain of repetitive DNA tracts can generate MSI, and it has been regarded as a diagnostic phenotype for cancers. In this study, cuproptosis-related genes highly related to MSI should be focused on, such as FDX1 in ACC, GBMLGG, KIRC, LIPT1 in KIPAN, DLD in COAD, KIPAN, DLAT in STAD, PDHA1 in KIPAN, LUAD, LUSC, TGCT, PDHB in ACC, KIPAN, SLC31A1 in GBMLGG, THCA, UCS((|r|\u0026gt;0.2)). In addition, cuproptosis-related genes highly correlated with NEO should be focused on, such as FDX1 in TGCT, UCEC, LIPT1 in PCPG, DLD in ACC, DLAT in ACC, TGCT, PDHB in PCPG, SLC31A1 in CHOL, PCPG, UCEC. TMB, MSI, and NEO are novel clinical indexes, and cuproptosis-related genes highly correlated with them would be potential biomarkers for cancer-targeted therapy.\u003c/p\u003e \u003cp\u003ePreviously, there was no study reporting that cuproptosis-related genes prevent immune cells from lipoylation and cuproptosis. This study showed that most cuproptosis-related genes negatively correlated with ImmuneScore, which might indicate that cuproptosis-related genes involved in the evolution of various cancers by negatively adjusting the infiltrating of different immune cells. In addition, this study also demonstrated that all cuproptosis-related gene expressions had a significant positive association with the infiltration of CLP cells, and Th2 cells, while negative association with NKT cells and Th1 cells. These results suggested that cuproptosis-related gene expression played a key role in adjusting tumor immunity by influencing the cuproptosis of CLP cells, Th2 cells, NKT cells, and Th1 cells. Thus, CLP cells and Th2 cells have the potential to become the target of cancer therapies through cuproptosis. Finally, this study also performed the correlation analysis of cuproptosis-related genes and drug sensibility, MI-503, BY-87-2243, Crizotinib, RX-3117, tic10, AT13387 were found to have significant correlations with cuproptosis-related genes and the results might offer a potential novel strategy for the therapy of pan-cancer.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCuproptosis-related genes altered extensively across various cancer types through pan-cancer systematical analysis. A comprehensive understanding of correlations between cuproptosis-related gene expression and MSI, TMB, NEO, clinical relevance, cells in TME, tumor stemness, and drug sensitivity is conducive to understanding the mechanism of Cuproptosis-related genes in various cancer types. As a consequence, the necessary and effective methods would be found to perform the individualized treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eWe declare that we have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJianpeng Zhou: Conceptualization; Data curation; Formal analysis; Software; Writing - original draftChuanlei Wang: Data curation; Formal analysis; SoftwareJia Li: Data curation; Formal analysis; Supervision; ValidationGuangyi Wang: Conceptualization; Writing- review \u0026amp; editing. All of the authors have read and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the present study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsvetkov, P. \u003cem\u003eet al\u003c/em\u003e. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 375, 1254\u0026ndash;1261 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu, S., Singh, M.K., Singh, T.B., Bhartiya, S.K., Singh, S.P., Shukla, V.K. Heavy and trace metals in carcinoma of the gallbladder. World J Surg. 37, 2641\u0026ndash;2646 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, X. \u003cem\u003eet al\u003c/em\u003e. Analysis of serum levels of 15 trace elements in breast cancer patients in Shandong, China. Environ Sci Pollut Res Int. 22, 7930\u0026ndash;7935 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavithra, V., Sathisha, T.G., Kasturi, K., Mallika, D.S., Amos, S.J., Ragunatha, S. Serum levels of metal ions in female patients with breast cancer. J Clin Diagn Res. 9, BC25-c27 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaltaci, A.K., Dundar, T.K., Aksoy, F., Mogulkoc, R. Changes in the Serum Levels of Trace Elements Before and After the Operation in Thyroid Cancer Patients. Biol Trace Elem Res. 175, 57\u0026ndash;64 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStepien, M. \u003cem\u003eet al\u003c/em\u003e. Pre-diagnostic copper and zinc biomarkers and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort. Carcinogenesis. 38, 699\u0026ndash;707 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., Yang, Q. Association between serum copper levels and lung cancer risk: A meta-analysis. J Int Med Res. 46, 4863\u0026ndash;4873 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, F. \u003cem\u003eet al\u003c/em\u003e. Serum copper and zinc levels and the risk of oral cancer: A new insight based on large-scale case-control study. Oral Dis. 25, 80\u0026ndash;86 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAubert, L. \u003cem\u003eet al\u003c/em\u003e. Copper bioavailability is a KRAS-specific vulnerability in colorectal cancer. Nat Commun. 11, 3701 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh, S.A.K., Adly, H.M., Abdelkhaliq, A.A., Nassir, A.M. Serum Levels of Selenium, Zinc, Copper, Manganese, and Iron in Prostate Cancer Patients. Curr Urol. 14, 44\u0026ndash;49 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichniewicz, F. \u003cem\u003eet al\u003c/em\u003e. Copper: An Intracellular Achilles' Heel Allowing the Targeting of Epigenetics, Kinase Pathways, and Cell Metabolism in Cancer Therapeutics. ChemMedChem. 16, 2315\u0026ndash;2329 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanbhag, V.C., Gudekar, N., Jasmer, K., Papageorgiou, C., Singh, K., Petris, M.J. Copper metabolism as a unique vulnerability in cancer. Biochim Biophys Acta Mol Cell Res. 1868, 118893 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinbrueck, A. \u003cem\u003eet al\u003c/em\u003e. Transition metal chelators, pro-chelators, and ionophores as small molecule cancer chemotherapeutic agents. Chem Soc Rev. 49, 3726\u0026ndash;3747 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLizio, M. \u003cem\u003eet al\u003c/em\u003e. Update of the FANTOM web resource: expansion to provide additional transcriptome atlases. Nucleic Acids Res. 47, D752-D758 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUhlen, M. \u003cem\u003eet al\u003c/em\u003e. Proteomics. Tissue-based map of the human proteome. Science. 347, 1260419 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConsortium, G.T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 369, 1318\u0026ndash;1330 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonneville, R. \u003cem\u003eet al\u003c/em\u003e. Landscape of Microsatellite Instability Across 39 Cancer Types. \u003cem\u003eJCO Precis Oncol.\u003c/em\u003e 2017, (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThorsson, V. \u003cem\u003eet al\u003c/em\u003e. The Immune Landscape of Cancer. Immunity. 48, 812\u0026ndash;830 e814 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng, S.Z., Lai, M.F., Li, Y.P., Xu, C.H., Zhang, H.R., Kuang, J.G. Human marrow stromal cells secrete microRNA-375-containing exosomes to regulate glioma progression. Cancer Gene Ther. 27, 203\u0026ndash;215 (2020).\u003c/span\u003e\u003c/li\u003e\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":"cuproptosis-related genes, pan-cancer, immunoregulation, cuproptosis, cancer therapy","lastPublishedDoi":"10.21203/rs.3.rs-4403303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4403303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe primary drawbacks of current cancer therapies are lower selectivity for cancer cells, more side effects, and obscure resistance mechanisms. Novel approaches to overcome these drawbacks comprise the utilization of ionophores and metalliferous chelators to change the concentration of trace metal elements in cancer cells. As the concept of cuproptosis emerged, it might be a novel strategy to enhance the curative effects for resistant cancer cells potentially. FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, and SLC31A1 are the major regulators of cuproptosis. However, the expression landscape and clinical roles of these regulators remain to be addressed. This study explored the expression pattern and clinical role of these cuproptosis-related genes in pan-cancer by evaluating the association of tumor mutation burden, immune-related scores, cells in tumor microenvironment, and drug sensibility. The results displayed that the expressions of cuproptosis-related genes were significantly different in various cancer types, all cuproptosis-related gene upregulates significantly in LAML, ALL, PAAD, GBM, GBMLGG, LGG, and all significantly downregulated in cancers KIPP, WT, KIPAN, KIRC. Furthermore, the higher the level of cuproptosis-related genes expressed, the higher the survival in patients suffering from KIRC, and KIPAN increased. In addition, the expression of cuproptosis-related genes was negatively associated with immune-related scores, while SLC31A1 had a positive association with StromalScore, ImmuneScore, and EstimateScore in LAML. Importantly, the level of cuproptosis-related gene expressions is positively associated with CLP cells or Th2 cells, but negatively associated with NKT cells or Th1 cells. In summary, cuproptosis-related genes are disordered in various cancer types have prognostic value for different cancers, and also can evaluate the cells infiltrating in tumor microenvironment.\u003c/p\u003e","manuscriptTitle":"The potential role of cuproptosis-related genes for therapy and immunoregulation in pan-cancer ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 18:47:10","doi":"10.21203/rs.3.rs-4403303/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":"20dd2903-9097-432f-b8dc-7b3157904362","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33290579,"name":"Biological sciences/Cancer"},{"id":33290580,"name":"Biological sciences/Cell biology"}],"tags":[],"updatedAt":"2024-07-10T09:07:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 18:47:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4403303","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4403303","identity":"rs-4403303","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}