Research Paper Volume 15, Issue 13 pp 6467—6486

Exploration of cuprotosis-related genes for predicting prognosis and immunological characteristics in acute myeloid leukaemia based on genome and transcriptome

Yanhui Wei1,2, , Zhaoxu Miao2, , Xuejun Guo2,3, , Songwei Feng4, &, ,

  • 1 School of Medicine, Southeast University, Nanjing, China
  • 2 Department of Haematology, Puyang Oilfield General Hospital, Puyang, China
  • 3 Puyang Translational Medicine Engineering and Technology Research Center, Puyang, China
  • 4 Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China

Received: April 17, 2023       Accepted: June 19, 2023       Published: July 13, 2023      

https://doi.org/10.18632/aging.204864
How to Cite

Copyright: © 2023 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Acute myeloid leukemia (AML) is a common hematologic malignancy with a generally unfavorable prognosis. Cuprotosis as a new form of programmed cell death has been shown to play an important role in tumorigenesis and progression; However, the relationship between cuprotosis and the prognosis of AML patients remains unclear.

Methods: Transcriptomic and genomics data, along with clinical information, were obtained from the TCGA and GEO databases. Especially, unsupervised clustering and machining learning were used to identify molecular subtypes and cuprotosis-related risk scores respectively. Kaplan-Meier analysis, univariate and multivariate Cox regression, and Receiver Operator Characteristic curve (ROC) were performed to assess the prognosis based on cuprotosis-related genes (CRGs). Moreover, multiple algorithms were used to evaluate immunological heterogeneity among patients with different risk scores. For in vitro analysis, the expression of genes involved in CRGs was detected by Quantitative Reverse Transcription Polymerase (qRT-PCR) in AML patients.

Results: Transcriptomic and genome data indicated the immense heterogeneity in the CRGs landscape of normal and tumor samples. Cuprotosis subtype A and cuprotosis regulatory subtype B in the genomics map and biological characteristics were significantly different from the other groups. Furthermore, these two subtypes had lower risk scores and longer survival times compared to other groups. Cox analyses indicated that risk score was an independent prognostic factor for AML patients. In addition, our risk score could be an indicator of survival outcomes in immunotherapy datasets.

Conclusions: Our study demonstrates the potential of CRGs in guiding the prognosis, treatment, and immunological characteristics of AML patients.

Abbreviations

AML: Acute myeloid leukemia; CLL: chronic lymphocytic leukemia; CRGs: cuprotosis-related genes; DEGs: differentially expressed genes; TME: tumor microenvironment; PCA: principal component analysis; TCGA: The Cancer Genome Atlas; CNV: copy number variation; GEO: Gene Expression Omnibus; GSVA: gene set variation analysis; ssGSEA: single sample gene set enrichment analysis; LASSO: least absolute shrinkage and selection operator; T-ALL: T acute lymphocytic leukemia; CYT: cytolytic activity.