Screen Necroptosis-related Genes and evaluate the prognostic capacity, clinical value and the affection of their copy number variations in acute myeloid leukemia

Screen Necroptosis-related Genes and evaluate the prognostic capacity, clinical value and the affection of their copy number variations in acute myeloid leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Screen Necroptosis-related Genes and evaluate the prognostic capacity, clinical value and the affection of their copy number variations in acute myeloid leukemia Dake Wen, Ru Yan, Lin Zhang, Haoyang Zhang, Xuyang Chen, Jian Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4313518/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in BMC Cancer → Version 1 posted 12 You are reading this latest preprint version Abstract Background： Acute myeloid leukemia (AML) is an aggressive hematological neoplasm. Little improvement in survival rates has been achieved over the past few decades. Necroptosis has relationship with certain types of malignancies outcomes. Here, we evaluated the diagnostic ability, prognostic capacity of necroptosis-related genes (NRGs) and the affection of their copy number variations (CNVs) in AML. Methods： Necroptosis-related differentially expressed genes (NRDEGs) were acquired after intersecting Differentially expressed genes (DEGs) from Gene Expression Omnibus(GEO) database with NRGs from GeneCards, Molecular Signatures Database (MsigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. The expression levels of 6 hub-NRDEGs were validated in vitro. mRNA-miRNA and mRNA-TF interaction networks with hub-NRDEGs were screened by Cytoscape @ . Single-sample gene set enrichment analysis (ssGSEA) was utilized to calculate correlationships between hub-NRDEGs and immune cells. CNVs analysis on hub-NRDEGs was carried out based on TCGA-LAML datasets from the TCGA database. Kaplan–Meier(K-M) survival analyses was utilized to evaluate the prognostic values along with COX model. Results： 6 hub-NRDEGs ( SLC25A5, PARP1, CTSS, ZNF217, NFKB1, PYGL ) were obtained and their expression changes derived from CNVs in AML were visualized. 65 mRNA-miRNA and 80 mRNA-TF interaction networks with hub-NRDEGs was screened. ssGSEA result showed the expression of RAPR1 is inversely related with CD56 dim natural killer cell and CTSS positive with MDSCs in AML. K-M results demonstrated ZNF217 had significant difference in duration of survival in AML patients. Cox regression models revealed hub-NRDEGs had better predictive power at year-1 and year-5. Conclusion： These screened NRDEGs could be exploited as clinical prognosis predictions in AML patients, as well as potential biomarkers for diagnosis and therapeutic targeting. Necroptosis Acute myeloid leukemia (AML) Necroptosis-Related differentially expressed Genes(NRDEGs) regulated cell death（RCD） copy number variations (CNVs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Acute myeloid leukemia (AML) is an invasive hematologic neoplasm and comprises the highest proportion (62%) of fatalities attributed to all subtypes of leukemia. [1] Many cytotoxic therapies and radiotherapies were shown to trigger malignant cells to undergo apoptosis. [2] BCL-2 inhibition, which can trigger intrinsic apoptosis, was a wonderful instance showing how a rapid development of drugs resulting from the molecular comprehension of regulated cell death (RCD) which altered the therapeutic option available to AML patients. However, 10% to 50% of freshly diagnosed AML individuals remains ineffective to BCL-2 inhibition and the overall survival curves do not plateau. [3, 4] Chemotherapy-resistant AML, typically propelled by clonal evolution, has a poor outcome, and the impairment of cell cycle arrest as a clinically important factor. [5] It is imperative to put further research into therapeutic mechanisms of AML. Copy number variations (CNVs) is an abbreviation for "duplication, inversion, or deletion" of a DNA sequence that surpasses 50 bp when compared to a reference genome. [6] By modifying gene dosage, CNVs affect gene expression, adaptation, and phenotypic variation, increase the susceptibility to harmful genetic alterations. [7] CNAs typically affect larger fractions of the genome in cancers than do any other type of somatic genetic alteration. [8] Nearly 40% of cancer-related genes are interrupted by a CNV. [9] Necroptosis, a form of caspase-independent RCD, is triggered by the same stimuli with apoptosis and their signaling pathways are highly interconnected. [10-12] As a fail-proof mechanism of cell death that occurs as apoptosis failed be triggered, necroptosis has demonstrated the ability to defend against specific kinds of cancers and improve patient’s outcomes. [13] Nevertheless, necroptosis can induce inflammatory reactions and is reported to encourage the proliferation, metastasis, and immunosuppression related to cancer. [14, 15] Because of the positive relationship between cell death and inflammation within necroptosis, necroptosis induction in apoptosis refractory AML is considered a potent second line and a most promising anti-leukemia therapeutic strategies. [16] Here we screened the hub-NRDEGs in AML through establishing diagnostic and prognostic models, evaluated the prognostic capacity, clinical value and the affection of their CNVs in AML. Materials and Methods 1.1 Data sources The flowchart of this study is shown in Figure 1. Relevant AML datasets GSE7186, GSE23143 and GSE84334 all originate from Homo sapiens were downloaded from the GEO database. GPL4861 is the data platform GSE7186 used. We selected 29 samples, 23 tissue samples from AML individuals and 6 cases from health individuals, to be included in this analysis; the data platform used by GSE23143 are GPL8650, GPL8651, GPL8652, GPL8653, GPL8654, we selected 87 cases of AML samples from GPL8653 to be included in this analysis; the data set GSE84334 using the GPL570 data platform and comprises 45 cases of AML samples in total. All samples were selected for inclusion in this analysis. (See supplementary table 1) Necroptosis-related genes (NRGs) are collected from the database GeneCards [17] , MsigDB [18] and literature [19] . The GeneCards database shares integrated data about human genomes. We use the word "Necroptosis" to search genes related to necroptosis whose molecular type is mRNA. 654 NRGs were gathered in total. The MSigDB database is a pre-annotated functional gene set specially collected for GSEA analysis. We used the word "Inflammatory" as a search keyword and obtained a total of 8 NRGs through the "GOBP NECROPTOTIC SIGNALING PATHWAY" pathway. We obtained a total of 159 NRGs through literature [19] . The above results were taken and combined. A total of 756 NRGs were obtained. (See supplementary table2) 1.2 AML cells line , bone marrow mononuclear cells (BM-MNCs) and the ethics approval Human Acute Monocytic Leukemia Cells (THP-1) (C6960, Beyotime Biotech Inc) were obtained from Beyotime Biotech Inc .Shanghai. Bone marrow from 3 healthy donors were collected at Nanjing Medical University affiliated Wuxi People’s Hospital, Wuxi Children’s Hospital between July 2022 and December 2023. All health donors who donated bone marrow for the study gave informed consent. The procedures for the collection and use of samples in this investigation were authorized by the Ethics Committee of Nanjing Medical University Affiliated Wuxi People's Hospital, Wuxi Children’s Hosptial. The ethics approval ,“WXCH2023-11-090”, certified that the study was performed in accordance with the ethical standards as laid down in the 2013 Declaration of Helsinki. 1.3 Necroptosis-related Differentially expressed genes To obtain the DEGs between AML and Normal groups, we merged the data sets aforementioned and performed batch processing (see detail in supplementary data). We used the R package limma [20] to perform differential analysis on the expression profile data of the amalgamated datasets. The genes were selected basing on the criteria of |logFC|>1 with p.adj<0.05 and used as DEGs for further research. The volcano map was drawn using the R package ggplot2 [21] to display the results of differential analysis, and the heat map related to DEGs was created using the R package pheatmap. NRDEGs were indentified after intersecting the selected DEGs and NRGs. RT-qPCR was used to validated the differences in expression of identified NRDEGs between AML and Normal groups according to Human Acute Monocytic Leukemia Cells (THP-1) and Bone marrow mononuclear cells (BM-MNCs) of health donors (see detail in supplementary data). 1.4 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis GO [22] analysis is a prevalent method in conducting comprehensive investigations on functional enrichment on a grand scale. It includes three main categories: biological process (BP), molecular function (MF), and cellular component (CC). Kyoto Encyclopedia of Genes and Genomes [23] database encompasses a broad range of information concerning genomes, biological pathways, diseases, medicines, etc. Annotations to KEGG and GO using the R package clusterProfiler [24] were performed according to NRDEGs. The statistical significance entrance screening criteria was p-value<0.05 with q (FDR-value) <0.25, Benjamini-Hochberg (BH) was the statistical approach used to adjust p-values. Pathway maps of the findings carried out by KEGG enrichment analysis were poltted utilizing the R package Pathview. [25] 1.5 Gene Set Enrichment Analysis(GSEA) GSEA [26] is an approach for assessing the distribution pattern of a predetermined set of genes among a gene table summoned by phenotypic correlation in order to estimate how it contributes to the phenotypic. After differential genes in the combined data set were sorted, R package clusterProfiler was utilized. The following criteria were utilized for the GSEA: seeds is 10000, every gene set contains 10 genes or more, the largest number of genes is limited to 500, and the statistical method used for p-value adjustment was the Benjamini-Hochberg (BH). From MSigDB database, the gene set "c2.all.v2023.1.Hs.entrez" was acquired, and the statistical significance entrance screening criteria was p value <0.05 with q (FDR value) <0.25. 1.6 Diagnosis model based on NRDEGs Support vector machine (SVM) [27] is one supervised machine learning method capable of learning from data and making decisions. It incorporates automatic complexity control to prevent over-fitting and utilizes a flexible representation of the class boundaries. SVM model on the NRDEGs was constructed. According to the number of NRDEGs with the maximum accuracy rate and the minimum error rate, outcome was screened while a solitary global minimum was discovered in polynomial time. Random Forest [28] is a computational method that employs ensemble learning to integrate many decision trees. It is a part of the bagging integration method, which incorporates bootstrap aggregation and self-service sampling. When a specific specimen needs to be forecasted, each tree among the forest is predicted for that specimen, which is accomplished by constructing multiple decision trees. Then the prediction results are obtained from these prediction results by voting to pick the final result. We use the R package randomForest [29] to carry out the model according to NRDEGs expression of the combined data set, the parameter is set.seed(234), ntree = 1000. We selected the genes screened by both SVM and RFE analysis for subsequent analysis. functional similarity analysis of Friends analysis and the semantic of GO annotations provides a numerical approach for assessing genetic and genomic similarities, and has emerged as a fundamental foundation for various approaches of bioinformatics investigation. The NRDEGs' GO semantic similarity is calculated by R package GOSemSim [30] and the geometric mean of NRDEGs at the BP, CC and MF levels is further calculated to obtain the final score, the functional similarity analysis results are finally showed with the R package ggplot2. 1.7 Least absolute shrinkage and selection operator regression To obtain a more accuracy result, we use the R package glmnet [31] and carried out LASSO [32] regression on NRDEGs using the following settings: seed=2020, family = "binomial". 1000 rounds were run to avoid over-fitting. By adding a punitive item (λ × the absolute value of slope) to a linear regression model, over-fitting is mitigated and the model's ability of generalization is enhanced. Then, we displayed each gene's molecular expression in the diagnostic model by utilizing the LASSO regression results. DCA (Decision curve analysis) [33] is a basic method used in clinical evaluating for diagnostic test and to make decisions about test selection and use. We use DCA to assess the LASSO model's accuracy and resolution, and create a DCA plot using the R package ggDCA and visualize the LASSO regression model’s effect. 1.8 Copy number variations analysis associated with Hub-NRDEGs To assess the affection of CNVs on hub-NRDEGs in AML,we analyzed CNVs in TCGA-LAML groups. We acquired the TCGA-LAML datasets from the TCGA database and designated them as AML groups after selecting the sub-options "copy number variation" and "masked copy number segment". After the data was processed, we performed CNVs analysis using GISTIC2.0 [34] , Genes in CNV regions were annotated using Genome Research Consortium Human build 38 (GRCh38) as the reference genome. All analytic parameters were initialized to their corresponding default values. R package maftools [35] was used to display the distribution of CNVs and R package RCircos [36] to plot the location of hub-NRDEGs association with CNVs on chromosomes. The gains and losses of CNVs of the hub-NRDEGs were visualized in a barplot. 1.9 Single-sample Gene Set Enrichment Analysis (ssGSEA) ssGSEA [37] ,an extension GSEA approach that analyzes enrichment scores for individual samples and gene sets separately rather than multiple objects. Every score of ssGSEA reflects how concurrently up- or down-regulated the input gene in a particular gene set is in a sample. We calculate the infiltration abundance of immune cells in LASSO risk groups of AML and performed correlation analysis between NRDEGs’ expressions and infiltration abundance of immune cells in the samples. 1.10 Kaplan-Meier analysis Kaplan-Meier analysis is a statistical technique allows the construction of survival curves for a specific outcome and to assess the relative risk of a given exposure and the incidence rate of a relevant clinical event in a cohort of individuals followed-up for a predefined time period. In order to assess the impact of the 6 hub-NRDEGs on survival and prognosis in AML, we performed Kaplan-Meier analysis in a new data set GSE37642 for the hub-NRDEGs 1.11 Cox Model To assess the predict value of the clinical prognostic of hub-NRDEGs, we carried out a univariate Cox regression analysis of the age(>= 65, < 65) and screened NRDEGs’ expression as the clinical variables in a new data set GSE37642 and mapped the forest graph. Genes with P.adj < 0.1 were then selected to create a multivariate Cox regression model. We predict the probability of progression-free survival at year- 1, year-3, and year-5 in AML individuals using nomograms on the basis of multivariate Cox regression. The nomogram is a plot of the functions of multiple independent variables in a plane-diagonal coordinate system using a set of interrelated lines. Using a multivariable regression analysis, the regression model's variables are scored by a set of scales, the overall score is measured to forecast the possibility of occurrences. Finally, we evaluated the nomograms' accuracy and resolution with an adjusted curve. Calibration plots are used to estimate the prognostic impact of models on reality outcomes by visualizing the alignment of the probabilities of the real and model predictions in different situations, and to analyze the alignment of the models with the real situation, based on the Cox regression method. R package rms was used to demonstrate nomograms and calibration curves and R package ggDCA was used to estimated the ability of the nomogram model to predict survival outcomes of year-1, year-3, and year-5 in AML patients.. 1.12 Statistical Analysis Data manipulation and statistical analyses in this research were carried out using R software (Version 4.2.1).The independent Student t test was utilized for determining statistical significance of variables with normal distributions when comparing two sets of continuous variables. The Mann-Whitney U test was utilized to evaluate differences between variables with non-normal distributions. Using the Chi-square test or Fisher's exact test, the statistical significance of two categories of variables was compared and analyzed. Spearman correlation analysis was used to measure the correlation coefficients between distinct compounds. Unless otherwise specified, all statistical P values in are considered as bidirectional (symmetrical \two-tailed), and P<0.05 is considered to have statistically significant. Results 2.1 Identification of NRDEGs in AML After eliminating the batching impact in the merged datasets comprised of GSE7186, GSE23143, and GSE84334 (see supplementary Figure S1A–D), we divided the data into the AML and Normal groups. Differential expression analysis was conducted and the result was displayed in a volcano map (Figure 2A). 178 genes was identified fulfilling the criteria |logFC| > 1 and p. adj < 0.05 and defined as DEGs, with 70 displayed up-regulation and 108 down-regulation. The DEGs and NRGs obtained aforementioned were intersected and 12 NRDEGs ( SLC25A5, TUBB6, PLEKHA5, PARP1, CTSS, IVNS1ABP, ZNF217, NFKB1, PYGL, PAWR, CXCL1, JUN ) were identified (Figure 2C). Heat-map (Figure 2B) demonstrated the 12 NRDEGs’ expression in AML and Normal groups. Histogram of group comparison (Figure 2E) revealed the differences of the expression levels of the 12 NRDEGs between AML and Normal groups (P<0.05). RT-qPCR validated the differences in expression of selected NRDEGs between AML and Normal groups according to THP-1 and BM-MNCs of health donors. (see supplementary Figure S2) The correlation coefficients among distinct molecules measured by Spearman correlation analysis was visualized in the correlation heat map (Figure 2D). 2.2 Enrichment analysis of NRDEGs To find out the biological function of the identified NRDEGs, GO and KEGG enrichment analysis were carried out separately and in combination with NRDEGs by providing logFC from DEGs and calculating a Z-score for each NRDEG. GSEA was performed to estimate how these gene expressions impact of on the occurrence of AML. GO results (Figure 3A，see detail in supplementary Table 4) showed NRDEGs are mainly enriched in vesicle lumen(GO:0031983),cytoplasmic vesicle lumen(GO:0060205),secretory granule lumen (GO:0034774), etc. and transcription regulator Complex(GO:0005667) in cellular components (CC) and DNA-binding transcription repressor activity, RNA polymerase II-specific (GO:0001227), R−SMAD binding (GO:0070412), SMAD binding(GO:0046332) , etc. in molecular functions (MF). KEGG result (Fig. 3B,see detail in supplementary Table 4) showed NRDEGs are mainly enriched in Apoptosis(map04210), IL−17 signaling pathway(map04657), NF-kappa B signaling pathway(map04064) ,TNF signaling pathway (map04668), Necroptosis(map04217),NOD-like receptor signaling pathway(map04621). We used a chord diagram (Figure 3C) to demonstrate the detailed GO terms that each NRDEG enriched in and a circleplot (Figure 3D) to show the distribution of the NRDEGs enriched in GO analysis that was in combination with NRDEGs by providing logFC. The relationship between the 12 NRDEGs and the outputs of GO and KEGG enrichment analysis was displayed in a circular network diagram (Figure 3E). Pathway heat-map (Figure 3F) displayed the relationship between NRDEGs and GO terms while circular network diagram (Figure 3G) displayed the relationship between the results of KEGG enrichment analysis. The crossed lines denote the corresponding molecules and the annotations for each item. GSEA results revealed a high enrichment of genes in MANALO HYPOXIA DN (Figure 4A), REACTOME TP53 REGULATES TRANSCRIPTION OF DNA REPAIR GENES (Figure. 4B), HAMAI APOPTOSIS VIA TRAIL UP (Figure 4C), VERHAAK AML WITH NPM1 MUTATED UP (Figure 4D), KEGG MAPK SIGNALING PATHWAY (Figure 4E), PEREZ TP53 TARGETS (Figure 4F), KEGG JAK STAT SIGNALING PATHWAY (Figure 4G), ZHENG IL22 SIGNALING UP (Figure 4H), etc. (See detail in supplementary table 5) 2.3 Machine learning identified 6 hub-NRDEGs and validated the models’ accuracy We used SVM-RFE algorithms to perform feature selection in the 12 NRDEGs in order to screen diagnostic markers in the combined data set for AML. SVM analysis obtained the number of genes with the minimal rate of error (Figure 5A) and the maximum rate of accuracy (Figure 5B), achieved the maximum accuracy with a gene count of 7 ( PAWR, CTSS, SLC25A5, ZNK217, NFKB1, PYGL, PARP1 ). Selected IncNodePurity (improvement in Node Purity) > 0.3 as the criterion, 9 NRDEGs ( SLC25A5, PLEKHA5, PARP1, CTSS, IVNS1ABP, ZNF217, NFKB1, PYGL, TUBB6 ) of AML(Figure 5C-D) were obtained in RFE according to the expression levels of 12 NRDEGs. After intersecting the outcomes of the two algorithms, 6 NRDEGs ( SLC25A5, PARP1, CTSS, ZNF217, NFKB1, PYGL ) were obtained (Figure 5E). Friends functional similarity analysis was conducted through the R package GOSemSim [38] and the ranking of functional importance of the 6 NRDEGs was shown (Figure 5F). LASSO regression model was developed (Figure 6A) and variable trajectory (Figure 6B) was obtained. Calibration analysis showed excellent agreement between the actual and predicted probability of the model (see supplementary Figure S4). The 6 NRDEGs were then tagged as hub-NRDEGs and visualized in the forest plot (Figure 6C).ROC curve for the expression levels of AML due to the LASSO model showed a superior diagnostic accuracy (Figure 6D, AUC = 1). 2.4 GSEA according to the LASSO model showed NRDEGs are mainly enriched in pathways associate with AML To estimate the impaction of NRDEGs expression on the occurence of AML, new GSEA was performed based on the DEGs in the combined data set according to the LASSO high and low risk groups. Ridge plot (Figure 7A) were generated and the GSEA outcome based on LASSO models revealed the genes were significant enriched in ROSS AML WITH PML RARA FUSION (Figure 7B), ROSS AML OF FAB M7 TYPE (Figure 7C), ALCALAY AML BY NPM1 LOCALIZATION UP (Figure 7D), VERHAAK AML WITH NPM1 MUTATED DN (Figure 7E) ,etc. 2.5 CNV frequency of Hub-NRDEGs To analyze CNVs associated with six hub-NRDEGs in AML, , after the data was processed, we performed CNV analyze using GISTIC2.0 based on CNV datasets of TCGA-LAML from the TCGA database. Distribution of CNVs in TCGA-LAML group was show in Fig 8A. We then displayed the chromosomal localization maps of Hub-NRDEGs associated with CNVs in a circle diagram.(Fig.8B) The frequency of CNVs related to Hub-NRDEGs was presented in a barplot (Figure. 8C).The outcomes showed that PARP1 ， PYGL get only gain, NFKB1 ， CTSS get both gain and loss while gain was more than loss, SLC25A5 , ZNF217 get more gain than loss. 2.6 PARP1 and CTSS are associated with immune cell infiltration in the AML ssGSEA algorithm was utilized to reveal immune cells’ infiltration profiles between LASSO risk groups in AML. The comparison diagram (Figure 9A) showed the infiltration abundance of immune cells between LASSO risks groups and density-related heat-map (Figure 9B) revealed co-relationship between hub-NRDEGs and immune cells according to the infiltrate abundance. The correlation scatter plots according to the correlation analysis showed that the RARP1 is mild inversely related to the CD56 dim NK cell(R=-0.46 p＜0.001，Figure 9C）and CTSS is moderate positively associated with MDSC（R=0.55, p＜0.001，Figure 9D） 2.7 COX models showed hub-NRDEGs has better predictive value and ZNF217 has significant difference in duration of survival in AML patients To access the predictive value of hub-NRDEGs and the effect of them on survival, univariate Cox regression analysis was first conducted. Forest plot (Figure 11A) showed the association between expression of hub-NRDEGs and age in GSE37642. Factors with p<0.1 were selected for multivariate Cox regression analysis and nomograms (Figure 11B) was created. The calibration curves (Figure 11C-E) for the prognostic of the nomograms utilizing Calibration analysis revealed the model had better predictive power at year-1 and year-5. DCA plot (Figure 11F-H) demonstrated that the blue lines which represent the models for year-1, year-3 and year-5 were significantly higher than the red lines (all positive) and the gray lines (all negative), indicating that the model had a better predictive value. Kaplan-Meier analysis on GSE37642 (Figure 10A-F) showed ZNF217 expression had a statistically significant difference in the time to survival indicating it have predictive value for survival in patients with AML. Discussion AML is the most prevalent type of acute leukemia in adults and rank as the second in children. Within the age groups of younger than 20, 20 to 49, 50 to 64, and ages 65 and above, the relative 5-year survival rate over a 5- year period declines from 69% to 58%, 35%, and 9%. [39] Limited advancements have been achieved during the past decades and researches have been expanding the approach to AML treatment by exploring molecular pathways specific to AML cell proliferation and survival. Apoptosis is known to be induced by the majority of chemotherapies and radiotherapies in reaction to DNA damage or cellular stress in cancer cells. [2, 40] Drug resistance and carcinogenesis are frequently caused by resistance to apoptosis, which leads to chemotherapeutic failure. [40] So methods of triggering non-apoptotic forms of RCD is appealing to be discovered as alternative cancer therapies in order to conquer it. Necroptosis shares a mechanistic resemblance with apoptosis with their signaling pathways highly interconnected. [11, 12] The key regulators of necroptosis are Receptor-Interacting Protein-1 (RIP1), Receptor-Interacting Protein-3 (RIP3), and Mixed Lineage Kinase Domain-Like (MLKL). [10] It has been documented that lots of important molecules in necroptotic signaling pathways are down regulated in various malignancy cell types. [10] Necroptosis inducers have demonstrated significant benefit in apoptosis-resistant cancers. [41] SMAC mimetics in conjunction with caspase-8 inhibitions have been domonstrated to induce necroptosis in preclinical studies of AML. [42] Chidamide, an HDAC inhibitor, was more effective in treating FLT3-ITD positive AML when RIPK1 was inhibited. [43] In an animal model carrying a mutant AML driver gene in transplanted bone marrow cells, leukemogenesis was dramatically increased after the knockout of RIPK3, RIPK3 -/- mice had a shorter lifespan than RIPK3 +/+ mice. [13] Targeting necroptosis pathway may be proven promising in AML treatment. GO results showed the terms among the CC components such as vesicle lumen , cytoplasmic vesicle lumen and secretory granule lumen all were child terms of organelle lumen(GO:0043233 ), indicated NRDEGs act mainly on the secretion delivery and trafficking of intracellular components that responsible for intercellular information exchange, functional regulation. MF components as R−SMAD binding, SMAD binding are child terms of protein binding(GO:0005515). SMAD are critically important intracellular signal transducers for regulating cellular process. It form a complex with other components within the cell then transferred into the nucleus where it directly binding to the DNA or interacting with other cofactors [44] that may also act in DNA-binding transcription repressor activity, RNA polymerase II-specific (GO:0001227), play an important role in promoting tumorigenesis and cancer progression. [44] SMAD proteins are also intracellular signal transducers in the transforming growth factor-β ( TGF-β ) signaling pathway [45] ,which also play an important role in tumorigenesis. These above indicate that NRDEGs mainly influence signal transduction, which in turn may promote leukemogenesis and the advancement of AML. Among the KEGG results, IL-17 signaling pathway, NOD-like receptor signaling pathway primarily regulate the expression of antimicrobial peptides, cytokines, and chemokines. They are responsible for detecting various pathogens and generating innate immune responses or promoting inflammatory pathology in autoimmune disease. In TNF signaling pathway, the key cytokine TNF can activate a variety of intracellular signaling pathways, including apoptosis, cell survival, inflammation, and immunology. And the above three pathways can activate the downstream NF-κB, MAPK cascade pathway, cytokine production and apoptosis. NF-κB pathway itself was also included in our KEGG results, suggesting most NRDEGs can function via the NK-κB pathway in AML. NF-κB family is a transcription factor family that plays a significant role in multiple physiological and pathological processes, including inflammation, tumorigenesis, immunological response, cell proliferation and apoptosis. [46] Approximately 40% of AML individuls exhibit increased activity of NF-κB [47] .AML cells endure apoptosis when NF-κB is inhibited. [48] NFkB1, a member with no transcriptional activity among the NF-κB family [46] , was shown to be a important NRDEG in the result of Friends functional similarity analysis. NFKB1 must combine with RelA, RelB, or RelC to form a heterodimer to regulates the transcription of its target gene. [46, 49] The activation of NFKB1 homodimers and complexes of Bcl-3 have been observed in nasopharyngeal carcinoma. [50-52] The overexpression of NFKB1 has been demostrated both in rodent skin cancer and non-small cell lung cancer. [53, 54] In addition to NFKB1 itself, many crucial genes implicated in leukemogenesis such as RUNX1 and CEBP-A, were affected by NF-κB inhibition. [55] On the basis of our results, it may be feasible to provide a convincing rationale for targeting the NFKB1 in AML. Among the GSEA outcomes for LASSO results, gene sets contained in ROSS AML WITH PML RARA FUSION and ROSS AML OF FAB M7 TYPE were identified for pediatric AML through an ANN-based supervised learning algorithm, and include the 100 Top probe sets in AML subtype with PML-RARA fusion and subtype FAB-M7 each, an overall prognostically diagnostic accuracy of 93% was achieved. [56] ALCALAY AML BY NPM1 LOCALIZATION UP and VERHAAK AML WITH NPM1 MUTATED DN pathway separately contains Genes up-regulated [57] and down-regulated in AML individuals exhibiting mutated NPM1 [58] , and ALCALAY AML BY NPM1 LOCALIZATION UP pathway is crucial for p53 stabilization after stress. [57] These show the important roles for hub-NRDEGs in AML. Altered or misled immune responses may significantly impacted cancer initiation and progression. [59] ssGSEA algorithms showed the immune cell infiltration between LASSO risk groups on AML and revealed RAPR1 is negative correlate with CD56 dim NK cells while CTSS is positive correlate with MDSCs, indicating the two NRDEGs may affect AML via immune effects. Poly(ADP-ribose) polymerase 1 ( PARP1 ) ,a most well-known member of PARP proteins family, functions as vital factor in maintenance genetic stability. [60] It mediates DNA damage repair rely on binding to single- and double-strand breaks within the DNA [60] and regulates the cellular process including cell cycle, protein stabilization, protein-protein interaction, intracellular localization, and transcriptional. [61] According to reports, overexpression of PARP1 shows poorer OS (overall survival) rates in AML patients. [62] In preclinical investigations, AML with specific genetic alterations such as IDH1/2, RUNX1-RUNX1T1, PML-RARA, FLT3-ITD, co-occurring lesions such as P53 or BCOR was extremely sensitive to PARPi. [63] NK cells are induced to undergo PARP1-dependent apoptosis by leukemia cells. [64] According to the expression levels of CD56 and CD16 on the surface, NK cells can be mainly categorized into the canonical CD56 dim CD16 + subgroup which demonstrates anti-leukemia efficacy [65] and the CD56 bright CD16 subgroup that typically considered as immune-modulatory NK cells with less antitumor activity. [66] PARP1 can help leukemia germ cells selective in evading immune surveillance by NK cells through represses the expression of NKG2D ligands in leukemia germ cells, of which that bind to the NKG2D receptor on the surface of NK cells and activated CD8 + T cells to function as a co-stimulatory signal. [67] The aforementioned process renders AML cells more sensitive to the tumor necrosis factor-α-related apoptosis-inducing ligand (TRAIL), which is a crucial effector molecule of NK cells. [68] Similar to our result, studies had reported that high level of CD56 dim CD16 + NK cell count is related with worse outcomes in AML patients and the proportions of baseline according NK cells and their subgroups at the time of AML diagnosis is particular relevant. [66] Cathepsin-S ( CTSS ), a lysosomal cysteine cathepsin family member, plays a crucial function in the immunological response. [69] It’s suppression of Treg cells may decrease total immunity of T cells under normal circumstance but increases the CD8 + T-cell’s immunity when exposed to tumor cells [70, 71] .When activated, T cells enhance the polarization of M2-type macrophages and dendritic cells [71] , which promote myeloid-derived suppressor cells (MDSCs), a subset of myeloid cells with powerful immune suppression function, [72] and tumor-associated macrophages (TAM), then enhance the proliferation of Treg cells as alternative to cytotoxic CD8 + T cells. [71] T cells also represent the primary targets of MDSCs. [72] MDSCs inhibit T-cell proliferation and responses both in AML cell lines and animal models. [73] When elevated, MDSCs contribut to impeded immune surveillance in the bone marrow niche in AML subsets. [74] Higher MDSCs frequencies were linked to worse prognoses and shorter OS in a meta-analysis from 16 separate researches encompassing 1864 malignant disease individuals, [75] which is similar to our results. Cox model showed the 6 hub-NRDEGs had better predictive power and Kaplan-Meier curve showed the expression of zinc-finger protein 217( ZNF217 ) had positive correlation with survival duration in our result. ZNF217 , rarely reported in AML, is a transcription factor with oncogenic properties. It displayed 19 interactions with miRNAs and 45 with transcription factors in our results (see supplementary Figure S5, table 6 and table 7). ZNF217 plays a crucial role in the advancement of tumor development in variety malignancies encompassing both initial and late phases. [76] At early stages, ZNF217 interfered the apoptotic pathway by attenuating apoptotic signals induced by dysfunctional telomere [77] and to sustaining proliferative signals, resisting cell death [1] , promoting immortalization [1] ,which prolongs the lifespan of tumor cells and rises the possibility of DNA mutation. [2] While at later stages, by conferring resistance to chemotherapy. [77] TRF (Telomere repeat-binding factor)-1 and TRF-2 are two proteins that bind in a complex with other proteins to the double-stranded region of the telomere. [78] TRF-1 can induce p53-dependent apoptosis while TRF2 acts as a major protective factor that can trigger p53-independent apoptosis. Following ZNF217 overexpression, lethal treatments such as doxorubicin and a negative TRF2 mutant and TRF1 are unsuccessful in both benign and malignant cell lines. [77] ZNF217 adjusted the pattern of DNA methylation at important gene promoters to impede a certain non-coding RNA (ncRNA) or to improve the epitranscriptome [79] , function as a mediator that inhibits tumor-suppressive RNAs. [79] Overexpression of ZNF217 can prevent the uptake of cofactors essential for the active of demethylation for p15ink4b, a direct target gene of the ZNF217/CoREST transcriptional complex, thus impairs the anti-proliferation in the TGF-dependent pathway. [80] This impairs the cell cycle during the G1-S transition, leading to a dramatic increase of the cell numbers. [81] When binding to TGF2 or TGF3 promoters, ZNF217 was found to stimulate the expression and consequently resulted in production of activated TGFs. blocking of the TGF- pathway resulted in a reverse change of ZNF217-dependent EMT and invasion properties. [82] FTO, a canonical obesity gene which plays a significant role in carcinogenesis. [83] , is found to be a direct target gene mediated by Zfp217 (the murine homolog of ZNF217 ). [10] When binding to promoter of FTO , Zfp217 up-regulates FTO expression [10] ，hence, provides insights into the significance of Zfp217 in adipocyte metabolism as it pertains to malignant neoplasms. ZNF217 also regulates molecular signaling pathways (eg.PI3K/AKT, MAPK, ERK, BMP and mitochondrial apoptosis pathways) [79] to reprogram pro-metastatic circuits [76] that regulate signature properties in cancer cells. Stimulating of PI3K/Akt pathways by ZNF217 will result in resistance to many cancer medications (eg. trastuzumab, paclitaxel, and tamoxifen). [84, 85] Bcl-2 and Bcl-XL overexpression and down-regulation of Bad, Bak, and Bax are reversed, and factors downstream of p53 display less variation in their expression levels when ZNF217 is overexpressed. [86, 87] CNVs are associated with chemotherapy response. [8] Refractory disease occurred more often in AML with CNA marker compared with other AML. [8, 88] The World Health Organization (WHO) (2016) classified cytogenetic CNV abnormalities as the single greatest predictor of complete remission (CR) and OS in AML. [89] CNVs analysis showed PARP1 , PYGL , CTSS , NFKB1 get more gain than loss in TCGA-LAML groups, means their expression were up-regulated from baseline derived from CNV amplifications. PYGL , CTSS and NFKB1 are down-regulated NRDEGs in AML, as the hub-NRDEGs mainly responsible for tumorigenesis, the down-regulations of them originally meant favorable for the outcomes of AML, while the CNVs offset this portion of the benefits. PARP1 was up-regulated in AML and CNVs may enhanced its pro-leukemic activity. ZNF217 displayed oncogenic properties but our results demonstrated the expression of ZNF217 has positive correlation to survival possibility in AML, partly may be attributed to the fact that ZNF217 is a down-regulated DEG in AML, but extra loss in CNVs is undoubtedly responsible for additional expression decline of ZNF217 and possibly the attenuation of its leukemogenesis effect. Although our research provided theoretical underpinnings and research suggestions, it still has its limitations. First, this study were conducted across retrospective datasets and is the unavoidable issue of batch effects thus may increasing the possibility of bias. Second, there are insufficient publicly available external makes it difficult to evaluate the model's reliability, more validation data are required to confirm the applicability of our model. Third, because of the different subtypes of AML, the findings might not be applicable to all AML individuals. Finally, no additional functional or mechanistic research was carried out. Our result might provide novel clues for diagnosis, therapy, and prognosis of AML. Therefore, further research is required to validate the aforementioned conclusions. Conclusion Our study screened hub-NRDEGs in AML, demonstrated the affection of CNVs on them, showed relationship between hub-NRDEGs and immune cells, evaluated the prognostic capacity and clinical value of them on AML. These findings may contribute to the comprehensive understanding of the genomic pattern and molecular mechanism associated with necroptosis in AML, and would highlight the hub-NRDEGs especially ZNF217 as clinical prognostic predictors and therapeutic targets in AML. Abbreviations Abbreviation Definition AML acute myeloid leukemi NRGs Necroptosis-related genes CNVs Copy number variations NRDGEs Necroptosis-related differentially expressed genes. DEGs Differentially expressed genes GEO Gene Expression Omnibus MsigDB Molecular Signatures Database K-M Kaplan–Meier RCD regulated cell death GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes SVM Support Vector Machine RFE Random Forest, GSEA Gene Set Enrichment Analysis LASSO Least absolute shrinkage and selection operator ROC receiver operating characteristic ssGSEA Single-sample gene set enrichment analysis IncNodePurity improvement in Node Purity DCA Decision curve analysis BM bone marrow, COX Proportional Hazards Regression CR complete remission OS overall survival BM-MNCs Bone marrow mononuclear cells MDSCs myeloid-derived suppressor cells TAM tumor-associated macrophages RT-qPCR real-time polymerase chain reaction WHO The World Health Organization TRF Telomere repeat-binding factor RIP1 Receptor-Interacting Protein-1 RIP3 Receptor-Interacting Protein-3 MLKL Mixed Lineage Kinase Domain-Like Declarations Ethics approval and consent to participate: The procedures for the collection and use of samples in this investigation were authorized by the Ethics Committee of Nanjing Medical University Affiliated Wuxi People's Hospital, Wuxi Children’s Hosptial. All health donors gave informed consent. The ethics approval,“ WXCH2023-11-090 ” ,certified that the study was performed in accordance with the ethical standards as laid down in the 2013 Declaration of Helsinki . Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Consent for publication: Not applicable Availability of data and materials The datasets analysed during the current study are available in: Dataset Repositories GSE7186 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7186 GSE84334 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84334 GSE23143 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23143 GSE37642 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37642 CNVs datasets in TCGA-LAM https://portal.gdc.cancer.gov Author Contributions: Conceptualization: DK.W, JZ, Data analysis: DK.W, Cell experiment: RY, Code Modification Correction: HY. 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16:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4313518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4313518/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-13439-y","type":"published","date":"2025-01-13T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73947405,"identity":"6c4ca26e-2b65-439d-a288-04642baf6fd7","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":889968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Flowchart shows the algorithms used in screening hub-NRDEGs and the establishment of diagnostic and prognostic models.\u003cstrong\u003eDEG\u003c/strong\u003es: Differentially expressed genes. \u003cstrong\u003eNRGs:\u003c/strong\u003e Necroptosis-related\u003cstrong\u003e \u003c/strong\u003egenes, \u003cstrong\u003eBM:\u003c/strong\u003e bone marrow, \u003cstrong\u003eSVM: \u003c/strong\u003eSupport Vector Machine, \u003cstrong\u003eRFE: \u003c/strong\u003eRandom Forest, \u003cstrong\u003eNRDEGs:\u003c/strong\u003e Necroptosis-related differentially expressed genes. \u003cstrong\u003eGSEA: \u003c/strong\u003eGene Set Enrichment Analysis. \u003cstrong\u003eLASSO: \u003c/strong\u003eLeast absolute shrinkage and selection operator. \u003cstrong\u003essGSEA\u003c/strong\u003e: Single-sample gene set enrichment analysis. \u003cstrong\u003eK-M: \u003c/strong\u003eKaplan-Meier, \u003cstrong\u003eCOX:\u003c/strong\u003e Cox Proportional Hazards Regression. \u003cstrong\u003eCNVs:\u003c/strong\u003e copy number variations.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/fe5d2b375e5c21647fb91a05.png"},{"id":73947404,"identity":"51d1801f-5d29-4f8e-b25c-96ce0cfbf02a","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":648562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of necroptosis related differentially expressed genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Volcano map for DEGs between the AML and Normal groups. Marked genes are NRDEGs. \u003cstrong\u003eB.\u003c/strong\u003e Heat map for expression levels of the 12 NRDEGs according to the AML and Normal group. \u003cstrong\u003eC.\u003c/strong\u003eVenn diagram for DEGs and NRGs. \u003cstrong\u003eD.\u003c/strong\u003e Correlation heat map of NRDEGs. \u003cstrong\u003eE. \u003c/strong\u003eHistogram of group comparison for NRDEGs according to the AML-Normal groups.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/03d5f07f08457877e1922791.png"},{"id":73947406,"identity":"21fe3df3-c5c0-430d-911d-ca0146256508","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1190566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO/KEGG enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA and B.\u003c/strong\u003e Bubble plots of GO and KEGG enrichment analysis on NRDEGs. \u003cstrong\u003eC and D.\u003c/strong\u003e Chord diagram and Circle diagram of GO and KEGG enrichment analyses that was in combination with 12 NRDEGs by providing logFC. \u003cstrong\u003eE.\u003c/strong\u003e Circular network of the relationship between NRDEGs and GO/KEGG analysis. \u003cstrong\u003eF. \u003c/strong\u003ePathway heat map of GO functional enrichment analysis on 12 NRDEGs. \u003cstrong\u003eG.\u003c/strong\u003e Circular network diagram of the relationship between the results of KEGG enrichment analysis on 12 NRDEGs.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/806e91b38444aa73d112aef8.png"},{"id":73947407,"identity":"5be08524-7ee7-481c-a569-a2643b57f705","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":743068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenes in the combined data set revealed a significant enrichment in MANALO HYPOXIA DN \u003cstrong\u003e(A)\u003c/strong\u003e, REACTOME TP53 REGULATES TRANSCRIPTION OF DNA REPAIR GENES \u003cstrong\u003e(B)\u003c/strong\u003e, HAMAI APOPTOSIS VIA TRAIL UP \u003cstrong\u003e(C)\u003c/strong\u003e, VERHAAK AML WITH NPM1 MUTATED UP \u003cstrong\u003e(D)\u003c/strong\u003e, KEGG MAPK SIGNALING PATHWAY (E), PEREZ TP53 TARGETS \u003cstrong\u003e(F)\u003c/strong\u003e, KEGG JAK STAT SIGNALING PATHWAY \u003cstrong\u003e(G)\u003c/strong\u003e, ZHENG IL22 SIGNALING UP (H)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/df96069b1399d4db312a2791.png"},{"id":73948794,"identity":"3f353491-d8c2-472b-ad2c-be221d9bb784","added_by":"auto","created_at":"2025-01-16 09:12:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":357131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSVM analysis and construction of random forest model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B.\u003c/strong\u003e The number of genes with the lowest error rate(\u003cstrong\u003eA\u003c/strong\u003e) and with the highest accuracy rate(\u003cstrong\u003eB\u003c/strong\u003e) obtained by the SVM algorithm. \u003cstrong\u003eC.\u003c/strong\u003e Diagnostic markers of AML displayed by the random forest model based on the descending order of IncNodePurity \u0026gt; 0.3. \u003cstrong\u003eD.\u003c/strong\u003e Model training error plot for the Random Forest algorithm. \u003cstrong\u003eE. \u003c/strong\u003eVenn diagram for the results of two algorithms. \u003cstrong\u003eF\u003c/strong\u003e. Functional similarity analysis of Friends of the intersection genes of the two algorithms.The increase in node purity is shown by IncNodePurity (improvement in Node Purity). As the purity of a node increases, the presence of impurities decreases (that is, the smaller the Gini coefficient).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/f34d5e0d219c7f6a952fa24c.png"},{"id":73947409,"identity":"96fd5678-2f05-4903-9810-5cef99d35793","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":298819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Variable trajectory plot of the LASSO model.\u003cstrong\u003e B. \u003c/strong\u003eScreening factor plot of the LASSO model. \u003cstrong\u003eC. \u003c/strong\u003eForest plot of hub-NRDEGs in the LASSO model. \u003cstrong\u003eD.\u003c/strong\u003e ROC curve for the expression levels of 6-hub-NRDEGs between LASSO risk groups of AML in the combined data set. The closer the value of AUC is to 1, the better the power of diagnose is. When the value of AUC is greater than 0.9, the diagnostic power is high. \u003cstrong\u003eROC: \u003c/strong\u003ereceiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/c75e30a1f008b50bf6e97deb.png"},{"id":73947413,"identity":"a79176ac-5915-4c89-be03-6a9f6dc87b14","added_by":"auto","created_at":"2025-01-16 09:04:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":581314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e GSEA rideg plot. \u003cstrong\u003eB-D\u003c/strong\u003e. In the LASSO high and low risk groups, the sample genes were significantly enriched in ROSS AML WITH PML RARA FUSION (\u003cstrong\u003eB\u003c/strong\u003e), ROSS AML OF FAB M7 TYPE (\u003cstrong\u003eC\u003c/strong\u003e), ALCALAY AML BY NPM1 LOCALIZATION UP (\u003cstrong\u003eD\u003c/strong\u003e), VERHAAK AML WITH NPM1 MUTATED DN (\u003cstrong\u003eE\u003c/strong\u003e). \u003cstrong\u003eGSEA:\u003c/strong\u003e Gene Set Enrichment Analysis. \u003cstrong\u003eAML:\u003c/strong\u003e Acute myeloid leukemia.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/315ba7bd3721d13835d969f9.png"},{"id":73947408,"identity":"b9049b19-484e-4fba-81cf-d9540894fdc9","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1473296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCNV frequency associated with 6 Hub-NRDEGs in the TCGA-LAML disease group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eDistribution of CNVs on chromosomes in the TCGA-LAML group. \u003cstrong\u003eB:\u003c/strong\u003e Chromosomal localization maps of Hub-NRDEGs associated with CNVs. \u003cstrong\u003eC. \u003c/strong\u003eThe frequency of CNVs related to Hub-NRDEGs. \u003cstrong\u003eTCGA，\u003c/strong\u003eThe Cancer Genome Atlas. \u003cstrong\u003eCNV，\u003c/strong\u003eCopy Number Variations.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/f410ef12805496a40d19e14d.png"},{"id":73947427,"identity":"aaa8d05c-5c3f-4de6-a87e-de6385c74937","added_by":"auto","created_at":"2025-01-16 09:04:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1034009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-sample gene set enrichment analysis（ssGSEA）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Comparative of immune cells in the combined data set of LASSO high- and low-risk groups for AML. \u003cstrong\u003eB.\u003c/strong\u003e Immune cell infiltration density-related heat map in combined datasets.\u003cstrong\u003e C-D.\u003c/strong\u003e Correlation scatter plots of the association of \u003cem\u003eRAPR1\u003c/em\u003e with CD56\u003csup\u003edim\u003c/sup\u003e NK cell (\u003cstrong\u003eC\u003c/strong\u003e) and \u003cem\u003eCTSS\u003c/em\u003e association with MDSCs (\u003cstrong\u003eD\u003c/strong\u003e). Symbol ns equal to P ≥ 0.05 was not significant; symbol * equal to P \u0026lt; 0.05 was significant; symbol ** equal to P \u0026lt; 0.01 was highly significant; symbol*** equal to P \u0026lt; 0.001 was extremely significant.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/36abc50d3bfd20b6711650b6.png"},{"id":73947411,"identity":"798d918b-4f96-49b9-9be9-251ed27fed10","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":421208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-F\u003c/strong\u003e Kaplan–Meier curve for 6 hub-NRDEGs in the data set GSE37642. \u003cstrong\u003eK-M Curve:\u003c/strong\u003eKaplan-Meier curve\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/9c122879d6f765f8731fab18.png"},{"id":73948795,"identity":"bde6c382-a77c-4417-8707-7491a953dfa5","added_by":"auto","created_at":"2025-01-16 09:12:23","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":457808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-Multivariate Cox modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Forest plots for univariate Cox regression analyses. \u003cstrong\u003eB.\u003c/strong\u003e Nomograms for multivariate Cox regression analysis. \u003cstrong\u003eC-E\u003c/strong\u003e. Calibration curves at year-1, year-3, and year-5 for the nomograms of the multivariable Cox regression model. \u003cstrong\u003eF-G. \u003c/strong\u003eDCA plots at year-1, year-3, and year-5 in the Cox regression model. Vertical lines at the top of the calibration curve plots represent the corresponding probability of survival (distribution of survival rate) for a given sample. The more the dense is, the greater the probability of survival in this sample. The DCA map's x-axis reflects the probability threshold or threshold Probability, while the y-axis reflects the net gain. \u003cstrong\u003eDCA,\u003c/strong\u003edecision curve analysis.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/c51e5175c242c6818b8f66ba.png"},{"id":74284440,"identity":"ce26f8c8-ad2a-49e5-bc93-0433e25509b9","added_by":"auto","created_at":"2025-01-20 16:05:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9718374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/31eedcc4-fccd-4604-af42-3733a4b98158.pdf"},{"id":73947412,"identity":"9e196831-52e1-41e1-8d11-06660d4d9b3e","added_by":"auto","created_at":"2025-01-16 09:04:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6838172,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-4313518/v1/df0a4d03de181ad067ae5714.docx"}],"financialInterests":"","formattedTitle":"Screen Necroptosis-related Genes and evaluate the prognostic capacity, clinical value and the affection of their copy number variations in acute myeloid leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emyeloid leukemia (AML) is an invasive hematologic neoplasm and\u0026nbsp;comprises the highest proportion (62%) of fatalities attributed to all subtypes of leukemia.\u003csup\u003e[1]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMany cytotoxic therapies and radiotherapies were shown to trigger malignant cells to undergo apoptosis.\u003csup\u003e[2]\u003c/sup\u003e BCL-2 inhibition, which can trigger intrinsic apoptosis, was a wonderful instance showing how a rapid development of drugs resulting from the molecular comprehension of regulated cell death (RCD) which altered the therapeutic option available to AML patients. However, 10% to 50% of freshly diagnosed AML individuals remains ineffective to BCL-2 inhibition and the overall survival curves do not plateau.\u003csup\u003e[3, 4]\u003c/sup\u003e Chemotherapy-resistant AML, typically propelled by clonal evolution, has a poor outcome, and the impairment of cell cycle arrest as a clinically important factor.\u003csup\u003e[5]\u003c/sup\u003e It is imperative to put further research into therapeutic mechanisms of AML.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCopy number variations (CNVs) is an abbreviation for \u0026quot;duplication, inversion, or deletion\u0026quot; of a DNA sequence that surpasses 50 bp when compared to a reference genome.\u003csup\u003e[6]\u003c/sup\u003e By modifying gene dosage, CNVs affect gene expression, adaptation, and phenotypic variation, increase the susceptibility to harmful genetic alterations.\u003csup\u003e[7]\u003c/sup\u003e CNAs typically affect larger fractions of the genome in cancers than do any other type of somatic genetic alteration.\u003csup\u003e[8]\u003c/sup\u003e Nearly 40% of cancer-related genes are interrupted by a CNV.\u003csup\u003e[9]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eNecroptosis, a form of caspase-independent RCD, is triggered by the same stimuli with apoptosis and their signaling pathways are highly interconnected.\u003csup\u003e[10-12]\u003c/sup\u003e As a fail-proof mechanism of cell death that occurs as apoptosis failed be triggered, necroptosis has demonstrated the ability to defend against specific kinds of cancers and improve patient\u0026rsquo;s outcomes.\u003csup\u003e[13]\u003c/sup\u003e Nevertheless, necroptosis can induce inflammatory reactions and is reported to encourage the proliferation, metastasis, and immunosuppression related to cancer.\u003csup\u003e[14, 15]\u003c/sup\u003e Because of the positive relationship between cell death and inflammation within necroptosis, necroptosis induction in apoptosis refractory AML is considered a potent second line and a most promising anti-leukemia therapeutic strategies.\u003csup\u003e[16]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eHere we screened the hub-NRDEGs in AML through establishing diagnostic and prognostic models, evaluated the prognostic capacity, clinical value and the affection of their CNVs in AML.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Data sources\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flowchart of this study is shown in Figure 1. Relevant AML datasets GSE7186, GSE23143 and GSE84334 all originate from Homo sapiens were downloaded from the GEO database. GPL4861 is the data platform GSE7186 used. We selected 29 samples, 23 tissue samples from AML individuals and 6 cases from health individuals, to be included in this analysis; the data platform used by GSE23143 are GPL8650, GPL8651, GPL8652, GPL8653, GPL8654, we selected 87 cases of AML samples from GPL8653 to be included in this analysis; the data set GSE84334 using the GPL570 data platform and comprises 45 cases of AML samples in total. All samples were selected for inclusion in this analysis. (See supplementary table 1)\u003c/p\u003e\n\u003cp\u003eNecroptosis-related genes (NRGs) are collected from the database GeneCards \u003csup\u003e[17]\u003c/sup\u003e , MsigDB \u003csup\u003e[18]\u003c/sup\u003e and literature\u003csup\u003e[19]\u003c/sup\u003e. The GeneCards database shares integrated data about human genomes. We use the word \u0026quot;Necroptosis\u0026quot; to search genes related to necroptosis whose molecular type is mRNA. 654 NRGs were gathered in total. The MSigDB database is a pre-annotated functional gene set specially collected for GSEA analysis. We used the word \u0026quot;Inflammatory\u0026quot; as a search keyword and obtained a total of 8 NRGs through the \u0026quot;GOBP NECROPTOTIC SIGNALING PATHWAY\u0026quot; pathway. We obtained a total of 159 NRGs through literature\u003csup\u003e[19]\u003c/sup\u003e. The above results were taken and combined. A total of 756 NRGs were obtained. (See supplementary table2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 AML cells line , bone marrow mononuclear cells (BM-MNCs) and the ethics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman Acute Monocytic Leukemia Cells (THP-1) (C6960,\u0026nbsp;Beyotime Biotech Inc) were obtained from Beyotime Biotech Inc .Shanghai.\u0026nbsp;Bone marrow from 3 healthy donors were collected at Nanjing Medical University affiliated Wuxi People\u0026rsquo;s Hospital, Wuxi Children\u0026rsquo;s Hospital\u0026nbsp;between July 2022 and December 2023. All health donors who donated bone marrow for the study gave informed consent.\u0026nbsp;The procedures for the collection and use of samples in this investigation were authorized by the Ethics Committee of\u0026nbsp;Nanjing Medical University Affiliated Wuxi People\u0026apos;s Hospital, Wuxi Children\u0026rsquo;s Hosptial. The ethics approval ,\u0026ldquo;WXCH2023-11-090\u0026rdquo;, certified that the study was performed in accordance with the ethical standards as laid down in the 2013 Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Necroptosis-related Differentially expressed genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain the DEGs between AML and Normal groups,\u0026nbsp;we merged the data sets aforementioned and performed batch processing (see detail in supplementary data). We used the R package limma\u003csup\u003e[20]\u003c/sup\u003eto perform differential analysis on the expression profile data of the amalgamated datasets. The genes were selected basing on the criteria of |logFC|\u0026gt;1 with p.adj\u0026lt;0.05 and used as DEGs for further research. The volcano map was drawn using the R package ggplot2\u003csup\u003e[21]\u003c/sup\u003e to display the results of differential analysis, and the heat map related to DEGs was created using the R package pheatmap.\u003c/p\u003e\n\u003cp\u003eNRDEGs were indentified after intersecting the selected DEGs and NRGs. RT-qPCR was used to validated the differences in expression of identified NRDEGs between AML and Normal groups according to Human Acute Monocytic Leukemia Cells (THP-1) and Bone marrow mononuclear cells (BM-MNCs) of health donors (see detail in supplementary data).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO\u003csup\u003e[22]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eanalysis is a prevalent method in conducting comprehensive investigations on functional enrichment on a grand scale. It includes three main categories: biological process (BP), molecular function (MF), and cellular component (CC). Kyoto Encyclopedia of Genes and Genomes\u003csup\u003e[23]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003edatabase encompasses a broad range of information concerning genomes, biological pathways, diseases, medicines, etc. Annotations to KEGG and GO using the R package clusterProfiler\u003csup\u003e[24]\u003c/sup\u003e were performed according to\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNRDEGs.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe statistical significance entrance screening criteria was p-value\u0026lt;0.05 with q (FDR-value) \u0026lt;0.25, Benjamini-Hochberg (BH) was the statistical approach used to adjust p-values. Pathway maps of the findings carried out by KEGG enrichment analysis were poltted utilizing the R package Pathview.\u003csup\u003e[25]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Gene Set Enrichment Analysis(GSEA)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA\u003csup\u003e[26]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eis an approach for assessing the distribution pattern of a predetermined set of genes among a gene table summoned by phenotypic correlation in order to estimate how it contributes to the phenotypic. After differential genes in the combined data set were sorted, R package clusterProfiler was utilized. The following criteria were utilized for the GSEA: seeds is 10000, every gene set contains 10 genes or more, the largest number of genes is limited to 500, and the statistical method used for p-value adjustment was the Benjamini-Hochberg (BH).\u0026nbsp;From MSigDB database, the gene set \u0026quot;c2.all.v2023.1.Hs.entrez\u0026quot; was acquired, and the statistical significance entrance screening criteria was p value \u0026lt;0.05 with q (FDR value) \u0026lt;0.25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6 Diagnosis model based on NRDEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupport vector machine (SVM)\u003csup\u003e[27]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e is one supervised machine learning method capable of learning from data and making decisions. It incorporates automatic complexity control to prevent over-fitting and utilizes a flexible representation of the class boundaries. SVM model on the NRDEGs was constructed. According to the number of NRDEGs with the maximum accuracy rate and the minimum error rate, outcome was screened while a solitary global minimum was discovered in polynomial time. Random Forest\u003csup\u003e[28]\u003c/sup\u003e is a computational method that employs ensemble learning to integrate many decision trees. It is a part of the bagging integration method, which incorporates bootstrap aggregation and self-service sampling. When a specific specimen needs to be forecasted, each tree among the forest is predicted for that specimen, which is accomplished by constructing multiple decision trees. Then the prediction results are obtained from these prediction results by voting to pick the final result. We use the R package randomForest \u003csup\u003e[29]\u003c/sup\u003eto carry out the model according to NRDEGs expression of the combined data set, the parameter is set.seed(234), ntree = 1000.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"221\" height=\"48\"\u003e\u003c/p\u003e\n\u003cp\u003eWe selected the genes screened by both SVM and RFE analysis for subsequent analysis.\u003c/p\u003e\n\u003cp\u003efunctional similarity analysis of Friends analysis\u0026nbsp;and the semantic of GO annotations provides a numerical approach for assessing genetic and genomic similarities, and has emerged as a fundamental foundation for various approaches of bioinformatics investigation. The NRDEGs\u0026apos; GO semantic similarity is calculated by R package GOSemSim\u003csup\u003e[30]\u003c/sup\u003e and the geometric mean of NRDEGs at the BP, CC and MF levels is further calculated to obtain the final score, the functional similarity analysis results are finally showed with the R package ggplot2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7 Least absolute shrinkage and selection operator regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain a more accuracy result, we use the R package glmnet\u003csup\u003e[31]\u003c/sup\u003e and carried out LASSO\u003csup\u003e[32]\u003c/sup\u003e regression on NRDEGs using the following settings: seed=2020, family = \u0026quot;binomial\u0026quot;. 1000 rounds were run to avoid over-fitting. By adding a punitive item (\u0026lambda; \u0026times; the absolute value of slope) to a linear regression model, over-fitting is mitigated and the model\u0026apos;s ability of generalization is enhanced. Then, we displayed each gene\u0026apos;s molecular expression in the diagnostic model by utilizing the LASSO regression results. DCA (Decision curve analysis)\u003csup\u003e[33]\u003c/sup\u003e is a basic\u0026nbsp;method used in clinical evaluating for diagnostic test and to make decisions about test selection and use. We use DCA to assess the LASSO model\u0026apos;s accuracy and resolution, and create a DCA plot using the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eR package\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eggDCA\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand visualize the LASSO regression model\u0026rsquo;s effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.8 Copy number variations analysis associated with Hub-NRDEGs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the affection of CNVs on hub-NRDEGs in AML,we analyzed CNVs in TCGA-LAML groups. We acquired the TCGA-LAML datasets from the TCGA database and designated them as AML groups after selecting the sub-options \u0026quot;copy number variation\u0026quot; and \u0026quot;masked copy number segment\u0026quot;. After the data was processed, we performed CNVs analysis using GISTIC2.0\u003csup\u003e[34]\u003c/sup\u003e, Genes in CNV regions were annotated using Genome Research Consortium Human build 38 (GRCh38) as the reference genome. All analytic parameters were initialized to their corresponding default values. R package maftools\u003csup\u003e[35]\u003c/sup\u003e was used to display the distribution of CNVs and R package RCircos\u003csup\u003e[36]\u003c/sup\u003e to plot the location of hub-NRDEGs association with CNVs on chromosomes. The gains and losses of CNVs of the hub-NRDEGs were visualized in a barplot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.9 Single-sample Gene Set Enrichment Analysis (ssGSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003essGSEA\u003csup\u003e[37]\u003c/sup\u003e,an extension GSEA approach that analyzes enrichment scores for individual samples and gene sets separately rather than\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emultiple objects. Every score of ssGSEA reflects how concurrently up- or down-regulated the input gene in a particular gene set is in a sample. We calculate the infiltration abundance of immune cells in LASSO risk groups of AML and performed correlation analysis between NRDEGs\u0026rsquo; expressions and infiltration abundance of immune cells in the samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.10 Kaplan-Meier\u003c/strong\u003e \u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analysis is a statistical technique allows the construction of survival curves for a specific outcome and to assess the relative risk of a given exposure and the incidence rate of a relevant clinical event in a cohort of individuals followed-up for a predefined time period. In order to assess the impact of the 6 hub-NRDEGs on survival and prognosis in AML, we performed Kaplan-Meier analysis in a new data set GSE37642 for the hub-NRDEGs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.11 Cox Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the predict value of the clinical prognostic of hub-NRDEGs, we carried out a univariate Cox regression analysis of the age(\u0026gt;= 65, \u0026lt; 65) and screened NRDEGs\u0026rsquo; expression as the clinical variables in a new data set GSE37642 and mapped the forest graph. Genes with P.adj \u0026lt; 0.1 were then selected to create a multivariate Cox regression model. We predict the probability of progression-free survival at year- 1, year-3, and year-5 in AML individuals using nomograms on the basis of multivariate Cox regression. The nomogram is a plot of the functions of multiple independent variables in a plane-diagonal coordinate system using a set of interrelated lines. Using a multivariable regression analysis, the regression model\u0026apos;s variables are scored by a set of scales, the overall score is measured to forecast the possibility of occurrences. Finally, we evaluated the nomograms\u0026apos; accuracy and resolution with an adjusted curve. Calibration plots are used to estimate the prognostic impact of models on reality outcomes by visualizing the alignment of the probabilities of the real and model predictions in different situations, and to analyze the alignment of the models with the real situation, based on the Cox regression method. R package rms was used to demonstrate nomograms and calibration curves and R package ggDCA was used to estimated the ability of the nomogram model to predict survival outcomes of year-1, year-3, and year-5 in AML patients..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.12 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData manipulation and statistical analyses in this research were carried out using R software (Version 4.2.1).The independent Student t test was utilized for determining statistical significance of variables with normal distributions when comparing two sets of continuous variables. The Mann-Whitney U test was utilized to evaluate differences between variables with non-normal distributions. Using the Chi-square test or Fisher\u0026apos;s exact test, the statistical significance of two categories of variables was compared and analyzed. Spearman correlation analysis was used to measure the correlation coefficients between distinct compounds. Unless otherwise specified, all statistical P values in are considered as bidirectional (symmetrical \\two-tailed), and P\u0026lt;0.05 is considered to have statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Identification of NRDEGs in AML\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter eliminating the batching impact in the merged datasets comprised of GSE7186, GSE23143, and GSE84334 (see supplementary Figure S1A\u0026ndash;D), we divided the data into the AML and Normal groups. Differential expression analysis was conducted and the result was displayed in a volcano map (Figure 2A). 178 genes was identified fulfilling the criteria |logFC| \u0026gt; 1 and p. adj \u0026lt; 0.05 and defined as DEGs, with 70 displayed up-regulation and 108 down-regulation. The DEGs and NRGs obtained aforementioned were intersected and 12 NRDEGs (\u003cem\u003eSLC25A5, TUBB6, PLEKHA5, PARP1, CTSS, IVNS1ABP, ZNF217, NFKB1, PYGL, PAWR, CXCL1, JUN\u003c/em\u003e) were identified (Figure 2C). Heat-map (Figure 2B) demonstrated the 12 NRDEGs\u0026rsquo; expression in AML and Normal groups. Histogram of group comparison (Figure 2E) revealed the differences of the expression levels of the 12 NRDEGs between AML and Normal groups (P\u0026lt;0.05). RT-qPCR validated the differences in expression of selected NRDEGs between AML and Normal groups according to THP-1 and BM-MNCs of health donors. (see supplementary Figure S2) The correlation coefficients among distinct molecules measured by Spearman correlation analysis was visualized in the correlation heat map (Figure 2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Enrichment analysis of NRDEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo find out the biological function of the identified NRDEGs, GO and KEGG enrichment analysis were carried out separately and in combination with NRDEGs by providing logFC from DEGs and calculating a Z-score for each NRDEG. GSEA was performed to estimate how these gene expressions impact of on the occurrence of AML.\u003c/p\u003e\n\u003cp\u003eGO results (Figure 3A，see detail in supplementary Table 4) showed NRDEGs are mainly enriched in vesicle lumen(GO:0031983),cytoplasmic vesicle lumen(GO:0060205),secretory granule lumen (GO:0034774), etc. and transcription regulator Complex(GO:0005667) in cellular components (CC) and DNA-binding transcription repressor activity, RNA polymerase II-specific (GO:0001227), R\u0026minus;SMAD binding (GO:0070412), SMAD binding(GO:0046332) , etc. in molecular functions (MF). KEGG result (Fig. 3B,see detail in supplementary Table 4) showed NRDEGs are mainly enriched in Apoptosis(map04210), IL\u0026minus;17 signaling pathway(map04657), \u0026nbsp;NF-kappa B signaling pathway(map04064) ,TNF signaling pathway (map04668), Necroptosis(map04217),NOD-like receptor signaling pathway(map04621). We used a chord diagram (Figure 3C) to demonstrate the detailed GO terms that each NRDEG enriched in and a circleplot (Figure 3D) to show the distribution of the NRDEGs enriched in GO analysis that was in combination with NRDEGs by providing logFC. The relationship between the 12 NRDEGs and the outputs of GO and KEGG enrichment analysis was displayed in a circular network diagram (Figure 3E). Pathway heat-map (Figure 3F) displayed the relationship between NRDEGs and GO terms while circular network diagram (Figure 3G) displayed the relationship between the results of KEGG enrichment analysis. The crossed lines denote the corresponding molecules and the annotations for each item.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA results revealed a high enrichment of genes in \u003cem\u003eMANALO HYPOXIA DN\u0026nbsp;\u003c/em\u003e(Figure 4A), \u003cem\u003eREACTOME TP53 REGULATES TRANSCRIPTION OF DNA REPAIR GENES\u003c/em\u003e (Figure. 4B), \u003cem\u003eHAMAI APOPTOSIS VIA TRAIL UP\u003c/em\u003e (Figure 4C), \u003cem\u003eVERHAAK AML WITH NPM1 MUTATED UP\u0026nbsp;\u003c/em\u003e(Figure 4D), \u003cem\u003eKEGG MAPK SIGNALING PATHWAY\u003c/em\u003e (Figure 4E), \u003cem\u003ePEREZ TP53 TARGETS\u003c/em\u003e (Figure 4F), \u003cem\u003eKEGG JAK STAT SIGNALING PATHWAY\u003c/em\u003e (Figure 4G), \u003cem\u003eZHENG IL22 SIGNALING UP\u003c/em\u003e (Figure 4H), etc. (See detail in supplementary table 5)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Machine learning identified 6 hub-NRDEGs and validated the models\u0026rsquo; accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used SVM-RFE algorithms to perform feature selection in the 12 NRDEGs in order to screen diagnostic markers in the combined data set for AML. SVM analysis obtained the number of genes with the minimal rate of error (Figure 5A) and the maximum rate of accuracy (Figure 5B), achieved the maximum accuracy with a gene count of 7 (\u003cem\u003ePAWR, CTSS, SLC25A5, ZNK217, NFKB1, PYGL, PARP1\u003c/em\u003e). Selected IncNodePurity (improvement in Node Purity) \u0026gt; 0.3 as the criterion, 9 NRDEGs (\u003cem\u003eSLC25A5, PLEKHA5, PARP1, CTSS, IVNS1ABP, ZNF217, NFKB1, PYGL, TUBB6\u003c/em\u003e) of AML(Figure 5C-D) were obtained in RFE according to the expression levels of 12 NRDEGs. After intersecting the outcomes of the two algorithms, 6 NRDEGs (\u003cem\u003eSLC25A5, PARP1, CTSS, ZNF217, NFKB1, PYGL\u003c/em\u003e) were obtained (Figure 5E). Friends functional similarity analysis was conducted through the R package GOSemSim\u003csup\u003e[38]\u003c/sup\u003e and the ranking of functional importance of the 6 NRDEGs was shown (Figure 5F).\u003c/p\u003e\n\u003cp\u003eLASSO regression model was developed (Figure 6A) and variable trajectory (Figure 6B) was obtained. Calibration analysis showed excellent agreement between the actual and predicted probability of the model (see supplementary Figure S4). The 6 NRDEGs were then tagged as hub-NRDEGs and visualized in the forest plot (Figure 6C).ROC curve for the expression levels of AML due to the LASSO model showed a superior diagnostic accuracy (Figure 6D, AUC = 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 GSEA according to the LASSO model showed NRDEGs are mainly enriched in pathways associate with AML\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate the impaction of NRDEGs expression on the occurence of AML, new GSEA was performed based on the DEGs in the combined data set according to the LASSO high and low risk groups. Ridge plot (Figure 7A) were generated and the GSEA outcome based on LASSO models revealed the genes were significant enriched in ROSS AML WITH PML RARA FUSION (Figure 7B), ROSS AML OF FAB M7 TYPE (Figure 7C), ALCALAY AML BY NPM1 LOCALIZATION UP (Figure 7D), VERHAAK AML WITH NPM1 MUTATED DN (Figure 7E) ,etc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.5 \u003cstrong\u003eCNV frequency of Hub-NRDEGs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze CNVs associated with six hub-NRDEGs in AML, , after the data was processed, we performed CNV analyze using GISTIC2.0 based on CNV datasets of TCGA-LAML from the TCGA database. Distribution of CNVs in TCGA-LAML group was show in Fig 8A. We then displayed the chromosomal localization maps of Hub-NRDEGs associated with CNVs in a circle diagram.(Fig.8B) The frequency of CNVs related to Hub-NRDEGs was presented in a barplot (Figure. 8C).The outcomes showed that\u003cem\u003e\u0026nbsp;PARP1\u003c/em\u003e，\u003cem\u003ePYGL\u0026nbsp;\u003c/em\u003eget only gain, \u003cem\u003eNFKB1\u003c/em\u003e，\u003cem\u003eCTSS\u0026nbsp;\u003c/em\u003eget both gain and loss while gain was more than loss, \u003cem\u003eSLC25A5\u003c/em\u003e,\u003cem\u003eZNF217\u003c/em\u003e get more gain than loss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 \u003cem\u003ePARP1\u003c/em\u003e and \u003cem\u003eCTSS\u003c/em\u003e are associated with immune cell infiltration in the AML\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003essGSEA algorithm was utilized to reveal immune cells\u0026rsquo; infiltration profiles between LASSO risk groups in AML. The comparison diagram (Figure 9A) showed the infiltration abundance of immune cells between LASSO risks groups and density-related heat-map (Figure 9B) revealed co-relationship between hub-NRDEGs and immune cells according to the infiltrate abundance. The correlation scatter plots according to the correlation analysis showed that the \u003cem\u003eRARP1\u003c/em\u003e is mild inversely related to the CD56\u003csup\u003edim\u003c/sup\u003e NK cell(R=-0.46 p＜0.001，Figure 9C）and \u003cem\u003eCTSS\u003c/em\u003e is moderate positively associated with MDSC（R=0.55, p＜0.001，Figure 9D）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 COX models showed hub-NRDEGs has better predictive value and \u003cem\u003eZNF217\u003c/em\u003e has significant difference in duration of survival in AML patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo access the predictive value of hub-NRDEGs and the effect of them on survival, univariate Cox regression analysis was first conducted. Forest plot (Figure 11A) showed the association between expression of hub-NRDEGs and age in GSE37642. Factors with p\u0026lt;0.1 were selected for multivariate Cox regression analysis and nomograms (Figure 11B) was created. The calibration curves (Figure 11C-E) for the prognostic of the nomograms utilizing Calibration analysis revealed the model had better predictive power at year-1 and year-5. DCA plot (Figure 11F-H) demonstrated that the blue lines which represent the models for year-1, year-3 and year-5 were significantly higher than the red lines (all positive) and the gray lines (all negative), indicating that the model had a better predictive value. Kaplan-Meier analysis on GSE37642 (Figure 10A-F) showed \u003cem\u003eZNF217\u003c/em\u003e expression had a statistically significant difference in the time to survival indicating it have predictive value for survival in patients with AML.\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eAML is the most prevalent type of acute leukemia in adults and rank as the second in children. Within the age groups of younger than 20, 20 to 49, 50 to 64, and ages 65 and above, the relative 5-year survival rate over a 5- year period declines from 69% to 58%, 35%, and 9%.\u003csup\u003e[39]\u003c/sup\u003e Limited advancements have been achieved during the past decades and researches have been expanding the approach to AML treatment by exploring molecular pathways specific to AML cell proliferation and survival.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApoptosis is known to be induced by the majority of chemotherapies and radiotherapies in reaction to DNA damage or cellular stress in cancer cells. \u003csup\u003e[2, 40]\u003c/sup\u003e Drug resistance and carcinogenesis are frequently caused by resistance to apoptosis, which leads to chemotherapeutic failure.\u003csup\u003e[40]\u003c/sup\u003e So methods of triggering non-apoptotic forms of RCD is appealing to be discovered as alternative cancer therapies in order to conquer it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNecroptosis shares a mechanistic resemblance with apoptosis with their signaling pathways highly interconnected.\u003csup\u003e[11, 12]\u003c/sup\u003e The key regulators of necroptosis are Receptor-Interacting Protein-1 (RIP1), Receptor-Interacting Protein-3 (RIP3), and Mixed Lineage Kinase Domain-Like (MLKL).\u003csup\u003e[10]\u003c/sup\u003e It has been documented that lots of important molecules in necroptotic signaling pathways are down regulated in various malignancy cell types.\u003csup\u003e[10]\u003c/sup\u003e Necroptosis inducers have demonstrated significant benefit in apoptosis-resistant cancers.\u003csup\u003e[41]\u003c/sup\u003e SMAC mimetics in conjunction with caspase-8 inhibitions have been domonstrated to induce necroptosis in preclinical studies of AML.\u003csup\u003e[42]\u003c/sup\u003e\u0026nbsp; Chidamide, an HDAC inhibitor, was more effective in treating \u003cem\u003eFLT3-ITD\u003c/em\u003e positive AML when RIPK1 was inhibited.\u003csup\u003e[43]\u003c/sup\u003eIn an animal model carrying a mutant AML driver gene in transplanted bone marrow cells, leukemogenesis was dramatically increased after the knockout of RIPK3, RIPK3\u003csup\u003e-/-\u003c/sup\u003e mice had a shorter lifespan than RIPK3\u003csup\u003e+/+\u003c/sup\u003e mice.\u003csup\u003e[13]\u003c/sup\u003e Targeting necroptosis pathway may be proven promising in AML treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO results showed the terms among the CC components such as \u003cem\u003evesicle lumen\u003c/em\u003e, \u003cem\u003ecytoplasmic vesicle lumen\u003c/em\u003e and \u003cem\u003esecretory granule lumen\u003c/em\u003e all were child terms of organelle lumen(GO:0043233\u003cstrong\u003e),\u0026nbsp;\u003c/strong\u003eindicated NRDEGs act mainly on the secretion delivery and trafficking of intracellular components that responsible for intercellular information exchange, functional regulation.\u0026nbsp;MF components as R\u0026minus;SMAD binding, SMAD binding are child terms of protein binding(GO:0005515).\u0026nbsp;SMAD\u0026nbsp;are critically important intracellular signal transducers\u0026nbsp;for regulating cellular process.\u0026nbsp;It\u0026nbsp;form a complex with other components within the cell then transferred into the nucleus where it directly binding to the DNA or interacting with other cofactors\u003csup\u003e[44]\u003c/sup\u003e that may also act in DNA-binding transcription repressor activity, RNA polymerase II-specific (GO:0001227), play an important role in promoting tumorigenesis and cancer progression.\u003csup\u003e[44]\u003c/sup\u003e SMAD proteins are also intracellular signal transducers in the transforming growth factor-\u0026beta; (\u003cem\u003eTGF-\u0026beta;\u003c/em\u003e) signaling pathway\u003csup\u003e[45]\u003c/sup\u003e,which also\u0026nbsp;play an important role in tumorigenesis.\u0026nbsp;These above indicate that NRDEGs mainly influence signal transduction, which in turn may promote leukemogenesis and the advancement of AML.\u003c/p\u003e\n\u003cp\u003eAmong the KEGG results, IL-17 signaling pathway, NOD-like receptor signaling pathway primarily regulate the expression of antimicrobial peptides, cytokines, and chemokines. They are responsible for detecting various pathogens and generating innate immune responses or promoting inflammatory pathology in autoimmune disease. In TNF signaling pathway, the key cytokine TNF can activate a variety of intracellular signaling pathways, including apoptosis, cell survival, inflammation, and immunology. And the above three pathways can activate the downstream NF-\u0026kappa;B, MAPK cascade pathway, cytokine production and apoptosis. NF-\u0026kappa;B pathway itself was also included in our KEGG results, suggesting most NRDEGs can function via the NK-\u0026kappa;B pathway in AML.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNF-\u0026kappa;B family is a transcription factor family that plays a significant role in multiple physiological and pathological processes, including inflammation, tumorigenesis, immunological response, cell proliferation and apoptosis.\u003csup\u003e[46]\u003c/sup\u003e Approximately 40% of AML individuls exhibit increased activity of NF-\u0026kappa;B\u003csup\u003e[47]\u003c/sup\u003e.AML cells endure apoptosis when NF-\u0026kappa;B is inhibited.\u003csup\u003e[48]\u003c/sup\u003e NFkB1, a member with no transcriptional activity among the NF-\u0026kappa;B family\u003csup\u003e[46]\u003c/sup\u003e, was shown to be\u0026nbsp;a important NRDEG in the result of Friends functional similarity analysis. NFKB1 must combine with RelA, RelB, or RelC to form a heterodimer to regulates the transcription of its target gene.\u003csup\u003e[46, 49]\u003c/sup\u003e The activation of NFKB1 homodimers and complexes of Bcl-3 have been observed in nasopharyngeal carcinoma.\u003csup\u003e[50-52]\u003c/sup\u003e\u0026nbsp; \u0026nbsp;The overexpression of NFKB1 has been demostrated both in rodent skin cancer and non-small cell lung cancer.\u003csup\u003e[53, 54]\u003c/sup\u003e In addition to NFKB1 itself, many crucial genes implicated in leukemogenesis such as RUNX1 and CEBP-A, were affected by NF-\u0026kappa;B inhibition.\u003csup\u003e[55]\u003c/sup\u003e On the basis of our results, it may be feasible to provide a convincing rationale for targeting the NFKB1 in AML.\u003c/p\u003e\n\u003cp\u003eAmong the GSEA outcomes for LASSO results, gene sets contained in \u003cem\u003eROSS AML WITH PML RARA FUSION\u003c/em\u003e and \u003cem\u003eROSS AML OF FAB M7 TYPE\u003c/em\u003e were identified for pediatric AML through an ANN-based supervised learning algorithm, and include the 100 Top probe sets in AML subtype with PML-RARA fusion and subtype FAB-M7 each, an overall prognostically diagnostic accuracy of 93% was achieved.\u003csup\u003e[56]\u003c/sup\u003e \u003cem\u003eALCALAY AML BY NPM1 LOCALIZATION UP\u003c/em\u003e and \u003cem\u003eVERHAAK AML WITH NPM1 MUTATED DN\u003c/em\u003e pathway separately contains Genes up-regulated\u003csup\u003e[57]\u003c/sup\u003e and down-regulated in AML individuals exhibiting mutated \u003cem\u003eNPM1\u003c/em\u003e\u003csup\u003e[58]\u003c/sup\u003e, and \u003cem\u003eALCALAY AML BY NPM1 LOCALIZATION UP\u0026nbsp;\u003c/em\u003epathway is crucial for \u003cem\u003ep53\u003c/em\u003e stabilization after stress.\u003csup\u003e[57]\u003c/sup\u003e These show the important roles for hub-NRDEGs in AML.\u003c/p\u003e\n\u003cp\u003eAltered or misled immune responses may significantly impacted cancer initiation and progression.\u003csup\u003e[59]\u003c/sup\u003e ssGSEA algorithms showed the immune cell infiltration between LASSO risk groups on AML and revealed \u003cem\u003eRAPR1\u003c/em\u003e is negative correlate with CD56\u003csup\u003edim\u003c/sup\u003e NK cells while \u003cem\u003eCTSS\u003c/em\u003e is positive correlate with MDSCs, indicating the two NRDEGs may affect AML via immune effects.\u003c/p\u003e\n\u003cp\u003ePoly(ADP-ribose) polymerase 1 (\u003cem\u003ePARP1\u003c/em\u003e) ,a most well-known member of PARP proteins family, functions as vital factor in maintenance genetic stability.\u003csup\u003e[60]\u003c/sup\u003e It mediates DNA damage repair rely on binding to single- and double-strand breaks within the DNA\u003csup\u003e[60]\u003c/sup\u003e and regulates the cellular process including cell cycle, protein stabilization, protein-protein interaction, intracellular localization, and transcriptional.\u003csup\u003e[61]\u003c/sup\u003e According to reports, overexpression of PARP1 shows poorer OS (overall survival) rates in AML patients.\u003csup\u003e[62]\u003c/sup\u003e In preclinical investigations, AML with specific genetic alterations such as IDH1/2, RUNX1-RUNX1T1, PML-RARA, FLT3-ITD, co-occurring lesions such as \u003cem\u003eP53\u003c/em\u003e or \u003cem\u003eBCOR\u003c/em\u003e was extremely sensitive to PARPi.\u003csup\u003e[63]\u003c/sup\u003e NK cells are induced to undergo PARP1-dependent apoptosis by leukemia cells.\u003csup\u003e[64]\u003c/sup\u003e According to the expression levels of CD56 and CD16 on the surface, NK cells can be mainly categorized into the canonical CD56\u003csup\u003edim\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e subgroup which demonstrates anti-leukemia efficacy\u003csup\u003e[65]\u003c/sup\u003e and the CD56\u003csup\u003ebright\u003c/sup\u003eCD16 subgroup that typically considered as immune-modulatory NK cells with less antitumor activity.\u003csup\u003e[66]\u003c/sup\u003e \u003cem\u003ePARP1\u003c/em\u003e can help leukemia germ cells selective in evading immune surveillance by NK cells through represses the expression of NKG2D ligands in leukemia germ cells, of which that bind to the NKG2D receptor on the surface of NK cells and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells to function as a co-stimulatory signal.\u003csup\u003e[67]\u003c/sup\u003e The aforementioned process renders AML cells more sensitive to the tumor necrosis factor-\u0026alpha;-related apoptosis-inducing ligand (TRAIL), which is a crucial effector molecule of NK cells.\u003csup\u003e[68]\u003c/sup\u003e Similar to our result, studies had reported that high level of CD56\u003csup\u003edim\u003c/sup\u003e CD16\u003csup\u003e+\u003c/sup\u003e NK cell count is related with worse outcomes in AML patients and the proportions of baseline according NK cells and their subgroups at the time of AML diagnosis is particular relevant.\u003csup\u003e[66]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCathepsin-S (\u003cem\u003eCTSS\u003c/em\u003e), a lysosomal cysteine cathepsin family member, plays a crucial function in the immunological response.\u003csup\u003e[69]\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIt\u0026rsquo;s suppression of Treg cells may decrease total immunity of T cells under normal circumstance but increases the CD8\u003csup\u003e+\u003c/sup\u003e T-cell\u0026rsquo;s immunity when exposed to tumor cells \u003csup\u003e[70, 71]\u003c/sup\u003e.When activated, T cells enhance the polarization of M2-type macrophages and dendritic cells\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003csup\u003e[71]\u003c/sup\u003e, which promote myeloid-derived suppressor cells (MDSCs), a subset of myeloid cells with powerful immune suppression function,\u003csup\u003e[72]\u003c/sup\u003eand tumor-associated macrophages (TAM), then enhance the proliferation of Treg cells as alternative to cytotoxic CD8\u003csup\u003e+\u003c/sup\u003e T cells. \u003csup\u003e[71]\u003c/sup\u003e T cells also represent the primary targets of MDSCs.\u003csup\u003e[72]\u003c/sup\u003e MDSCs inhibit T-cell proliferation and responses both in AML cell lines and animal models.\u003csup\u003e[73]\u003c/sup\u003e When elevated, MDSCs contribut to impeded immune surveillance in the bone marrow niche in AML subsets.\u003csup\u003e[74]\u003c/sup\u003e Higher MDSCs frequencies were linked to worse prognoses and shorter OS in a meta-analysis from 16 separate researches encompassing 1864 malignant disease individuals,\u003csup\u003e[75]\u003c/sup\u003e which is similar to our results.\u003c/p\u003e\n\u003cp\u003eCox model showed the 6 hub-NRDEGs had better predictive power and Kaplan-Meier curve showed the expression of zinc-finger protein 217(\u003cem\u003eZNF217\u003c/em\u003e)\u0026nbsp;had positive correlation with survival duration in our result.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZNF217\u003c/em\u003e,\u0026nbsp;rarely reported in AML, is a transcription factor with oncogenic properties. It displayed 19 interactions with\u0026nbsp;miRNAs and 45 with transcription factors\u0026nbsp;in our results (see supplementary Figure S5, table 6 and table 7).\u0026nbsp;ZNF217 plays a crucial role in the advancement of tumor development in variety malignancies encompassing both initial and late phases.\u003csup\u003e[76]\u003c/sup\u003e At early stages, \u003cem\u003eZNF217\u003c/em\u003e interfered the apoptotic pathway by attenuating apoptotic signals induced by dysfunctional telomere\u003csup\u003e[77]\u003c/sup\u003e and to sustaining proliferative signals, resisting cell death\u003csup\u003e[1]\u003c/sup\u003e,\u0026nbsp;promoting immortalization\u003csup\u003e[1]\u003c/sup\u003e,which prolongs the lifespan of tumor cells and rises the possibility of DNA mutation.\u003csup\u003e[2]\u003c/sup\u003e While at later stages, by conferring resistance to chemotherapy.\u003csup\u003e[77]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTRF (Telomere repeat-binding factor)-1 and TRF-2 are two proteins that bind in a complex with other proteins to the double-stranded region of the telomere.\u003csup\u003e[78]\u003c/sup\u003e TRF-1 can induce p53-dependent apoptosis while TRF2 acts as a major protective factor that can trigger p53-independent apoptosis. Following \u003cem\u003eZNF217\u003c/em\u003e overexpression, lethal treatments such as doxorubicin and a negative TRF2 mutant and TRF1 are unsuccessful in both benign and malignant cell lines.\u003csup\u003e[77]\u003c/sup\u003e \u003cem\u003eZNF217\u003c/em\u003e adjusted the pattern of DNA methylation at important gene promoters to impede a certain non-coding RNA (ncRNA) or to improve the epitranscriptome\u003csup\u003e[79]\u003c/sup\u003e, function as a mediator that inhibits tumor-suppressive RNAs.\u003csup\u003e[79]\u003c/sup\u003e Overexpression of \u003cem\u003eZNF217\u003c/em\u003e can prevent the uptake of cofactors essential for the active of demethylation for p15ink4b, a direct target gene of the ZNF217/CoREST transcriptional complex, thus impairs the anti-proliferation in the TGF-dependent pathway.\u003csup\u003e[80]\u003c/sup\u003e This impairs the cell cycle during the G1-S transition, leading to a dramatic increase of the cell numbers.\u003csup\u003e[81]\u003c/sup\u003e When binding to TGF2 or TGF3 promoters, \u003cem\u003eZNF217\u0026nbsp;\u003c/em\u003ewas found to stimulate the expression and consequently resulted in production of activated TGFs. blocking of the TGF- pathway resulted in a reverse change of ZNF217-dependent EMT and invasion properties.\u003csup\u003e[82]\u003c/sup\u003e\u0026nbsp; \u003cem\u003eFTO,\u003c/em\u003e a canonical obesity gene which plays a significant role in carcinogenesis.\u003csup\u003e[83]\u003c/sup\u003e, is found to be a direct target gene mediated by \u003cem\u003eZfp217\u0026nbsp;\u003c/em\u003e(the murine homolog of \u003cem\u003eZNF217\u003c/em\u003e).\u003csup\u003e\u0026nbsp;[10]\u003c/sup\u003e When binding to promoter of \u003cem\u003eFTO\u003c/em\u003e, \u003cem\u003eZfp217\u003c/em\u003e up-regulates \u003cem\u003eFTO\u003c/em\u003e expression\u003csup\u003e[10]\u003c/sup\u003e，hence, provides insights into the significance of \u003cem\u003eZfp217\u003c/em\u003e in adipocyte metabolism as it pertains to malignant neoplasms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZNF217\u003c/em\u003e also regulates molecular signaling pathways (eg.PI3K/AKT, MAPK, ERK, BMP and mitochondrial apoptosis pathways)\u003csup\u003e[79]\u003c/sup\u003e to reprogram pro-metastatic circuits\u003csup\u003e[76]\u003c/sup\u003e that regulate signature properties in cancer cells. Stimulating of PI3K/Akt pathways by \u003cem\u003eZNF217\u003c/em\u003e will result in resistance to many cancer medications (eg. trastuzumab, paclitaxel, and tamoxifen).\u003csup\u003e[84, 85]\u003c/sup\u003e Bcl-2 and Bcl-XL overexpression and down-regulation of Bad, Bak, and Bax are reversed, and factors downstream of p53 display less variation in their expression levels when \u003cem\u003eZNF217\u003c/em\u003e is overexpressed.\u003csup\u003e[86, 87]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCNVs are associated with chemotherapy response.\u003csup\u003e[8]\u003c/sup\u003e Refractory disease occurred more often in AML with CNA marker compared with other AML.\u003csup\u003e[8, 88]\u003c/sup\u003e The World Health Organization (WHO) (2016) classified cytogenetic CNV abnormalities as the single greatest predictor of complete remission (CR) and OS in AML.\u003csup\u003e[89]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCNVs analysis showed \u003cem\u003ePARP1\u003c/em\u003e, \u003cem\u003ePYGL\u003c/em\u003e, \u003cem\u003eCTSS\u003c/em\u003e, \u003cem\u003eNFKB1\u003c/em\u003e get more gain than loss in TCGA-LAML groups, means their expression were up-regulated from baseline derived from CNV amplifications. \u003cem\u003ePYGL\u003c/em\u003e, \u003cem\u003eCTSS\u003c/em\u003e and \u003cem\u003eNFKB1\u003c/em\u003e are down-regulated NRDEGs in AML, as the hub-NRDEGs mainly responsible for tumorigenesis, the down-regulations of them originally meant favorable for the outcomes of AML, while the CNVs offset this portion of the benefits. \u003cem\u003ePARP1\u003c/em\u003e was up-regulated in AML and CNVs may enhanced its pro-leukemic activity. \u003cem\u003eZNF217\u0026nbsp;\u003c/em\u003edisplayed oncogenic properties but our results demonstrated the expression of \u003cem\u003eZNF217\u003c/em\u003e has positive correlation to survival possibility in AML, partly may be attributed to the fact that \u003cem\u003eZNF217\u003c/em\u003e is a down-regulated DEG in AML, but extra loss in CNVs is undoubtedly responsible for additional expression decline of \u003cem\u003eZNF217\u003c/em\u003e and possibly the attenuation of its leukemogenesis effect.\u003c/p\u003e\n\u003cp\u003eAlthough our research provided theoretical underpinnings and research suggestions, it still has its limitations. First, this study were conducted across retrospective datasets and is the unavoidable issue of batch effects thus may increasing the possibility of bias. Second, there are insufficient publicly available external makes it difficult to evaluate the model\u0026apos;s reliability, more validation data are required to confirm the applicability of our model. Third, because of the different subtypes of AML, the findings might not be applicable to all AML individuals. Finally, no additional functional or mechanistic research was carried out. Our result might provide novel clues for diagnosis, therapy, and prognosis of AML. Therefore, further research is required to validate the aforementioned conclusions.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study screened hub-NRDEGs in AML, demonstrated the affection of CNVs on them, showed relationship between hub-NRDEGs and immune cells, evaluated the prognostic capacity and clinical value of them on AML. These findings may contribute to the comprehensive understanding of the genomic pattern and molecular mechanism associated with necroptosis in AML, and would highlight the hub-NRDEGs especially \u003cem\u003eZNF217\u003c/em\u003e as clinical prognostic predictors and therapeutic targets in AML.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eacute myeloid leukemi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNecroptosis-related genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNVs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCopy number variations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRDGEs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNecroptosis-related differentially expressed genes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifferentially expressed genes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMsigDB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMolecular Signatures Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK-M\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKaplan\u0026ndash;Meier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eregulated cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSupport Vector Machine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRandom Forest,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLASSO\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003essGSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle-sample gene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncNodePurity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eimprovement in Node Purity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebone marrow,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProportional Hazards Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecomplete remission\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eoverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBM-MNCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBone marrow mononuclear cells\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDSCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emyeloid-derived suppressor cells\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etumor-associated macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRT-qPCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereal-time polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe World Health Organization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTelomere repeat-binding factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRIP1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceptor-Interacting Protein-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRIP3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceptor-Interacting Protein-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLKL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed Lineage Kinase Domain-Like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e The procedures for the collection and use of samples in this investigation were authorized by the Ethics Committee of Nanjing Medical University Affiliated Wuxi People\u0026apos;s Hospital, Wuxi Children\u0026rsquo;s Hosptial. All health donors gave informed consent. The ethics approval,\u0026ldquo;\u003cstrong\u003eWXCH2023-11-090\u003c/strong\u003e\u0026rdquo; ,certified that the study was performed in accordance with the ethical standards as laid down in \u003cstrong\u003ethe 2013 Declaration of Helsinki\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepositories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE7186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE84334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE23143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE37642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCNVs datasets in TCGA-LAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://portal.gdc.cancer.gov\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions: Conceptualization:\u003c/strong\u003e DK.W, JZ, \u003cstrong\u003eData analysis:\u003c/strong\u003e DK.W, \u003cstrong\u003eCell experiment:\u003c/strong\u003e RY, \u003cstrong\u003eCode Modification Correction:\u003c/strong\u003e HY. Z, \u003cstrong\u003eWriting-Reviewing and editing:\u003c/strong\u003e DK.W, RY, LZ, JZ, \u003cstrong\u003eFigure conception and drawing:\u003c/strong\u003e XY.C, LZ, \u003cstrong\u003eSupervision:\u003c/strong\u003e JZ.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We would like to express our gratitude to all those who helped us during our project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShallis RM, Wang R, Davidoff A, Ma X, Zeidan AM. Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges. Blood Rev. 2019. 36: 70-87.\u003c/li\u003e\n\u003cli\u003eSaxena K, DiNardo C, Daver N, Konopleva M. Harnessing Apoptosis in AML. Clin Lymphoma Myeloma Leuk. 2020. 20 Suppl 1: S61-S64.\u003c/li\u003e\n\u003cli\u003eMaiti A, Rausch CR, Cortes JE, et al. Outcomes of relapsed or refractory acute myeloid leukemia after frontline hypomethylating agent and venetoclax regimens. Haematologica. 2021. 106(3): 894-898.\u003c/li\u003e\n\u003cli\u003eMaiti A, Carter BZ, Andreeff M, Konopleva MY. Beyond BCL-2 Inhibition in Acute Myloid Leukemia: Other Approaches to Leverage the Apoptotic Pathway. Clin Lymphoma Myeloma Leuk. 2022. 22(9): 652-658.\u003c/li\u003e\n\u003cli\u003eLing VY, Straube J, Godfrey W, et al. Targeting cell cycle and apoptosis to overcome chemotherapy resistance in acute myeloid leukemia. Leukemia. 2023. 37(1): 143-153.\u003c/li\u003e\n\u003cli\u003eZarrei M, MacDonald JR, Merico D, Scherer SW. A copy number variation map of the human genome. Nat Rev Genet. 2015. 16(3): 172-83.\u003c/li\u003e\n\u003cli\u003eRedon R, Ishikawa S, Fitch KR, et al. Global variation in copy number in the human genome. 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The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016. 127(20): 2391-405.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Necroptosis, Acute myeloid leukemia (AML), Necroptosis-Related differentially expressed Genes(NRDEGs), regulated cell death（RCD）, copy number variations (CNVs) ","lastPublishedDoi":"10.21203/rs.3.rs-4313518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4313518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground：\u003c/strong\u003eAcute myeloid leukemia (AML) is an aggressive hematological neoplasm. Little improvement in survival rates has been achieved over the past few decades. Necroptosis has relationship with certain types of malignancies outcomes. Here, we evaluated the diagnostic ability, prognostic capacity of necroptosis-related genes (NRGs) and the affection of their copy number variations (CNVs) in AML.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003eNecroptosis-related differentially expressed genes (NRDEGs) were acquired after intersecting Differentially expressed genes (DEGs) from Gene Expression Omnibus(GEO) database with NRGs from GeneCards, Molecular Signatures Database (MsigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. The expression levels of 6 hub-NRDEGs were validated in vitro. mRNA-miRNA and mRNA-TF interaction networks with hub-NRDEGs were screened by Cytoscape\u003csup\u003e@\u003c/sup\u003e. Single-sample gene set enrichment analysis (ssGSEA) was utilized to calculate correlationships between hub-NRDEGs and immune cells. CNVs analysis on hub-NRDEGs was carried out based on TCGA-LAML datasets from the TCGA database. Kaplan–Meier(K-M) survival analyses was utilized to evaluate the prognostic values along with COX model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003e6 hub-NRDEGs (\u003cem\u003eSLC25A5, PARP1, CTSS, ZNF217, NFKB1, PYGL\u003c/em\u003e) were obtained and their expression changes derived from CNVs in AML were visualized. 65 mRNA-miRNA and 80 mRNA-TF interaction networks with hub-NRDEGs was screened. ssGSEA result showed the expression of \u003cem\u003eRAPR1\u003c/em\u003e is inversely related with CD56\u003csup\u003edim\u003c/sup\u003e natural killer cell and CTSS positive with MDSCs in AML. K-M results demonstrated \u003cem\u003eZNF217\u003c/em\u003e had significant difference in duration of survival in AML patients. Cox regression models revealed hub-NRDEGs had better predictive power at year-1 and year-5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003e These screened NRDEGs could be exploited as clinical prognosis predictions in AML patients, as well as potential biomarkers for diagnosis and therapeutic targeting.\u003c/p\u003e","manuscriptTitle":"Screen Necroptosis-related Genes and evaluate the prognostic capacity, clinical value and the affection of their copy number variations in acute myeloid leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 09:04:18","doi":"10.21203/rs.3.rs-4313518/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-04T04:04:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-09T18:47:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T00:56:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77104840332560906503237505241339729684","date":"2024-10-23T00:51:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261318485565956223979697730755311591664","date":"2024-10-20T09:54:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64924059265260383883754953959603532962","date":"2024-05-09T11:24:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244475783453548766382512902944108653030","date":"2024-05-08T05:58:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-08T05:03:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-08T04:56:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-08T00:35:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-04T07:52:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-05-04T07:50:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"236687d7-e654-4882-88f5-b2a7a80413ff","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T15:58:35+00:00","versionOfRecord":{"articleIdentity":"rs-4313518","link":"https://doi.org/10.1186/s12885-025-13439-y","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-01-13 15:56:53","publishedOnDateReadable":"January 13th, 2025"},"versionCreatedAt":"2025-01-16 09:04:18","video":"","vorDoi":"10.1186/s12885-025-13439-y","vorDoiUrl":"https://doi.org/10.1186/s12885-025-13439-y","workflowStages":[]},"version":"v1","identity":"rs-4313518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4313518","identity":"rs-4313518","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}