Predictive potential of cuproptosis-related genes in multiple myeloma: Comprehensive analysis based on bone marrow whole-genome sequencing

Predictive potential of cuproptosis-related genes in multiple myeloma: Comprehensive analysis based on bone marrow whole-genome sequencing | 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 Predictive potential of cuproptosis-related genes in multiple myeloma: Comprehensive analysis based on bone marrow whole-genome sequencing Zhongmin Kang, Licheng Li, Yuying Huang, Jishi Wang, Mengxing Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4201066/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 Purpose: The primary objective of this study was to identify potential CRGs in patients with MM and develop a predictive model to enhance prognostic outcomes for individuals with MM. Methods: We leveraged transcriptome sequencing data from patients with MM, combined with clinical information from the TCGA-MMRF dataset and the GSE4581 dataset from the GEO database. Through analysis, we pinpointed three genes—CDKN2A, PDE3B, and UBE2D1 that exhibited a significant association with the prognosis of patients with MM. This association was confirmed through a combination of univariate and multivariate Cox regression analyses. Subsequently, we employed LASSO-Cox regression analysis to construct a risk-prognostic model centered around these three CRGs. Results: Notably, the model revealed that high-risk patients with MM experienced significantly shorter overall survival times. Intriguingly, We have unveiled a propensity for high-risk patients with MM to develop an immunosuppressive tumor microenvironment. Finally, to substantiate our findings, we conducted in-depth examinations of the expression of these three CRGs at the cellular level using quantitative reverse transcription–polymerase chain reaction and Western blotting. Conclusion: Our research collectively reveals the molecular scenery in the MM microenvironment through the development of a prognostic model focused on CRGs. Biological sciences/Cancer/Haematological cancer/Myeloma Health sciences/Oncology Health sciences/Risk factors Multiple myeloma cuproptosis prognostic model tumor microenvironment immune-targeted therapies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Multiple myeloma (MM) is a hematologic malignancy characterized by the abnormal accumulation of cancerous plasma cells in the bone marrow, secretion of monoclonal immunoglobulins, development of osteolytic lesions in the advanced stages, and invasion of extramedullary organs [1] . It ranks as the second most prevalent hematologic malignancy following malignant lymphoma, with an annual incidence of 4 per 100,000 individuals and constituting 10% of all hematologic neoplasms [2] . Despite the utilization of different treatments like Inhibitors of protease and Targeted towards immunity strategies, which have demonstrated certain effectiveness During clinical practice, the persistent occurrence of the disease and the development of drug resistance still have a detrimental effect on the predicted outcome of patients. Moreover, the exact cause of MM is still unknown. Hence, It's essential to find fresh predictive biomarkers to uncover the underlying mechanisms of MM, ultimately improving patient results and the effectiveness of treatment. Copper plays a vital role in numerous biological functions, such as mitochondrial respiration and iron absorption, protection against antioxidants, and pathways for detoxification. Notably, copper levels exhibit a significant surge during tumor growth and metastasis. Numerous researches have shown increased amounts of copper in tumorous tissues or the patient's serum, encompassing different forms of cancer such as breast, lung, gastrointestinal, oral, thyroid, gallbladder, gynecological, pancreatic, and prostate cancers. Moreover, increased levels of copper in the blood are associated with more advanced tumor stages and the progression of colorectal, lung, and breast cancers. Moreover, copper plays an active role in stimulating angiogenesis, which is a vital element in the advancement and spread of tumors. The participation of copper in essential cellular processes, such as PI3K/AKT and MAPK/ERK signaling pathways, energy production (glycolysis and the tricarboxylic acid [TCA] cycle), and the control of epigenetic alterations, is responsible for these impacts [3–6] . Nevertheless, once copper surpasses the threshold set by the in vivo system, it transitions into a cytotoxic entity. Copper-dependent cell death, known as "Cuproptosis," was described by Tsvetkov et al. During their investigation of the anticancer properties of elesclomol, a transporter of copper ions, in the year 2019. Copper-induced cytotoxicity has been identified in recent studies as a unique type of cellular death known as copper death. It distinguishes itself from other mechanisms that regulate death of cells, including phenomena like apoptosis and autophagy, iron death, and pyroptosis. Cuproptosis is characterized by the excessive accumulation of copper ions within the TCA cycle, which subsequently interacts with lipid-acylated mitochondrial enzymes, culminating in cell death [7] . Significantly, Cu(II) bound to elastoinositol interacts with ferredoxin 1 (FDX1), an enzyme found in mitochondria, and undergoes reduction to generate Cu(I), resulting in the production of reactive oxygen species (ROS) [8,9] . Targeted therapy using elesclomol reduced tumor burden in MM mouse models. The growing body of evidence indicates that targeting copper death shows great potential as a strategy for cancer treatment. Nevertheless, the exact molecular mechanisms and regulatory roles of cuproptosis in the development and advancement of tumors are not fully comprehended. The objective of the study was to examine the correlation between genes associated with cuproptosis and the prognosis of individuals diagnosed with MM. As a result, a prognostic model for MM was developed. The TCGA-MMRF cohort was utilized as a test set, while the GSE4581 dataset served as the validation set. To determine the prognosis of MM patients, we utilized both single and multifactorial Cox regression analyses, along with LASSO-Cox regression techniques, in order to identify CRGs linked to their outcome. Using these results, we developed a predictive risk model and then confirmed its precision by testing it on an independent validation dataset. Significantly, this risk model exhibited robust associations with different various kinds of immune cells exist within the MM microenvironment, as uncovered by analyses of immune infiltration and correlation. Furthermore, it demonstrated distinct expression in plasma cells and macrophages at the individual cell level. Furthermore, we validated the elevated expression of the UBE2D1 and CDKN2A genes and the reduced expression of the PDE3B gene in U266, RPMI8266, and MMIS myeloma cells using quantitative reverse transcription–polymerase chain reaction (qRT-PCR) and western blot analysis. Additional understanding was acquired by identifying multiple potentially closely associated CRGs from the GDSC database, which encompassed therapeutic medications like Nutlin-3a(-), PD-0332991, bleomycin (50 µm), PI-103, cytarabine, and dasatinib. The combined results contribute to a broader comprehension of the predictive characteristics and biochemical processes involved in copper-induced mortality among individuals diagnosed with MM. This knowledge provides a solid foundation for the current and future design of immunologic and targeted therapies for MM. 2 Materials and Methods 2.1 Accumulating and managing information Data from 859 cases, including whole-genome sequencing data and Patients' survival statistics, were acquired from the TCGA-MMRF database (https //portal.gdc.cancer.gov/)(Table S1 ). Additionally, transcription and clinical survival data for 414 patients were obtained from the GEO database (https // www.ncbi.nlm.nih.gov/geo/ ) using the accession number GSE4581. The information was analyzed using the resilient multiple array averaging technique, utilizing the affy software [10] , and the sva package was employed to minimize batch effects [11] . 2.2 Screening for CRGs A set of 44 genes relevant to cuproptosis was collected from existing literature [12] . This gene set constituted the 44 CRGs found in the MMRF dataset. 2.3 Screening for CRGs associated with overall survival We screened CRGs in the TCGA-MMRF training set, which included 787 MM patients, to reveal the potential risk profile linked to these CRGs in MM. Afterwards, a one-way Cox proportional hazards regression analysis was utilized to discover CRGs associated with overall survival (OS) at P-value lower than 0.05, the findings hold statistical significance. 2.4 Analysis focused on OS-associated CRGs enrichment We used the ClusterProfiler package [13] for performing gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment study. The specific roles of OS-related CRGs in MM were elucidated by determining statistical significance using corrected P values less than 0.05. 2.5 Investigation of molecular interactions involving CRGs associated with OS In order to examine the connection between OS-associated CRGs, we computed Pearson correlation coefficients to evaluate the gene correlations in MM. 2.6 The creation and verification of a predictive model associated with CRGs in MM In order to reduce the possibility of overfitting, we employed LASSO-Cox regression [14] analysis in the training set to identify crucial CRGs related to overall survival (OS) for developing the prognostic model. The CRGs were then subjected to analysis using multivariate Cox proportional hazard regression, where variables were chosen in a stepwise manner using the Akaike information criterion (AIC) [15] for selection.The formula was used to calculate the ultimate risk score. Risk score \(=\sum _{i}^{n}Coefi\times Ai\) The regression coefficient is represented by Coef, CRGs are denoted by i, every CRG in the signature's relative expression value is denoted by A, and the total number of genes is signified by n. The patients were classified into two categories, a low-risk group and a high-risk group, depending on their median risk scores.The comparisons of cohorts were conducted using the log-rank tests and Kaplan-Meier analysis, while the accuracy of the prognostic model was assessed through the creation of time-varying receiver operating characteristic (ROC) curves [16] . To ascertain the accuracy of the prognostic model's predictions, a validation set consisting of 414 patients from GSE4581 was utilized. A single formula was employed to compute risk scores for every individual, and Kaplan-Meier diagrams were developed to reevaluate the survival and prognosis differences among the two cohorts. 2.7 Analysis of gene enrichment and the identification of genes expressed differentially In order to distinguish DEGs (differentially expressed genes) between the high-risk and low-risk groups, the Limma software package [17] was utilized. The cluster analysis software 2 Immunology Research Package (ClusterProfiler) [13] was utilized to execute GO and KEGG enrichment evaluations, providing important knowledge about cell components (CC), molecular function (MF), biological process (BP), and KEGG pathways. This analysis aimed to gain a better understanding of the potential functions and pathways associated with MM. Furthermore, ClustVis [18] was used to categorize differentially expressed genes and produce heatmaps for visual illustration. 2.8 Analysis of gene set variations As previously discussed, the risk score median served as the defining factor distinguishing between low and high-risk cohorts. In order to further describe the main pathways enriched in the high and low-risk categorizations, we conducted a Gene set enrichment analysis (GSEA) [19] . Establishing statistical significance employed enrichment, necessitating p-values beneath 0.05 and a false discovery rate under 0.25. 2.9 Evaluating the predictive precision of OS-associated CRGs in clinicopathologic diagrams The RMS [20] software package was utilized to perform prognostic analysis on MM patients, and their prognostic characteristics were incorporated into the clinicopathological analysis of the training group. The AIC was used to select the ultimate model, and calibration curves were created to evaluate the predictive precision of the column-line graphs [21] . 2.10 The immunological milieu of Multiple Myeloma and potential targets for immunotherapy aiding in prognosis prediction. We used CIBERSORT and Spearman correlation analysis to assess the connection between risk profiles, which are determined by OS-related CRGs, and the infiltration of immune cells in the tumor microenvironment (TME). The immune cells considered include natural killer (NK) cells, plasma cells, B cells, and T cells. Additionally, the association between the risk scores and 24 prevalent immune checkpoints, 46 potential immunosuppressive molecules targeted towards tumors, and chemokines commonly found in the tumor microenvironment (TME) was evaluated [22] .All subsequent analyses were subjected to a significance threshold of P < 0.05. 2.11 Expression analysis of prognostically relevant CRGs at the single-cell level The information utilized in this research was obtained from the GSE176131 document found in the GEO repository (https // www.ncbi.nlm.nih.gov/geo/ ). The Seurat software enabled the creation of objects and the removal of low-quality cells. Standard data preprocessing involved the calculation of gene count percentages, cell counts, and mitochondrial content. We filtered out genes found in less than 3 cells and cells with less than 200 detected genes, as well as cells showing low or high mitochondrial content (> 5%). Each cell's UMI counts were normalized using scale factor = 10,000. The ScaleData function in Seurat (v3.0.2) was utilized after performing a logarithmic transformation. The standardized analysis utilized the rectified normalized data metrics. For principal component analysis, the top 10 variable genes were chosen, and the first 5 principal components were kept for visualization and clustering using Uniform Manifold Approximation and Projection or t-distributed stochastic neighbor embedding (t-SNE). The Seurat R package was utilized to perform clustering by employing the FindClusters function. 2.12 Drug sensitivity analysis. Analyses of drug sensitivity were performed for the three main prognostic CRGs using information from the GDSC database ( www.cancerRxgene.org ) [23] , which is the most extensive public repository for molecular markers related to drug sensitivity and response in cancer. Significance was determined at P < 0.05. 2.13 Cell lines and cell culture Human MM cell lines, including MMIS, U266, and RPMI8226, were obtained from the Cell Center of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. Normal human bone marrow cells were derived from healthy medical examiners' bone marrow, and single-nucleated cells were isolated from human peripheral blood lymphocyte isolation solution FICO (Beijing Solepol Science and Technology Co., Ltd.) for use as normal control cells. Human MM cell lines were grown in RPMI-1640 medium (Gibco, USA) with the addition of 10% fetal bovine serum. The cells underwent cultivation in an incubator with 5% CO 2 at 37°C. 2.14 Quantitative reverse transcription-polymerase chain reaction in real-time TRIzol reagent (Takara, Japan) was used to extract total RNA according to the instructions provided by the manufacturer. TBGreenMasterMix (Takara, Japan) was used for qRT-PCR to measure the mRNA expression of CDKN2A, PDE3B, and UBE2D1.Shanghai Sangong Bioengineering Co. synthesized the primer pairs. GAPDH served as a control for normalization, and the method was used to determine the relative expression levels. 2.15 Western blotting To analyze proteins, MM cell lines and normal control cells were lysed using a suitable quantity of high-performance radioimmunoprecipitation assay lysis solution (from Beijing Solebrite Technology Co., Ltd.). The lysates were then placed on ice and shaken on an oscillator for 30 s at 15-second intervals to ensure complete lysis. This process was repeated 10 times for thorough circulation. Following centrifugation at a speed of 12,000 rpm at 4°C for a duration of 20 min, the resulting liquid was combined with 5 times the amount of protein sampling buffer (provided by Beijing Solebrite Technology Co., Ltd.) and subjected to heating at 100°C for 10 min. Proteins underwent separation via sodium dodecyl sulfate–polyacrylamide gel electrophoresis on a 12% gel, followed by transfer onto polyvinylidene fluoride membranes measuring 0.22 mm (microtiter wells, USA). The membranes were obstructed with 5% skim milk for about 2 h, followed by incubation with particular antibodies. The visualization was achieved by adding ECL chemiluminescent solution (Millipore, USA) drop by drop. The CDKN2A antibody was obtained from Wan Class Biotechnology Co., Ltd., whereas the PDE3B and UBE2D1 antibodies were procured from Wuhan Three Eagles Biotechnology Co., Ltd., and GAPDH was acquired from Bioworld Technologies Ltd. Quantitative analysis was conducted by utilizing ImageJ software, relying on relative gray values for assessment. 2.16 Statistical analysis GraphPad Prism 9.4 and R software, version 4.2.1, were utilized for conducting statistical analysis. To identify disparities between groups of normally distributed variables, the Student t-test was employed for quantitative variables, while the Wilcoxon test was utilized for skewed data.Statistical significance was determined for p-values less than 0.05, p-values less than 0.01, and p-values less than 0.001 denoted as *, **, and *** respectively. 3 Results 3.1 Identifying MM-associated CRGs related to the operating system Prognostic-related factors for patients with MM were identified using univariate Cox regression analysis, with a significance level of P < 0.05. A grand total of 44 CRGs were obtained from the available literature on copper fatality. We acquired 16 CRGs linked to MM prognosis by intersecting these genes (Table 1 , Fig. 1 A-B). GO analysis of these CRGs showed that they are mainly involved in the regulation of copper ions, including processes related to mitochondrial respiration, modification of cellular proteins, and binding of copper ions (Fig. 1 C). Moreover, the KEGG enrichment analysis revealed that these CRGs were also involved in pathways associated with mitochondrial metabolism, the TCA cycle, and cancer-related pathways like the HIF-1 signaling pathway (Fig. 1 D). In order to explore the connections between OS-related CRGs, we discovered six crucial genes - DLD, COA6, CDKN2A, SOD1, PDHA1, and UBE2D1 - by analyzing their significant correlation with one another among the 16 CRGs, utilizing Pearson correlation analysis (Fig. 1 E). The discovery implies that the control mechanisms related to these CRGs might have significant impacts on the development of MM. Table 1 CRGs associated with overall survival in MM patients (P < 0.05) Genes HR Lower Upper Likelihood logrank Wald COX11 1.453835 1.009534 2.093675 0.043438 0.045385 0.04433 PDHB 1.543364 1.068996 2.228234 0.018404 0.020634 0.020556 PDE3B 1.600773 1.359239 1.885227 1.70E-06 6.67E-09 1.72E-08 COA6 1.895057 1.514929 2.370568 2.79E-08 2.46E-08 2.19E-08 UBE2D1 1.773009 1.315833 2.389026 Genes 0.000185 0.000167 CD274 0.689696 0.549889 0.865049 0.001182 0.001374 0.001308 MAP2K1 1.621814 1.143145 2.300915 0.007935 0.006893 0.006735 PDK1 0.813345 0.692437 0.955365 0.012657 0.011765 0.011868 SCO1 1.474794 1.000335 2.174289 0.048249 0.049921 0.049803 DLD 1.736625 1.237513 2.43704 0.001416 0.001409 0.00141 SOD1 2.09423 1.51107 2.902445 1.10E-05 9.08E-06 9.03E-06 SLC31A2 0.585425 0.393917 0.870036 0.007598 0.008048 0.008081 ULK1 0.73544 0.547325 0.98821 0.039098 0.042537 0.041485 GCSH 1.365105 1.028938 1.811102 0.030999 0.031014 0.030948 PDHA1 2.322373 1.498823 3.598434 0.000158 0.00019 0.000162 CDKN2A 1.888524 1.467956 2.429585 7.11E-07 1.11E-06 7.56E-07 3.2 Prognostic screening and modeling for OS-associated CRGs In order to avoid possible overfitting, we employed LASSO COX regression analysis to select key OS-related CRGs for modeling purposes. Subsequently, four CRGs—CDKN2A, PDE3B, UBE2D1, and COA6—were identified through stepwise multivariate Cox proportional risk regression analysis (Fig. 2 A). Patients’ risk score was determined using the following formula: risk score = [CDKN2A expression level ∗ (0.4529)] + PDE3B expression level ∗ (0.3191)] + UBE2D1 expression level ∗ (0.5280)] + COA6 expression level ∗ (0.3833)]. Based on their median risk scores, all patients were categorized into either a high-risk or low-risk cohort. It was showed that as the risk scores increased, there was a noticeable rise in the mortality rate among patients(Fig. 2 B). Furthermore, the expression profiles of these four genes were investigated in both the high-risk and low-risk groups, indicating that CDKN2A, PDE3B, UBE2D1, and COA6 exhibited elevated expression levels in the high-risk cohort (Fig. 2 C). This implies that these four CRGs could act as cancer-causing genes, potentially facilitating the advancement of MM. 3.3 Assessment of predictive attributes of OS-associated CRGs in individuals diagnosed with MM The analysis of the ROC curve on the training set (MMRF) showed that the model had high predictability, with area under the curve (AUC) values of 0.63, 0.71, and 0.78 for predicting OS at 1, 3, and 5 years, respectively (Fig. 3 A). In the meantime, the precision of the risk model was assessed by examining Kaplan-Meier survival plots, which indicated a considerably shorter overall survival (OS) for individuals in the high-risk group compared to those in the low-risk group (P < 0.001, hazard ratio = 2.18) (Fig. 3 B). To further evaluate the autonomous predictive significance of the risk profile for individuals diagnosed with MM, a unidirectional Cox regression analysis was performed, integrating clinical factors like age, sex, International Staging System (ISS) stage, and risk score. The findings validated that the risk score could autonomously function as a predictive marker and was linked to the outcome of individuals diagnosed with MM (Fig. 3 C). Furthermore, following the adjustment for these clinicopathologic factors, the multifactorial Cox regression analysis confirmed that the risk profile continued to be a separate prognostic predictor for MM. Stages II and III in ISS staging were also identified as independent prognostic predictors (Fig. 3 D).To gauge the diagnostic efficacy of risk scores in comparison with other clinical variables in predicting the prognosis of patients with MM, we employed ROC curves. The AUC values for risk score predictions of OS over 1 year were higher than those for sex (Figure S2 A), the AUC value risk score predicting OS over 3 years was higher than the age and sex (Figure S2 B), and interestingly, the AUC value risk score predicting OS over 5 years was higher than the age, sex, and ISS stage, all of which were clinical variables for MM (Figure S2 C), respectively. This finding underscores the superior diagnostic efficacy of the established prognostic model, particularly in predicting long-term survival outcomes such as 5-year OS in patients with MM. To thoroughly evaluate the prognostic model's accuracy, we generated a column-line graph that incorporated risk score, age, sex, and ISS stage (Fig. 3 E). Calibration plots demonstrated outstanding agreement in predicting OS at 1, 3, and 5 years (Fig. 3 F). Combining clinical variables with the risk score highlighted the remarkable predictive precision of OS in patients with MM, as demonstrated by these findings. 3.4 GSEA of CRGs We carried out Gene Set Enrichment Analysis (GSEA) to explore the biological roles and corresponding signaling channels in both patient groups after observing the disparities in Overall Survival (OS) between the low-risk and high-risk cohorts.The low-risk cohort exhibited significant enrichment in functions related to cellular responses to starvation, EIF2AK4 GCN2 response to amino acid deficiency, and translational elongation in eukaryotes (Figure S3A).Conversely, the high-risk cohort displayed significant enrichment in pathways associated with cell cycle, immunity, and apoptosis (Figure S3B). These findings suggest that the signature genes related to copper death in high-risk patients with MM may interact with immune pathways through apoptosis, thereby influencing MM disease progression. 3.5 Validation of Prognostic Risk Models The risk score algorithm was then utilized on the validation set (GSE4581) to compute risk scores for individuals in the high- and low-risk groups, confirming the dependability of the prognostic model.In accordance with the training set, patients belonging to the high-risk group demonstrated a reduced overall survival (OS) and a more unfavorable prognosis compared to the low-risk group (Fig. 4 A).Furthermore, the AUC values obtained from ROC analysis for predicting OS at 1, 3, and 5 years were 0.656, 0.657, and 0.797, correspondingly (Fig. 4 B).The findings indicated that the risk model also showed strong dependability in the validation group of individuals with MM and demonstrated exceptional precision in forecasting long-term prognostic survival. 3.6 Enrichment and characterization of DEGs Moreover, we performed DEG screening in the training dataset to explore potential molecular pathways that regulate patients with MM exhibiting copper-induced cell death. By utilizing the Limma software package, we detected 188 differentially expressed genes (DEGs) in the training dataset, demonstrating unique patterns of gene expression in the low-risk and high-risk groups (110 genes were found to be upregulated, whereas 78 genes were downregulated) ( Fig. 5 A-B). Analysis of gene ontology (GO) indicated that CRGs played a role in diverse immune-related biological processes, such as the conventional mechanism of cellular growth, the adaptive immune response, and immunity mediated by B-cells, among other functions (Fig. 5 C).The analysis of KEGG emphasized the primary participation of CRGs in signaling pathways associated with tumors, including the cell cycle, the pathway related to cancer, and P53, in addition to other signaling pathways related to tumors (Fig. 5 D).Given the enrichment of these crucial biological functions in patients with MM, we postulate that DEGs in high- and low-risk patients characterized by copper death may mediate disease progression through immunomodulation within the TME. 3.7 Correlation analysis of prognostically relevant CRGs with the MM immune microenvironment The communication between tumorinfiltrating immune cells (TILs) and cancer cells in the tumor microenvironment (TME) is crucial for the advancement of tumors and the development of resistance to drugs. Multiple studies have reported that cuproptosis, a cell death mechanism that relies on copper, is linked to immune-infiltrating cells in the tumor microenvironment (TME) of different types of cancer, such as bladder and breast cancer. As a result, we examined the relationship between the risk profile of associated CRGs based on OS in the training set and the infiltration of immune cells in the TME. In the high-risk group, our analysis showed a notable decrease in immune cells like plasma cells and CD4 + T cells, whereas memory B cells, memory CD4 + T cells, activated NK cells, and dendritic cells were considerably increased (Fig. 6 A-B). The results indicate a strong association between the risk profile and the existence of multiple TILs in the MM microenvironment. Recently, MM has been the subject of investigation in preclinical treatments and clinical trials, focusing on immune checkpoint inhibitors and innovative targeted immunotherapies [24] . In order to explore their possible significance, we employed Spearman correlation analysis to investigate the connection between risk scores and 24 immune checkpoints, such as CD274 (PD-L1), CTLA-4, LAG3, HAVCR2 (TIM3), and TIGIT (Fig. 6 C). The findings demonstrated a significant positive correlation between the risk scores and CTLA-4, whereas a marked negative association was noted with LGALS9 and LAG3. Furthermore, we performed a correlation analysis on 46 potential checkpoints for tumor-targeted therapy, revealing noteworthy positive correlations between risk scores and TNFSF4 and ENTPD1 (Fig. 6 D). Moreover, an examination of 40 MM microenvironmental chemokines revealed noteworthy positive correlations between risk scores and CXCL16, CCL8, and CCL16, in addition to a notable negative correlation with CXCL17 (Fig. 6 E). The results suggest that individuals with MM who have a copper mortality risk profile might experience more advantages from therapies that focus on CTLA4, TNSF4, ENTPD1, CXCL16, CCL8, and CCL16. These treatments involve immune checkpoints, immunosuppressive elements, and chemokines. Conversely, the efficacy of targeted therapies involving LGALS9, LAG3, and CXCL17 appears to be less promising. 3.8 4 CRGs with prognostic relevance confirmed through single-cell validation To gain further insights into the expression patterns of the four prognosis-related CRGs in MM at the single-cell level, we leveraged the R package "Seurat" to down-converge and cluster single-cell MM data from GSE176131, resulting in five distinct clusters (Fig. 7 A). Following the analysis of individual cells, it was found that plasma cells showed increased levels of CDKN2A and COA6 expression, whereas macrophages exhibited higher expression of UBE2D1(Fig. 7 B). These findings indicate that CRGs might have a crucial function in facilitating cell-to-cell interactions between plasma cells and macrophages within the MM microenvironment. 3.9 Drug sensitivity analysis of prognostically relevant CRGs In order to investigate potential targeted therapeutic drugs based on the prognosis-related key CRGs, we performed a drug sensitivity analysis of the three relevant CRGs using the GDSC database. The analysis revealed a strong and favorable association between CDKN2A and Nutlin-3a (-), PD-0332991, whereas PDE3B exhibited a notable and positive correlation with Bleomycin (50 µm). UBE2D1 displayed positive correlation with PI-103(Fig. 8 ). The results indicate that these medications may be effective in targeting copper-induced death treatment approaches in MM patients, leading to improved outcomes in terms of relapse and treatment response. 3.10 Prognosis-related CRGs are validated for differential expression in MM cells To further confirm the expression levels of prognosis-related CRGs in MM at the cellular level, we chose MMIS, U266, and RPMI82263 MM cell lines. We evaluated the levels of mRNA and protein expression using qRT-PCR and western blot techniques, respectively. The findings indicated that CDKN2A and UBE2D1 displayed elevated levels of expression in all three MM cell lines compared to normal bone marrow mononuclear cells, whereas PDE3B showed decreased levels of expression. Interestingly, the mRNA patterns of UBE2D1 and PDE3B aligned with their corresponding protein levels. In contrast, the mRNA level of CDKN2A exhibited a different pattern when compared to the protein level(Fig. 9 ). This implies that CDKN2A could be controlled by specific epigenetic mechanisms at the post-transcriptional and translational levels throughout the progression of MM. The protein-level differential expression of these three CRGs in MM cell lines provides evidence for their role in MM pathogenesis. Discussion Multiple Myeloma, the adult population's second most prevalent blood malignancy, is diagnosed by the harmful accumulation of cancer-bearing plasma cells within the bone marrow that subsequently damages critical organs. Despite the progress made in targeted and immunotherapies, many patients with MM continue to experience unfavorable outcomes as a result of the difficulties presented by frequent relapses and resistance to medication. Through this research, we have developed an accurate forecast model focused on copper mortality, providing medical professionals with a valuable resource to design personalized treatment plans for each patient. A significant association was noticed between CRGs and OS in individuals diagnosed with MM. Furthermore, by utilizing four CRGs that are relevant for prognosis, we have created a risk score model that can accurately predict survival prognosis and uncover potential biological functions and molecular mechanisms in MM. Validation set analysis underscored the independence, stability, and reliability of this prognostic model, establishing it as a robust diagnostic indicator. Moreover, our analysis of immune correlation revealed the complex connection between risk scores and tumorinfiltrating immune cells (TILs), immune checkpoint proteins, and chemokines in the tumor microenvironment (TME) for both cohorts with low and high risks. This sheds light on the unique immune landscape associated with copper death in MM and hints at potential immunotherapeutic avenues. At the same time, we identified possible drug targets that are specific to patients with copper death-associated MM. Additionally, we examined the expression patterns of CRGs at the single-cell level and confirmed their potential role in MM pathogenesis by validating the protein and mRNA expression levels of CRGs in MM cell lines. Copper death signifies a recently characterized type of cellular demise that is different from other types of planned cellular demise. This condition is primarily characterized by proteotoxic stress, caused when copper ions within the cell directly interact with lipid-acylated elements of the tricarboxylic acid cycle. The interaction ultimately leads to the buildup of lipoylated proteins and the destabilization of proteins containing Fe-S clusters, causing proteotoxic stress that eventually leads to cell death [25] . It is believed that copper exerts its impact on cancer cells by attaching to and activating crucial molecules in different signaling pathways. For example, a study conducted in 2013 using human leukemia K562 cells showed that copper ions have the ability to oxidize ascorbic acid and combine with hydrogen peroxide, which is produced through ES (copper ion carrier), resulting in the production of even more harmful ROS [26] . Copper triggers the oncogenic signaling pathway of phosphoinositide 3-kinase (PI3K)-protein kinase B (PKB or AKT) in breast cancer, thereby facilitating the development of tumors. Significantly, the AKT signaling pathway and the development of tumors can be reduced by depleting copper transporter protein 1 (CTR1) or obstructing the copper transporter protein 1 (CTR1)-copper connection through the use of copper chelators [27] . Furthermore, copper has a direct binding effect on MEK1 in colon cancer cells, leading to an increase in phosphorylation of ERK1/2. This, in turn, triggers the activation of downstream JNK, thereby controlling the growth of tumors [28] . Maintaining a balanced copper ion metabolism is crucial for the progression of tumors in the body. With the emergence of copper death, therapeutic strategies targeting this phenomenon in tumors have garnered increasing attention. Therefore, it is crucial to identify significant prognostic indicators associated with copper-induced mortality in individuals diagnosed with MM. Accurate prognostic survival estimation and effective categorization of treatment effectiveness are essential measures to enhance the outcomes of MM patients dealing with relapse and resistance to medication. By employing univariate COX regression analysis, we were able to identify a total of 16 CRGs in individuals diagnosed with MM in this particular study. Certain CRGs have significant impacts on the advancement of cancer and the development of resistance in hematologic malignancies. For example, inhibiting SOD1 leads to the initiation of caspases, the p53/p21 signaling pathway, and the response to endoplasmic reticulum stress, resulting in the demise of MM cells and ultimately enhancing the prognosis of patients [29] . Moreover, UBE2D1, a constituent of the E2 ubiquitin-coupled enzyme and part of the UBE2D lineage, has been linked to the proliferation of liver tumors by reducing p53 expression via pathways that rely on ubiquitination [30] . CDKN2A (P16) is a tumor suppressor gene located on the short arm of chromosome 9, reported in a variety of solid tumors and blood tumors. CDKN2A suppresses the growth and spread of cervical cancer cells in breast cancer by blocking the AKT-mTOR pathway, which is facilitated by LDHA [31] . CDKN2A deficiency in non-specific peripheral T-cell lymphoma (PTCL-NOS) is associated with a negative prognosis in lymphoma patients [32] . The findings of our research indicate that these CRGs show a notable enrichment in pathways linked to the process of mitochondrial respiration, modification of proteins in the cytosol, binding of copper ions, and other pathways related to copper ions. It indicates that these CRGs rely on copper ion metabolism processes to regulate copper death, and are involved in the progression of MM. Establishing a tumor-related prognostic model based on transcriptome expression profile characteristics has strong application prospects for risk assessment [33] . This study determines the key OS-related CRGs through LASSO-Cox regression analysis and establishes a risk model. The model was developed utilizing the four CRGs that had the most potent prognostic traits. Through univariate/multifactorial Cox regression analysis, it was found that the prognosis of patients with MM could be predicted independently by risk scores, in contrast to clinical variables. The area under the curve (AUC) values for overall survival (OS) predicted by receiver operating characteristic (ROC) curves at 1, 3, and 5 years were 0.63, 0.71, and 0.78. Furthermore, the integration of clinical variables of MM into ROC curves resulted in higher AUC values for OS beyond 5 years when compared to using clinical variables alone. This underscores the significant influence of risk scores on the long-term outcomes in MM. The accuracy of the model in predicting patient prognosis was further confirmed by column-line plots that incorporated ISS stage, age, sex, risk score, and calibration curves. Immune cell heterogeneity in patients with MM has been highlighted by recent studies, which have shown that various subpopulations of immune cells, such as plasma cells, CTLs, T cells, and NK cells, display elevated levels of expression [34] . In the microenvironment of MM, cancerous plasma cells discharge mitochondrial DNA, a type of mtDAMPs, into the microenvironment of myeloma BM. This action activates macrophages through STING signaling and encourages the advancement of myeloma [35] . At the individual cell level, our examination revealed that plasma cells exhibited significant overexpression of CDKN2A and COA6, whereas macrophages displayed high expression of UBE2D1. The results indicate that the characteristics of copper-induced death may rely on the interactions between plasma cells and macrophages in the microenvironment of MM, providing insight into the progression mechanism of MM. Furthermore, differentially expressed genes (DEGs) exhibit notable enrichment in signaling pathways associated with adaptive immune responses, immunity mediated by B-cells, regulation of the cell cycle, P53, and FoxO pathways. According to recent research, p53 mainly controls the formation of iron-sulfur clusters and copper glutathione chelates, thus affecting copper metabolism and contributing to copper-induced cell death [36] . In addition, copper binds with the arginine 117 and arginine 203 sites of Pyruvate Dehydrogenase Kinase 1 (PDK1), leading to the activation of AKT. Following this, the activation of AKT by copper leads to the phosphorylation and subcellular redistribution of FoxO1a and FoxO4 within cells, thereby stimulating the proliferation of cancer cells and facilitating tumor growth. TILs are one of the main components of TME, and the density and types of TILs have significant prognostic associations in many aspects of cancer [37] . Significant changes were observed in the levels of different types of immune cells, including plasma cells, memory B cells, monocytes, eosinophils, and neutrophils, within the MM microenvironment, distinguishing the high-risk and low-risk cohorts. The findings indicate that the risk profile is strongly linked to the existence of TILs within the microenvironment of MM. Excessive amounts of copper death over an extended period might play a role in the significantly immunosuppressive surroundings seen in individuals suffering from MM. Effective therapies for hematologic tumors and different solid tumors have been identified in the form of immune checkpoint inhibitors [38] . Nevertheless, the task of attaining long-lasting response rates in certain individuals suffering from cancer continues to be difficult because of the evasion of the immune system and the development of resistance. Our analysis of immune correlation indicates that patients with MM who display characteristics associated with copper death are more inclined to experience positive outcomes from therapies that focus on immune checkpoints, immunosuppressive elements, and chemokines like CTLA4, TNSF4, ENTPD1, CXCL16, CCL8, and CCL16.The therapeutic effect is unsatisfactory for MM patients who are treated with therapies targeting LGALS9, LAG3, and CXCL17 as the targets. Hence, continuously stimulating or blocking copper demise may present novel treatment options for enhancing the results of individuals with MM who have a limited response to immune checkpoint therapy. To tackle the difficulties in the development of new medications and enhance the healing capabilities of current drugs, it is necessary to enhance the process of selecting targets and repurposing drugs that have already been approved. Significant positive correlations were found between CDNK2A and Nutlin-3a (-), PD-0332991, PDE3B, and bleomycin (50 µm) in our analysis using GDSC drug sensitivity. Additionally, UBE2D1 and PI-103 showed a correlation. Nutlin-3a could enhance natural killer cell-mediated neuromasts by restoring the p53-dependent NKG2D and DNAM − 1 receptor ligand expression and enhances natural killer cell-mediated neuroblastoma killing [39] . Additionally, PD-0332991 (pabocinib), a cell cycle protein-dependent inhibitor of kinase 4 and 6, exhibits efficacy against metastatic tumors. Bleomycin, at a concentration of 50 µm, plays a vital role in the gold-standard treatment for different types of tumors, such as Hodgkin’s lymphoma, testicular cancer, and germ cell cancer. The involvement of ASH2L in the growth and susceptibility of Hodgkin's lymphoma and testicular cancer cells to bleomycin has been established [40] . PI-103, a potentially effective drug, shows promise in inhibiting PI3K and mTOR. This suggests that it has the ability to effectively target different types of cancer cells, like gliomas and breast cancers, at various points in the pathway. The results of drug sensitivity analysis based on CRG offer potential for the advancement of novel targeted treatments for individuals with MM. Additionally, we confirmed the protein and mRNA levels of three clinically significant CRGs in MM cell lines through western blot and qRT-PCR analysis. Surprisingly, there was a contrasting pattern observed in the mRNA and protein expression of CDKN2A. The discrepancy implies that CDKN2A might undergo post-transcriptional epigenetic alterations in MM tumors and senescence, potentially including promoter methylation and regulation of histone acetylation [41,42] . As a result, we hypothesized that MM may make CDKN2A more prone to post-transcriptional epigenetic alterations, leading to decreased mRNA expression and impacting the progression of the disease. Additional analysis is required to identify the specific molecular interactions involved. Although limitations exist, this study has created a risk-prognostic model by analyzing patients with MM and copper death characteristics. The lack of a clinical cohort with a large sample size in this study impedes the further validation and comprehensive comprehension of the effect of genes linked to copper-induced death on the prognostic survival of MM patients. Additionally, it is necessary to conduct additional research using in vitro and in vivo models to further investigate the regulatory pathways, molecular mechanisms, and impacts on the immune microenvironment of CRGs. These efforts will aid in gaining a more profound comprehension of the significance of copper-induced cell death in multiple myeloma and potentially in other types of tumors as well. Conclusions To summarize, this research has developed a prognostic framework for MM that includes four CRGs and has shown its ability to predict MM OS independently in both the training and validation groups. Furthermore, it offers a deeper understanding of the molecular terrain of MM, encompassing the complex interaction between governing pathways, the tumor microenvironment (TME), and possible targets for therapeutic intervention. Although initial verification of important CRGs has been performed on cell cultures, additional investigation is required to substantiate their clinical significance through prospective data analysis. Further experimental investigation is necessary to examine the precise regulatory mechanisms of copper-induced death-associated molecules. Abbreviations MM Multiple myeloma CRGs Cuproptosis-related genes TCA Tricarboxylic acid ROS Reactive oxygen species qRT-PCR Quantitative reverse transcription–polymerase chain reaction OS Overall survival GO Gene ontology KEGG Kyoto Encyclopedia of Genes and Genomes AIC Akaike information criterion ROC Receiver operating characteristic DEGs Differentially expressed genes CC Cell components MF Molecular function BP Biological process GSEA Gene set enrichment analysis TME Tumor microenvironment NK Natural killer t-SNE t-distributed stochastic neighbor embedding ISS International Staging System TILs Tumorinfiltrating immune cells PTCL-NOS non-specific peripheral T-cell lymphoma AUC area under the curve GDSC Genomics of Drug Sensitivity in Cancer Declarations Supplementary Information supplementary document Acknowledgements We express our gratitude to the individuals whose publicly available datasets were utilized in this research. Authors’ contributions Responsible for conceptualization and design tasks by ZK, LL and ML. QL contributed important ideas. Data collection is allocated to ZK, LL and YH. ZK, LL and ML also contributed to the methodology. Additionally, they played a role in analyzing and interpreting the data. Given in written form (initial version). ZK is assigned to write (the original manuscript). ZK, LL, ML, JW were involved in writing (reviewing and editing). The article was contributed to by all authors and the submitted version was approved by them. Funding The present study was supported by the National Natural Science Foundation of China (81960476, 81460365, 81760039, 81402451, 82173378), Guizhou Provincial Science and Technology Projects ([2019]1270, [2020]4Y160), Guizhou Provincial Health and Health Commission Fund (gzwkj2021-160), Guiyang Science and Technology Projects (GY2015-35, J-2015–09), and Guizhou Medical University Science and Technology Projects (21NSFCP12). Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/) , Gene Expression Omnibus (GEO-GSE4581) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4581) and Genomics of Drug Sensitivity in Cancer(GDSC) databases were employed for compiling the entire original data. Employing R 4.2.1, source data were combined, and we validated the website database investigation outcomes. Competing interests The authors declare that they have no competing interests. References Tai, Y.-T., Cho, S.-F. & Anderson, K. C. Osteoclast Immunosuppressive Effects in Multiple Myeloma: Role of Programmed Cell Death Ligand 1. Front Immunol 2018; 9(1822. doi:10.3389/fimmu.2018.01822 Kumar, S. K., Rajkumar, V., Kyle, R. A. et al. Multiple myeloma. Nat Rev Dis Primers 2017; 3(17046. doi:10.1038/nrdp.2017.46 Michniewicz, F., Saletta, F., Rouaen, J. R. C. et al. 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ASH2L drives proliferation and sensitivity to bleomycin and other genotoxins in Hodgkin's lymphoma and testicular cancer cells. Cell Death Dis 2020; 11(11). 1019. doi:10.1038/s41419-020-03231-0 Zhao, R., Choi, B. Y., Lee, M.-H., Bode, A. M. & Dong, Z. Implications of Genetic and Epigenetic Alterations of CDKN2A (p16(INK4a)) in Cancer. EBioMedicine 2016; 8(30-9. doi:10.1016/j.ebiom.2016.04.017 Rodríguez, S., Bermúdez, L. G., González, D. et al. Transcriptional regulation of CDKN2A/p16 by sirtuin 7 in senescence. Mol Med Rep 2022; 26(5). doi:10.3892/mmr.2022.12861 Additional Declarations No competing interests reported. Supplementary Files SIwithchangesmarked.zip supplementaryfile.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-4201066","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291881370,"identity":"d9a9870a-2997-4cc3-a093-ded835f403a2","order_by":0,"name":"Zhongmin Kang","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongmin","middleName":"","lastName":"Kang","suffix":""},{"id":291881371,"identity":"ef284715-ab22-4d6c-b05a-c0f2e20de986","order_by":1,"name":"Licheng Li","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Licheng","middleName":"","lastName":"Li","suffix":""},{"id":291881372,"identity":"d07fcea7-44bf-4864-8472-ec4cb98704a0","order_by":2,"name":"Yuying Huang","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuying","middleName":"","lastName":"Huang","suffix":""},{"id":291881373,"identity":"86e8b36d-75a2-4f7d-bb81-3c6d554cd372","order_by":3,"name":"Jishi Wang","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jishi","middleName":"","lastName":"Wang","suffix":""},{"id":291881374,"identity":"d22f38a7-a921-4911-8f7e-e9f54e6d1322","order_by":4,"name":"Mengxing Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie2PsQrCMBRFUwJxCXZ9gtRfCAhOgr/yQiGTiiBIB0FBSQcVVz/D0VEpZIrOHesnuLlpnRVTN4ec6Q7v8O4lxOP5Q1htcSow6c5CSl9h6lbq3MSisAobqX4F41Yi6HcaV52hsLYMS1qhGD+2hdR0SHKlEjlnJExX6NgylwVe2DjYxSaXhyYBe967vmQCJzxYgFK5tIwIGDgUkBqQQaDLUaOyYRUlZoBayDW3HVJN4YYKtNiGmo4BreHOLa10e7vek0fUy+jpdk+mUZhuvitv8N/OPR6Px/ORJ2OSTiW+NMwZAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mengxing","middleName":"","lastName":"Li","suffix":""},{"id":291881375,"identity":"451d5ffa-6702-4faf-b623-633658a3dc28","order_by":5,"name":"Qinshan Li","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qinshan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-01 13:50:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4201066/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4201066/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55003867,"identity":"7da92138-0217-4b62-af2f-189a31ffa911","added_by":"auto","created_at":"2024-04-19 18:44:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97053,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of OS-Related CRGs in MM. (A) A Venn diagram depicting the intersection of genes related to copper death (44) with genes related to OS. (B) Utilization of one-way COX regression to analyze the 16 OS-associated CRGs. (C) Results from GO-enrichment analysis of OS-associated CRGs. (D) Results from KEGG-enrichment analysis of OS-associated CRGs. (E) Illustration of the interaction between the 16 OS-associated CRGs, with correlation data analyzed using Pearson correlation analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/8f1f1befbcf3039ca1065669.png"},{"id":55003915,"identity":"86c1e641-12e2-4358-880a-773d883bc54b","added_by":"auto","created_at":"2024-04-19 18:44:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59776,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and Modeling of Prognosis for OS-associated CRGs. (A) A graphical representation of stepwise multivariate Cox regression analysis is shown in a forest plot, which screens for the four OS-related CRGs. (B) Displaying the arrangement of patient risk scores and their corresponding survival time, while differentiating between patients at high and low risk as their risk scores escalate. (C) Illustration of the manifestation of the 4 OS-associated CRGs in MM patients belonging to the high- and low-risk groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/4996b8e531ba40a35f7f1f43.png"},{"id":55003866,"identity":"6bd2fe2e-6cb4-4075-96cd-f9adb7eae910","added_by":"auto","created_at":"2024-04-19 18:44:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81121,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic Characteristics of Patients with MM Evaluating OS-Related CRGs. (A) ROC curves that predict overall survival (OS) at 1, 3, and 5 years. (B) Comparing the survival status of the low-risk and high-risk groups. (C) Analysis of clinicopathologic variables and risk scores using univariate Cox regression. (D) Using multivariate Cox regression analysis, we evaluated clinical characteristics and risk scores. (E) Combining age, gender, ISS stage, and risk scores to predict the overall survival (OS) of patients with MM at 1, 3, and 5 years.Overall survival (OS) in patients with multiple myeloma (MM) at 1, 3, and 5-year intervals. (F) The precision of projected survival at 1, 3, and 5 years is confirmed by examining calibration curves for column line graphs.The dotted line represents a perfect column line graph, while the solid lines in green, blue, and red depict the real usefulness of the column line graph.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/8d1880498b525fd8d2d76228.png"},{"id":55003907,"identity":"6507552a-d1b4-4d0d-8dd3-bb707d467749","added_by":"auto","created_at":"2024-04-19 18:44:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46244,"visible":true,"origin":"","legend":"\u003cp\u003eAssessing the Accuracy of Prognostic Risk Models in Validation Set. (A) Validation set displays Kaplan-Meier curves illustrating prognostic features. (B) ROC curves were generated to predict overall survival (OS) at 1, 3, and 5 years in the validation dataset.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/512715bd303278fc6fa1cb0b.png"},{"id":55003911,"identity":"a85152fb-f8b9-4ffa-9914-5bc2c3b520c6","added_by":"auto","created_at":"2024-04-19 18:44:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116761,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the identification and enrichment of genes that are expressed differently. (A) Heatmaps showcasing DEGs. (B) Volcano plots presenting CRGs with significant differential expression. (C) GO functional enrichment analysis of the DEGs. (D) KEGG pathway analysis of the DEGs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/6c8b521ce99fdbe1c381c6db.png"},{"id":55003899,"identity":"4873f0f9-e449-46c4-84dc-2794857146ff","added_by":"auto","created_at":"2024-04-19 18:44:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187412,"visible":true,"origin":"","legend":"\u003cp\u003eImmunocorrelation Analysis of Risk-Scored Patients with MM in the Tumor Microenvironment. (A) Heatmap displaying correlation analysis of each immune infiltrating cell. (B) The histogram displays the variation in the levels of immune infiltrating cells among patients with MM in the high and low-risk groups. The low-risk and high-risk cohorts are represented by the red and blue columns, respectively. (C) Correlation analysis between prognostic risk scores and 24 immune checkpoints. (D) Correlation analysis between prognostic risk scores and 46 tumor-targeted checkpoints. (E) Correlation analysis between prognostic risk scores and 40 chemokines in the MM microenvironment.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/9941696abc9cc456e3f81c83.png"},{"id":55003910,"identity":"9d8e2c66-de68-4327-a5bb-44a2e48ccaae","added_by":"auto","created_at":"2024-04-19 18:44:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43619,"visible":true,"origin":"","legend":"\u003cp\u003eExamining the Expression of CRGs in Cell Clusters through the Analysis of Single-Cell Sequencing Data. (A) Clusters of cells identified in the single-cell dataset GSE176131. (B) The 4 prognosis-associated CRGs were visualized in 5 distinct cell clusters to observe differential expression.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/7ea336686685780e97ab7471.png"},{"id":55003913,"identity":"f8a772ab-3eba-4e3c-a15b-e6951cf19c65","added_by":"auto","created_at":"2024-04-19 18:44:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":78481,"visible":true,"origin":"","legend":"\u003cp\u003eGDSC-Based Drug Sensitivity Analysis of the Three Prognostically Relevant CRGs.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/7f30d87458975f69c30a3c90.png"},{"id":55003865,"identity":"013ea8e3-3e16-415b-a465-2828f5027926","added_by":"auto","created_at":"2024-04-19 18:44:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":99041,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression Validation of Prognosis-Associated CRGs in MM Cells. (A) Protein expression levels of the 3 prognosis-related CRGs in MM1S, U266, and RPMI8226 cell lines were detected using Western blot. The comparison was made with normal bone marrow cells having a single nucleus, and representative protein grayscale bands were observed. Statistical histograms quantify protein bands in grayscale. (B) The quantities of the 3 prognostic-related CRGs in MM1S, U266, and RPMI8226 were assessed using qRT-PCR, contrasting these levels with those from individual nucleus cells in healthy bone marrow. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.Three repetitions were performed for every experiment.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/e4a574c74348fc0e70277da3.png"},{"id":66420991,"identity":"a8aede6d-30d0-4fa4-a60a-cca5626e837a","added_by":"auto","created_at":"2024-10-11 15:43:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1720081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/191d282c-755d-4ecc-a60c-9ed33e876c46.pdf"},{"id":55003880,"identity":"69f06a32-eb37-4039-be52-fa562815b73c","added_by":"auto","created_at":"2024-04-19 18:44:30","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":415197,"visible":true,"origin":"","legend":"","description":"","filename":"SIwithchangesmarked.zip","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/d47992203420b04c3cc40a9a.zip"},{"id":55003902,"identity":"497a8b9c-4c0f-44bc-b6e3-2a11c42fea80","added_by":"auto","created_at":"2024-04-19 18:44:31","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1710601,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.zip","url":"https://assets-eu.researchsquare.com/files/rs-4201066/v1/0a5c09a1f4eff8c7bbd1b701.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive potential of cuproptosis-related genes in multiple myeloma: Comprehensive analysis based on bone marrow whole-genome sequencing","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a hematologic malignancy characterized by the abnormal accumulation of cancerous plasma cells in the bone marrow, secretion of monoclonal immunoglobulins, development of osteolytic lesions in the advanced stages, and invasion of extramedullary organs\u003csup\u003e[1]\u003c/sup\u003e. It ranks as the second most prevalent hematologic malignancy following malignant lymphoma, with an annual incidence of 4 per 100,000 individuals and constituting 10% of all hematologic neoplasms\u003csup\u003e[2]\u003c/sup\u003e. Despite the utilization of different treatments like Inhibitors of protease and Targeted towards immunity strategies, which have demonstrated certain effectiveness During clinical practice, the persistent occurrence of the disease and the development of drug resistance still have a detrimental effect on the predicted outcome of patients. Moreover, the exact cause of MM is still unknown. Hence, It's essential to find fresh predictive biomarkers to uncover the underlying mechanisms of MM, ultimately improving patient results and the effectiveness of treatment.\u003c/p\u003e \u003cp\u003eCopper plays a vital role in numerous biological functions, such as mitochondrial respiration and iron absorption, protection against antioxidants, and pathways for detoxification. Notably, copper levels exhibit a significant surge during tumor growth and metastasis. Numerous researches have shown increased amounts of copper in tumorous tissues or the patient's serum, encompassing different forms of cancer such as breast, lung, gastrointestinal, oral, thyroid, gallbladder, gynecological, pancreatic, and prostate cancers. Moreover, increased levels of copper in the blood are associated with more advanced tumor stages and the progression of colorectal, lung, and breast cancers. Moreover, copper plays an active role in stimulating angiogenesis, which is a vital element in the advancement and spread of tumors. The participation of copper in essential cellular processes, such as PI3K/AKT and MAPK/ERK signaling pathways, energy production (glycolysis and the tricarboxylic acid [TCA] cycle), and the control of epigenetic alterations, is responsible for these impacts\u003csup\u003e[3\u0026ndash;6]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, once copper surpasses the threshold set by the in vivo system, it transitions into a cytotoxic entity. Copper-dependent cell death, known as \"Cuproptosis,\" was described by Tsvetkov et al. During their investigation of the anticancer properties of elesclomol, a transporter of copper ions, in the year 2019. Copper-induced cytotoxicity has been identified in recent studies as a unique type of cellular death known as copper death. It distinguishes itself from other mechanisms that regulate death of cells, including phenomena like apoptosis and autophagy, iron death, and pyroptosis. Cuproptosis is characterized by the excessive accumulation of copper ions within the TCA cycle, which subsequently interacts with lipid-acylated mitochondrial enzymes, culminating in cell death\u003csup\u003e[7]\u003c/sup\u003e. Significantly, Cu(II) bound to elastoinositol interacts with ferredoxin 1 (FDX1), an enzyme found in mitochondria, and undergoes reduction to generate Cu(I), resulting in the production of reactive oxygen species (ROS)\u003csup\u003e[8,9]\u003c/sup\u003e. Targeted therapy using elesclomol reduced tumor burden in MM mouse models. The growing body of evidence indicates that targeting copper death shows great potential as a strategy for cancer treatment. Nevertheless, the exact molecular mechanisms and regulatory roles of cuproptosis in the development and advancement of tumors are not fully comprehended.\u003c/p\u003e \u003cp\u003eThe objective of the study was to examine the correlation between genes associated with cuproptosis and the prognosis of individuals diagnosed with MM. As a result, a prognostic model for MM was developed. The TCGA-MMRF cohort was utilized as a test set, while the GSE4581 dataset served as the validation set. To determine the prognosis of MM patients, we utilized both single and multifactorial Cox regression analyses, along with LASSO-Cox regression techniques, in order to identify CRGs linked to their outcome. Using these results, we developed a predictive risk model and then confirmed its precision by testing it on an independent validation dataset. Significantly, this risk model exhibited robust associations with different various kinds of immune cells exist within the MM microenvironment, as uncovered by analyses of immune infiltration and correlation. Furthermore, it demonstrated distinct expression in plasma cells and macrophages at the individual cell level. Furthermore, we validated the elevated expression of the UBE2D1 and CDKN2A genes and the reduced expression of the PDE3B gene in U266, RPMI8266, and MMIS myeloma cells using quantitative reverse transcription\u0026ndash;polymerase chain reaction (qRT-PCR) and western blot analysis. Additional understanding was acquired by identifying multiple potentially closely associated CRGs from the GDSC database, which encompassed therapeutic medications like Nutlin-3a(-), PD-0332991, bleomycin (50 \u0026micro;m), PI-103, cytarabine, and dasatinib. The combined results contribute to a broader comprehension of the predictive characteristics and biochemical processes involved in copper-induced mortality among individuals diagnosed with MM. This knowledge provides a solid foundation for the current and future design of immunologic and targeted therapies for MM.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":" \u003ch2\u003e2.1 Accumulating and managing information\u003c/h2\u003e \u003cp\u003eData from 859 cases, including whole-genome sequencing data and Patients' survival statistics, were acquired from the TCGA-MMRF database (https //portal.gdc.cancer.gov/)(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, transcription and clinical survival data for 414 patients were obtained from the GEO database (https //\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the accession number GSE4581. The information was analyzed using the resilient multiple array averaging technique, utilizing the affy software\u003csup\u003e[10]\u003c/sup\u003e, and the sva package was employed to minimize batch effects\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e \u003ch2\u003e2.2 Screening for CRGs\u003c/h2\u003e \u003cp\u003eA set of 44 genes relevant to cuproptosis was collected from existing literature\u003csup\u003e[12]\u003c/sup\u003e. This gene set constituted the 44 CRGs found in the MMRF dataset.\u003c/p\u003e \u003ch2\u003e2.3 Screening for CRGs associated with overall survival\u003c/h2\u003e \u003cp\u003eWe screened CRGs in the TCGA-MMRF training set, which included 787 MM patients, to reveal the potential risk profile linked to these CRGs in MM. Afterwards, a one-way Cox proportional hazards regression analysis was utilized to discover CRGs associated with overall survival (OS) at P-value lower than 0.05, the findings hold statistical significance.\u003c/p\u003e \u003ch2\u003e2.4 Analysis focused on OS-associated CRGs enrichment\u003c/h2\u003e \u003cp\u003eWe used the ClusterProfiler package\u003csup\u003e[13]\u003c/sup\u003e for performing gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment study. The specific roles of OS-related CRGs in MM were elucidated by determining statistical significance using corrected P values less than 0.05.\u003c/p\u003e \u003ch2\u003e2.5 Investigation of molecular interactions involving CRGs associated with OS\u003c/h2\u003e \u003cp\u003eIn order to examine the connection between OS-associated CRGs, we computed Pearson correlation coefficients to evaluate the gene correlations in MM.\u003c/p\u003e \u003ch2\u003e2.6 The creation and verification of a predictive model associated with CRGs in MM\u003c/h2\u003e \u003cp\u003eIn order to reduce the possibility of overfitting, we employed LASSO-Cox regression\u003csup\u003e[14]\u003c/sup\u003e analysis in the training set to identify crucial CRGs related to overall survival (OS) for developing the prognostic model. The CRGs were then subjected to analysis using multivariate Cox proportional hazard regression, where variables were chosen in a stepwise manner using the Akaike information criterion (AIC) \u003csup\u003e[15]\u003c/sup\u003e for selection.The formula was used to calculate the ultimate risk score.\u003c/p\u003e \u003cp\u003eRisk score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\sum _{i}^{n}Coefi\\times Ai\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe regression coefficient is represented by Coef, CRGs are denoted by i, every CRG in the signature's relative expression value is denoted by A, and the total number of genes is signified by n. The patients were classified into two categories, a low-risk group and a high-risk group, depending on their median risk scores.The comparisons of cohorts were conducted using the log-rank tests and Kaplan-Meier analysis, while the accuracy of the prognostic model was assessed through the creation of time-varying receiver operating characteristic (ROC) curves\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo ascertain the accuracy of the prognostic model's predictions, a validation set consisting of 414 patients from GSE4581 was utilized. A single formula was employed to compute risk scores for every individual, and Kaplan-Meier diagrams were developed to reevaluate the survival and prognosis differences among the two cohorts.\u003c/p\u003e \u003ch2\u003e2.7 Analysis of gene enrichment and the identification of genes expressed differentially\u003c/h2\u003e \u003cp\u003eIn order to distinguish DEGs (differentially expressed genes) between the high-risk and low-risk groups, the Limma software package\u003csup\u003e[17]\u003c/sup\u003e was utilized. The cluster analysis software 2 Immunology Research Package (ClusterProfiler) \u003csup\u003e[13]\u003c/sup\u003e was utilized to execute GO and KEGG enrichment evaluations, providing important knowledge about cell components (CC), molecular function (MF), biological process (BP), and KEGG pathways. This analysis aimed to gain a better understanding of the potential functions and pathways associated with MM. Furthermore, ClustVis \u003csup\u003e[18]\u003c/sup\u003e was used to categorize differentially expressed genes and produce heatmaps for visual illustration.\u003c/p\u003e \u003ch2\u003e2.8 Analysis of gene set variations\u003c/h2\u003e \u003cp\u003eAs previously discussed, the risk score median served as the defining factor distinguishing between low and high-risk cohorts. In order to further describe the main pathways enriched in the high and low-risk categorizations, we conducted a Gene set enrichment analysis (GSEA)\u003csup\u003e[19]\u003c/sup\u003e. Establishing statistical significance employed enrichment, necessitating p-values beneath 0.05 and a false discovery rate under 0.25.\u003c/p\u003e \u003ch2\u003e2.9 Evaluating the predictive precision of OS-associated CRGs in clinicopathologic diagrams\u003c/h2\u003e \u003cp\u003eThe RMS \u003csup\u003e[20]\u003c/sup\u003e software package was utilized to perform prognostic analysis on MM patients, and their prognostic characteristics were incorporated into the clinicopathological analysis of the training group. The AIC was used to select the ultimate model, and calibration curves were created to evaluate the predictive precision of the column-line graphs\u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e \u003ch2\u003e2.10 The immunological milieu of Multiple Myeloma and potential targets for immunotherapy aiding in prognosis prediction.\u003c/h2\u003e \u003cp\u003eWe used CIBERSORT and Spearman correlation analysis to assess the connection between risk profiles, which are determined by OS-related CRGs, and the infiltration of immune cells in the tumor microenvironment (TME). The immune cells considered include natural killer (NK) cells, plasma cells, B cells, and T cells. Additionally, the association between the risk scores and 24 prevalent immune checkpoints, 46 potential immunosuppressive molecules targeted towards tumors, and chemokines commonly found in the tumor microenvironment (TME) was evaluated \u003csup\u003e[22]\u003c/sup\u003e .All subsequent analyses were subjected to a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003ch2\u003e2.11 Expression analysis of prognostically relevant CRGs at the single-cell level\u003c/h2\u003e \u003cp\u003eThe information utilized in this research was obtained from the GSE176131 document found in the GEO repository (https //\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Seurat software enabled the creation of objects and the removal of low-quality cells. Standard data preprocessing involved the calculation of gene count percentages, cell counts, and mitochondrial content. We filtered out genes found in less than 3 cells and cells with less than 200 detected genes, as well as cells showing low or high mitochondrial content (\u0026gt;\u0026thinsp;5%). Each cell's UMI counts were normalized using scale factor\u0026thinsp;=\u0026thinsp;10,000. The ScaleData function in Seurat (v3.0.2) was utilized after performing a logarithmic transformation. The standardized analysis utilized the rectified normalized data metrics. For principal component analysis, the top 10 variable genes were chosen, and the first 5 principal components were kept for visualization and clustering using Uniform Manifold Approximation and Projection or t-distributed stochastic neighbor embedding (t-SNE). The Seurat R package was utilized to perform clustering by employing the FindClusters function.\u003c/p\u003e \u003ch2\u003e2.12 Drug sensitivity analysis.\u003c/h2\u003e \u003cp\u003eAnalyses of drug sensitivity were performed for the three main prognostic CRGs using information from the GDSC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cancerRxgene.org\u003c/span\u003e\u003cspan address=\"http://www.cancerRxgene.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[23]\u003c/sup\u003e, which is the most extensive public repository for molecular markers related to drug sensitivity and response in cancer. Significance was determined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003ch2\u003e2.13 Cell lines and cell culture\u003c/h2\u003e \u003cp\u003eHuman MM cell lines, including MMIS, U266, and RPMI8226, were obtained from the Cell Center of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. Normal human bone marrow cells were derived from healthy medical examiners' bone marrow, and single-nucleated cells were isolated from human peripheral blood lymphocyte isolation solution FICO (Beijing Solepol Science and Technology Co., Ltd.) for use as normal control cells. Human MM cell lines were grown in RPMI-1640 medium (Gibco, USA) with the addition of 10% fetal bovine serum. The cells underwent cultivation in an incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C.\u003c/p\u003e \u003ch2\u003e2.14 Quantitative reverse transcription-polymerase chain reaction in real-time\u003c/h2\u003e \u003cp\u003eTRIzol reagent (Takara, Japan) was used to extract total RNA according to the instructions provided by the manufacturer. TBGreenMasterMix (Takara, Japan) was used for qRT-PCR to measure the mRNA expression of CDKN2A, PDE3B, and UBE2D1.Shanghai Sangong Bioengineering Co. synthesized the primer pairs. GAPDH served as a control for normalization, and the method was used to determine the relative expression levels.\u003c/p\u003e \u003ch2\u003e2.15 Western blotting\u003c/h2\u003e \u003cp\u003eTo analyze proteins, MM cell lines and normal control cells were lysed using a suitable quantity of high-performance radioimmunoprecipitation assay lysis solution (from Beijing Solebrite Technology Co., Ltd.). The lysates were then placed on ice and shaken on an oscillator for 30 s at 15-second intervals to ensure complete lysis. This process was repeated 10 times for thorough circulation. Following centrifugation at a speed of 12,000 rpm at 4\u0026deg;C for a duration of 20 min, the resulting liquid was combined with 5 times the amount of protein sampling buffer (provided by Beijing Solebrite Technology Co., Ltd.) and subjected to heating at 100\u0026deg;C for 10 min. Proteins underwent separation via sodium dodecyl sulfate\u0026ndash;polyacrylamide gel electrophoresis on a 12% gel, followed by transfer onto polyvinylidene fluoride membranes measuring 0.22 mm (microtiter wells, USA). The membranes were obstructed with 5% skim milk for about 2 h, followed by incubation with particular antibodies. The visualization was achieved by adding ECL chemiluminescent solution (Millipore, USA) drop by drop. The CDKN2A antibody was obtained from Wan Class Biotechnology Co., Ltd., whereas the PDE3B and UBE2D1 antibodies were procured from Wuhan Three Eagles Biotechnology Co., Ltd., and GAPDH was acquired from Bioworld Technologies Ltd. Quantitative analysis was conducted by utilizing ImageJ software, relying on relative gray values for assessment.\u003c/p\u003e \u003ch2\u003e2.16 Statistical analysis\u003c/h2\u003e \u003cp\u003eGraphPad Prism 9.4 and R software, version 4.2.1, were utilized for conducting statistical analysis. To identify disparities between groups of normally distributed variables, the Student t-test was employed for quantitative variables, while the Wilcoxon test was utilized for skewed data.Statistical significance was determined for p-values less than 0.05, p-values less than 0.01, and p-values less than 0.001 denoted as *, **, and *** respectively.\u003c/p\u003e "},{"header":"3 Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identifying MM-associated CRGs related to the operating system\u003c/h2\u003e \u003cp\u003ePrognostic-related factors for patients with MM were identified using univariate Cox regression analysis, with a significance level of P \u0026lt; 0.05. A grand total of 44 CRGs were obtained from the available literature on copper fatality. We acquired 16 CRGs linked to MM prognosis by intersecting these genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). GO analysis of these CRGs showed that they are mainly involved in the regulation of copper ions, including processes related to mitochondrial respiration, modification of cellular proteins, and binding of copper ions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Moreover, the KEGG enrichment analysis revealed that these CRGs were also involved in pathways associated with mitochondrial metabolism, the TCA cycle, and cancer-related pathways like the HIF-1 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eIn order to explore the connections between OS-related CRGs, we discovered six crucial genes - DLD, COA6, CDKN2A, SOD1, PDHA1, and UBE2D1 - by analyzing their significant correlation with one another among the 16 CRGs, utilizing Pearson correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The discovery implies that the control mechanisms related to these CRGs might have significant impacts on the development of MM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCRGs associated with overall survival in MM patients (P \u0026lt; 0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLikelihood\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elogrank\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOX11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.453835\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.009534\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.093675\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043438\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045385\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04433\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDHB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.543364\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.068996\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.228234\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018404\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020634\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020556\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDE3B\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.600773\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.359239\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.885227\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67E-09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.72E-08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOA6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.895057\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.514929\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.370568\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79E-08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46E-08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.19E-08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBE2D1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.773009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.315833\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.389026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000167\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD274\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.689696\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.549889\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.865049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001182\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001374\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001308\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP2K1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.621814\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.143145\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.300915\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007935\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006893\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006735\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDK1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.813345\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.692437\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.955365\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012657\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011765\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011868\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCO1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.474794\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000335\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.174289\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048249\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049921\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049803\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.736625\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.237513\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.43704\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001416\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001409\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00141\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOD1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.09423\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.902445\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.08E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.03E-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC31A2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585425\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.393917\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870036\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007598\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008048\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008081\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eULK1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73544\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.547325\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.041485\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCSH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.365105\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.028938\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.811102\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030999\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031014\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030948\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDHA1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.322373\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.498823\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.598434\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000158\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000162\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN2A\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.888524\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.467956\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.429585\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.11E-07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.56E-07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prognostic screening and modeling for OS-associated CRGs\u003c/h2\u003e \u003cp\u003eIn order to avoid possible overfitting, we employed LASSO COX regression analysis to select key OS-related CRGs for modeling purposes. Subsequently, four CRGs—CDKN2A, PDE3B, UBE2D1, and COA6—were identified through stepwise multivariate Cox proportional risk regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Patients’ risk score was determined using the following formula:\u003c/p\u003e \u003cp\u003erisk score = [CDKN2A expression level ∗ (0.4529)] + PDE3B expression level ∗ (0.3191)] + UBE2D1 expression level ∗ (0.5280)] + COA6 expression level ∗ (0.3833)].\u003c/p\u003e \u003cp\u003eBased on their median risk scores, all patients were categorized into either a high-risk or low-risk cohort. It was showed that as the risk scores increased, there was a noticeable rise in the mortality rate among patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, the expression profiles of these four genes were investigated in both the high-risk and low-risk groups, indicating that CDKN2A, PDE3B, UBE2D1, and COA6 exhibited elevated expression levels in the high-risk cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This implies that these four CRGs could act as cancer-causing genes, potentially facilitating the advancement of MM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Assessment of predictive attributes of OS-associated CRGs in individuals diagnosed with MM\u003c/h2\u003e \u003cp\u003eThe analysis of the ROC curve on the training set (MMRF) showed that the model had high predictability, with area under the curve (AUC) values of 0.63, 0.71, and 0.78 for predicting OS at 1, 3, and 5 years, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the meantime, the precision of the risk model was assessed by examining Kaplan-Meier survival plots, which indicated a considerably shorter overall survival (OS) for individuals in the high-risk group compared to those in the low-risk group (P \u0026lt; 0.001, hazard ratio = 2.18) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To further evaluate the autonomous predictive significance of the risk profile for individuals diagnosed with MM, a unidirectional Cox regression analysis was performed, integrating clinical factors like age, sex, International Staging System (ISS) stage, and risk score. The findings validated that the risk score could autonomously function as a predictive marker and was linked to the outcome of individuals diagnosed with MM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Furthermore, following the adjustment for these clinicopathologic factors, the multifactorial Cox regression analysis confirmed that the risk profile continued to be a separate prognostic predictor for MM. Stages II and III in ISS staging were also identified as independent prognostic predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).To gauge the diagnostic efficacy of risk scores in comparison with other clinical variables in predicting the prognosis of patients with MM, we employed ROC curves. The AUC values for risk score predictions of OS over 1 year were higher than those for sex (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA), the AUC value risk score predicting OS over 3 years was higher than the age and sex (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB), and interestingly, the AUC value risk score predicting OS over 5 years was higher than the age, sex, and ISS stage, all of which were clinical variables for MM (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC), respectively. This finding underscores the superior diagnostic efficacy of the established prognostic model, particularly in predicting long-term survival outcomes such as 5-year OS in patients with MM.\u003c/p\u003e \u003cp\u003eTo thoroughly evaluate the prognostic model's accuracy, we generated a column-line graph that incorporated risk score, age, sex, and ISS stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Calibration plots demonstrated outstanding agreement in predicting OS at 1, 3, and 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Combining clinical variables with the risk score highlighted the remarkable predictive precision of OS in patients with MM, as demonstrated by these findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GSEA of CRGs\u003c/h2\u003e \u003cp\u003eWe carried out Gene Set Enrichment Analysis (GSEA) to explore the biological roles and corresponding signaling channels in both patient groups after observing the disparities in Overall Survival (OS) between the low-risk and high-risk cohorts.The low-risk cohort exhibited significant enrichment in functions related to cellular responses to starvation, EIF2AK4 GCN2 response to amino acid deficiency, and translational elongation in eukaryotes (Figure S3A).Conversely, the high-risk cohort displayed significant enrichment in pathways associated with cell cycle, immunity, and apoptosis (Figure S3B). These findings suggest that the signature genes related to copper death in high-risk patients with MM may interact with immune pathways through apoptosis, thereby influencing MM disease progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of Prognostic Risk Models\u003c/h2\u003e \u003cp\u003eThe risk score algorithm was then utilized on the validation set (GSE4581) to compute risk scores for individuals in the high- and low-risk groups, confirming the dependability of the prognostic model.In accordance with the training set, patients belonging to the high-risk group demonstrated a reduced overall survival (OS) and a more unfavorable prognosis compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).Furthermore, the AUC values obtained from ROC analysis for predicting OS at 1, 3, and 5 years were 0.656, 0.657, and 0.797, correspondingly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).The findings indicated that the risk model also showed strong dependability in the validation group of individuals with MM and demonstrated exceptional precision in forecasting long-term prognostic survival.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Enrichment and characterization of DEGs\u003c/h2\u003e \u003cp\u003eMoreover, we performed DEG screening in the training dataset to explore potential molecular pathways that regulate patients with MM exhibiting copper-induced cell death. By utilizing the Limma software package, we detected 188 differentially expressed genes (DEGs) in the training dataset, demonstrating unique patterns of gene expression in the low-risk and high-risk groups (110 genes were found to be upregulated, whereas 78 genes were downregulated) ( Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Analysis of gene ontology (GO) indicated that CRGs played a role in diverse immune-related biological processes, such as the conventional mechanism of cellular growth, the adaptive immune response, and immunity mediated by B-cells, among other functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).The analysis of KEGG emphasized the primary participation of CRGs in signaling pathways associated with tumors, including the cell cycle, the pathway related to cancer, and P53, in addition to other signaling pathways related to tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).Given the enrichment of these crucial biological functions in patients with MM, we postulate that DEGs in high- and low-risk patients characterized by copper death may mediate disease progression through immunomodulation within the TME.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Correlation analysis of prognostically relevant CRGs with the MM immune microenvironment\u003c/h2\u003e \u003cp\u003eThe communication between tumorinfiltrating immune cells (TILs) and cancer cells in the tumor microenvironment (TME) is crucial for the advancement of tumors and the development of resistance to drugs. Multiple studies have reported that cuproptosis, a cell death mechanism that relies on copper, is linked to immune-infiltrating cells in the tumor microenvironment (TME) of different types of cancer, such as bladder and breast cancer. As a result, we examined the relationship between the risk profile of associated CRGs based on OS in the training set and the infiltration of immune cells in the TME. In the high-risk group, our analysis showed a notable decrease in immune cells like plasma cells and CD4 + T cells, whereas memory B cells, memory CD4 + T cells, activated NK cells, and dendritic cells were considerably increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). The results indicate a strong association between the risk profile and the existence of multiple TILs in the MM microenvironment. Recently, MM has been the subject of investigation in preclinical treatments and clinical trials, focusing on immune checkpoint inhibitors and innovative targeted immunotherapies\u003csup\u003e[24]\u003c/sup\u003e. In order to explore their possible significance, we employed Spearman correlation analysis to investigate the connection between risk scores and 24 immune checkpoints, such as CD274 (PD-L1), CTLA-4, LAG3, HAVCR2 (TIM3), and TIGIT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The findings demonstrated a significant positive correlation between the risk scores and CTLA-4, whereas a marked negative association was noted with LGALS9 and LAG3. Furthermore, we performed a correlation analysis on 46 potential checkpoints for tumor-targeted therapy, revealing noteworthy positive correlations between risk scores and TNFSF4 and ENTPD1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Moreover, an examination of 40 MM microenvironmental chemokines revealed noteworthy positive correlations between risk scores and CXCL16, CCL8, and CCL16, in addition to a notable negative correlation with CXCL17 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). The results suggest that individuals with MM who have a copper mortality risk profile might experience more advantages from therapies that focus on CTLA4, TNSF4, ENTPD1, CXCL16, CCL8, and CCL16. These treatments involve immune checkpoints, immunosuppressive elements, and chemokines. Conversely, the efficacy of targeted therapies involving LGALS9, LAG3, and CXCL17 appears to be less promising.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.8 4 CRGs with prognostic relevance confirmed through single-cell validation\u003c/h2\u003e \u003cp\u003eTo gain further insights into the expression patterns of the four prognosis-related CRGs in MM at the single-cell level, we leveraged the R package \"Seurat\" to down-converge and cluster single-cell MM data from GSE176131, resulting in five distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Following the analysis of individual cells, it was found that plasma cells showed increased levels of CDKN2A and COA6 expression, whereas macrophages exhibited higher expression of UBE2D1(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). These findings indicate that CRGs might have a crucial function in facilitating cell-to-cell interactions between plasma cells and macrophages within the MM microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Drug sensitivity analysis of prognostically relevant CRGs\u003c/h2\u003e \u003cp\u003eIn order to investigate potential targeted therapeutic drugs based on the prognosis-related key CRGs, we performed a drug sensitivity analysis of the three relevant CRGs using the GDSC database. The analysis revealed a strong and favorable association between CDKN2A and Nutlin-3a (-), PD-0332991, whereas PDE3B exhibited a notable and positive correlation with Bleomycin (50 µm). UBE2D1 displayed positive correlation with PI-103(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The results indicate that these medications may be effective in targeting copper-induced death treatment approaches in MM patients, leading to improved outcomes in terms of relapse and treatment response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Prognosis-related CRGs are validated for differential expression in MM cells\u003c/h2\u003e \u003cp\u003eTo further confirm the expression levels of prognosis-related CRGs in MM at the cellular level, we chose MMIS, U266, and RPMI82263 MM cell lines. We evaluated the levels of mRNA and protein expression using qRT-PCR and western blot techniques, respectively. The findings indicated that CDKN2A and UBE2D1 displayed elevated levels of expression in all three MM cell lines compared to normal bone marrow mononuclear cells, whereas PDE3B showed decreased levels of expression. Interestingly, the mRNA patterns of UBE2D1 and PDE3B aligned with their corresponding protein levels. In contrast, the mRNA level of CDKN2A exhibited a different pattern when compared to the protein level(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This implies that CDKN2A could be controlled by specific epigenetic mechanisms at the post-transcriptional and translational levels throughout the progression of MM. The protein-level differential expression of these three CRGs in MM cell lines provides evidence for their role in MM pathogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMultiple Myeloma, the adult population's second most prevalent blood malignancy, is diagnosed by the harmful accumulation of cancer-bearing plasma cells within the bone marrow that subsequently damages critical organs. Despite the progress made in targeted and immunotherapies, many patients with MM continue to experience unfavorable outcomes as a result of the difficulties presented by frequent relapses and resistance to medication. Through this research, we have developed an accurate forecast model focused on copper mortality, providing medical professionals with a valuable resource to design personalized treatment plans for each patient. A significant association was noticed between CRGs and OS in individuals diagnosed with MM. Furthermore, by utilizing four CRGs that are relevant for prognosis, we have created a risk score model that can accurately predict survival prognosis and uncover potential biological functions and molecular mechanisms in MM. Validation set analysis underscored the independence, stability, and reliability of this prognostic model, establishing it as a robust diagnostic indicator. Moreover, our analysis of immune correlation revealed the complex connection between risk scores and tumorinfiltrating immune cells (TILs), immune checkpoint proteins, and chemokines in the tumor microenvironment (TME) for both cohorts with low and high risks. This sheds light on the unique immune landscape associated with copper death in MM and hints at potential immunotherapeutic avenues. At the same time, we identified possible drug targets that are specific to patients with copper death-associated MM. Additionally, we examined the expression patterns of CRGs at the single-cell level and confirmed their potential role in MM pathogenesis by validating the protein and mRNA expression levels of CRGs in MM cell lines.\u003c/p\u003e\u003cp\u003eCopper death signifies a recently characterized type of cellular demise that is different from other types of planned cellular demise. This condition is primarily characterized by proteotoxic stress, caused when copper ions within the cell directly interact with lipid-acylated elements of the tricarboxylic acid cycle. The interaction ultimately leads to the buildup of lipoylated proteins and the destabilization of proteins containing Fe-S clusters, causing proteotoxic stress that eventually leads to cell death\u003csup\u003e[25]\u003c/sup\u003e. It is believed that copper exerts its impact on cancer cells by attaching to and activating crucial molecules in different signaling pathways. For example, a study conducted in 2013 using human leukemia K562 cells showed that copper ions have the ability to oxidize ascorbic acid and combine with hydrogen peroxide, which is produced through ES (copper ion carrier), resulting in the production of even more harmful ROS\u003csup\u003e[26]\u003c/sup\u003e. Copper triggers the oncogenic signaling pathway of phosphoinositide 3-kinase (PI3K)-protein kinase B (PKB or AKT) in breast cancer, thereby facilitating the development of tumors. Significantly, the AKT signaling pathway and the development of tumors can be reduced by depleting copper transporter protein 1 (CTR1) or obstructing the copper transporter protein 1 (CTR1)-copper connection through the use of copper chelators\u003csup\u003e[27]\u003c/sup\u003e. Furthermore, copper has a direct binding effect on MEK1 in colon cancer cells, leading to an increase in phosphorylation of ERK1/2. This, in turn, triggers the activation of downstream JNK, thereby controlling the growth of tumors\u003csup\u003e[28]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMaintaining a balanced copper ion metabolism is crucial for the progression of tumors in the body. With the emergence of copper death, therapeutic strategies targeting this phenomenon in tumors have garnered increasing attention. Therefore, it is crucial to identify significant prognostic indicators associated with copper-induced mortality in individuals diagnosed with MM. Accurate prognostic survival estimation and effective categorization of treatment effectiveness are essential measures to enhance the outcomes of MM patients dealing with relapse and resistance to medication.\u003c/p\u003e\u003cp\u003eBy employing univariate COX regression analysis, we were able to identify a total of 16 CRGs in individuals diagnosed with MM in this particular study. Certain CRGs have significant impacts on the advancement of cancer and the development of resistance in hematologic malignancies. For example, inhibiting SOD1 leads to the initiation of caspases, the p53/p21 signaling pathway, and the response to endoplasmic reticulum stress, resulting in the demise of MM cells and ultimately enhancing the prognosis of patients\u003csup\u003e[29]\u003c/sup\u003e. Moreover, UBE2D1, a constituent of the E2 ubiquitin-coupled enzyme and part of the UBE2D lineage, has been linked to the proliferation of liver tumors by reducing p53 expression via pathways that rely on ubiquitination\u003csup\u003e[30]\u003c/sup\u003e. CDKN2A (P16) is a tumor suppressor gene located on the short arm of chromosome 9, reported in a variety of solid tumors and blood tumors. CDKN2A suppresses the growth and spread of cervical cancer cells in breast cancer by blocking the AKT-mTOR pathway, which is facilitated by LDHA\u003csup\u003e[31]\u003c/sup\u003e. CDKN2A deficiency in non-specific peripheral T-cell lymphoma (PTCL-NOS) is associated with a negative prognosis in lymphoma patients\u003csup\u003e[32]\u003c/sup\u003e. The findings of our research indicate that these CRGs show a notable enrichment in pathways linked to the process of mitochondrial respiration, modification of proteins in the cytosol, binding of copper ions, and other pathways related to copper ions. It indicates that these CRGs rely on copper ion metabolism processes to regulate copper death, and are involved in the progression of MM.\u003c/p\u003e\u003cp\u003eEstablishing a tumor-related prognostic model based on transcriptome expression profile characteristics has strong application prospects for risk assessment\u003csup\u003e[33]\u003c/sup\u003e. This study determines the key OS-related CRGs through LASSO-Cox regression analysis and establishes a risk model. The model was developed utilizing the four CRGs that had the most potent prognostic traits. Through univariate/multifactorial Cox regression analysis, it was found that the prognosis of patients with MM could be predicted independently by risk scores, in contrast to clinical variables. The area under the curve (AUC) values for overall survival (OS) predicted by receiver operating characteristic (ROC) curves at 1, 3, and 5 years were 0.63, 0.71, and 0.78. Furthermore, the integration of clinical variables of MM into ROC curves resulted in higher AUC values for OS beyond 5 years when compared to using clinical variables alone. This underscores the significant influence of risk scores on the long-term outcomes in MM. The accuracy of the model in predicting patient prognosis was further confirmed by column-line plots that incorporated ISS stage, age, sex, risk score, and calibration curves. Immune cell heterogeneity in patients with MM has been highlighted by recent studies, which have shown that various subpopulations of immune cells, such as plasma cells, CTLs, T cells, and NK cells, display elevated levels of expression\u003csup\u003e[34]\u003c/sup\u003e. In the microenvironment of MM, cancerous plasma cells discharge mitochondrial DNA, a type of mtDAMPs, into the microenvironment of myeloma BM. This action activates macrophages through STING signaling and encourages the advancement of myeloma\u003csup\u003e[35]\u003c/sup\u003e. At the individual cell level, our examination revealed that plasma cells exhibited significant overexpression of CDKN2A and COA6, whereas macrophages displayed high expression of UBE2D1. The results indicate that the characteristics of copper-induced death may rely on the interactions between plasma cells and macrophages in the microenvironment of MM, providing insight into the progression mechanism of MM.\u003c/p\u003e\u003cp\u003eFurthermore, differentially expressed genes (DEGs) exhibit notable enrichment in signaling pathways associated with adaptive immune responses, immunity mediated by B-cells, regulation of the cell cycle, P53, and FoxO pathways. According to recent research, p53 mainly controls the formation of iron-sulfur clusters and copper glutathione chelates, thus affecting copper metabolism and contributing to copper-induced cell death\u003csup\u003e[36]\u003c/sup\u003e. In addition, copper binds with the arginine 117 and arginine 203 sites of Pyruvate Dehydrogenase Kinase 1 (PDK1), leading to the activation of AKT. Following this, the activation of AKT by copper leads to the phosphorylation and subcellular redistribution of FoxO1a and FoxO4 within cells, thereby stimulating the proliferation of cancer cells and facilitating tumor growth. TILs are one of the main components of TME, and the density and types of TILs have significant prognostic associations in many aspects of cancer\u003csup\u003e[37]\u003c/sup\u003e. Significant changes were observed in the levels of different types of immune cells, including plasma cells, memory B cells, monocytes, eosinophils, and neutrophils, within the MM microenvironment, distinguishing the high-risk and low-risk cohorts. The findings indicate that the risk profile is strongly linked to the existence of TILs within the microenvironment of MM. Excessive amounts of copper death over an extended period might play a role in the significantly immunosuppressive surroundings seen in individuals suffering from MM.\u003c/p\u003e\u003cp\u003eEffective therapies for hematologic tumors and different solid tumors have been identified in the form of immune checkpoint inhibitors\u003csup\u003e[38]\u003c/sup\u003e. Nevertheless, the task of attaining long-lasting response rates in certain individuals suffering from cancer continues to be difficult because of the evasion of the immune system and the development of resistance. Our analysis of immune correlation indicates that patients with MM who display characteristics associated with copper death are more inclined to experience positive outcomes from therapies that focus on immune checkpoints, immunosuppressive elements, and chemokines like CTLA4, TNSF4, ENTPD1, CXCL16, CCL8, and CCL16.The therapeutic effect is unsatisfactory for MM patients who are treated with therapies targeting LGALS9, LAG3, and CXCL17 as the targets. Hence, continuously stimulating or blocking copper demise may present novel treatment options for enhancing the results of individuals with MM who have a limited response to immune checkpoint therapy.\u003c/p\u003e\u003cp\u003eTo tackle the difficulties in the development of new medications and enhance the healing capabilities of current drugs, it is necessary to enhance the process of selecting targets and repurposing drugs that have already been approved. Significant positive correlations were found between CDNK2A and Nutlin-3a (-), PD-0332991, PDE3B, and bleomycin (50 µm) in our analysis using GDSC drug sensitivity. Additionally, UBE2D1 and PI-103 showed a correlation. Nutlin-3a could enhance natural killer cell-mediated neuromasts by restoring the p53-dependent NKG2D and DNAM − 1 receptor ligand expression and enhances natural killer cell-mediated neuroblastoma killing\u003csup\u003e[39]\u003c/sup\u003e. Additionally, PD-0332991 (pabocinib), a cell cycle protein-dependent inhibitor of kinase 4 and 6, exhibits efficacy against metastatic tumors. Bleomycin, at a concentration of 50 µm, plays a vital role in the gold-standard treatment for different types of tumors, such as Hodgkin’s lymphoma, testicular cancer, and germ cell cancer. The involvement of ASH2L in the growth and susceptibility of Hodgkin's lymphoma and testicular cancer cells to bleomycin has been established\u003csup\u003e[40]\u003c/sup\u003e. PI-103, a potentially effective drug, shows promise in inhibiting PI3K and mTOR. This suggests that it has the ability to effectively target different types of cancer cells, like gliomas and breast cancers, at various points in the pathway. The results of drug sensitivity analysis based on CRG offer potential for the advancement of novel targeted treatments for individuals with MM.\u003c/p\u003e\u003cp\u003eAdditionally, we confirmed the protein and mRNA levels of three clinically significant CRGs in MM cell lines through western blot and qRT-PCR analysis. Surprisingly, there was a contrasting pattern observed in the mRNA and protein expression of CDKN2A. The discrepancy implies that CDKN2A might undergo post-transcriptional epigenetic alterations in MM tumors and senescence, potentially including promoter methylation and regulation of histone acetylation\u003csup\u003e[41,42]\u003c/sup\u003e. As a result, we hypothesized that MM may make CDKN2A more prone to post-transcriptional epigenetic alterations, leading to decreased mRNA expression and impacting the progression of the disease. Additional analysis is required to identify the specific molecular interactions involved.\u003c/p\u003e\u003cp\u003eAlthough limitations exist, this study has created a risk-prognostic model by analyzing patients with MM and copper death characteristics. The lack of a clinical cohort with a large sample size in this study impedes the further validation and comprehensive comprehension of the effect of genes linked to copper-induced death on the prognostic survival of MM patients. Additionally, it is necessary to conduct additional research using in vitro and in vivo models to further investigate the regulatory pathways, molecular mechanisms, and impacts on the immune microenvironment of CRGs. These efforts will aid in gaining a more profound comprehension of the significance of copper-induced cell death in multiple myeloma and potentially in other types of tumors as well.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo summarize, this research has developed a prognostic framework for MM that includes four CRGs and has shown its ability to predict MM OS independently in both the training and validation groups. Furthermore, it offers a deeper understanding of the molecular terrain of MM, encompassing the complex interaction between governing pathways, the tumor microenvironment (TME), and possible targets for therapeutic intervention. Although initial verification of important CRGs has been performed on cell cultures, additional investigation is required to substantiate their clinical significance through prospective data analysis. Further experimental investigation is necessary to examine the precise regulatory mechanisms of copper-induced death-associated molecules.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Multiple myeloma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cuproptosis-related genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tricarboxylic acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Reactive oxygen species\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eqRT-PCR \u0026nbsp; \u0026nbsp; \u0026nbsp;Quantitative reverse transcription–polymerase chain reaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Overall survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Akaike information criterion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cell components\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Molecular function\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Biological process\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTME \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor microenvironment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNK \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Natural killer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003et-SNE \u0026nbsp; \u0026nbsp; \u0026nbsp; t-distributed stochastic neighbor embedding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eISS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; International Staging System\u003c/p\u003e\n\u003cp\u003eTILs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumorinfiltrating immune cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTCL-NOS \u0026nbsp; non-specific peripheral T-cell lymphoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGDSC \u0026nbsp; \u0026nbsp; \u0026nbsp; Genomics of Drug Sensitivity in Cancer\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esupplementary document\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the individuals whose publicly available datasets were utilized in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResponsible for conceptualization and design tasks by ZK, LL and ML. QL contributed important ideas. Data collection is allocated to ZK, LL and YH. \u0026nbsp;ZK, LL and ML also contributed to the methodology. Additionally, they played a role in analyzing and interpreting the data. Given in written form (initial version). ZK is assigned to write (the original manuscript). ZK, LL, ML, JW were involved in writing (reviewing and editing). The article was contributed to by all authors and the submitted version was approved by them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (81960476, 81460365, 81760039, 81402451, 82173378), Guizhou Provincial Science and Technology Projects ([2019]1270, [2020]4Y160), Guizhou Provincial Health and Health Commission Fund (gzwkj2021-160), Guiyang Science and Technology Projects (GY2015-35, J-2015–09), and Guizhou Medical University Science and Technology Projects (21NSFCP12).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/) , Gene Expression Omnibus (GEO-GSE4581) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4581) and Genomics of Drug Sensitivity in Cancer(GDSC) databases were employed for compiling the entire original data. Employing R 4.2.1, source data were combined, and we validated the website database investigation outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTai, Y.-T., Cho, S.-F. \u0026amp; Anderson, K. C. Osteoclast Immunosuppressive Effects in Multiple Myeloma: Role of Programmed Cell Death Ligand 1. \u003cem\u003eFront Immunol\u003c/em\u003e 2018; 9(1822. doi:10.3389/fimmu.2018.01822\u003c/li\u003e\n \u003cli\u003eKumar, S. K., Rajkumar, V., Kyle, R. A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Multiple myeloma. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e 2017; 3(17046. doi:10.1038/nrdp.2017.46\u003c/li\u003e\n \u003cli\u003eMichniewicz, F., Saletta, F., Rouaen, J. R. 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G., Gonz\u0026aacute;lez, D.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Transcriptional regulation of CDKN2A/p16 by sirtuin 7 in senescence. \u003cem\u003eMol Med Rep\u003c/em\u003e 2022; 26(5). doi:10.3892/mmr.2022.12861\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Multiple myeloma, cuproptosis, prognostic model, tumor microenvironment, immune-targeted therapies","lastPublishedDoi":"10.21203/rs.3.rs-4201066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4201066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The primary objective of this study was to identify potential CRGs in patients with MM and develop a predictive model to enhance prognostic outcomes for individuals with MM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We leveraged transcriptome sequencing data from patients with MM, combined with clinical information from the TCGA-MMRF dataset and the GSE4581 dataset from the GEO database. Through analysis, we pinpointed three genes—CDKN2A, PDE3B, and UBE2D1 that exhibited a significant association with the prognosis of patients with MM. This association was confirmed through a combination of univariate and multivariate Cox regression analyses. Subsequently, we employed LASSO-Cox regression analysis to construct a risk-prognostic model centered around these three CRGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Notably, the model revealed that high-risk patients with MM experienced significantly shorter overall survival times. Intriguingly, We have unveiled a propensity for high-risk patients with MM to develop an immunosuppressive tumor microenvironment. Finally, to substantiate our findings, we conducted in-depth examinations of the expression of these three CRGs at the cellular level using quantitative reverse transcription–polymerase chain reaction and Western blotting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our research collectively reveals the molecular scenery in the MM microenvironment through the development of a prognostic model focused on CRGs.\u003c/p\u003e","manuscriptTitle":"Predictive potential of cuproptosis-related genes in multiple myeloma: Comprehensive analysis based on bone marrow whole-genome sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:44:21","doi":"10.21203/rs.3.rs-4201066/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":"0ce6034d-d81a-494a-966f-9c0871572960","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30749586,"name":"Biological sciences/Cancer/Haematological cancer/Myeloma"},{"id":30749587,"name":"Health sciences/Oncology"},{"id":30749588,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-10-11T15:35:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:44:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4201066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4201066","identity":"rs-4201066","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}