Identifying Cuproptosis-Related Hub Genes and Their Clinical Significance in Tumors

doi:10.21203/rs.3.rs-3100141/v1

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Identifying Cuproptosis-Related Hub Genes and Their Clinical Significance in Tumors

https://doi.org/10.21203/rs.3.rs-3100141/v1

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The freshly detected Cuproptosis is a novel form of tumor cell death whose role in pan-cancer is still not completely unintelligible. Methods: We analyzed and assessed the Cuproptosis-related genes (FDX1, LIPT1, DLD, LIAS, DLAT, PDHA1, PDHB, MTF1, GLS and CDKN2A) conversion through the STING database and Sytoscpy software, performed functional enrichment analysis and identified the Hub genes. We extracted the expression data of Hub gene in tumor through TGCA and UCSC database, and analyzed its clinical prognosis. The cancer single-cell state atlas (CSEA) database was used to evaluate the effect of Hub genes on tumor phenotype in single cell lines. Finally, we analyzed the Hub genes mutation and evaluated its correlation with tumor immune infiltration. Results: DLD and LIAS were identified as the Hub genes of Cuproptosis, which have the same CCM score. Mutation analysis revealed that the expression of Hub genes is steady in pan-cancer, DLD and LIAS have been identified as tumor suppressor genes or oncogenes in different tumors. ROC curve analysis showed that DLD and LIAS had a very high predictive effect on survival and prognosis in LAML, and DLD also had a very high predictive effect in the other 4 tumor types. Through the expression of Hub gene in single cell lines, we found that DLD and LIAS displayed significant inhibitory and facilitative effects in different phenotypes of different tumors. Lastly, we explored the relationship between Hub genes and immune infiltration, and found that DLD was substantially associated with immune infiltration of 23 different tumors, and LIAS was significantly associated with immune infiltration of 31 different tumors. Conclusion: We preliminarily evaluated the effect of Cuproptosis on pan-cancer through the hub gene of Cuproptosis, suggesting that copper death has different effects in different tumors.

Bioinformatics

Cuproptosis

Hub genes

Pan-cancer

cell death

immune infiltration

Copper ions are essential trace elements involved in various fundamental biological processes, including energy metabolism and oxidative metabolism. They are one of the basic substances necessary for cell survival(1). The level of copper ions in the body is regulated by the cellular environment and metabolic levels. Too much or too few copper ions can cause serious damage to cell functions(2). Previous studies have shown that an imbalance in copper ion regulation can have critical effects on the development of the liver, nervous system, and can even lead to serious diseases such as Wilson's and Parkinson's disease(3). Additionally, it has long been observed that the level of copper ions in cancer patients is significantly different from that of healthy individuals(4, 5). Further research has identified that an imbalance in copper ions may induce cytotoxicity, which can affect tumor prognosis(6). Some researchers have also discovered that copper ions can enhance the killing effect of chemotherapy and radiotherapy on tumor cells during cancer treatment. Thus, copper ion compounds are used for anticancer therapy(7, 8). Recent studies have identified a direct binding between copper ions and the fatty acylated components of the tricarboxylic acid (TCA) cycle. This leads to fatty acylated protein aggregation, resulting in the subsequent loss of iron-sulfur cluster proteins, proteotoxic stress, and ultimately, an independent apoptotic pathway leading to cell death. This novel form of cell death is referred to as Cuproptosis, and it is a completely unknown mode of cell death(9), Ten genes related to Cuproptosis, including FDX1, LIPT1, DLD, LIAS, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A, were obtained from the aforementioned study. We then conducted bioinformatics analyses on these genes to obtain pan-cancer information on Cuproptosis, which provides useful insights to aid in oncology planning.

2.1.1. Ethics approval of research

This study is an analysis of existing data, The Ruikang hospital Research Ethics Committee has confirmed that no ethical approval is required.

2.2. Data acquisition

We obtained TCGA pan-cancer and genotype-tissue expression data from the University of California, Santa Cruz (UCSC) Xena browser (https://xena.ucsc.edu/) and (https://gtexportal.org/home/datasets) (GTEx). We then carefully screened the sample sources to obtain a high-quality TCGA prognostic data set, supplemented with TARGET follow-up data from UCSC’s Cancer Browser and excluded samples with short follow-up times of less than 30 days. We extracted gene expression data for ENSG00000091140 (DLD) and ENSG00000121897 (LIAS) in each sample and performed a log2(x + 0.001) transformation on each expression value. We also eliminated cancer types with less than 10 samples in a single cancer type, resulting in a final set of 44 cancer types (TCGA-GBM, TCGA-GBMLGG, TCGA-LGG, TCGA-UCEC, TARGET-LAML, TCGA-BRCA, TCGA-CESC, TCGA-LUAD, TCGA-ESCA, TCGA-STES, TCGA-SARC, TCGA-KIRP, TCGA-KIPAN, TCGA-COAD, TCGA-COADREAD, TCGA-PRAD, TCGA-STAD, TCGA-HNSC, TCGA-KIRC, TCGA-LUSC, TCGA-THYM, TCGA-LIHC, TARGET-WT, TCGA-SKCM-P, TCGA-SKCM, TCGA-BLCA, TCGA-SKCM-M, TCGA-THCA, TARGET-NB, TCGA-MESO, TCGA-READ, TCGA-OV, TCGA-UVM, TCGA-PAAD, TCGA-TGCT, TCGA-UCS, TCGA-LAML, TARGET-ALL, TCGA-PCPG, TCGA-ACC, TARGET-ALL-R, TCGA-DLBC, TCGA-KICH, TCGA-CHOL).We used the coxph function of the R software package survival (version 3.2-7) to establish a Cox proportional hazards regression model and analyze the relationship between gene expression and prognosis in each tumor (Overall survival). We also performed statistical tests using the Logrank test to obtain prognostic significance.

2.3. Cuproptosis-related gene enrichment analysis and Hub gene screening

To gain further insight into Cuprottosis-related genes, we uploaded the 10 gene lists mentioned above to the STRING database to create a protein-protein interaction (PPI) network. The PPI network was constructed using the STRING database and Cytoscape software (version 3.9.1) with a minimum required interaction score of 0.9. The PPI network graph was visualized using Cytoscape software, and we subsequently conducted KEGG/GO functional enrichment analysis of cuproptosis-related genes using the CLUEGO plugin. Finally, we utilized cytoHubba's plugin to score Cuprottosis-related genes with 12 different algorithms and identified the gene with the highest score as the hub gene of Cuprottosis-related genes.

2.4. Hub gene expression in single-cell lines

We utilized the Cancer Single-Cell State Atlas (CSEA) database to investigate the impact of Cupproptosis-related Hub genes on the functional states of tumor cells. The CSEA database extensively studies 14 functional states of diverse tumor cells at the single-cell level, including stemness, invasion, metastasis, proliferation, epithelial-mesenchymal transition (EMT), angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, and quiescence. We began by examining the expression of Hub genes in different tumor cell lines and then assessed expression differences of target genes in various samples. We then obtained data on the effect of the Hub gene on the functional status of tumor cells in different tumor single-cell lines, which we visualized using Prism 8.0 software.

2.5. Immuno-infiltration analysis of Hub genes

To further investigate the role of Cuproptosis-related Hub genes in cancer, we analyzed their impact on immune infiltration in pan-cancer. Specifically, we examined immunomodulators, immune infiltration levels, MSI (microsatellite instability), TMB (tumor mutational burden), neoantigens, and immune cell types. We used the cbioportal website to analyze the expression and genetic alterations of the HUB genes DLD and LIAS across various cancer types. We then explored the mutations of DLD and LIAS and investigated the potential effects of these mutations on the function of the Hub genes.

3.1. Cuproptosis-related gene enrichment analysis and Hub gene screening

We utilized the STRING database, a resource for known and predicted protein-protein interactions, to investigate the interactions among Cuproptosis-related genes. The resulting protein-protein interaction (PPI) network is presented in Fig. 1A. Next, we imported all the genes from the MEturquoise module into Cytoscape and performed pathway analysis using the ClueGO application. The enrichment analysis revealed that Cuproptosis-related genes primarily regulate programmed cell death through "pyruvate dehydrogenase complex" and "pyruvate dehydrogenase" (Fig. 1B, Fig. 1C), with pyruvate dehydrogenase being the key factor in Cuproptosis. We used the cytoHubba plug-in in Cytoscape to identify hub genes, resulting in a significant submodule (Fig. 1D) from which we selected the hub genes with the highest scores. DLD and LIAS genes were ultimately designated as the Hub genes of Cuproptosis-related genes, with MCC scores of 121 points in both cases. Finally, we examined the molecular association among the 10 genes associated with Cuproptosis (Fig. 1E).

3.2. The expression difference and survival analysis of Hub gene in pan-cancer

We examined the expression levels of Cuproptosis-related genes in both tumor and non-tumor tissues using data from the TCGA database. As shown in Fig. 2A, with the exception of DLAT, the expression levels of the other 9 genes were significantly different (P < 0.05) between tumor and non-tumor tissues. We then further analyzed the expression levels of the hub genes, DLD and LIAS, across different cancer types using TCGA samples (Fig. 2B). To investigate the expression differences between tumor and adjacent tissues in pan-cancer and to determine which cancers show significant expression differences, we used the R package to analyze the TCGA expression matrix (Fig. 2c). We found that there were no differences in LIAS expression between tumor and adjacent tissues in ACC, COAD, KIRC, KIRP, PCPG, while the other 25 tumor types showed significant differences in expression. In the case of DLD, there were no differences in expression between tumor and adjacent tissues in CESC, COAD, ESCA, HNSC, OV, UCEC, and UCS, while significant expression differences were observed in the other 23 tumor types (see Fig. 2C). We utilized the R software package to observe the expression of DLD in six types of tumors, namely TCGA-GBMLGG (N = 619, p = 3.6e-5, HR = 1.85 (1.39, 2.47)), TCGA-LGG (N = 474, p = 9.3e-4, HR = 2.14 (1.37, 3.32)), TARGET-LAML (N = 142, p = 1.3e-5, HR = 1.46 (1.23, 1.73)), TCGA-BRCA (N = 1044, p = 0.03, HR = 1.29 (1.02, 1.64)), TCGA-LUAD (N = 490, p = 0.03, HR = 1.30 (1.03, 1.64)), and TCGA-UVM (N = 74, p = 0.03, HR = 1.82 (1.07, 3.10)). We found that medium to high expression of DLD was associated with poor prognosis and showed significant carcinogenic effects. In addition, we observed that low expression of DLD in three tumor types, TCGA-KIPAN (N = 855, p = 1.0e-9, HR = 0.67 (0.59, 0.76)), TCGA-COADREAD (N = 368, p = 1.0e-2, HR = 0.66 (0.48, 0.90)), and TCGA-KIRC (N = 515, p = 7.2e-7, HR = 0.66 (0.56, 0.78)), was also associated with poor prognosis. Furthermore, we examined the expression of the target gene LIAS and found significant carcinogenic effects in three tumor types, TARGET-LAML (N = 142, p = 0.01, HR = 1.39 (1.08, 1.80)), TCGA-THCA (N = 501, p = 0.04, HR = 3.40 (1.09, 10.64)), and TCGA-KICH (N = 64, p = 0.04, HR = 4.63 (1.00, 21.42)). However, we observed lower expression of LIAS in five additional tumors that was associated with poorer prognosis, namely TCGA-GBMLGG (N = 619, p = 3.6e-4, HR = 0.56 (0.41, 0.77)), TCGA-KIRP (N = 276, p = 0.01, HR = 0.52 (0.31, 0.88)), TCGA-KIPAN (N = 855, p = 1.4e-7, HR = 0.56 (0.45, 0.70)), TCGA-KIRC (N = 515, p = 1.8e-6, HR = 0.56 (0.44, 0.71)), and TCGA-MESO (N = 84, p = 0.02, HR = 0.63 (0.43, 0.92)) (see Fig. 2D for visualization). To validate our findings, we obtained sequencing data of acute leukemia GSE37642 with survival data from the GEO database. We then analyzed the data using the R packages "GEOquery" and "survival". As shown in Fig. 2E and Fig. 2F, we found that the expression of HUB genes LIAS and DLD was higher than normal in LAML patients (P < 0.005). Moreover, the OS was lower in patients with high DLD expression than in those with low expression (p < 0.05857), which supports our conclusion. However, the difference in the expression of the HUB gene LIAS did not lead to an overall survival difference. This could be due to issues that occurred during data cleaning, and further validation analysis is needed.

3.3. Expression and functional analysis of Hub gene in single cell line

To gain a better understanding of the function of the Hub gene related to Cuproptosis, we conducted a search in the cancer single-cell state atlas (CSEA) database. We used "DLD" and "LIAS" as search terms, respectively, to retrieve information on their expression and function. Our search yielded results indicating that DLD and LIAS were detected in single-cell samples from various tumor types, including blood, the central nervous system, lung, skin, kidney, breast, head and neck, ovary, intestine, and eye, among others. Figure 3A shows the expression of DLD and LIAS in samples labeled EXP0052 in Braune EB. Stem Cell Reports. 2016 (PDX).(10). Figure 3B depicts the expression patterns of DLD and LIAS across various single-cell samples, and reveals that their expressions are not consistent in different samples. We then conducted a correlation analysis to determine the differential functions of Hub genes in single-cell lines across different tumors, as illustrated in Fig. 3C. Our findings suggest that DLD and LIAS exhibit different effects on the functions of different tumor cells. For instance, the expression of DLD inhibits apoptosis in AML and UM, but promotes apoptosis in RCC. On the other hand, the expression of LIAS inhibits apoptosis, cell cycle, DNA repair, EMT, Hypoxia, metastasis, proliferation, Quiescence, and Stemness.

3.4. Mutation of Hub gene

After retrieving data from the Cbioportal database, we discovered that the Hub genes DLD and LIAS have very low mutation frequencies in pan-cancer patients, both being less than 0.1%. This suggests that these genes are abnormally stable. Figure 4A shows the common mutation sites and mutation types of these Hub genes. Next, we utilized the GDC database and the Simple Nucleotide Variation dataset of level 4 of all TCGA samples processed by MuTect2 software to find that DLD and LIAS were significantly associated with specific cancers. In Fig. 4B, we can observe that DLD has the lowest mutation frequency of 0.2% in PRAD, all of which are deletion mutations, while the highest mutation in COAD is 2.5%, mainly deletion mutations and Frame shift Del. On the other hand, the mutation frequency of LIAS in CHOL is the highest at 2.9%, which is a deletion mutation; the mutation frequency in PRAD is the lowest at 0.2%, which is also a deletion mutation. In order to determine whether these mutations have an impact on patient survival, we further searched the Cbioportal database and discovered 73 patients with LIAS mutations and 206 patients with DLD mutations in the pan-cancer samples. Interestingly, we found that these mutations did not affect overall survival, with P values of 0.334 and 0.426, respectively, as shown in Fig. 4C.

3.5. Effect of Hub gene on immune infiltration

To investigate the immune roles of Cuproptosis-related Hub genes across various cancer types, pan-cancer analyses are crucial for identifying potential candidates for immunotherapy. Our findings demonstrated that DLD had a negative correlation with most immunomodulators in KIPAN, while it was positively correlated with most immunomodulators in OV. On the other hand, LIAS had a negative correlation with most immunomodulators in NB and THCA, but was positively correlated with most immunomodulators in PRAD (Fig. 5A). Additionally, we calculated the immune infiltration scores of 10,180 tumor samples across 44 tumor types and used the corr.test function in the R package to determine the Spearman's correlation coefficient between the genes and immune infiltration scores in each tumor. We determined the immune infiltration score that was significantly correlated with gene expression across 23 different cancer types. Of these, we observed four cancer types that were significantly positively correlated with immune infiltration, including TCGA-LIHC (N = 363, R = 0.11, P = 0.03), TCGA-PAAD (N = 177, R = 0.16, P = 0.04), TCGA-LAML (N = 214, R = 0.33, P = 7.1e-7), and TARGET-ALL-R (N = 99, R = 0.43, P = 9.6e-6) (Fig. 5B). We also found 19 significant negative correlations, including TCGA-UCEC (N = 178, R=-0.22, P = 3.7e-3), TARGET-LAML (N = 142, R=-0.23, P = 7.0e-3), TCGA-BRCA (N = 1077, R=-0.08, P = 6.9e-3), TCGA-CESC (N = 291, R=-0.14, P = 0.02), TCGA-LUAD (N = 500, R=-0.11, P = 0.01), TCGA-ESCA (N = 181, R=-0.20, P = 8.4e-3), TCGA-STES (N = 569, R=-0.22, P = 5.8e-8), TCGA-SARC (N = 258, R=-0.31, P = 2.7e-7), TCGA-KIPAN (N = 878, R=-0.41, P = 3.9e-36), TCGA-STAD (N = 388, R=-0.14, P = 5.7e-3), TCGA-KIRC (N = 528, R=-0.11, P = 0.01), TCGA-LUSC (N = 491, R=-0.28, P = 3.1e-10), TARGET-WT (N = 80, R=-0.32, P = 3.6e-3), TCGA-SKCM-P (N = 101, R=-0.26, P = 8.6e-3), TCGA-BLCA (N = 405, R=-0.18, P = 3.4e-4), TCGA-THCA (N = 503, R=-0.23, P = 1.2e-7), TARGET-NB (N = 153, R=-0.36, P = 4.3e-6), TCGA-TGCT (N = 132, R=-0.35, P = 3.1e-5), and TCGA-ACC (N = 77, R=-0.23, P = 0.04). In LIAS expression analysis, we found a significant correlation between gene expression and immune infiltration in 31 cancer types. Out of these, one showed significant positive correlation, namely TCGA-LAML (N = 214, R = 0.25, P = 2.1e-4) as shown in Fig. 5B. The remaining 30 cancer types showed significant negative correlations, including TCGA-GBMLGG, TCGA-LGG, TCGA-UCEC, TCGA-BRCA, TCGA-CESC, TCGA-LUAD, TCGA-ESCA, TCGA-STES, TCGA-SARC, TCGA-KIRP, TCGA-KIPAN, TCGA-COAD, TCGA-COADREAD, TCGA-STAD, TCGA-HNSC, TCGA-LUSC, TCGA-LIHC, TCGA-SKCM-P, TCGA-SKCM, TCGA-BLCA, TCGA-SKCM-M, TCGA-THCA, TARGET-NB, TCGA-READ, TCGA-UVM, TCGA-PAAD, TCGA-TGCT, TCGA-PCPG, TCGA-ACC, TCGA- KICH. Taken together, the hub genes related to cuproptosis are strongly correlated with immune infiltration in LAML, which may be a significant contributing factor to the observed carcinogenicity of these genes.

As shown in Fig. 5C, we calculated the Pearson correlation between the expression of Hub genes and MSI in each tumor. We observed that the expression of DLD was significantly correlated with MSI in 7 tumors, of which it was significantly positively correlated in 4 tumors: UCEC (P = 0.0001), STAD (P = 0.00000043), STAD (P = 0). Additionally, it was significantly negatively correlated in 3 tumors, such as DLBC (P = 0.028) and PRAD (P = 2.7e-8). Moreover, we found that the expression of LIAS was significantly correlated with MSI in 5 tumors, with significant positive correlations observed in 4 tumors: SARC (P = 0.016), COAD (P = 0.00074), SKCM (P = 0.021), and THCA (P = 0.0031). In addition, we observed a significant negative correlation in 1 tumor, COAD (P = 0.041). As shown in Fig. 5D, we calculated the Pearson correlation between Hub gene expression and Neoantigen in each tumor. We observed a significant positive correlation between DLD expression and Neoantigen in 3 tumors, including BRCA (P = 0.0035), READ (P = 0.00025), and STAD (P = 0.003). Additionally, LIAS expression was significantly positively correlated with Neoantigen in 5 tumors, including UCEC (P = 0.043), READ (P = 0.0014), PRAD (P = 0.024), LUAD (P = 0.0045), and STAD (P = 0.00004). We also observed an inverse correlation in 1 tumor, specifically BRCA (P = 2.6*e6). As depicted in Fig. 5E, we computed the Pearson correlation between the expression of Hub genes and TMB in each tumor. Our analysis revealed that DLD expression was significantly positively correlated with TMB in 5 tumors, including LUAD (P = 0.00043), UCEC (P = 0.00025), CESE (P = 0.0082), MESO (P = 0.005), and STAD (P = 0.00001). Similarly, we observed that the expression of LIAS was significantly positively correlated with TMB in 5 tumors, such as PRAD (P = 0.00071), UCEC (P = 0.00049), COAD (P = 0.00026), STAD (P = 9.1*e11), and READ (p = 0.00063). However, we observed negative correlations in 3 tumors, including LGG (P = 0.049), BLCA (P = 0.026), and BRCA (P = 0.024). As shown in Fig. 5F, we further investigated the association of Cuproptosis-related Hub genes with immune cells in various tumors to understand their impact on immune infiltration. Our analysis revealed a significant negative correlation between DLD expression and immune cell infiltration in TGCT, SARC, LUSC, and THCA. However, for DLBC, KICH, and UCS, there is little to no effect on immune cells. We also observed a significant negative correlation between LIAS expression and immune cell infiltration in THCA, KIRP, LUSC, ACC, and LUAD. However, in ESCA, CHOL, DLBC, KICH, and UCS, an immune desert phenomenon was observed.

In this study, we began by screening the 10 related genes of Cuproptosis and identifying the hub genes, followed by conducting bioinformatics analysis on them. Our initial findings revealed that DLD and LIAS had the same maximum CCM score among the 10 related genes of Cuproptosis. We then explored the expression of DLD and LIAS in pan-cancer and examined their impact on survival. As anticipated, we observed that DLD and LIAS exhibited varying effects on different types of tumors. Subsequently, we endeavored to uncover the underlying cause of this phenomenon. To this end, we analyzed the effect of Hub gene expression on the major phenotypes of tumor cells in single-cell lines, as well as their impact on immune infiltration in different tumors. Our results indicate that the expression of DLD and LIAS can have a significant effect on immune infiltration in multiple tumors, while some may also exhibit immune deserts.

Our research has several notable strengths. Firstly, we utilized Sytoscape for the first time to screen Cuproptosis-related genes, which allowed us to identify their main functional genes. Furthermore, we analyzed the expression of hub genes in different tumors and their correlation with prognosis. In this process, we conducted enrichment analysis on the functions of Cuproptosis-related genes and discovered that pyruvate dehydrogenase and its complexes played a crucial role in Cuproptosis. Pyruvate dehydrogenase and its complexes are essential mediators in the process of glycolysis and the tricarboxylic acid cycle, and are fundamental components of biological cell energy metabolism. The current research on pyruvate dehydrogenase and its complexes in cancer is not very extensive, making our study an important contribution to this field.(11, 12). Currently, there is no existing research on the action pathway of Cuproptosis, and our findings could serve as a valuable reminder for future research. In addition, our study revealed that the Cuproptosis Hub Genes have extremely high diagnostic predictive value in a small number of cancers, which has not been previously reported. Notably, both Hub genes, DLD and LIAS, displayed high diagnostic value in LAML, where neither of the two genes have been reported before. Moreover, we found that the Cuproptosis Hub Genes were significantly associated with LAML immune infiltration and exhibited a significant oncogenic role. The mechanism of LAML Cuproptosis has not been previously reported, which underscores the importance of our findings as a foundation for future studies. Moving forward, we investigated the expression of Hub genes and their impact on different tumor phenotypes in single cell lines. Our results demonstrate that Hub genes have a profound effect on various phenotypes in different tumors, which could provide useful guidance for future research. Therefore, our study serves as a crucial screening tool for understanding the mechanism of Hub genes in tumors.

To complete our investigation, we conducted an immunological analysis of the Cuproptosis Hub genes, which illuminated the correlation between Cuproptosis and tumor immunophenotype. Currently, only a limited number of studies have reported on the association between Cuproptosis and tumor immune infiltration.(13–15), Furthermore, to date, no comprehensive study has been conducted on this subject. Our research revealed that the Cuproptosis-related Hub genes exhibit significant immune hotspots and immune deserts in pan-cancer, which indicates that Cuproptosis may play a unique role in tumorigenesis. Currently, research on the relationship between Cuproptosis and tumor immunity is limited, and we hope that our study on Cuproptosis provides a novel perspective for future investigations.

Our study also has some limitations that should be acknowledged. Firstly, we conducted our analysis based on publicly available databases, and our findings need to be verified in tissue samples. Secondly, while we observed the effects of the Hub genes on tumor behavior and function, we did not explore the underlying mechanisms by which these effects occur.

In conclusion, this study provides important insights into the role of Cuproptosis-related Hub genes in pan-cancer. Our findings suggest that DLD and LIAS have significant roles and predictive power in pan-cancer, and are closely related to tumor immunity. Our study provides a foundation for further investigation into the mechanisms by which Cuproptosis affects tumor survival.

Acknowledgments (All sources of funding of the study must be disclosed)

This study was supported by grants from the Guangxi Science Foundation of China, Grant/Award Number: GZZC2020087.

Conflict of interest

The authors declare there is no conflict of interest.

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Identifying Cuproptosis-Related Hub Genes and Their Clinical Significance in Tumors

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1.1. Ethics approval of research

2.2. Data acquisition

2.3. Cuproptosis-related gene enrichment analysis and Hub gene screening

2.4. Hub gene expression in single-cell lines

2.5. Immuno-infiltration analysis of Hub genes

3. Results

3.1. Cuproptosis-related gene enrichment analysis and Hub gene screening

3.2. The expression difference and survival analysis of Hub gene in pan-cancer

3.3. Expression and functional analysis of Hub gene in single cell line

3.4. Mutation of Hub gene

3.5. Effect of Hub gene on immune infiltration

4. Discussion

5. Conclusions

Declarations

References

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