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. 2022 Aug 19:13:996836.
doi: 10.3389/fimmu.2022.996836. eCollection 2022.

Identification of cuproptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Zhi Li  1   2 , Hua Zhang  1 , Xixi Wang  1 , Qun Wang  1 , Jiapeng Xue  1 , Yun Shi  1 , Minghua Wang  1 , Geng Wang  1 , Jianquan Zhang  3
Affiliations

Identification of cuproptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Zhi Li et al. Front Immunol. .

Abstract

Breast cancer (BC) is now the most frequent and lethal cancer among women. Cuproptosis is a newly identified programmed cell death process that has been connected to tumor therapeutic sensitivity, patient outcomes, and the genesis of cancer. Cuproptosis-related genes (CRGs) are involved in breast cancer, although their roles and potential mechanisms are still unclear. First, we examined the effect of gene mutations and copy number changes on overall survival in 1168 breast cancer samples. Breast cancer patients were split into two molecular categories as determined by the variation in CRG based on clinicopathological traits, overall survival, and cell-infiltrating traits in tumor microenvironments. In addition, we created and validated a CRG score to calculate breast cancer patients' OS. Finally, we created a comprehensive nomogram for the clinical use of the CRG score. Patients whose CRG scores were low showed increased odds of developing OS, a larger mutation load, and immunological activation than those with high CRG scores. The CRG score, the cancer stem cell index, and the responsiveness to chemotherapy or targeted therapies were also shown to be statistically significantly correlated. Our thorough examination of CRGs in breast cancer patients demonstrated that they may be useful predictors of prognosis, clinical characteristics, and tumor microenvironment. These findings provide fresh insight into CRGs in breast cancer and might inspire brand-new approaches to both diagnosing and treating patients there.

Keywords: CRG; breast cancer; cuprotosis; prognosis; tumor microenvironments.

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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
The analysis of 19 CRGs' expression and association in the TCGA cohort. (A) The expression of the 19 CRGs in BC tissues and healthy breast tissues (*p < 0.05; ***p < 0.001). (B) Data on the frequency of CRG mutations for 976 BC patients. (C) The sites of CNV variation in CRGs on the 23 chromosomes. (D) The distributions of CNV gain, loss, and non-CNV among CRGs. (E–N) The association between 10 CRGs and overall survival in British Columbia.
Figure 2
Figure 2
Biological and clinicopathological characteristics of CRG subtypes. (A) The interactions between CRGs in BC (the red and blue strings denote positive and negative correlation, respectively; the intensity of the correlation is indicated by the color shades). (B) The consensus matrix's heatmap of two clusters (κ = 2). (C) A considerable transcriptome divergence between the two subtypes is seen by PCA analysis. (D) Subtype-specific Kaplan-Meier OS curves. (E) CRG expression levels and clinicopathological traits vary across subtypes.
Figure 3
Figure 3
Cuproptosis subtypes linked to TME invasion. (A) GSVA of two cuproptosis subtype-related cellular pathways (Red means activated and blue means inhibited). (B) Correlations between immune cell infiltration levels in the two subtypes associated with cuproptosis. (C, D) DEG enrichment studies across two cuproptosis-related subgroups using GO and KEGG. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 4
Figure 4
DEGs are used to identify gene subtypes. (A) Heatmap of the consensus matrix defining four clusters ( κ = 4). (B) Differences in clinicopathologic characteristics among the four gene subtypes. (C) The four gene subtypes' Kaplan-Meier OS curves. (D) Variations in the expression of ten CRGs across four gene subtypes. **p < 0.01, ***p < 0.001.
Figure 5
Figure 5
The CRG score was created in the TCGA and GSE61304 cohorts. (A) The subtype distributions among groups, CRG scores and survival outcomes. (B) Variations in CRG scores among cuproptosis subtypes. (C) Variations in PRG scores among different gene subtypes. (D) CRG regression using LASSO. (E) Cross-validation of LASSO regression parameter selection. (F) CRG score differences in ten CRGs. *p < 0.05, ***p < 0.001.
Figure 6
Figure 6
The patient survival status and CRG score distribution vary between the train and test sets. (A, C, E) The patient survival status and CRG score distribution in the train set. (B, D, F) The patient survival status and CRG score distribution in the test set.
Figure 7
Figure 7
Creating and evaluating a nomogram. (A) The nomogram used to calculate the survival rates of 1-, 3-, and 5-years for patients with BC. (B) Calibration curve for nomograms. (C–E) ROC curves for the train set and test set, respectively, for forecasting 1-, 3-, and 5-year OS in the cohorts. *p < 0.05, ***p < 0.001.
Figure 8
Figure 8
Comprehensive analysis of the CRG scores in BC. (A) Correlations between immune cell types and CRG score. (B) The six genes from the proposed model are correlated with the number of immune cells. (C) CRG score and TMB spearman correlation analysis. (D, E) The somatic mutation features waterfall plot determined by high and low CRG scores. One patient was represented by each column. The correct number represented each gene's frequency of mutation, and the upper barplot displayed TMB. The proportion of each variant type was displayed in the right barplot. (F) Associates between the CSC index and the CRG score.
Figure 9
Figure 9
Relationships between the CRG score and susceptibility to chemotherapy or targeted therapies for BC. (A) paclitaxel. (B) Vinblastine. (C) Bleomycin. (D) AUY922. (E) ATRA. (F) AZD6244.
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References

    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. . Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2021) 71(3):209–49. doi: 10.3322/caac.21660 - DOI - PubMed
    1. DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, et al. . Breast cancer statistics, 2019. CA Cancer J Clin (2019) 69(6):438–51. doi: 10.3322/caac.21583 - DOI - PubMed
    1. Crozier JA, Pezzi TA, Hodge C, Janeva S, Lesnikoski BA, Samiian L, et al. . Addition of chemotherapy to local therapy in women aged 70 years or older with triple-negative breast cancer: a propensity-matched analysis. Lancet Oncol (2020) 21(12):1611–9. doi: 10.1016/S1470-2045(20)30538-6 - DOI - PubMed
    1. Voorwerk L, Slagter M, Horlings HM, Sikorska K, van de Vijver KK, de Maaker M, et al. . Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial. Nat Med (2019) 25(6):920–8. doi: 10.1038/s41591-019-0432-4 - DOI - PubMed
    1. Dirix LY, Takacs I, Jerusalem G, Nikolinakos P, Arkenau HT, Forero-Torres A, et al. . Avelumab, an anti-PD-L1 antibody, in patients with locally advanced or metastatic breast cancer: a phase 1b JAVELIN solid tumor study. Breast Cancer Res Treat (2018) 167(3):671–86. doi: 10.1007/s10549-017-4537-5 - DOI - PMC - PubMed