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. 2022 Oct 6:13:948042.
doi: 10.3389/fimmu.2022.948042. eCollection 2022.

Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma

Bao Wang  1 , Qiang Song  1 , Yuang Wei  1 , Xiangzheng Wu  1 , Tian Han  1 , Hengtao Bu  1 , Sensheng Tang  1 , Jian Qian  1 , Pengfei Shao  1
Affiliations

Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma

Bao Wang et al. Front Immunol. .

Abstract

Background: Copper-induced cell death has been widely investigated in human diseases as a form of programmed cell death (PCD). The newly recognized mechanism underlying copper-induced cell death provided us creative insights into the copper-related toxicity in cells, and this form of PCD was termed cuproptosis.

Methods: Through consensus clustering analysis, ccRCC patients from TCGA database were classified into different subgroups with distinct cuproptosis-based molecular patterns. Analyses of clinical significance, long-term survival, and immune features were performed on subgroups accordingly. The cuproptosis-based risk signature and nomogram were constructed and validated relying on the ccRCC cohort as well. The cuproptosis scoring system was generated to better characterize ccRCC patients. Finally, in vitro validation was conducted using ccRCC clinical samples and cell lines.

Result: Patients from different subgroups displayed diverse clinicopathological features, survival outcomes, tumor microenvironment (TME) characteristics, immune-related score, and therapeutic responses. The prognostic model and cuproptosis score were well validated and proved to efficiently distinguish the high risk/score and low risk/score patients, which revealed the great predictive value. The cuproptosis score also tended out to be intimately associated with the prognosis and immune features of ccRCC patients. Additionally, the hub cuproptosis-associated gene (CAG) FDX1 presented a dysregulated expression pattern in human ccRCC samples, and it was confirmed to effectively promote the killing effects of copper ionophore elesclomol as a direct target. In vitro functional assays revealed the prominent anti-cancer role of FDX1 in ccRCC.

Conclusion: Cuproptosis played an indispensable role in the regulation of TME features, tumor progression, and long-term prognosis of ccRCC.

Keywords: ccRCC; copper-induced cell death; cuproptosis; immunotherapy; prognosis; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A landscape of key cuproptosis-associated genes (CAGs) in ccRCC. (A) Venn diagram of CAGs and ccRCC-derived DEGs. (B) Expression level of CAGs between tumors and normal tissues in ccRCC. (C) PPI network of CAGs. (D) Genetic mutation of CAGs in ccRCC samples. (E) CNA frequency of CAGs in ccRCC samples. (F) CNV sites of CAGs on chromosomes (p < 0.001 ***). DEGs: differentially expressed genes.
Figure 2
Figure 2
Survival correlation of CAGs in ccRCC patients. (A) Kaplan–Meier survival curves of 10 CAGs. (B) Interactive plot of 10 CAGs in the ccRCC cohort. The significance was labeled in different colors and sizes of dots (p < 0.05 *; p < 0.01 **; p < 0.001 ***. NS, not significant). CAGs: cuproptosis-associated genes.
Figure 3
Figure 3
CAG pattern-based grouping of patients with distinct clinical and immune features. (A) PCA analysis indicates the separation of subgroups in the ccRCC cohort from TCGA database. (B) K-means curve suggests the optimal number of clusters. (C) Kaplan–Meier survival curves of three subgroups. (D) Heatmap of different clinicopathological features in three subgroups. Analysis of immune cell infiltration level (E) and immune-related function (F) between three subgroups. PCA: principal component analysis. TCGA: The Cancer Genome Atlas. KEGG: Kyoto Encyclopedia of Genes and Genomes. GSVA: gene set variation analysis (p < 0.05 *; p < 0.01 **; p < 0.001 ***. NS, not significant).
Figure 4
Figure 4
Generation and validation of the prognostic model. (A) Venn diagram of the DEGs via pairwise comparison among three subgroups. (B) LASSO regression analysis used to construct the prognostic model. (C) ROC curves for the predictive risk model of 1 year (left), 3 years (middle), and 5 years (right). (D) Overall survival analysis at the cutoff values: 1.400 (left), 0.941 (middle), and 1.231 (right). (E) Progression-free survival analysis at the cutoff values: 1.400 (left), 0.941 (middle), and 1.231 (right).
Figure 5
Figure 5
The correlation analysis of risk score and clinicopathological variables in ccRCC. The (A) uniCox and (B) multiCox analyses explored the independent prognostic value of risk score and multiple clinical factors. (C) The comparative ROC curves showed that the risk score was superior to most clinical factors in predicting patients’ long-term survival. (D) Ranked dot and scatter plots showing the risk score distribution and patient survival status. (E) Nomogram combining the risk signature and clinical factors. (F) Calibration curves for the nomogram-predicted OS at 1, 3, and 5 years (p < 0.001 ***).
Figure 6
Figure 6
Gene clustering-based grouping of patients with distinct clinical and immune features. (A) Heatmap of the consensus matrix depicting the proper clusters (k = 3) and correlation area. (B) Kaplan–Meier survival curves of three subgroups. Analysis of immune cell infiltration level (C) and immune-related function (D) between three subgroups. (E) Heatmap of different clinicopathological features in three subgroups (*p < 0.05; **p < 0.01; ***p < 0.001).
Figure 7
Figure 7
Construction and verification of the cuproptosis score for ccRCC patients from TCGA database. (A) Distribution of ccRCC patients in groups based on CAGs, DEGs, cuproptosis score, and surviving status. (B, C) Cuproptosis score of diverse subgroups based on CAGs (B) and DEGs (C). (D) Kaplan–Meier survival curves of ccRCC patients with low and high cuproptosis scores. (E) Proportion of dead or alive patients in low and high cuproptosis score groups. (F) Overall level of cuproptosis score for patients in alive and dead groups. (G, H) KEGG pathway analysis on patients in low and high cuproptosis score groups (p < 0.05 *; p < 0.01 **; p < 0.001 ***). CAGs, cuproptosis-associated genes; DGEs, differentially expressed genes.
Figure 8
Figure 8
Clinical and genetic correlation of cuproptosis score. (A) Clinicopathological information of ccRCC patients in low and high cuproptosis score groups. (B) Spearman’s correlation analysis on cuproptosis score and stem cell level. (C) Relationships between cuproptosis score and TMB. (D) Kaplan–Meier survival curves of patients with high and low TMB. (E) Kaplan–Meier survival curves of patients with diverse TMB and cuproptosis scores. (F, G) Genetic mutation landscape of patients with high (F) and low (G) cuproptosis scores. TMB, tumor mutation burden.
Figure 9
Figure 9
Immune and immunotherapeutic correlation of cuproptosis score. (A) Tumor purity of patients with high and low cuproptosis score. (B) Correlations between immune-related cells and cuproptosis score. (C, D) Abundance of immune cell infiltration (C) and immune-related functions (D) in patients with high and low cuproptosis scores. (E) Abundance of immunotherapy markers in patients with high and low cuproptosis scores. (F) TIDE in different cuproptosis score groups (p < 0.05 *; p < 0.01 **; p < 0.001 ***. NS, not significant).
Figure 10
Figure 10
Therapeutic response correlation of the cuproptosis score in ccRCC. (A) IPS level in the high and low cuproptosis score groups. pos: positive; neg: negative. (B) Estimated treating response of agents in patients with high and low cuproptosis scores. (C) GO analysis on DEGs from patients with high and low cuproptosis scores. (D) Circle plot of representative pathways and hub genes. IPS: immunophenotype score. GO: gene ontology. DEGs, differentially expressed genes.
Figure 11
Figure 11
FDX1 regulates cuproptosis to suppress the progress of ccRCC. (A) FDX1 mRNA level in 23 paired clinical ccRCC samples. Overexpressing efficiency of FDX1 in ccRCC cell lines validated by q-PCR (B) and Western blot (C). (D) Relative viability of cells after being treated with elesclomol (20 nM) for 12 h. (E–H) CCK 8 assay (E), colony formation assay (F), Transwell assay (G), and wounding healing assay (H) on ccRCC cells after transfection of plasmid vector carrying FDX1 (p < 0.01 **; p < 0.001 ***; p < 0.0001 ****).
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