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. 2022 Oct 3:13:992431.
doi: 10.3389/fphar.2022.992431. eCollection 2022.

Comprehensive analysis of cuproptosis in immune response and prognosis of osteosarcoma

Mingzhe Li  1 , Qiang Song  2 , Yunfeng Bai  1 , Feng Hua  1 , Tao Wu  1 , Jun Liu  1
Affiliations

Comprehensive analysis of cuproptosis in immune response and prognosis of osteosarcoma

Mingzhe Li et al. Front Pharmacol. .

Abstract

Copper-induced cell death, a form of apoptosis, has been extensively investigated in human diseases. Recent studies on the mechanisms underlying copper-induced cell death have provided innovative insights into copper-related toxicity in cells, and this form of programmed cell death was termed cuproptosis. Herein, we conducted a comprehensive analysis to determine the specific role of cuproptosis in osteosarcoma. Using consensus clustering analysis, patients with osteosarcoma from the TARGET database were classified into subgroups with distinct cuproptosis-based molecular patterns. Accordingly, these patients displayed diverse clinicopathological features, survival outcomes, tumor microenvironment (TME) characteristics, immune-related scores, and therapeutic responses. Furthermore, we constructed a cuproptosis-based risk signature and nomogram, as well as developed a cuproptosis score for improved patient characterization. The prognostic model and cuproptosis score were well validated and confirmed to efficiently distinguish high- and low-risk patients, thereby affording great predictive value. Finally, we verified the abnormal expression of prognostic CUG in OS patients by immunohistochemistry. In conclusion, we suggest that cuproptosis may play an important role in regulating the tumor microenvironment features, tumor progression and the long-term prognosis of osteosarcoma.

Keywords: copper-induced cell death; cuproptosis; immunotherapy; osteosarcoma; 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 flow chart showing the details of the analysis.
FIGURE 2
FIGURE 2
The prognostic value of 10 CUGs in patients with OS. (A) Kaplan-Meier curves for the 10 CUGs in OS patients from TARGET database. (B) The PPI network acquired from the STRING database among the CUGs. (C) A network of correlations including CUGs in the TARGET cohort. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 3
FIGURE 3
Identification of cuproptosis subtypes in OS. (A) t-SNE of the mRNA expression profiles of CUGs from the OS samples in the TARGET dataset confirmed the two clusters: C1 and C2. (B) Kaplan-Meier curves for the two molecular patterns of OS patients. (C) Heatmap depicted the correlation between the subtypes and different clinicopathological characteristics. (D) GSVA enrichment analysis of biological pathways between the two distinct subtypes. Boxplots showed abundance of 23 infiltrating immune cell types (E) and differences in immune scores (F) in the two cuproptosis subtypes. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 4
FIGURE 4
Generation and validation of the prognostic model. (A) Venn diagram of the DEGs via pairwise comparison among two subgroups. (B,C) LASSO regression analysis used to construct the prognostic model. (D) Survival analysis in the TARGET-OS cohort. (E) ROC curves for the predictive survival in TARGET-OS cohort. (F) Ranked dot and scatter plots showing the risk score distribution and patient survival status in TARGET-OS cohort. (G–I) Validation of prediction results from GSE21257 dataset against TARGET database. (J,K) Expression patterns of three selected prognostic genes in high-and low-risk groups.
FIGURE 5
FIGURE 5
Construction and validation of a nomogram for predicting the prognosis of OS patients. (A) Nomogram for predicting the 1-, 3-, and 5-years OS of osteosarcoma patients in the TARGET-OS cohort. (B) Calibration curves for validating the established nomogram. (C–E) The ROC curves of the nomograms compared for 1-, 3-, and 5-years OS in osteosarcoma patients, respectively. (F–H) The DCA curves of the nomograms compared for 1-, 3-, and 5-years OS in osteosarcoma patients, respectively. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 6
FIGURE 6
Distinct TME characteristics and mutation of OS patients according to the risk score. (A) Correlations between risk score and both immune and stromal scores. (B) Correlations between risk score and immune cells. Boxplots showed (C) abundance of 23 infiltrating immune cell types and (D) differences in immune scores in the two risk score groups. (E) The boxplot shows variations in the expression of CUGs between the two risk score groups. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 7
FIGURE 7
Prognosis and TME characteristics in two cuproptosis gene clusters for OS patients. (A) Consensus matrix heatmap defining two gene clusters according to the prognostic DEGs. (B) Kaplan-Meier survival analysis for patients in the two gene clusters. (C) Clinical features of the two gene clusters. (D) The boxplot shows variations in the expression of CUGs between the two gene clusters. Boxplots showed (E) abundance of 23 infiltrating immune cell types and (F) differences in immune scores in the two gene clusters. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 8
FIGURE 8
Development and validation of the cuproptosis scoring system for OS. (A) Sankey Diagram of cuproptosis clusters, gene clusters, cuproptosis score, and clinical outcomes. Differences in cuproptosis score between (B) the two cuproptosis subtypes and (C) the two gene clusters. (D) Kaplan-Meier analysis of the OS between the two cuproptosis score groups. (E) GSEA showed the different pathways significantly enriched in the high score group. (F) Clinical characteristics for the high and low cuproptosis score groups. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 9
FIGURE 9
Distinct TME characteristics and mutation of OS patients according to the cuproptosis score. (A) Correlations between cuproptosis score and both immune and stromal scores. (B) Correlations between cuproptosis score and immune cells. Boxplots showed (C) abundance of 23 infiltrating immune cell types and (D) differences in immune scores in the two cuproptosis score groups. (E–G) The boxplots showed variations in the expression levels of PD1, PD-L1, and CTLA4 between the two cuproptosis score groups. (p < 0.05 *; p < 0.01 **; p < 0.001 ***).
FIGURE 10
FIGURE 10
(A) Cuproptosis score predicts the responsiveness of OS to chemotherapy. (B) The 3D structure tomographs of the candidate small-molecule drugs targeting CDKN2A.
FIGURE 11
FIGURE 11
IHC analysis of ki-67, PCNA, and CDKN2A in OS and tumor-adjacent tissues (Magnification, ×200, scale bars indicated 50 μm).
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