Exploring prognostic markers for patients with acute myeloid leukemia based on cuproptosis related genes

Abstract

Background: Acute myeloid leukemia (AML), a common form of acute leukemia, is due to tumor changes and clonal proliferation caused by genetic variants. Cuproptosis is a novel form of regulated cell death. This study aimed to explore the role of cuproptosis-related genes (CRGs) in AML.

Methods: Initially, differentially expressed genes (DEGs) between AML samples and normal samples were obtained by differential analysis, which were further intersected with the cuproptosis score-related genes (CSRGs) acquired by weighted gene co-expression network analysis (WGCNA) to obtain cuproptosis score-related differentially expressed genes (CS-DEGs). Then, a risk model was constructed by Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis. Finally, immune infiltration analysis was performed and the functions and pathways of model genes were explored by single sample gene set enrichment analysis (ssGSEA).

Results: Thirty-two CS-DEGs were obtained by overlapping 11,160 DEGs and 132 CSRGs. These 32 CS-DEGs were mainly enriched to cytoplasmic microtubule organization, RNA methylation, mTOR signaling pathway, and notch signaling pathway. Two model genes, PACS2 and NDUFV1, were finally screened for the construction of the risk model. In addition, PACS2 and NDUFV1 were significantly positively correlated with activated B cells, CD56dim natural killer (NK) cells, and negatively correlated with effector memory CD4 T cells and activated CD4 T cells. PACS2 gene was significantly enriched to inositol phosphate metabolism, histone modification, etc. NDUFV1 was mainly enriched to ncRNA metabolic process, 2-oxocarboxylic acid metabolism, and other pathways.

Conclusions: A cuproptosis-related risk model consisting of PACS2 and NDUFV1 was built, which provided a new direction for the diagnosis and treatment of AML.

Keywords: Acute myeloid leukemia (AML); bioinformatics; cuproptosis; prognostic markers; risk model.

Figure 1

Screening for DEGs between AML and normal samples. (A) The volcano map of DEGs between AML patients and healthy samples. Abscissa denotes log₂FC, ordinate denotes −log₁₀ (P value). Every dot in the image corresponds to a gene. The red and blue dots represent significant differential expression, and the red dots indicate upregulated expression, blue dots indicate downregulation in AML samples, and black dots indicate no significant difference. (B) The heat map of DEGs between AML patients and healthy samples. Each small square represents the normalized expression level of different genes in each sample. Each row represents the expression level of the same gene in the sample, and each column represents the expression level of the gene in the same sample. The bar with gradient color on the right (2, −2) represents the color corresponding to the specific value of different genes after normalization. DEGs, differentially expressed genes; AML, acute myeloid leukemia; FC, fold change.

Figure 2

Identification of cuproptosis score-related genes by WGCNA. (A) The sample and trait tree diagram. (B) Screening for scale-free soft threshold. The horizontal axis represents the power value of the weight parameter, the vertical axis of the left figure represents the square of the correlation coefficient between log(k) and log(p(k)) in the corresponding network, that is, signedR². The higher the square of the correlation coefficient, the closer the network is to the scale-free distribution. The vertical axis of the right graph represents the mean of all gene adjacency functions in the corresponding gene module. (C) The systematic clustering tree. (D) Identification and combination of modules. Different colors represent different modules, where gray defaults to genes that cannot be classified in any module. (E) Heatmap of correlations between modules and clinical traits. The ordinate represents different modules, the abscissa represents different traits, and each square represents the correlation coefficient between a module and a trait. (F) The correlations between yellow module and trait. The abscissa represents the connectivity within the yellow module, and the ordinate represents the significance of the cuproptosis score and the module. WGCNA, weighted gene co-expression network analysis.

Figure 3

Screening and functional enrichment analysis of cuproptosis score-related differentially expressed genes. (A) Intersection of DEGs and hub genes. (B) Expression of 32 cuproptosis score-related differentially expressed genes modules between AML and normal samples. (C) GO enrichment analysis of 32 cuproptosis score-related differentially expressed genes. (D) KEGG enrichment analysis of 32 cuproptosis score-related differentially expressed genes. ****, P<0.0001. TCGA, The Cancer Genome Atlas; DEGs, differentially expressed genes; AML, acute myeloid leukemia; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 4

Screening for model genes. (A) The forest map of univariate Cox regression analysis. (B,C) Gene coefficient plot (B) and cross-validation error plot (C) of LASSO regression analysis. LASSO, least absolute shrinkage and selection operator.

Figure 5

Construction and validation of the risk model. (A-C) Risk curves, scatter plots, model gene expression heatmaps for high- and low-risk groups of patients in the training set, test set, and validation set. (D-F) Survival analysis of patients in the training set, test set, and validation set. (G-I) The ROC curves in the training set, test set, and validation set. (B,D,F) The x-axis is the order in which the AML patient samples are ranked according to the size of the risk score value, with red and green points indicating the samples. (C,E) The x-axis represents patient survival time. TCGA, The Cancer Genome Atlas; OS, overall survival; ROC, receiver operating characteristic; GEO, Gene Expression Omnibus; AML, acute myeloid leukemia.

Figure 6

Independent prognostic analysis. (A) The forest map of univariate cox regression analysis. (B) The forest map of multivariate cox regression analysis. (C) The nomogram of independent prognostic model. (D) Calibration curve of nomogram. (E) ROC curves for the nomogram, risk score, and age at 1, 3, and 5 years. OS, overall survival; ROC, receiver operating characteristic; AUC, area under the curve.

Figure 7

Functional enrichment analysis of high- and low-risk groups. (A) Top 10 enriched pathway in high- and low-risk groups (C2). (B) Pathway top 10 enriched in GSEA high- and low-risk groups (C5). GSEA, gene set enrichment analysis.

Figure 8

ssGSEA of model genes. (A) The GO, KEGG, REACTOME enrichment results of PACS2. (B) The GO, KEGG, REATCOM enrichment results of NDUFV1. GSEA, gene set enrichment analysis; ssGSEA, single-sample gene set enrichment analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.