Cuproptosis differential genes involved in ccRCC collection and functional enrichment analysis
We downloaded GSE53757, a microarray expression profiling dataset, from the GEO database and obtained 8567 differential genes at |log2 (fold-change) | > 0.5 and P < 0.05 (Fig. 1A and C). Also, cuproptosis-related genes were gained including 10 genes from a previous study[15] and intersected them with GSE53757 to identify cuproptosis differential genes in ccRCC. The results indicate that six down-regulated genes and only one up-regulated genes (Fig. 1C and Table S1). The Top 20 up- and down-regulated genes from ccRCC patients were exhibited in the heatmap (Fig. 1B). As we identified, functional and pathway enrichment analysis of seven cuproptosis differential genes was carried out using the online tool Metascape. The results of functional analysis revealed that acetyl-CoA biosynthetic process, acetyl-CoA metabolism process, acyl-CoA biosynthetic process, mitochondrial matrix, S-acyltransferase activity and 2 iron, 2 sulfur cluster binding were dramatically triggered in the gene sets (Fig. 1D). The activation of the citrate cycle (TCA cycle) signaling pathway, pyruvate metabolism, glycolysis/gluconeogenesis and carbon metabolism were all regulated by PDHB, PDHA1, DLAT and DLD; among them, PDHB and PDHA1 were involved in the glucagon signaling pathway, HIF-1 signaling pathway and diabetic cardiomyopathy (Fig. 1E).
Validation And Mapping Of Cuproptosis Differential Gene Expression
We further analyzed the mRNA expression of CDGs in ccRCC through the TCGA database (Unmatched renal clear cell carcinoma consisted of 72 paracancer tissues and 539 tumor samples). The results demonstrated that FDX1, DLD, DLAT, PDHA1, GLS and PDHB exhibited significantly lower expression in ccRCC tissues than normal tissues. Interestingly, only CDKN2A showed higher expression in ccRCC tissues than normal tissues (Fig. 2A). In addition, we found that the expressions of FDX1, DLD, DLAT, PDHA1, GLS and PDHB were significantly down-regulated in pathologic state, whereas CDKN2A was up-regulated as compared to comparable normal tissues (Fig. 2B). The results from the HPA database showed that protein expression levels of CDGs had also been consistent with mRNA expression in ccRCC (Fig. 2C). Surprisingly, we observed that all CDGs were expressed in mitochondria in human cells, except for CDKN2A, which was expressed in the nucleus (Fig. 2D). Based on the above results, we validated the expression of FDX1 and DLAT in 10 matched normal and ccRCC clinical samples, and found that their expressions were poorly expressed in ccRCC tissues compared to adjacent tissues (Fig. 2E). The results illustrated that cuproptosis differential genes are abnormally expressed in ccRCC patients in comparison to those of normal tissues, which may be predictive of poor prognosis and is related to disease stage progression in ccRCC patients.
Effects Of Cuproptosis Differential Genes On Ccrcc Patient Prognosis
Next, we evaluated the correlation between the expression of cuproptosis differential genes and the prognosis in ccRCC patients. The results indicated that ccRCC patients with low expression of FDX1, DLD, DLAT, PDHA1, GLS and PDHB had poor survival probability (HR < 1, P < 0.05), but not CDKN2A (Fig. 3A-G). Furthermore, the evaluation of univariate cox regression model reveals that low expression of FDX1 (HR 1.971; 95% CI 1.444–2.692, P < 0.001), DLAT (HR 2.422; 95% CI 1.758–3.337, P < 0.001), PDHA1 (HR 1.605; 95% CI 1.183–2.178, P = 0.002), PDHB (HR 1.608; 95% CI 1.183–2.185, P = 0.002) and GLS (HR 1.408; 95% CI 1.041–1.905, P = 0.027) were poor predictors for OS in ccRCC patients (Fig. 3I). Conversely, low CDKN2A expression (HR 0.734; 95% CI 0.544–0.992, P = 0.044) predicted a favorable prognosis in patients suffering from ccRCC. The multivariate cox results suggested that only FDX (HR 1.479; 95% CI 1.023–2.140, P = 0.038) and DLAT (HR 2.001; 95% CI 1.373–2.917, P < 0.001) were identified as independent prognostic factors affecting OS in patients with ccRCC (Fig. 3J). For the diagnostic value of cuproptosis differential genes in ccRCC patients, the AUCs of FDX1, DLD, DLAT, PDHA1, GLS, PDHB and CDKN2A were 0.965, 0.909, 0.813, 0.939, 0.956, 0.849 and 0.991, respectively (Fig. 3H). These findings suggest that ccRCC patients with strong (CDKN2A) or weak (FDX1, DLAT, etc.) CDGs expressions possess high diagnostic accuracy. Overall, FDX and DLAT act as independent prognostic factors and have a highly accurate diagnostic value for ccRCC patients. Thus, they may have important implications for the treatment of ccRCC patients. The correlation between FDX1 and DLAT in ccRCC was investigated by the “corrplot” R package. Notably, the results found that FDX1 and DLAT exhibited a strong positive correlation (r = 0.621, P < 0.01, Fig. 3K).
Co-expression Network Selection And Gene Functional Enrichment Analysis Of Fdx1 And Dlat Genes
We further identified differentially expressed genes related to FDX1 and DLAT in ccRCC using the LinkedOmics database. The differentially expressed genes associated with FDX1 and DLAT were found under the Pearson test (Fig. 4A, D), and their top 50 positively (r > 0) and negatively (r < 0) correlated genes are shown in the heatmap (Fig. 4B, C, E, F). The positive and negative correlation genes of FDX1 and DLAT were selected based on the following criteria: |r| > 0.5, P < 0.05. Eventually, 101 genes positively and 83 genes negatively correlated with FDX1 and 625 genes positively and 642 genes negatively correlated with DLAT were selected, respectively. Among them, a total of 43 positively and 30 negatively associated genes with FDX1 and DLAT were identified for further analysis (Fig. 4G, H). These genes were used for GO and KEGG enrichment analysis. For genes positively associated with FDX1 and DLAT, the functional analysis demonstrated that acetyl-CoA biosynthetic process, respiratory electron transport chain, lipid oxidation, mitochondrial respiratory chain, active ion transmembrane transporter activity and acetyl-CoA metabolic process were severely affected. The KEGG results indicated that these genes were primarily involved in TCA cycle, oxidative phosphorylation and secretion, and carbon metabolism (Fig. 4I). For genes negatively correlated with FDX1 and DLAT, the functional analysis indicated that the regulation of signal transduction by p53 class mediator, mitotic cytokinetic process, host intracellular domains, iron-sulfur cluster binding, and metal cluster binding were impacted on the biological processes, molecular functions, and cellular component terms. The KEGG results revealed that these genes were mainly involved in the process of endocytosis, base excision repair and ether lipid metabolism (Fig. 4J).
Genomic Alterations And Methylation Analysis
The mutation frequencies of FDX1 and DLAT in ccRCC patients were explored through the cBioPortal database. The dataset including 392 patients (TCGA-ccRCC, Nature 2013, RNA Seq V2 RSEM) was selected for analysis. The somatic mutation frequency of DLAT in ccRCC was 0.3%, consisting mainly of missense mutations (Figure S1B), which were comparatively rare, with only 1 in 392 samples. Surprisingly, FDX1 did not exhibit any somatic mutations (Figure S1A). Therefore, FDX1 and DLAT mutations were not found to affect the overall survival of patients (Figure S1C, D). Furthermore, we found that the total FDX1 and DLAT methylation levels were reduced in ccRCC tissues compared with normal tissues (Fig. 5C, D), and their methylation status was both dramatically correlated with OS and CpG sites in ccRCC patients (Fig. 5A, B). Thus, the methylation sites of FDX1 and DLAT genes were examined, along with the prognostic value of each CpG, based on the TCGA database. The data showed that cg02239377, cg06674932 and cg26061355 of FDX1 and cg08065721 of DLAT were the most methylated sites (Fig. 5E, F). Nevertheless, the methylation sites of the FDX1 gene included cg05485370 (HR:0.518, 95%CI: 0.311–0.863, P = 0.012), cg13258606 (HR:0.42, 95%CI: 0.245–0.718, P = 0.0015), cg23587050 (HR:0.411, 95%CI: 0.269–0.639, P < 0.001) and cg26061355 (HR:0.584, 95%CI: 0.351–0.974, P = 0.039), suggesting a good prognosis for patients suffering from ccRCC. In contrast, patients with cg05741490 (HR:2.054, 95%CI: 1.198–3.548, P = 0.0099), cg06674932 (HR:1.996, 95%CI: 1.172-3.4, P = 0.011), cg09762563 (HR:1.557, 95%CI: 1.061–2.286, P = 0.024) and cg26763524 (HR:1.655, 95%CI: 1.007–2.721, P = 0.047) conferred a poor prognosis (Fig. 5G). For DLAT gene, cg00327185 (HR:0.555, 95%CI: 0.34–0.905, P = 0.018) and cg13372927 (HR:0.498, 95%CI: 0.328–0.756, P = 0.0011) revealed a good prognosis in ccRCC patients, but cg10616121 (HR:2.442, 95%CI: 1.647–3.623, P < 0.001) and cg27191019 (HR:3.243, 95%CI: 1.774–5.929, P < 0.001) were associated with adverse patient outcomes (Fig. 5H).
Correlation Analysis Of Fdx1 And Dlat Expression With Immune Infiltration Level In Ccrcc
To understand the relationship between FDX1 and DLAT in cellular immunity, the potential correlation of their expression with 24 types of immune cells was analyzed by ssGSEA from the R package with a Spearman test. The findings showed that FDX1 expression was significantly related to neutrophils, mast cells, eosinophils, TReg, aDC and cytotoxic cells (Fig. 6A). However, DLAT was significantly associated with eosinophils, neutrophils, T helper cells, cytotoxic cells, B cells, NK CD56bright cells and TReg (Fig. 6B). Further studies demonstrated that FDX1 expression was positively correlated with infiltration levels of eosinophils and mast cells (r = 0.168, P < 0.001), neutrophils (r = 0.172, P < 0.001), but negatively correlated with cytotoxic cells (r = -0.239, P < 0.001) and TReg (r = -0.314, P < 0.001), while there was no significant correlation with B cells (r = -0.073, P < 0.090). DLAT expression was positively correlated with infiltration levels of eosinophils (r = 0.310, P < 0.001), neutrophils (r = 0.224, P < 0.001), T helper cells (r = 0.205, P < 0.001), but negatively correlated with CD8 T cells (r = -0.266, P < 0.001), cytotoxic cells (r = -0.393, P < 0.001) and B cells (r = -0.135, P < 0.002) (Figure S2). Subsequently, the expression of FDX1 and DLAT were classified into high and low groups according to their expression levels. Significant differences were found in the levels of infiltrating immune cells, such as neutrophils, mast cells, eosinophils, TReg, aDC and cytotoxic cells (P < 0.05), while FDX1 was not significant different in B cells. But for DLAT, there were obvious differences in the levels of infiltrating immune cells, including eosinophils, neutrophils, T helper cells, cytotoxic cells, B cells, NK CD56bright cells and TReg (P < 0.05) (Fig. 6C). Finally, we found that high levels of mast cells and Treg were remarkably associated with survival in ccRCC patients with low FDX1 expression (Fig. 6E, G), and high levels of eosinophils, neutrophils and NK cells were significantly correlated with survival in patients with high DLAT expression.
Correlation Analysis Of Fdx1 And Dlat Expression With Immune Checkpoints
The correlation of FDX1 and DLAT genes with immunosuppressant checkpoints in pan-cancer were shown in Fig. 7A and B. Specially, FDX1 and DLAT genes were closely associated with EDNRB, CD274, HAVCR2, IL10, TGFB1, LAG3, PDCD1, IL4 and VTCN1 in ccRCC patients. Further studies revealed that FDX1 was positively correlated with CD274 (r = 0.309, P < 0.001) and EDNRB (r = 0.421, P < 0.001) (Figure S3A, B) according to the following criteria: |r| > 0.3 and P < 0.05. DLAT was also related to CD274 (r = 0.415, P < 0.001) and EDNRB (r = 0.418, P < 0.001) (Figure S3C, D). Additionally, the correlations between immunostimulants and FDX1 and DLAT genes was investigated by Spearman correlation. The results showed that FDX1 and DLAT expression were markedly correlated with HMGB1, TLR4, CX3CL1, CD27, CCL5, CXCL10 and TNFRSF18 (Fig. 7C, D). We found that FDX1 expression was positively correlated with CX3CL1 (r = 0.395, P < 0.001), TLR4 (r = 0.353, P < 0.001) and HMGB1 (r = 0.466, P < 0.001), but negatively correlated with TNFRSP18 (r = -0.342, P < 0.001) (Figure S3E-H). As of interest, DLAT expression was also positively correlated with CX3CL1 (r = 0.331, P < 0.001), TLR4 (r = 0.551, P < 0.001) and HMGB1 (r = 0.448, P < 0.001), but negatively correlated with TNFRSP18 (r = -0.461, P < 0.001) in patients with ccRCC (Figure S3I-L). These results suggest that FDX1 and DLAT genes are highly relevant to immune checkpoints, which may lead to a favorable response from patients to immune checkpoint therapy.
Exploring The Sensitivity Of Ccrcc To Cuproptosis
To explore the sensitivity of ccRCC to cuproptosis, we analyzed the cytotoxic effect of the cuproptosis inducer elesclomol in the presence of CuCl2 on 786-O and A498 cells. The results profiled that different elesclomol (0-100 nM) induced cell death in a concentration-dependent manner. Contrarily, cells grown in the absence of CuCl2 were resistant to elesclomol (Fig. 8A, B). The brightfield images of cell growth showed that co-treatment with elesclomol (100 nM) and CuCl2 (1 µM) resulted in the death of 786-O and A498 cells after 6 and 12 hours, while chelating copper with 20 µM tetra-thiomolybdate (TTM) prevented cell death (Fig. 8C). Of note, the expression of both FDX1 and DLAT proteins was up-regulated when 786-O and A498 cells were co-treated with 30 nM elesclomol and 1 µM CuCl2 (Fig. 8D, E).
Screening And Validation Of Potential Drug Targets For Fdx1 And Dlat
Based on the above analysis results showing that FDX1 and DLAT are promising targets for ccRCC therapy, we predicted the corresponding drug targets through the DrugBank database, a drug target information source. The data indicated that FDX1 was assigned only one agent named mitotane, whereas NADH, radicicol and dihydrolipoic acid are potential drug targets for DLAT (Fig. 9A, B). Molecular docking results revealed that Leu460/209, Phe458, Val35/353 and Ile84 of FDX1 were key sites for mitotane binding, its binding affinity up to -8.1 kcal. mol− 1. NADH, radicicol and dihydrolipoic acid docked to several critical sites of DLAT, including Phe48/35/32, His168, Met47, Asn39, Gln31, etc., and the binding affinities were 8.1 kcal. mol− 1, 6,4 kcal. mol− 1 and 5,3 kcal. mol− 1, respectively (Fig. 9C, D).