Skip to main content
Download PDF

Potential value of immunogenic cell death related-genes in refining European leukemiaNet guidelines classification and predicting the immune infiltration landscape in acute myeloid leukemia

Cancer Cell International volume 25, Article number: 52 (2025) Cite this article

Abstract

Immunogenic cell death (ICD) is the kind of cell death that triggers the immune system. It affects several tumors, whereas its significance for prognosis in acute myeloid leukemia (AML) remains uncertain. AML categorization by cytogenetic variables is inaccurate. In addition, risk stratification of AML based on cytogenetics is imprecise. The data of AML patients were extracted from 4 databases, a total of 1,537 patients. Univariate and LASSO Cox regression analyses were conducted to construct an ICD risk signature (ICDRS). The ICDRS showed strong prognostic value for AML through Kaplan-Meier, Cox, ROC analyses and nomogram. Combining the ICDRS with the European LeukemiaNet (ELN) classification to redefine the risk stratification can better predict the prognosis of AML. Moreover, the ICDRS was examined to identify gene functional enrichment, immunological characteristics, drug susceptibility, and somatic mutation, which revealed considerable variations among different risk categories. We further validated the expression of ICDRS in the AML bone marrow microenvironment by single-cell RNA (scRNA) analysis. Ultimately, the functional role of CASP1 was proven in AML by a series of in-vitro experiments. Our study highlights the significant impact of ICDRS on AML, which may improve ELN risk classification, predict immune landscapes, and guide personalized therapy.

Introduction

Acute myeloid leukemia (AML) is a cancerous condition where abnormal hematopoietic cells rapidly multiply [1]. Although treatments such as intensive chemotherapy and allogeneic hematopoietic stem cell transplantation have gained significant momentum in recent years, many AML patients are still incurable, with a 5-year overall survival (OS) rate of merely 20–30% and most patients experiencing drug resistance or relapse [2]. Hence, it is imperative to identify prospective molecular biomarkers to enhance the diagnosis, individualized therapy, and prognosis of AML.

Characterization of cytogenetic subsets with varying outcomes improves survival prediction, further refined by the integration of recurrent somatic mutations [3]. European LeukemiaNet (ELN) guidelines include the most significant cytogenetic and molecular abnormalities that can provide a good classification of leukemia prognosis [4]. Recently, the ELN2017 guidelines have been updated to ELN2022, which show improved ability to diagnose and assess AML prognosis [5]. Nevertheless, ELN2022 guidelines remain highly controversial. Mrózek et al. compared de novo AML patients receiving first-line therapy in Cancer and Leukemia Group B, which showed no advantage of ELN2022 over ELN2017 [6]. In our previous work, we have also found that ELN2022 is not well suited for patients who cannot be treated with intensive chemotherapy or FLT3 inhibitors [7]. Emerging data indicate that the discovery of novel biomarkers for AML enhances our comprehension of the disease’s molecular foundation and assists in the identification, diagnosis, prognosis, and monitoring of AML [8, 9].

Immunogenic cell death (ICD) represents a form of regulated cell death that activates an adaptive immune response against antigens associated with deceased cells, thereby inducing immunogenicity in tumor cells [10]. Common triggers of ICD include cancer chemotherapy and radiotherapy. An enhanced understanding of ICD may help to modify current anti-cancer treatment regimens, particularly immunotherapy. ICD is followed by exposure and generation of various molecular patterns associated with damage that are highly adjuvant to dead cancer cells by favoring recruitment and activation of antigen-presenting cells [11]. Currently, chemotherapy and immunotherapy are also widely used in the clinical treatment of AML, yet the role of the ICD in the treatment of AML remains to be explored. Of note, there remains a lack of sufficient evidence concerning the clinical application of ICD, particularly in relation to the identification of biomarkers associated with ICD.

This study created an ICD risk signature(ICDRS) derived from ICD-related genes and examined its implications for prognosis and immunity, to improve risk classification compared with ELN2017 and ELN2022. Additionally, we investigated the tumor immune microenvironment and drug susceptibility across different ICD risk groups and validated ICDRS by single-cell RNA transcriptome analysis. In particular, we have verified the importance of CASP1 in vitro. We anticipate that the findings from this study will aid in the identification of novel prognostic markers for the diagnosis and personalized treatment of AML, as well as offer benefits for prognostic prediction and clinical therapy stratification.

Materials and methods

Datasets

The flowchart of the full text is presented in Fig. 1. Clinical and transcriptome data for the TCGA-LAML and TARGET cohorts were obtained from the UCSC Xena database (http://xena.ucsc.edu/) [12]. To validate our predictive model, we gathered clinical and transcriptome data from the GSE37642 cohort using the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) [13]. We also downloaded data from the Beat AML2.0 database (https://biodev.github.io/BeatAML2/) for our analysis [14]. The study algorithm is shown in Figure S1. The single-cell RNA transcriptome profile GSE116256 was downloaded from the GEO database [15]. The mRNA expression matrix of AML cell lines was acquired from the CCLE dataset (https://portals.broadinstitute.org/ccle) [16]. ICD-related genes were provided from previous literature [17]. Table S1 contains a compilation of 34 pertinent genes.

Fig. 1
figure 1

Workflow of this study

Full size image

Construction and validation of the ICD risk signature

The batch effect of TCGA-LAML, TARGET, Beat AML2.0, and GSE37642 datasets was corrected using the combat function in the R package “SVA”. The impact of related genes on the prognosis in AML was evaluated using univariate Cox regression analysis. Subsequently, to create a risk signature, we conducted a LASSO Cox regression analysis utilizing the genes that showed statistical significance. This analysis allows us to calculate the regression coefficients for each gene. The risk scores were calculated using the following formula:

$$\:\text{R}\text{i}\text{s}\text{k}\:\text{s}\text{c}\text{o}\text{r}\text{e}=\sum_{\text{i}=1}^{\text{n}}\text{C}\text{o}\text{e}\text{f}\text{i}\ast\:\text{X}\text{i}$$

The symbol Coefi represents the coefficient, while Xi represents the expression of each mRNA.

To compare the OS values of the high and low ICD risk groups, Kaplan-Meier (K-M) analysis was performed using the R packages “survminer” and “survival”. The specificity and sensitivity of the model were assessed by calculating the area under the curve (AUC). Univariate and multivariate Cox analyses were employed to demonstrate that the signature functioned as an independent prognostic model. We created a nomogram scoring method using clinicopathological variables and risk score to improve AML prediction. The “rms” package created a nomogram to forecast the likelihood of survival at 1, 3, and 5 years. The nomogram was calibrated for accuracy.

Improvement of the ELN risk stratification system

Based on the reports in previous literature, we carried out an adjustment to the ELN risk stratification [18,19,20]. Patients with AML were divided into three new categories: ELN favorable/ICD risk score high and ELN adverse/ICD risk score low patients were reassigned to the intermediate group, and ELN intermediate/ICD risk score high patients were reassigned to the adverse group. We evaluated the prognostic importance of the new risk classification method using K-M analysis.

Identification of DEGs in different risk groups and functional enrichment analysis

We used the R package “limma” to examine DEGs in two risk groups and set a filter to account for false positive TCGA data by adjusting for p-values less than 0.05 and logFC abs larger than 1. GO functional enrichment analysis and gene set enrichment analysis were performed using the R packages “clusterProfiler” and “GSVA”.

Comparative analysis of immunological landscape between two ICD subgroups

In order to evaluate the stromal score, estimated score, immunological score, and tumor purity of various risk groups, we utilized the “estimate” software. To determine the immune profiles of AML samples, the expression data of these samples were input into CIBERSORT (https://cibersort.stanford.edu/) [21]. This process was repeated 1000 times to calculate the relative percentage of 22 different types of immune cells. We conducted a comparison of the relative proportions of 22 different types of immune cells between the two categories classified by the ICD. Heatmaps depicting the infiltration of immune cells in TCGA-AML were created using several methods. The immuno-infiltration data used in this analysis were obtained from the TIMER website (http://timer.comp-genomics.org/infiltration_estimation_for_tcga.csv.gz) [22]. The expression levels of the human leukocyte antigen (HLA) gene and the immunological checkpoint (ICP) were examined using the “ggplot2” software, utilizing the RNA profiles of TCGA-LAML.

Mutation and drug sensitivity analysis

The “maftools” R package generated mutation annotation formats (MAF) using the TCGA database to detect somatic mutations in high-risk and low-risk AML patients. To explore potential clinical drugs for the treatment of AML, we used the R package “oncoPredict” to predict the semi-inhibitory concentration (IC50) values of drugs based on data in TCGA-LAML.

scRNA-seq data analysis

Single-cell data from 21 AML patients were acquired from GEO (GSE116256) [15]. The “Seurat” package was used to standardize the single - cell analysis procedure. Normalization and scaling were performed on cells expressing at least 500 genes and having a mitochondrial fraction below 5%. After principal component analysis (PCA) with the RunPCA function, the top 30 PCs were selected for UMAP reduction. Major cell cluster marker genes were used to annotate cell types. Finally, the “AddModuleScore” function mapped the expression of ICDRS in UMAP. We additionally visualized single cells using the “SCP” R package, “scRNAtoolVis” R package, and “InferCNA” R package.

Cell culture and siRNA treatment

AML cell lines (including THP-1, MOLM13, and NB4) and HS-5 were purchased from the COBIOER (Nanjing, China). MOLM13 and THP-1 cells were transfected with small interfering RNAs (siRNAs) purchased from General biological system (Anhui, China). The transfection was carried out using CALNPTM RNAi in vitro provided by D-Nano Therapeutics (Beijing, China). SiRNA sequences targeting CASP1 were:

siNC

5’-UUCUCCGAACGUGUCACGUTT-3’

siRNA#1

5’-GGUGUGGUUUAAAGAUUCATT-3’

siRNA#2

5’-GAAGACUCAUUGAACAUAUTT-3’

Cell growth inhibition and apoptosis

CellCounting-Lite 2.0 (Vazyme, China) was employed to assess the viability of Molm13 and THP-1 cells following treatment with siRNA and Belnacasan (MedChemExpress, China). To conduct cell death assay, the cells to be measured were collected. The Annexin V-FITC/PI Apoptosis Detection Kit (Vazyme, China) was utilized to detect cell apoptosis.

RT-qPCR

After collecting cells, the total RNA was extracted by Super FastPure Cell RNA Isolation Kit (Vazyme, China). The expression levels of CASP1 mRNA were measured using the Hieff qPCR SYBR Green Master Mix (Yeason, China).

The PCR Primers utilized for RT-qPCR were:

CASP1

F:

5’-GCTTTCTGCTCTTCCACACC-3’

R:

5’-TCCTCCACATCACAGGAACA-3’

β-actin

F:

5’-CCTGTACGCCAACACAGTGC-3’

R:

5’-ATACTCCTGCTTGCTGATCC-3’

Western blot

The Western blot experiment was carried out as described in our previous research [23]. The antibodies were used as follows: anti-CASP1 (Cat# 81482-1-RR, Proteintech, Wuhan, China) and Beta-ACTIN(Cat# 81115-1-RR, Proteintech, Wuhan, China).

Statistical analysis

The R software (v4.1.3) and GraphPad Prism8 was utilized for all statistical studies. The correlation matrices were computed using either Pearson or Spearman correlation coefficients. Student’s t-test or one-way ANOVA was used to assess the statistical significance. A Wilcoxon test was performed to compare the two groups. Survival disparities were assessed by comparing K-M methods.

Results

Development and verification of the ICD risk signature

We performed principal component analysis (PCA) on the four AML datasets (TCGA-LAML, GSE37642, TARGET, and Beat AML2.0) and extracted the expression profiles of ICD-related genes (Figure S2A and S2B). A comprehensive analysis of literature revealed the presence of 34 genes associated with ICD. Univariate Cox regression analysis on the TCGA dataset determined that 8 ICD- related genes were significantly correlated with OS in AML patients. These genes included 5 risk factors (hazards ratio [HR] > 1), namely, CASP1, CD4, IFNGR1, IL10, LY96, and 3 protective factors (HR < 1), namely, CALR, EIF2AK3, PIK3CA (Figure S2C; Table S2). Subsequently, the LASSO Cox regression analysis was conducted to determine the 5 most suitable genes for the ICD-related gene signature, as depicted in Figure S2D and S2E. Based on this foundation, we computed the risk score of our new prognostic model utilizing the subsequent formula: ICD Risk score = (− 0.662*CALR expression) + (0.009*CASP1 expression) + (0.064*IFNGR1 expression) + (0.044*IL10 expression) + (-0.726*PIK3CA expression)(Table S3). The AML samples in the TCGA, GEO, Beat AML, and TARGET datasets were categorized into high and low ICD risk groups based on the median ICD risk score. The K-M survival curve demonstrated that the OS rate of AML patients in the high risk score category was considerably inferior to that of individuals in the low risk score category (Fig. 2A and B, S3A and S3B). In addition, the distribution curve of ICD score showed that survival time was significantly shorter with increasing ICD score (Fig. 2C and D, S3C and S3D). The scatter plot showed that survival time of AML patients was negatively correlated with ICD score (Fig. 2E and F, S3E and S3F). Moreover, from the visual heatmap results, we derived the expression differences of 5 prognostic genes in low and high ICD risk groups (Fig. 2G and H, S3G and S3H).

Fig. 2
figure 2

Validating the ICD risk signature. (A, B) Overall survival, (C, D) risk scores distribution, (E, F) survival status of each patient, and (G, H) heatmaps of ICD risk signature in TCGA-LAML cohort and GSE37642 dataset

Full size image

Comprehensive analysis of independence and superiority of ICD risk signature in AML

In order to determine if ICDRS could serve as an independent prognostic factor for AML, we examined the autonomy of ICDRS in relation to clinicopathological characteristics in AML. In the train cohort, namely the TCGA-LAML cohort, the results of both univariate Cox analysis (p < 0.001, HR = 3.245 (2.126–4.954)) and multivariate Cox analysis (p < 0.001, HR = 2.334 (1.410–3.864)) indicated that ICDRS was identified as a risk factor associated with a poor clinical outcome (Fig. 3A and B). In the GSE37642 cohort, we found that ICDRS was identified as an unfavorable prognostic factor through both univariate Cox analysis (p < 0.001, HR = 1.449 (1.175–1.787)) and multivariate Cox analysis (p < 0.001, HR = 1.497 (1.203–1.863)) (Fig. 3C and D). The TARGET cohort study found that both univariate Cox analysis (p < 0.001, HR = 2.712 (1.647–4.466)) and multivariate Cox analysis (p < 0.001, HR = 3.779 (2.145–6.659)) also showed that the ICDRS was an independent prognostic predictor and was linked to a negative clinical prognosis in AML (Fig. 3E and F).

Fig. 3
figure 3

The independent prognostic analyses of combining riskscore and clinico-pathological characteristics in AML patients. (A, C, E) The left is univariate in TCGA- LAML (A), GSE37642 (C), and TARGET cohort (E). (B, D, F) The right is multivariate independent prognostic analysis in TCGA-LAML (B), GSE37642 (D), and TARGET cohort (F)

Full size image

The examination of the ROC curve for the TCGA-LAML cohort demonstrated that the ICD risk model had a high level of prediction accuracy (AUC = 0.745 for 1-year, 0.745 for 3-year and 0.872 for 5-year survival) (Fig. 4A). Furthermore, the ICD risk score had a higher AUC value compared with stratification by gender, WBC, ELN, and age for predicting 1-year, 3-year, and 5-year OS of patients with AML (Fig. 4B–D). The C-index confirmed the promising ability of ICD risk score to predict the survival of patients (Fig. 4E). Even further, our risk model showed a superior predictive value compared to the reported risk models (Fig. 4F–H) [8, 24, 25]. As Fig. 4I shows the c-index score of ICDRS was better than other risk models. In addition, based on the ICDRS and clinical characteristics, we developed a nomogram to assess the clinical survival rates of AML at 1-, 2-, and 3- years. As displayed in Fig. 4J, the nomogram model also indicated the importance of ICDRS in AML. Besides, the calibration curve showed that the OS of AML patients at 1-, 2-, and 3- years, as determined by the nomogram, was in satisfactory concordance with the actual OS (Fig. 4K). The ROC curve demonstrated that the nomogram model outperformed other independent factors in predicting the prognosis of AML (Fig. 4L).

Fig. 4
figure 4

Prognostic analysis of ICD risk score. (A) ROC curves to predict the sensiti- vity and specificity of 1-, 3- and 5-year survival based on the ICD risk scores. (B-D) Predictive accuracy of clinical characteristics and the risk model at 1-, 3-, 5- years in TCGA-LAML cohort. (E) C-index scores of clinical features and the risk model. (F-H) Comparing AUC values for different risk models at 1-, 3-, 5- years. (I) C-index scores of four risk models. (J) The nomogram integrated the Age, ELN and risk score to predict OS. (K) Calibration plots for consistency between predicted and observed 1-, 2-, 3-year survival. (L) ROC curves to predict the sensitivity and specificity of nomogram score and AML related clinical parameters

Full size image

Improvement of ELN risk stratification system by ICD risk signature

The ELN risk stratification system for AML is widely used. Firstly, we classified AML patients of datasets of TCGA, TARGET, and Beat AML into three risk groups: favorable, intermediate, and adverse, based on ELN2017. Survival analysis demonstrated that, according to the ELN2017 classification, there was an inability to satisfactorily differentiate between intermediate and adverse (Fig. 5A–C). Then we combed ICDRS and ELN2017 to adjust the risk classification. AML patients were stratified into three new groups: ELN favorable/ICD risk score high and ELN adverse/ICD risk score low patients were reassigned to the intermediate group, and ELN intermediate/ICD risk score high patients were reassigned to the adverse group (Fig. 5D–F). After stratification of AML patients, we found that combining ELN2017 with ICDRS AML stratification system could more clearly classify OS (Fig. 5G–I). Recently, with intensive research into the molecular biology of AML pathogenesis, great progress has been made in AML diagnosis, risk stratification, MRD assessment, and molecularly targeted therapies, resulting in a revision of the ELN2022 guidelines for AML. Based on ELN2022, we classified AML patients of TARGET and Beat AML into three risk groups: favorable, intermediate, and adverse. Similarly, we combined the ICDRS with ELN2022 for prognostic classification, and the reclassification had a better prognostic value than ELN2022 (Fig. 6). In addition, we also explored ICDRS and ICDRS + ELN to obtain the same results in disease-free survival (Fig. 7). Consequently, our findings demonstrated that the integration of the ICDRS into the 2017 and 2022 ELN risk classification systems had the potential to significantly enhance the predictive accuracy of survival outcomes for AML patients.

Fig. 5
figure 5

ICD risk signature refines ELN2017 classification to better stratify AML survival. (A, D, G) ELN2017 and ELN2017 + ICDRS stratification system in TCGA database; (B, E, H) ELN2017 and ELN2017 + ICDRS stratification system in TARGET database; (C, F, I) ELN2017 and ELN2017 + ICDRS stratification system in Beat AML2.0 database. The picture’s left side indicates the ELN2022 categories and the picture’s right side indicates ELN + ICDRS categories. If the lines are parallel, the patient’s catagories are not adjusted. If not, the patient’s catagories has changed

Full size image
Fig. 6
figure 6

ICD risk signature can improve ELN2022 risk stratification system. (A, B) Kaplan–Meier analysis for AML patients stratifified by ELN2022 stratifification system in TARGET and Beat AML2.0 databases; (C, D) Re-stratification of patients from the ELN2022 categories to the novel ELN2022 + ICDRS categories in TARGET and Beat AML2.0 databases. The picture’s left side indicates the ELN2022 categories and the picture’s right side indicates ELN + ICDRS categories. If the lines are parallel, the patient’s catagories are not adjusted. If not, the patient’s catagories has changed; (E, F) Kaplan–Meier analysis for AML patients stratifified by ELN2022 + ICDRS stratification system in TARGET and Beat AML2.0 databases

Full size image
Fig. 7
figure 7

ICD risk signature refines ELN2017 classification to clearly stratify Disease-Free Survival in TCGA-LAML cohort. (A) AML Disease-Free Survival in different ICD risk groups; (B) Disease-Free Survival classified by ELN 2017; (C) Disease-Free Survival classified by ELN 2017 + ICDRS

Full size image

Functional enrichment analysis of DEGs

To better understand the pathogenic molecular mechanisms responsible for the differences in prognosis, we performed genetic differential analysis for different risk groups and identified 211 up-regulated genes and 121 down-regulated genes (Fig. 8A and B). GO functional enrichment analysis revealed that these genes mostly were associated with immune receptor activity, cytokine binding, inhibitor MHC class I receptor activity, and MHC class I receptor activity (Fig. 8C). GSEA for GO and KEGG terms was also performed on the ICD high risk group, showing that DEGs were related to immune signature (Fig. 8D and F). We also analyzed GSVA between two risk groups and the results, which were also significant in T cell receptor signaling pathway, chemokine signaling pathway and B cell receptor signaling pathway, were shown in Fig. 8E. Hence, DEGs functional enrichment in different ICD risk groups pointed to immune signature.

Fig. 8
figure 8

Analysis of DEGs and functional enrichment in different ICD risk scores. (A) Volcano plots and (B) heatmap of DEGs between two risk groups. (C) GO enrichment analysis(p < 0.05, q < 0.05; BP, biological process; CC, cellular component; MF, molecular function). (D) GSEA results of GO term. (E) GSVA results of KEGG pathways. (F) GSEA results of KEGG pathways

Full size image

Characterization of the immune landscape in different risk groups

To determine the effect of ICD risk score on immune infiltration, we compared the ESTIMATE score of the ICD high risk and low risk groups. The high risk group had higher stromal, immune, and estimate scores (Fig. 9A). Subsequently, we focused on assessing disparities in immunological infiltration of immune cells between the two groups by employing various methods (Fig. 9B). We evaluated the variations in immune infiltration of 22 immune cells between the two risk groups using the CIBERSORT approach. In further detail, AML patients in the ICD high risk group had significantly higher percentages of monocytes and lower percentages of B cells naive, plasma, mast cells resting, and eosinophils (Fig. 9C). In addition, most human HLA genes and some ICPs were up-regulated in the high risk group. In contrast, the opposite trend was observed in the low risk group (Fig. 9D and E).

Fig. 9
figure 9

Tumor immune microenvironment in different risk groups. (A) The relationship between the TME score and the ICD-related subtypes. (B) The distribution traits of immunocyte infifiltration in ICD-based clusters. (C) Box plots showing differences of 22 immune cells between two ICD risk groups. (D, E) Box plots of differential expressed HLA (D) and immune checkpoints (E) genes between the high and low ICD risk groups

Full size image

Drug sensitivity and mutation analysis

We observed distinct somatic mutation profiles between the ICD low risk and ICD high risk groups. RUNX1, IDH2, and NPM1 were the most frequent mutations in high risk group, while NPM1, DNMT3A, FLT3, and KIT were more likely to be mutated in low risk group (Figure S4). In addition, TP53, associated with AML poor prognosis, had a higher mutation frequency in the high risk group than in the low risk group (7% vs. 2%). Additionally, the sensitivity score of Venetoclax, Navitoclax, ABT737, UMI-77, and AZD5991 was higher, while that of PI3K inhibitors, including Dactolisib, Taselisib, Pictilisib, AZD8186 and GNE-317, was lower in the high risk group in comparison with the AML patients of the low risk group (Fig. 10; Table S4). In conclusion, these results demonstrated that the high risk group might be more sensitive to most bcl-2 family inhibitors, which might contribute to the better prognosis of the high risk group.

Fig. 10
figure 10

Drug susceptibility analysis of different risk groups. (A) Association between two risk groups and sensitivity of Bcl-2 inhibitors. (B) Relationship between two risk groups and Mcl − 1 inhibitors. (C) Correlation of two risk groups with sensitivity of PI3K inhibitors

Full size image

Single cell analysis of ICD in AML bone marrow cells

To explore the value of ICDRS in the AML bone marrow microenvironment, we integrated a single-cell expression matrix of bone marrow cells from the GEO dataset (GSE116256). After filtering for cell quality, 5175 cells from the sixteen de novo AML samples were clustered by UMAP, resulting in 12 cell clusters (Fig. 11A). To accurately annotate cell clusters, we identified highly variable genes in each cell subpopulation (Fig. 11B and C) and featured the function of each cell subpopulation by gene function enrichment (Fig. 11D), combined with marker gene annotation for 12 cell clusters (Fig. 11E). To verify the accuracy of the cell subpopulation classification annotation, it was verified by InferCNV and bubble plots, which showed that the accuracy of annotation results was high (Fig. 11F and G). Finally, in order to further analyze the expression differences of ICDRS in different cell types, we used UMAP plots for visualization analysis. The result demonstrated that the expression of ICDRS in AML malignant subpopulation was higher than in other cell types (Fig. 11H).

Fig. 11
figure 11

Single-cell transcriptome analysis of bone marrow microenvironment to validate ICDRS related genes. (A) UMAP plots of 21 patients samples that were classified 12 clusters. (B, C) Vocalno plot and heatmap showing the characteristic genes exhibiting significant differences between cell types. (D) Differential expression markers between various enrichment cell types were identified and aligned with pathway mapping. (E) An UMAP plot displayed the comprehensive composition of cell types. (F) Heatmap showing large-scale CNV profile of each AML bone marrow cell cluster. Red and blue colors represent high and low CNV level, respectively. T cell, NK, CD4 + T, and CD8 + T cells are defined as reference cells. (G) Bubble plot showing 25 marker genes expressed in the twelve-cell types. (H) UMAP plot showing the distribution of ICD riskscore

Full size image

Validation of CASP1 in vitro experiment

Subsequently, we sought to investigate the role of CASP1 in AML. Firstly, we found that CASP1 had a high expression level in AML based on the TCGA dataset (Fig. 12A and B), and it was also closely correlated with prognosis and was of high diagnostic value (Fig. 12C and D). Through the CCLE database, we found that CASP1 was differentially expressed in different AML cell lines (Fig. 12E), and verified it by Western Blot and qPCR (Fig. 12F and G). We selected two AML cell lines with high relative expression, Molm13, and THP-1, and found that the proliferation rate of AML cells was inhibited by si-RNA knockdown of CASP1 (Fig. 12H–K). In addition, cell viability assay indicated that Belnacasan, a CASP1 inhibitor, could suppress the proliferation of Molm13 and THP-1 cells (Fig. 12L), and flow cytometric analyses revealed that the rate of apoptosis in Molm13 and THP-1 cells showed an increase with the increase of drug concentration(Fig. 12M). These results indicate that knocking down CASP1 can inhibit the proliferation and enhance the apoptotic ability of AML cells.

Fig. 12
figure 12

Validation of CASP1 by bioinformatics analysis and in vitro experiment. (A) The expression of CASP1 in Pan-cancer analysis. (B) Expression of CASP1 gene in TCGA-LAML and GTEx. (C, D) OS and ROC curve of CASP1 in TCGA-LAML. (E) Expression of different AML celllines by CCLE datasets. (F, G) Differential expression of CASP1 in HS-5 and AML cells by Western blot and RT-qPCR analysis. (H-K) Growth curves of cells were examined by CellTiter-Glo assay after the knockdown of CASP1 in Molm13 and THP-1 cells. (L) The effect of Belnacasan on cell viability were evaluated by CellTiter-Glo assay in Molm13 and THP-1 cells for 48 h. (M) Apoptosis in Molm13 and THP-1 cell lines measured by Annexin V/PI dual strain assay after 48 h treatment of Belnacansan. Data were shown as mean ± SD. N = 3, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001

Full size image

Discussion

Acute myeloid leukemia, one of the most severe hematologic malignancies, is characterized by diverse genetic abnormalities and immature myeloid progenitor cell buildup in bone marrow and peripheral blood [26, 27]. A great deal of effort has been devoted to the development of targeted therapies or combination therapies for it, but the 5-year survival rate for AML patients remains low, at around 30% [2]. Recently, the ELN 2022 risk stratification model was used to predict treatment response and prognosis [5], but it remains controversial. The growing abundance of multi-omics data presents a chance to enhance ELN modeling by integrating molecular expression data. In fact, this integration can contribute to the improvement of patient classification and treatment decisions by boosting accuracy [28].

ICD is a unique form of regulated cell death in which dead cells signal the immune system to trigger an immune response, accompanied by massive damage-associated molecular patterns (DAMP) release [29]. The release of DAMP during ICD and its binding to certain pattern recognition receptors produced by DCs initiate a series of cellular cascades that ultimately lead to the activation of innate and acquired immune responses [30, 31]. ICD has been shown to have prognostic predictive value in many tumors [32,33,34,35,36,37,38,39,40], whereas the prognostic predictive value of ICD in AML has rarely been studied.

In this study, we used the ICD-related genes set summarized by Abhishek et al. [17] and integrated literature reports to ascertain 34 ICD-related genes. We developed and validated a 5-gene ICD risk model, which combined with clinical data, can better predict 1-, 3-, and 5-year prognosis of AML patients and showed that ICD risk score was an independent prognostic factor. At the same time, our risk model showed superiority when compared with risk models proposed by other studies [8, 24, 25]. ELN includes the most significant cytogenetic and molecular abnormalities that can provide a good classification of leukemia prognosis [4]. The ELN2017 guidelines have lately been updated with the study showing that ELN2022 can diagnose and assess AML prognosis better than ELN2017 [41]. Interestingly, we reclassified the ELN guidelines combined with ICDRS for TCGA, TARGET, and Beat AML database risk stratification, and the OS of the reclassified AML cohort can be better distinguished. ICDRS can refine ELN risk classification in AML.

The TME has been demonstrated to have a significant impact on the development, advancement, and resistance to drugs in cancer [42, 43]. AML emerged in the context of a bone marrow microenvironment characterized by an immune-suppressed environment that promotes tumor growth and immune escape [44]. To fully comprehend the relationship between risk scores and immunological profiles, it is crucial to consider the immunosuppressive microenvironment of AML. Comparing the different TME scores of the two groups, the stromal score, immune score, and estimate score were higher in the high risk group than in the low risk group, indicating an adverse prognosis in the high risk group. Meanwhile, we found significant differences in HLA and some ICPs expression in two groups. CTLA4 and PD-1 have been proven to play an essential role in blocking immune evasion by T cells in AML [45, 46]. The expression of ICPs and HLA genes is a potential factor in tumor immune escape, which suppresses immune cell function and possibly leads to adverse prognosis.

Drug sensitivity analysis found that the ICD high risk group was sensitized to Bcl-2 family targets, while the low risk group was sensitized to PI3K inhibitors. Venetoclax, a very effective and specific inhibitor of Bcl-2, has received FDA approval for the treatment of chronic lymphocytic leukemia and AML. It is recommended in clinical guidelines and commonly prescribed for therapeutic purposes [47]. A close follow-up is being developed for similar drugs targeting Bcl-2 or similar function protein, particularly Mcl-1 inhibitors. Moreover, PI3K/AKT inhibitors are an effective strategy for the treatment of AML [48]. Our findings may provide prospective treatment options for AML patients and influence individualized oncology treatment. Bulk RNA seq can only reflect the average expression of overall mRNA levels in AML patients and does not accurately describe the expression of malignant hematologic tumor cells in the bone marrow microenvironment. We demonstrated by single-cell analysis that ICDRS was highly expressed by malignant tumor cells in the bone marrow microenvironment. In addition, we examined the expression and biological function of CASP1 by database analysis and experiment. CASP1, a key component of the inflammasome, is known to have profound effects on tumor formation and progression in several human cancers [49, 50]. Additionally, a report demonstrated that Increased expression of CASP1 may be a molecular marker for a high risk subset of AML. Moreover, the increase in CASP1 expression in AML can have extensive impacts on cell proliferation, inflammatory processes, and immunological responses [51]. We also demonstrated that the CASP1 gene was able to affect the proliferation and apoptosis of AML cells in vitro experiments.

However, this study has several limitations. Firstly, it is based on retrospective data retrieved from public databases, and thus still requires verification through real-world studies. Secondly, there are five key genes related to ICD, yet we have only verified CASP1 so far. Therefore, further verification of the remaining four genes is necessary.

Conclusions

This study aimed to create an ICDRS for AML, serving as an independent prognostic tool. Integrating this signature into ELN2017 and ELN2022 classifications enhanced AML prognosis stratification. We discovered that ICD high-risk AML patients are linked to immune suppression in the tumor microenvironment and identified Bcl-2 inhibitors for high-risk patients, and PI3K inhibitors for low-risk patients. The ICDRS was validated through scRNA analysis and CASP1 gene function experiments. This research provides a valuable resource for AML prognosis and therapy by focusing on ICD-associated genes.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, Potter NE, Heuser M, Thol F, Bolli N, et al. Genomic classification and prognosis in Acute myeloid leukemia. N Engl J Med. 2016;374(23):2209–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tamamyan G, Kadia T, Ravandi F, Borthakur G, Cortes J, Jabbour E, Daver N, Ohanian M, Kantarjian H, Konopleva M. Frontline treatment of acute myeloid leukemia in adults. Crit Rev Oncol/Hematol. 2017;110:20–34.

    Article  PubMed  Google Scholar 

  3. Grimwade D, Hills RK, Moorman AV, Walker H, Chatters S, Goldstone AH, Wheatley K, Harrison CJ, Burnett AK. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood. 2010;116(3):354–65.

    Article  CAS  PubMed  Google Scholar 

  4. Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T, Dombret H, Ebert BL, Fenaux P, Larson RA, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129(4):424–47.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, Ebert BL, Fenaux P, Godley LA, Hasserjian RP, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345–77.

    Article  PubMed  Google Scholar 

  6. Mrózek K, Kohlschmidt J, Blachly JS, Nicolet D, Carroll AJ, Archer KJ, Mims AS, Larkin KT, Orwick S, Oakes CC, et al. Outcome prediction by the 2022 European LeukemiaNet genetic-risk classification for adults with acute myeloid leukemia: an Alliance study. Leukemia. 2023;37(4):788–98.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chen E, Jiao C, Yu J, Gong Y, Jin D, Ma X, Cui J, Wu Z, Zhou J, Wang H, et al. Assessment of 2022 European LeukemiaNet risk classification system in real-world cohort from China. Cancer Med. 2023;12(24):21615–26.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chen Z, Song J, Wang W, Bai J, Zhang Y, Shi J, Bai J, Zhou Y. A novel 4-mRNA signature predicts the overall survival in acute myeloid leukemia. Am J Hematol. 2021;96(11):1385–95.

    Article  CAS  PubMed  Google Scholar 

  9. Li R, Ding Z, Jin P, Wu S, Jiang G, Xiang R, Wang W, Jin Z, Li X, Xue K, et al. Development and validation of a Novel Prognostic Model for Acute myeloid leukemia based on Immune-related genes. Front Immunol. 2021;12:639634.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kroemer G, Galassi C, Zitvogel L, Galluzzi L. Immunogenic cell stress and death. Nat Immunol. 2022;23(4):487–500.

    Article  CAS  PubMed  Google Scholar 

  11. Galluzzi L, Vitale I, Warren S, Adjemian S, Agostinis P, Martinez AB, Chan TA, Coukos G, Demaria S, Deutsch E et al. Consensus guidelines for the definition, detection and interpretation of immunogenic cell death. J Immunother Cancer. 2020;8(1).

  12. Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Herold T, Metzeler KH, Vosberg S, Hartmann L, Röllig C, Stölzel F, Schneider S, Hubmann M, Zellmeier E, Ksienzyk B, et al. Isolated trisomy 13 defines a homogeneous AML subgroup with high frequency of mutations in spliceosome genes and poor prognosis. Blood. 2014;124(8):1304–11.

    Article  CAS  PubMed  Google Scholar 

  14. Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, Long N, Schultz AR, Traer E, Abel M, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562(7728):526–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S, Verga JA, Stephansky J, Pastika TJ, Lombardi Story J, et al. Single-cell RNA-Seq reveals AML hierarchies relevant to Disease Progression and Immunity. Cell. 2019;176(6):1265–e12811224.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER 3rd, Kalocsay M, Jané-Valbuena J, Gelfand E, Schweppe DK, Jedrychowski M, et al. Quantitative proteomics of the Cancer Cell Line Encyclopedia. Cell. 2020;180(2):387–e402316.

  17. Garg AD, De Ruysscher D, Agostinis P. Immunological metagene signatures derived from immunogenic cancer cell death associate with improved survival of patients with lung, breast or ovarian malignancies: a large-scale meta-analysis. Oncoimmunology. 2016;5(2):e1069938.

    Article  PubMed  Google Scholar 

  18. Jentzsch M, Bischof L, Ussmann J, Backhaus D, Brauer D, Metzeler KH, Merz M, Vucinic V, Franke G-N, Herling M et al. Prognostic impact of the AML ELN2022 risk classification in patients undergoing allogeneic stem cell transplantation. Blood Cancer J. 2022;12(12).

  19. Wilop S, Chou W-C, Jost E, Crysandt M, Panse J, Chuang M-K, Brümmendorf TH, Wagner W, Tien H-F. Kharabi Masouleh B: a three-gene expression-based risk score can refine the European LeukemiaNet AML classification. J Hematol Oncol. 2016;9(1).

  20. Li R, Ding Z, Jin P, Wu S, Jiang G, Xiang R, Wang W, Jin Z, Li X, Xue K et al. Development and validation of a Novel Prognostic Model for Acute myeloid leukemia based on Immune-related genes. Front Immunol. 2021;12.

  21. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for Comprehensive Analysis of Tumor-infiltrating Immune cells. Cancer Res. 2017;77(21):e108–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang H, Zhou J, Ma X, Jiao C, Chen E, Wu Z, Zhang Y, Pan M, Cui J, Luan C et al. Dexamethasone enhances venetoclax-induced apoptosis in acute myeloid leukemia cells. Med Oncol. 2023;40(7).

  24. Jiang F, Mao Y, Lu B, Zhou G, Wang J. A hypoxia risk signature for the tumor immune microenvironment evaluation and prognosis prediction in acute myeloid leukemia. Sci Rep. 2021;11(1):14657.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fu D, Zhang B, Wu S, Zhang Y, Xie J, Ning W, Jiang H. Prognosis and characterization of Immune Microenvironment in Acute myeloid leukemia through identification of an autophagy-related signature. Front Immunol. 2021;12:695865.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Marando L, Huntly BJP. Molecular Landscape of Acute myeloid leukemia: prognostic and therapeutic implications. Curr Oncol Rep. 2020;22(6):61.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Thomas D, Majeti R. Biology and relevance of human acute myeloid leukemia stem cells. Blood. 2017;129(12):1577–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ng SW, Mitchell A, Kennedy JA, Chen WC, McLeod J, Ibrahimova N, Arruda A, Popescu A, Gupta V, Schimmer AD, et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature. 2016;540(7633):433–7.

    Article  CAS  PubMed  Google Scholar 

  29. Galluzzi L, Buqué A, Kepp O, Zitvogel L, Kroemer G. Immunogenic cell death in cancer and infectious disease. Nat Rev Immunol. 2017;17(2):97–111.

    Article  CAS  PubMed  Google Scholar 

  30. Paludan SR, Reinert LS, Hornung V. DNA-stimulated cell death: implications for host defence, inflammatory diseases and cancer. Nat Rev Immunol. 2019;19(3):141–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gong T, Liu L, Jiang W, Zhou R. DAMP-sensing receptors in sterile inflammation and inflammatory diseases. Nat Rev Immunol. 2020;20(2):95–112.

    Article  CAS  PubMed  Google Scholar 

  32. Xu G, Jiang Y, Li Y, Ge J, Xu X, Chen D, Wu J. A novel immunogenic cell death-related genes signature for predicting prognosis, immune landscape and immunotherapy effect in hepatocellular carcinoma. J Cancer Res Clin Oncol. 2023;149(18):16261–77.

    Article  CAS  PubMed  Google Scholar 

  33. Zhang P, Zhang H, Tang J, Ren Q, Zhang J, Chi H, Xiong J, Gong X, Wang W, Lin H, et al. The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy. Aging. 2023;15(19):10305–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Xu J, Yang J, Pan X, Wang J. Prognostic and immunotherapeutic significance of immunogenic cell death-related genes in colon adenocarcinoma patients. Sci Rep. 2023;13(1):19188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Li C, Guo F, Zhai J, Liu X, Li Z, Xu B. An immunogenic cell death-related signature for prediction of prognosis and response to immunotherapy in breast cancer. Chin Med J. 2024;137(4):487–9.

    Article  PubMed  Google Scholar 

  36. Chen L, Lin J, Wen Y, Chen Y, Chen CB. Development and validation of a model based on immunogenic cell death related genes to predict the prognosis and immune response to bladder urothelial carcinoma. Front Oncol. 2023;13:1291720.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dong Y, Yu X, Song H, Chen Q, Zheng B, Ji X, Xu M, Liu J, Sun X, Wang Q et al. Identification of molecular subtypes and prognostic model to reveal immune infiltration and predict prognosis based on immunogenic cell death-related genes in lung adenocarcinoma. Cell Cycle (Georgetown Tex). 2024:1–18.

  38. Li Q, Tang Y, Wang T, Zhu J, Zhou Y, Shi J. Novel immunogenic cell death-related risk signature to predict prognosis and immune microenvironment in lung adenocarcinoma. J Cancer Res Clin Oncol. 2023;149(1):307–23.

    Article  CAS  PubMed  Google Scholar 

  39. Liu Z, Liu B, Feng C, Li C, Wang H, Zhang H, Liu P, Li Z, He S, Tu C. Molecular characterization of immunogenic cell death indicates prognosis and tumor microenvironment infiltration in osteosarcoma. Front Immunol. 2022;13:1071636.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hu Y, Cai J, Ye M, Mou Q, Zhao B, Sun Q, Lou X, Zhang H, Zhao Y. Development and validation of immunogenic cell death-related signature for predicting the prognosis and immune landscape of uveal melanoma. Front Immunol. 2022;13:1037128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lachowiez CA, Long N, Saultz J, Gandhi A, Newell LF, Hayes-Lattin B, Maziarz RT, Leonard J, Bottomly D, McWeeney S, et al. Comparison and validation of the 2022 European LeukemiaNet guidelines in acute myeloid leukemia. Blood Adv. 2023;7(9):1899–909.

    Article  PubMed  Google Scholar 

  42. Turley SJ, Cremasco V, Astarita JL. Immunological hallmarks of stromal cells in the tumour microenvironment. Nat Rev Immunol. 2015;15(11):669–82.

    Article  CAS  PubMed  Google Scholar 

  43. Hinshaw DC, Shevde LA. The Tumor Microenvironment innately modulates Cancer Progression. Cancer Res. 2019;79(18):4557–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pyzer AR, Stroopinsky D, Rajabi H, Washington A, Tagde A, Coll M, Fung J, Bryant MP, Cole L, Palmer K, et al. MUC1-mediated induction of myeloid-derived suppressor cells in patients with acute myeloid leukemia. Blood. 2017;129(13):1791–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hobo W, Hutten TJA, Schaap NPM, Dolstra H. Immune checkpoint molecules in acute myeloid leukaemia: managing the double-edged sword. Br J Haematol. 2018;181(1):38–53.

    Article  PubMed  Google Scholar 

  46. Zhang L, Gajewski TF, Kline J. PD-1/PD-L1 interactions inhibit antitumor immune responses in a murine acute myeloid leukemia model. Blood. 2009;114(8):1545–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pollyea DA, Bixby D, Perl A, Bhatt VR, Altman JK, Appelbaum FR, de Lima M, Fathi AT, Foran JM, Gojo I, et al. NCCN guidelines insights: Acute Myeloid Leukemia, Version 2.2021. J Natl Compr Cancer Network: JNCCN. 2021;19(1):16–27.

    Article  PubMed  Google Scholar 

  48. Wang K, Ou Z, Deng G, Li S, Su J, Xu Y, Zhou R, Hu W, Chen F. The Translational Landscape revealed the sequential treatment containing ATRA plus PI3K/AKT inhibitors as an efficient strategy for AML therapy. Pharmaceutics. 2022;14(11).

  49. Martinon F, Burns K, Tschopp J. The inflammasome: a molecular platform triggering activation of inflammatory caspases and processing of proIL-beta. Mol Cell. 2002;10(2):417–26.

    Article  CAS  PubMed  Google Scholar 

  50. Zitvogel L, Kepp O, Galluzzi L, Kroemer G. Inflammasomes in carcinogenesis and anticancer immune responses. Nat Immunol. 2012;13(4):343–51.

    Article  CAS  PubMed  Google Scholar 

  51. Liu J, Zhao M, Feng X, Zeng Y, Lin D. Expression and prognosis analyses of CASP1 in acute myeloid leukemia. Aging. 2021;13(10):14088–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to express our appreciation to UCSC Xena (http://xena.ucsc.edu/), GEO (https://www.ncbi.nlm.nih.gov/geo/) and Beat AML2.0 databases (https://biodev.github.io/BeatAML2/) for providing the open- access databases utilized in this study.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82370175 and 81200371); Postgraduate Innovation Research and Practice Program of Anhui Medical University (Grant No. YJS20230016); Key Research and development Project of Anhui Province (Grant No. 201904a07020057); Research Foundation of Anhui Medical University (Grant No.2020xkj166); Clinical Trial Initiative Projects of The First Affiliated Hospital of Anhui Medical University (Grant No. LCYJ2021YB009); Research Foundation of Anhui Institute of Translational Medicine (Grant No. 2021zhyx-C32).

Author information

Authors and Affiliations

  1. Department of Hematology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China

    Changqing Jiao, Xiaoyu Ma, Jianling Cui, Bobin Su, Fei Xu, Enbo Chen, Junjie Zhou, Jifei Dai, Mengya Pan, Zhangbiao Long & Jian Ge

  2. Anhui Medical University, Hefei, 230032, China

    Changqing Jiao, Xiaoyu Ma, Jianling Cui, Bobin Su, Fei Xu, Enbo Chen, Jifei Dai, Mengya Pan, Zhangbiao Long & Jian Ge

  3. National Clinical Medical Research Centre for Blood System Diseases, Anhui Branch Centre, Hefei, 230032, China

    Jifei Dai, Zhangbiao Long & Jian Ge

Authors
  1. Changqing Jiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoyu Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jianling Cui
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Bobin Su
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Fei Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Enbo Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Junjie Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Jifei Dai
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Mengya Pan
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Zhangbiao Long
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Jian Ge
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

JG conceived the idea and provided the leadership. CQJ, XYM, and JLC performed the analyses. BBS, FX, EBC, JJZ, JFD, ZBL, and MYP discussed the data. CQJ performed the in vitro experiment. CQJ and XYM wrote the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Jian Ge.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

All authors declare no confict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiao, C., Ma, X., Cui, J. et al. Potential value of immunogenic cell death related-genes in refining European leukemiaNet guidelines classification and predicting the immune infiltration landscape in acute myeloid leukemia. Cancer Cell Int 25, 52 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03670-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03670-9

Keywords

Cancer Cell International

ISSN: 1475-2867

Contact us