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Characterization of cuproptosis identified immune microenvironment and prognosis in acute myeloid leukemia

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Abstract

Background

Recent studies have reported that cuproptosis, a novel cell death pathway, strongly correlates with mitochondrial metabolism. In addition, the studies reported that cuproptosis plays a role in the development of several cancers and is regulated by protein lipoylation. During cuproptosis, copper binds to the lipoylated proteins and mediates cancer progression. However, the role of cuproptosis in acute myeloid leukemia (AML) patients is yet to be explored.

Methods

This study curated seven cuproptosis-related-genes (CRGs): FDX1, DLAT, PDHB, PDHA1, DLD, LIAS, and LIPT1 to determine cuproptosis modification patterns and the CRGs signature in AML. The CIBERSORT and ssGSEA algorithms were utilized to evaluate the infiltration levels of different immune cell subtypes. A cuproptosis score system based on differentially expressed genes (DEGs) was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The developed cuproptosis score system was validated using two immunotherapy datasets, IMvigor210 and GSE78220.

Results

Three distinct cuproptosis regulation patterns were identified using the Beat AML cohort. The results demonstrated that the three cuproptosis regulation patterns were correlated with various biological pathways and clinical outcomes. Tumor microenvironment (TME) characterization revealed that the identified cuproptosis regulation patterns were consistent with three immune profiles: immune-desert, immune-inflamed, and immune-excluded. The AML patients were grouped into low- and high-score groups based on the cuproptosis score system abstracted from 486 cuproptosis-related DEGs. Patients with lower cuproptosis scores were characterized by longer survival time and attenuated immune infiltration. It was found that lower cuproptosis scores were strongly correlated with lower somatic mutation frequency. Moreover, patients with lower cuproptosis scores presented more favorable immune responses and dual clinical benefits among external validation cohorts.

Conclusions

Cuproptosis phenotypes are significantly correlated with immune microenvironment complexity and variety. Cuprotopsis regulates the response of cancer cells to the immune system. Quantitatively assessing cuproptosis phenotypes in AML improves the understanding and knowledge regarding immune microenvironment characteristics and promotes the development of therapeutic interventions.

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Data availability

The data could be download at Beat AML dataset (http://www.vizome.org/aml/); AMLCG (GSE106291:https://portal.gdc.cancer.gov/); TCGA-LAML dataset (https://www.cancer.gov/aboutnci/organization/ccg/research/-structuralgenomics/tcga). Immunotherapy datasets were downloaded from IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/packageVersions/) and GSE78220 (https://www.ncbi.nlm.nih.gov/geo/).

Abbreviations

AMLCG:

AML Cooperative Group

AML:

Acute myeloid leukemia

CRGs:

Cuproptosis-related genes

DEGs:

Differentially expressed genes

GEO:

Gene expression omnibus

GO:

Gene ontology

GSEA:

Gene set enrichment analysis

GSVA:

Gene set variation analysis

ICB:

Immune checkpoint blockade

ICIs:

Immune checkpoint inhibitors

KEGG:

Kyoto encyclopedia of genes and genomes

LASSO:

The least absolute shrinkage and selection operator

ROC:

Receiver operating characteristic

TME:

Tumor microenvironment

TCGA-LAML:

The cancer genome atlas-acute myeloid leukemia

TCA:

The tricarboxylic acid

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Funding

This work was supported by the National Natural Science Foundation of China (No. 81700104) and Natural Science Foundation of Guangdong, China (No.2019A043135067).

Author information

Author notes

  1. Dongmei Luo and Songyang Liu contributed equally to the work as co-first authors.

Authors and Affiliations

  1. Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Dongmei Luo, Hong Chen, Zherou He, Zicheng Gao, Ziyu Wen, Xiaoli Liu & Na Xu

  2. The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Songyang Liu

  3. Department of Hematology, First Affiliated Hospital of Hainan Medical College, Haikou, 570100, Hainan, China

    Jie Luo

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Contributions

NX revised the manuscript and make final approval of the version. DL designed the study, interpreted results and wrote the manuscripts. SL analyzed data and prepared figures. JL, ZH, ZG, HC, and XL performed review and revision of the manuscripts. All authors read and approved the final paper.

Corresponding author

Correspondence to Na Xu.

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Conflict of interest

The authors declare no competing interests.

Ethical approval

The study was reviewed by the Medical Ethics Committee of Southern Medical University Nanfang Hospital, and confirmed the research range as part of ‘can be exempted from medical ethical review on’ the article 3: for always archived data file records pathological specimens or diagnostic specimen collection or research, and these resources are public resources, or in the way they were unable to contact (directly or through identifier) records the information. Therefore, the application for exemption from medical ethics review of this study was approved.

Human and animal rights

This study dose not involving any human or animal participants directly, since it is a retrospective analysis from open access databases.

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Supplementary Information

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Supplementary file1 (XLSX 7196 KB)

12094_2023_3118_MOESM2_ESM.png

Supplementary file2 Identification of consistent clustering based on the copper cell death-related DEGs in Beat AML and AMLCG cohort. A The result of consistency clustering when k = 3. B, E The cumulative distribution function (CDF) when k = 2–10 (B Beat AML cohort; C AMLCG cohort). C, F The relative change of the area under the CDF curve when k = 2–10 (C Beat AML cohort; F AMLCG cohort). G, H The expression of 4 copper cell death-related DEGs. (G Beat AML cohort, H AMLCG cohort) (PNG 215 KB)

12094_2023_3118_MOESM3_ESM.png

Supplementary file3 The relative abundances of the immune cells or pathways in AMLCG cohort. A The different distribution of 22 TME infiltrating cells in three clusters. B Comparison of the AML immunity among three different clusters. C The different value of ESTIMATE score in three clusters. (****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05) (PNG 1059 KB)

12094_2023_3118_MOESM4_ESM.png

Supplementary file4 The relationship of biological signatures in Beat AML cohort. A Comparison of different biological signatures among three different clusters. (****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05). B The correlation of different biological signatures. (***p < 0.001; **p < 0.01; *p < 0.05) (PNG 351 KB)

12094_2023_3118_MOESM5_ESM.png

Supplementary file5 The development of copper cell death-related model. A Identification of the DEGs among three subclusters. B, C LASSO-cox regression of the genes extracted by univariate cox regression (PNG 283 KB)

12094_2023_3118_MOESM6_ESM.png

Supplementary file6 Kaplan–Meier estimates of BeatAML patients’ OS. A THG1L, B SYPL1, C MRPS36, D ACADS, E KLF9 (PNG 99 KB)

12094_2023_3118_MOESM7_ESM.png

Supplementary file7 Analysis of factors affecting the prognosis of patients with AML. A, B Principal component analysis for the expression of 5 genes extracted from LASSO (A Beat AML cohort; B AMLCG cohort). C Rate of clinical response predicted by 29-gene signature defined by a previous study. D The landscape of Top 20 frequently mutated genes of the patients from TCGA-AML cohort (right bottom: bar plot of the alteration type). E, F Forest plot summary of the multivariable Cox analysis of the copper score (risk score) and clinicopathological characteristics (*p < 0.05; **p < 0.01; ***p < 0.001) (PNG 630 KB)

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Luo, D., Liu, S., Luo, J. et al. Characterization of cuproptosis identified immune microenvironment and prognosis in acute myeloid leukemia. Clin Transl Oncol 25, 2393–2407 (2023). https://doi.org/10.1007/s12094-023-03118-4

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