Expression of cuproptosis-related genes in KIRC
It is reported that FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A are recognized as cuproptosis-related genes . We first analyzed the expression of these 10 genes in 530 KIRC and 72 corresponding paracancer samples from TCGA database, and the results showed that the expression of FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1 and GLS in KIRC was significantly lower than that in adjacent tissues. Only CDKN2A expression was significantly higher in KIRC than in adjacent tissues（figure 1A）.Spearman correlation analysis showed that CDKN2A was negatively correlated with FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1 and GLS in KIRC in TCGA database, while FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1 and GLS genes were positively correlated (figure 1B). Similarly, we used THE GSE53757 dataset in GEO database to analyze the expression of cuproptosis-related genes in KIRC and adjacent to corresponding cancers. The results showed that the other results were consistent with TCGA database except MTF1 expression in KIRC and adjacent to corresponding cancers(figure 1C). Subsequently, we also analyzed the association between cuproptosis-related genes in the GSE53757 dataset (figure 1D).
Correlation analysis of cuproptosis-related genes in KIRC and clinicopathology
To investigate the association between the expression of cuproptosis-related genes and clinicopathological features of KIRC, we assessed the expression of cuproptosis-related genes in patients with stage I, II, III, and IV KIRC. Results showed that FDX1, LIAS, DLD, DLAT, PDHB, MTF1, CDKN2A expression was significantly different in patients with stage I, II, III and IV KIRC, while LIPT1, PDHA1 and GLS expression were not significantly difference (figure 2A-J). We also demonstrated the high-low expression distribution trend of cuproptosis-related genes on pT stage, pN stage, pM stage and patient survival in KIRC samples (figure 2K-T).
Enrichment analysis of cuproptosis-related genes
To elucidate the function of cuproptosis-related genes in tumors, we analyzed these genes using KEGG and GO databases. KEGG analysis showed that the 10 cuproptosis-related genes were mainly related to the Citrate cycle (TCA cycle)，Pyruvate metabolism，Glycolysis / Gluconeogenesis，Carbon metabolism，Central carbon metabolism in cancer，Glucagon signaling pathway，HIF−1 signaling pathway (figure 3A). GO database analysis showed that copper death related genes participated in sulfur compound biosynthetic process, Coenzyme biosynthetic process, cofactor biosynthetic process, etc (figure 3B-D).
Prognostic analysis of cuproptosis-related genes in KIRC
In order to investigate the correlation between the expression of cuproptosis-related genes and the prognosis of KIRC, seven cuproptosis-related genes with prognostic value were screened out by univariate Cox regression analysis，Kaplan-meier survival curve results showed that high CDKN2A expression was significantly associated with poor prognosis in patients with KIRC，and low DLAT,DLD,FDX1,LIAS,MTF1,and PDHB expression was significantly associated with poor prognosis in patients with KIRC(figure 4A-G). There were no significant differences in the other three genes.
Construction of a cuproptosis-related prognostic gene model
The LASSO Cox regression model was used to select the most predictive genes as prognostic indicators. λ was selected when the median of the sum of squared residuals was the smallest. Five potential predictors (figure 5A-B). MTF1，LIAS，FDX1，DLAT，CDKN2A were identified as prognostic factors for KIRC. The risk score ==(0.1403)*CDKN2A+(-0.1883)*DLAT+(-0.3737)*FDX1+(-0.1023)*LIAS+(-0.1865)*MTF1. Patients with KIRC were divided into two groups based on risk score. The distribution of risk score, survival status and expression of these 5 genes are shown in Figure 5C. Kaplan-meier curves showed that patients with high risk KIRC had a lower overall survival rate than patients with low risk KIRC (median time=5.4 years，p=1.46e-05)(figure 5D)，AUC in the 1-year, 3-year and 5-year ROC curves were 0.702, 0.669 and 0.663, respectively (figure 5E). These results suggest that copper death-related genes can be used as biomarkers for the prognosis of KIRC.
Building a predictive nomogram
Considering the clinicopathologic features and these five prognostic cuproptosis-related genes, we also built a predictive nomogram to predict the survival probability. Univariate and multivariate analyses showed that FDX1 expression, age and pM stage were independent factors affecting the prognosis of KIRC patients (figure 6A-B). The predictive nomogram suggested that the 3‐year and 5‐year overall survival rates could be predicted relatively well compared with an ideal model in the entire cohor (figure 6C-D).
Correlation analysis of cuproptosis-related genes prognosis model and tumor immune infiltration in KIRC
Cuproptosis plays an important role in the development of tumor immune microenvironment. In this study, we also used TIMER and MCPCOUNTER methods to analyze the correlation between cuproptosis-related genes prognosis model and various immune cells，The results of TIMER method showed that B cell and Macrophage was negatively correlated with cuproptosis-related genes prognosis model(figure 7A) .cuproptosis-related genes prognosis model was positively correlated with T cell CD8+，cytotoxicity score, negatively correlated with Monocyte，Macrophage/Monocyte，Myeloid dendritic cell，Neutrophil，Endothelial cell by MCPCOUNTER method (figure 7B).
Cuproptosis prognosis related genes and immunoassay site related genes expression differences and correlation analysis with TMB,MSI
Immune checkpoint molecules are inhibitory regulatory molecules in the immune system, which are essential for maintaining tolerance, preventing autoimmune reactions, and minimizing tissue damage by controlling the timing and intensity of immune responses. Therefore, we analyzed the expression differences of immunoassay site-related genes in the high and low expression groups of cuproptosis prognosis related genes MTF1, LIAS, FDX1, DLAT and CDKN2A，The results showed that LAG3, PDCD1, CD274 and PDCD1LG2 were significantly different in the high and low expression groups of five cuproptosis prognosis genes(figure 8A-E). TMB and MSI are two emerging biomarkers related to immunotherapy response. We also evaluated the correlations of MTF1, LIAS, FDX1, DLAT, and CDKN2A with TMB and MSI，The results showed that FDX1 and CDKN2A were positively correlated with MSI(p＜0.05)(figure 8F）. FDX1 and DLAT were negatively correlated with TMB, while CDKN2A was positively correlated with TMB (p＜0.05)(figure 8G).