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. 2022 Oct 21:13:983445.
doi: 10.3389/fgene.2022.983445. eCollection 2022.

Molecular subtypes based on cuproptosis regulators and immune infiltration in kidney renal clear cell carcinoma (V体育2025版)

Aibin Liu  1 , Yanyan Li  2 , Lin Shen  3 , Na Li  3 , Yajie Zhao  4 , Liangfang Shen  3 , Zhanzhan Li  3   5
Affiliations

V体育官网入口 - Molecular subtypes based on cuproptosis regulators and immune infiltration in kidney renal clear cell carcinoma

Aibin Liu et al. Front Genet. .

Abstract

Copper toxicity involves the destruction of mitochondrial metabolic enzymes, triggering an unusual mechanism of cell death called cuproptosis, which proposes a novel approach using copper toxicity to treat cancer. However, the biological function of cuproptosis has not been fully elucidated in kidney renal clear cell carcinoma (KIRC). Using the expression profile of 13 cuproptosis regulators, we first identified two molecular subtypes related to cuproptosis defined as "hot tumor" and "cold tumor", having different levels of biological function, clinical prognosis, and immune cell infiltration. We obtained three gene clusters using the differentially expressed genes between the two cuproptosis-related subtypes, which were associated with different molecular activities and clinical characteristics. Next, we developed and validated a cuproptosis prognostic model that included two genes (FDX1 and DBT). The calculated risk score could divide patients into high- and low-risk groups. The high-risk group had a poorer prognosis, lower level of immune infiltration, higher frequency of gene alterations, and greater levels of FDX1 methylation and limited DBT methylation. The risk score was also an independent predictive factor for overall survival in KIRC. The established nomogram calculating the risk score achieved a high predictive ability for the prognosis of individual patients (area under the curve: 0. 860). We then identified small molecular inhibitors as potential treatments and analyzed the sensitivity to chemotherapy of the signature genes VSports手机版. Tumor immune dysfunction and exclusion (TIDE) showed that the high-risk group had a higher level of TIDE, exclusion and dysfunction that was lower than the low-risk group, while the microsatellite instability of the high-risk group was significantly lower. The results of two independent immunotherapy datasets indicated that cuproptosis regulators could influence the response and efficacy of immunotherapy in KIRC. Our study provides new insights for individualized and comprehensive therapy of KIRC. .

Keywords: KIRC; cuproptosis; immune infiltration; immunotherapy; molecular subtypes. V体育安卓版.

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Conflict of interest statement (VSports)

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
Flow chart showing data processing.
FIGURE 2
FIGURE 2
Landscape of cuproptosis regulators in KIRC. (A): Gene alterations of cuproptosis regulators. (B): Copy number variation frequency of cuproptosis regulators. (C): Chromosome ideograms and labelled chromosomes. (D): Correlation circle among cuproptosis regulators. (E): Differentially gene expression analysis of cuproptosis regulators between normal and tumor cells.
FIGURE 3
FIGURE 3
Molecular subtypes based on cuproptosis regulators in KIRC. (A): Correlations between the prognosis of cuproptosis regulators in KIRC. (B): The consensus matrix identified the optimal number of cuproptosis subtypes. (C,D): PCA and tSEN identified two components. (E): Kaplan-Meier survival curves of the two molecular subtypes. (F): Correlations of subtypes with clinical characteristics. (G): GSVA identified differentially expressed signaling pathways. (H–K): Tumor microenvironment and tumor purity levels of different subtypes. (L,M): Immune cells and functions of two cuproptosis subtypes. (N): Distribution of immune subtypes of the two cuproptosis subtypes. (O): Expression levels of immune-related checkpoint genes between the two cuproptosis subtypes.
FIGURE 4
FIGURE 4
Molecular clustering based on differentially expressed genes between two cuproptosis subtypes. (A): The consensus matrix identified the optimal gene clusters. (B,C): PCA and tSEN identified three gene clusters. (D): Kaplan-Meier survival curves of gene clusters. (E,F): Correlations of gene clusters with clinical characteristics and expression of cuproptosis regulators. (G): Association between cuproptosis clusters, gene clusters, and prognosis. (H–K): stromal and immune estimate scores, and tumor purity across three gene clusters. (L,M): Immune cells and levels of function of three gene clusters. (N): Expression of immune-related checkpoint genes among three groups. (O–Q): Differentially expressed signaling pathways among three gene clusters.
FIGURE 5
FIGURE 5
Development and validation of the cuproptosis-related prognostic model. (A,B): The Kaplan-Meier (K–M) survival curve and risk distribution of the patients in the training group. (C,D): The K-M survival curve and the risk distribution of the patients in the validation group. (E,F): The K-M survival curve and risk distribution of patients in the whole dataset. (G): The time-independent ROC of 1-year, 2-year, and 3-year overall survival. (H,I): Forest plot of univariate and multivariate Cox regression for the risk score. (J,K): Risk scores for cuproptosis clusters and gene clusters. (L–N): Correlations of risk groups with cuproptosis clusters, gene clusters, and clinical outcomes. (O): Nomogram that predicts individual risk based on risk groups and clinical parameters. (P–S): The calibration plot of the observed value and the probability predicted by the nomogram at 1 year, 3 years, and 5 years. (R): AUCs of the risk score and clinical parameters.
FIGURE 6
FIGURE 6
Characteristics of gene mutations of different risk groups. (A): Heat map showing the alterations of the cuproptosis regulator genes between the high and low-risk groups. (B): The yellow line indicates the level of methylation of the cuproptosis regulators between the high- and low-risk groups. (C,D): Top 20 gene alterations of the high and low risk groups. (E,F): Types of gene alterations in the high- and low-risk groups. (G,H): Summary of gene co-occurrences and mutually exclusive genes of the two risk groups.
FIGURE 7
FIGURE 7
Effects of cuproptosis regulators on chemotherapy sensitivity. (A–P): Small molecular compounds related to cuproptosis regulators. (Q–A2): IC50 levels of different chemotherapy drugs between the high- and low-risk groups.
FIGURE 8
FIGURE 8
Associations between cuproptosis regulators and immunotherapy. (A–D): TIDE, exclusion, dysfunction, and MSI levels of different risk groups. (E,F): The immune response and prognosis comparisons of different risk groups in the IMvigor cohort. (G,H): The immune response and prognosis comparisons of different risk groups in the GSE78220 cohort.
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