Pan-cancer profiles of the cuproptosis gene set

Abstract

A recent study has revealed a novel cell death pathway, called “cuproptosis”, a programmed cell death based on copper. A total of 12 genes were involved in the cuproptosis pathway, including 7 pro-cuproptosis genes (FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, and PDHB) genes, 3 anti-cuproptosis genes (MTF1, GLS, and CDKN2A), and 2 key copper transporters ATP7B and SLC31A1. The insight into these cuproptosis genes in cancer is necessary to understand cuproptosis-related tumorigenesis and to develop the cuproptosis pathway as a potential therapeutic target for clinical cancer treatment. By mining multi-omic profiling data, we performed a comprehensive and systematic characterization of the cuproptosis of these 12 genes across more than 9000 samples of 33 types of cancer. This letter not only revealed diverse mechanisms of the gene expression regulations of the cuproptosis gene set in cancer but also analyzed the potential associations between cuproptosis and other common cancer pathways, providing an overall picture of cuproptosis in cancer for future reference. This study comprehensively clarified the genomic pan-cancer profiles of the cuproptosis gene set regarding the SNV, CNV, methylation, mRNA expression, pathway cross-talk, and miRNA regulations across 33 solid tumors. Our findings revealed that genomic alterations and miRNA-mRNA network-mediated ectopic expression of cuproptosis genes were involved in the activation of other cancer-related pathways and also identified KIRC as a potential cancer type that might be affected by cuproptosis. We think, as the rase of the cuproptosis cancer research, these in-time profiles will provide a genetic overview and useful information for future studies on the cuproptosis in cancers.

Introduction

A recent paper [1] has revealed a novel cell death pathway, called “cuproptosis”, a programmed cell death based on copper, that is different from known cell death pathways such as apoptosis, necrosis, oncosis, pyroptosis, autophagy, and ferroptosis. Copper served as a co-factor for a number of key enzymes across almost all organisms [2], but the homeostatic level of intracellular copper is strictly regulated, and the accumulation of intracellular free copper can result in the death of cells [3,4]. The intracellular concentration of copper has been found to affect many types of cancers, such as breast cancer [5], head and neck cancer [6], and endometrial cancer [7], however, only until the recent publication did the pathway of cuproptosis sorted out with a set of cuproptosis genes identified. A total of 10 genes were involved in the cuproptosis pathway, including 7 pro-cuproptosis genes (FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, and PDHB) genes and 3 anti-cuproptosis genes (MTF1, GLS, and CDKN2A) [1]. In addition, two key copper transporters are key cuproptosis affecting genes, including copper exporter ATP7B [8] and copper importer SLC31A1 [9]. The insight into these cuproptosis genes in cancer is necessary to understand cuproptosis-related tumorigenesis and to develop the cuproptosis pathway as a potential therapeutic target for clinical cancer treatment. By mining multi-omic profiling data, we performed a comprehensive and systematic characterization of the cuproptosis of these 12 genes across more than 9000 samples of 33 types of cancer. This letter not only revealed diverse mechanisms of the gene expression regulations of the cuproptosis gene set in cancer but also analyzed the potential associations between cuproptosis and other common cancer pathways, providing an overall picture of cuproptosis in cancer for future reference. The detailed methods of the analyses were provided in the Supplementary Materials. We think, as the rase of the cuproptosis cancer research, these in-time profiles will provide a genetic overview and useful information for future studies on the cuproptosis in cancers.

Results and discussions

Pan-cancer single nucleotide variation profiles of cuproptosis gene set

The single nucleotide variation (SNV) analysis revealed that CDKN2A was the most frequently mutated cuproptosis gene across cancer types, especially in HNSC and PAAD, where the mutation rate was over 20%. ATP7B was the second most frequently mutated cuproptosis gene in cancer. Generally, UCEC had a relatively higher mutation rate in all cuproptosis genes than other cancer types (Figure 1A). The SNV landscape showed that most of the SNVs were missense-mutation, followed by a small proportion of other mutation types such as nonsense-mutation, except for the most frequently mutated gene, CDKN2A, which had a relatively high proportion of nonsense-mutation (Figure 1B). The profile of the survival association of cuproptosis gene SNVs revealed that the cancer types that were most likely to associate with cuproptosis genes SNV were BRCA and COAD. However, only a few genes were significant, and, for most of the other cancer types, no significance was found (Figure 1D). Based on the SNV analysis, although CDKN2A had a high SNV frequency, we suggested that the SNVs in cuproptosis genes might not be critical for cancers.

Figure 1.

Pan-cancer genetic profiles of cuproptosis gene set. A. Heatmap of the mutation frequency. Numbers represent the number of samples that have the corresponding mutated gene for a given cancer. “0” indicates that there was no mutation in the gene coding region, blank indicates there was no mutation in any region of the gene, and color represents the mutation frequency. B. The SNV landscaped plot of cuproptosis genes in cancers. C. Survival difference between mutant and wild-type cuproptosis genes. Copy number variation (CNV) and methylation analysis of cuproptosis genes in cancers. D. CNV distribution pie chart across cancers. Hete Amp = heterozygous amplification; Hete Del = heterozygous deletion; Homo Amp = homozygous amplification; Homo Del = homozygous deletion; None = no CNV. E, F. Heterozygous and homozygous CNV profile showing the percentage of amplification and deletion of heterozygous and homozygous CNVs for each gene in each cancer. Only genes with >5% CNV in a given cancer are shown as a point in the figure. G. Survival difference between CNV groups. H. The correlation of CNV and mRNA expression. I. Methylation differences between tumor and normal tissues. J. The correlation of methylation and mRNA expression. K. Survival difference between samples with high and low methylation of cuproptosis genes (only significant cancer types were plotted).

Pan-cancer copy number variation profiles of cuproptosis gene set

The copy number variation (CNV) profile showed that the CNV of the cuproptosis gene set across different cancer types had a variety of patterns. The pan-cancer pie chart and CNV plots of the cuproptosis gene set showed that the major CNV types were heterozygous CNV, with only very few cases of homozygous CNV (Figure 1D-F). The profile of the survival association of cuproptosis gene CNVs revealed that the cancer types that were most likely to associate with cuproptosis genes CNV were UCEC, KIRP, and ACC. However, for the other cancer types, there were only a few significances in part of the cuproptosis gene set (Figure 1G). The correlation profile of CNV and expression demonstrated that, for most cancer types, CNV was positively correlated with expressions of most of the cuproptosis genes (Figure 1H). Based on the CNV analysis, we suggested that the heterozygous CNV might potentially associate with cancers, especially in UCEC, KIRP, and ACC where cuproptosis genes CNV correlated with the survival of patients.

Pan-cancer methylation profiles of cuproptosis gene set

The profile of the methylation differences between tumor and normal tissues revealed that a few cancer types had a difference in methylation of some cuproptosis genes between tumor and normal tissues but there were no cancer-noncancer difference patterns shared across different cancer types (Figure 1I). The correlation profile of methylation and mRNA expression revealed that a few cancer types had significant correlations between methylation and mRNA expression in some cuproptosis genes and most of these correlations were negative correlations (Figure 1J). The survival correlation analysis of methylation showed that the methylation levels were not correlated with the survival of cancer in most cancer types (Figure 1K). Based on the CNV analysis, we suggested that hypermethylation was one of the potential mechanisms of down-regulation of some cuproptosis genes, but, overall, the methylation level of cuproptosis gene set was not closely associated with cancers.

Pan-cancer gene expression profiles of cuproptosis gene set

We analyzed the expression difference of cuproptosis genes between cancer and non-cancer tissues from TCGA. Results showed that KIRC had the most significantly different genes. DLD, PDHA1, GLS, DLAT, SLC31A1, PDHB, and FDX1 were down-regulated in KIRC, while CDKN2A and ATP7B were up-regulated in KIRC. CDKN2A was the gene that was significantly different between normal and cancer in most of the cancer types across all cuproptosis genes. Yet, for most cancer types, there were only a few significant differences in the expression of these genes between cancer and their non-cancer tissues (Figure 2A). We also analyzed the expression of these genes among cancer subtypes, results revealed that BRCA, KIRC, STAD, and LUAD were the top four cancer types that showed significance (Figure 2B). Survival analysis showed that KIRP was the cancer type whose survival was the most associated with cuproptosis genes. The high expressions of five pro-cuproptosis genes (LIAS, SLC31A1, DLD, FDX1, and DLAT) and two anti-cuproptosis genes (MTF1 and ATP7B) were associated with a low survival risk in KIRP, while the expression of anti-cuproptosis gene CDKN2A was associated with a high survival risk in KIRP. In addition, LGG was another cancer type whose survival might associate with cuproptosis, yet, for the other cancer types, there were only a few significances in survival associations (Figure 2C). These expression analyses identified KIRC as a potential cancer type that might be affected by cuproptosis.

Figure 2.

Pan-cancer expression and cross-talk profiles of cuproptosis gene set. A. Expression difference between cancer and non-cancer tissues from TCGA. B. Expression differences between subtypes of cancers. C. Survival difference between high and low expression groups. D. Pie chart of the percentage of the effect of cuproptosis gene cross-talk with other cancer pathway activity. E. Heat map of the percentage of the effect of cuproptosis genes on other cancer pathway activity. F. Pathway regulation network of cuproptosis genes. A line represents a connection between genes and different pathways, where a solid line represents activation, and a dashed line represents inhibition. The color of the line represents different cancer types. G. The microRNA (miRNA) network of cuproptosis genes. A miRNA and one regulator connection node represent miRNA regulation of a gene. Node size is positively correlated with the node’s degree, and edge width is defined by the absolute value of the correlation coefficient.

Pan-cancer pathway cross-talk profiles of cuproptosis gene set

To explore the potential cross-talk of cuproptosis and other cancer-related pathways, we analyzed the reverse-phase protein array (RPPA) data from the TCPA database to calculate scores for 7876 cancer samples and provided pan-cancer pathway cross-talk profiles of the cuproptosis gene set. As shown in the pie chart, the cuproptosis genes were also potentially involved in the activations of other cancer pathways, but most of these involvements were not remarkable except for a few genes in some pathways, such as PDHA1 in the cell cycle, MTF1 in RTK, ATP7B in RTK, and CDKN2A in the cell cycle (Figure 2D). Detailed percentage numbers of the activation and inactivation of these pathways were presented in Figure 2D. A pan-cancer pathway regulation network of cuproptosis genes was also constructed based on these results (Figure 2F). These pathway cross-talk profiles provided information on the potential connections between cuproptosis and other cancer-related pathways.

Pan-cancer pathway cross-talk profiles of cuproptosis gene set

This study also analyzed the miRNA expression data from TCGA and constructed a miRNA-regulation network of cuproptosis genes. Eleven of the twelve genes of the cuproptosis gene set were found in the databases with regulating miRNA. As shown in Figure 2G, multiple miRNAs might be involved in the regulation of the expression of the cuproptosis genes, including several co-regulators for some of the cuproptosis genes. ATP7B was regulated by 11 miRNA, where the top regulating miRNA were has-miR-185-5p and has-miR-98-5p. The has-miR-98-5p was also regulating PDHB and the has-miR-576-5p was regulating both ATP7B and LIPT1. DLAT was regulated by 15 miRNA, where the top regulating miRNA were has-miR-365a-3p, has-miR-664a-3p, and has-miR-1271-5p. has-miR-3133 was regulating both DLAT and PDHB and the has-miR-452-5p was regulating both DLAT and LIAS. The LIAS was only regulated by has-miR-452-5p and has-miR-1976. DLD was regulated by 14 miRNA, whose top regulated miRNA was has-miR-125b-5p, a miRNA that also regulated MTF1 and CDKN2A. Similar to has-miR-125b-5p, has-miR-876-5p and has-miR-125a-5p regulated both DLD and MTF1. CDKN2A was also regulated by multiple other miRNAs. FDX1 was regulated by 10 miRNA and the top regulating one was has-miR-21-5p, which was also regulating SLC31A1. SLC31A1 was regulated by 7 miRNA, especially by has-miR-708-5p and has-let-7i-5p. GAS has the most regulating miRNA. It was regulated by 27 miRNA, where the top correlated miRNAs were has-miR-9-5p, a miRNA also regulated CDKN2 and FDX1, and has-miR-141-3p (Figure 2G). The network provided an overview of the potential cuproptosis-regulating miRNAs for future reference.

Conclusions

This study comprehensively clarified the genomic pan-cancer profiles of the cuproptosis gene set regarding the SNV, CNV, methylation, mRNA expression, pathway cross-talk, and miRNA regulations across 33 solid tumors. Our findings revealed that genomic alterations and miRNA-mRNA network-mediated ectopic expression of cuproptosis genes were involved in the activation of other cancer-related pathways and also identified KIRC as a potential cancer type that might be affected by cuproptosis. The pan-cancer analysis of cuproptosis genes may provide additional insight into novel clinical therapy targeting cuproptosis.

Disclosure of conflict of interest

None.