In fasting and diabetes, nCD38 promotes sustained Ca2+ signals via transient receptor potential melastatin 2 (TRPM2) activation by ADP-ribose, which enhances the transcription of glucose-6 phosphatase and phosphoenolpyruvate carboxykinase 1. These findings shed light on the role of nCD38 in glucagon-induced gluconeogenesis and provide insight into nuclear Ca2+ signals that mediate the transcription of key genes in gluconeogenesis under physiological conditions.Hepatic glucose production by glucagon is crucial for glucose homeostasis during fasting, yet the underlying mechanisms remain incompletely delineated. Although CD38 has been detected in the nucleus, its function in this compartment is unknown. Here, we demonstrate that nuclear CD38 (nCD38) controls glucagon-induced gluconeogenesis in primary hepatocytes and liver in a manner distinct from CD38 occurring in the cytoplasm and lysosomal compartments. We found that the localization of CD38 in the nucleus is required for glucose production by glucagon and that nCD38 activation requires NAD Improved understanding of how glucose levels are linked to a key protein in liver cells may lead to novel treatments for diabetes. During fasting, hepatocytes (liver cells) convert stored glycogen into glucose in response to the hormone glucagon hormone. Using mouse models, Uh-Hyun Kim , Hun Taeg Chung (University of Ulsan), in South Korea, and co-workers showed that the mammalian ADP-ribosyl cyclase protein CD38 localises in the nucleus of hepatocytes where it plays a previously unknown but essential role in glucose production. In fasting and diabetes, nuclear CD38 prompts the production of ADP-ribose, triggering changes essential for generating the sustained calcium ion signals vital for responding to glucagon. The team found that ADP-ribose production was increased in diabetic mice. Using a drug to decrease ADP-ribose effects reduced abnormally high glucose levels. During the early stages of fasting, glucagon secreted by pancreatic α-cells acts on hepatocytes to convert stored glycogen to glucose via glycogenolysis3. As fasting progresses, glucagon stimulates glucose production from noncarbohydrate precursors, such as amino acids or lactate, via gluconeogenesis, which then becomes the main contributor to hepatic glucose production4. In hepatic gluconeogenesis, glucose-6-phosphatase (G6Pase) and phosphoenolpyruvate carboxykinase 1 (Pck1) represent the key regulatory enzymes; their expression is closely regulated by Ca2+ signaling6. Glucagon increases cytosolic Ca2+ through the protein kinase-A (PKA)-mediated phosphorylation of the inositol 1,4,5-trisphosphate receptor (IP3R), leading to increased calcineurin activity and the subsequent dephosphorylation of the cAMP response element-binding protein (CREB) coactivator CRTC25. We have previously demonstrated that glucagon induces CD38-mediated cyclic ADP-ribose (cADPR) production and sustained increases in cytosolic Ca2+, which in turn induce nuclear translocation of CRTC and gluconeogenic gene expression6.The liver is the primary organ in the maintenance of glucose homeostasis under fasting conditions2+-mobilizing messengers: cADPR, nicotinic acid adenine dinucleotide phosphate (NAADP), and ADP-ribose (ADPR)7. Although CD38 was initially identified as a type II transmembrane ectoenzyme in the plasma membrane, it was later revealed that CD38 exists in various intracellular organelles, including endosomes, lysosomes, and the nucleus10. The nuclear localization of CD38 has been demonstrated in a variety of cell types12. CD38 is assimilated in the inner nuclear membrane with its catalytic site within the nucleoplasm, suggesting that its product, cADPR, activates ryanodine receptors (RyR) on the inner nuclear membrane to cause an increase in nucleoplasmic Ca2+8. However, the specific characterization of this nuclear form of CD38 (nCD38) remains controversial, and the mechanism of its activation also remains unknown.CD38 is a mammalian ADP-ribosyl cyclase that catalyzes the synthesis of multiple Ca2+ signaling system that can generate its own Ca2+ transients that modulate processes such as gene transcription13. However, much remains unknown regarding how such spatially-distinct Ca2+ signals achieve specificity for gene transcription and how these nuclear Ca2+ signals are regulated. Numerous studies have demonstrated that the nucleus possesses the biochemical machinery required to produce Ca2+-mobilizing messengers15. These Ca2+-mobilizing messengers, IP3 and cADPR, stimulate the release of Ca2+ from the nuclear envelope (NE) into the nucleoplasm via IP3R and RyR located on the inner membrane of the nucleus18. Moreover, Gerasimenko et al.16 described ATP-dependent accumulation of Ca2+ in the NE, indicating that the NE serves as a “storage facility” for Ca2+ influx into the nucleoplasm. Together, these findings indicate that nuclear Ca2+ signaling operates independently of cytosolic signaling, although how these processes interact is not yet fully understood.The nucleus has an autonomous Ca2+ signaling as well as hepatic gluconeogenic gene expression. Our findings support the view that nCD38 in hepatocytes produces a novel Ca2+ signaling messenger, ADPR, in response to glucagon, and thereby plays a critical role in glucagon-induced gluconeogenic gene expression. We provide evidence that ADPR production is regulated by the transfer of the substrate NAD+ into the perinuclear space from the nucleoplasm via connexin43 (Cx43). Cx43 is in turn regulated by protein kinase C (PKC) δ-mediated phosphorylation. ADPR targets the transient receptor potential melastatin 2 (TRPM2) channel to gate Ca2+ from the NE into the nucleoplasm. Finally, we present evidence that ADPR production is upregulated in diabetic animals and that treating diabetic animals with an ADPR antagonist mitigates the abnormally increased glucose levels. Our findings emphasize that ADPR-mediated nuclear Ca2+ signaling is essential for hepatic gluconeogenesis under both normal physiological conditions and in diabetes.In this study, we investigated the role of nCD38 in glucagon-mediated CaCd38−/−; B6.129P2-Cd38tm/Lud) and B6. BKS(D)-Leprdb/J (db/db) mice were purchased from Jackson Laboratory . TRPM2 KO mice were kindly provided by Yasuo Mori . All experimental animals were used under a protocol approved by the institutional animal care and use committee at Jeonbuk National University Medical School (CBU 2014-00031).CD38 knockout mice  or pCMV3- Flag Cx43  by using primers to add NLS, which was inserted into the pCMV/myc/nuc vector . NLS-CD38 E230D and NLS-Flag Cx43 S368A were constructed by the addition of NLS via site-directed mutagenesis. The resulting construct was subcloned into the pENTR D-TOPO vector , after which a recombinant vector was generated using the pAd/CMV/V5-DEST adenoviral expression vector system . Recombinant adenovirus was amplified in HEK293A cells  and purified by cesium chloride gradient ultracentrifugation.Generation of NLS-R GECO Adenovirus: NLS R GECO adenovirus was constructed using NLS R GECO plasmid db/db mice as previously described20. Hepatocytes were transduced with adenoviral constructs 6 h after plating at a multiplicity of infection of 50 plaque-forming units per cell for 16 h, and experiments were performed 36 h after transduction. Glucose production assays were carried out as described21.Primary hepatocytes were isolated from 8- to 12-week-old male C57BL/6J mice or 9 p.f.u of CRE luciferase adenovirus  from Vector Biolabs. After 3 days, 6 h-fasted mice were injected intraperitoneally with 100 μg/kg glucagon 1 h before imaging. Then, the mice were injected intraperitoneally with 150 mg/kg firefly d‐luciferin . After 10 min, mice were anesthetized and imaged at 15 min using the IVIS Luminar XR Imaging System .Seven-week-old male C57BL/6 J mice were intravenously injected with 1 × 10Mice were fasted for 19 h and injected intraperitoneally with sodium pyruvate or glycerol (2 g/kg body weight). Tail vein blood was sampled for glucose measurement at the indicated time points after injection. Blood glucose values were determined using a LifeScan automatic glucometer.Plasma levels of insulin (ALPCO) and glucagon (MyBioSource), as well as glycogen content (BioAssay Systems), were assessed as per the manufacturer’s instructions.17. To prepare the outer membrane of the nucleus and nucleoplast (outer membrane removed nuclei), 2% (w/v) sodium citrate was added to the nuclear suspension, incubated for 1 h on ice while stirring and centrifuged for 15 min at 1000 g. The supernatant contained the outer membrane of the nucleus, and the pellet contained the nucleoplast.Intact nuclei from hepatocytes were isolated as previously described6-etheno-NAD as a substrate22.NAD glycohydrolase activity was determined fluorometrically by using 1,NTRPM2 siRNA-1, sense, 5’-GCACUCUGCAUACAAUCUAtt-3’, antisense, 5’-UAGAUUGUAUGCAGAGUGCgg-3’; TRPM2 siRNA-2, sense, 5’-GCGUCUUCACUGAGUGCUAtt-3’, antisense, 5’-UAGCACUCAGUGAAGACGCgg-3’; PKCδ siRNA, sense, 5’-GAUUCAAGGUUUAUAACUAtt-3’, antisense, 5’-UAGUUAUAAACCUUGAAUCgg-3’; PLCδ1 siRNA, sense, 5’-GCUACACUUUUACCUCUAAtt -3’, antisense, 5’-UUAGAGGUAAAAGUGUAGCca-3’; and PLCδ3 siRNA, sense, 5’-GAGUGAGGAUGGUCGAAUUtt-3’, antisense, 5’-AAUUCGACCAUCCUCACUCcg-3’.Hepatocytes were transfected with 100 nM siRNA specific for TRPM2 (Genolution), PKCδ, , PLCδ1 , PLCδ3 , or control siRNA  using Lipofectamine RNAi MAX reagent according to the manufacturer’s instructions. Transfected hepatocytes were cultured for 36 h before experiments. The siRNA sequences were as follows: G6pc, Pck1, and GAPDH were as follows: G6pc ; Pck1 ; GAPDH . GAPDH mRNA expression was quantified to normalize all data. Mouse β-Actin and PLC isoforms primers were as follows: β-Actin ; PLCβ1 ; PLCβ2 ; PLCβ3 ; PLCβ4 ; PLCδ1 ; PLCδ3 ; PLCδ4 ; PLCγ1 ; PLCγ2 .Total cellular RNA was extracted from primary hepatocytes using the RNeasy kit . cDNA was synthesized by reverse transcription from 50 ng of total RNA using a cDNA synthesis kit . PCR was carried out on a 384-well plate using the ABI Prism 7900HT Sequence Detection System (Applied Biosystems) and SYBR Green Master Mix . Real-time PCR primers for mouse Nuclear and cytoplasmic proteins from hepatocytes or liver tissue were extracted using the NE-PER Nuclear and Cytoplasmic Extraction Kit  according to the manufacturer’s instructions.+K+-ATPase , Calregulin , Nesprin 3 , Myc , CD38 , PLCβ1 , PLCβ3 , PLCβ4 , PLCδ1 , PLCδ3 , PLCδ4 , PLCγ1 , PLCγ2 , HSP90 , GAPDH , and LAMP-1 . After incubation with secondary antibodies conjugated with horseradish peroxidase , chemiluminescence was detected by using the LAS4000 system . Western blot densitometric quantification was performed using ImageJ .Cells were lysed in RIPA buffer . Lysates were boiled in Laemmli sample buffer for 10 min, separated by SDS-polyacrylamide gel electrophoresis, transferred to PVDF membranes, and probed with primary antibodies. We used antibodies for the following proteins: pCREB (Ser133) , pCaMKII , pCaMKIV , CREB , CaMKII , CaMKIV , PARP-1 , Actin , pCx43(Ser368) , Cx43 , Flag , Lamin B1 , PKCδ , TRPM2 , NaHepatocytes were grown on collagen-coated confocal dishes and fixed with 3.8% paraformaldehyde for 20 min, after which they were washed three times with ice-cold PBS. To permeabilize, cells and nuclei were treated with 0.25% Triton X-100 in PBS for 10 min. After blocking with 3% BSA, 0.25% Triton X-100, and PBS at RT for 1 h, samples were incubated overnight at 4 °C with the indicated antibodies: TRPM2 , CD38 , PKCδ , Cx43 , PLCδ1 , PLCδ3 , or Lamin B1 . Alexa Fluor-conjugated secondary antibodies  were incubated at 1:500 dilutions in the presence of 1% BSA at RT for 1 h. The nuclei were stained with DAPI . Cells and nuclei were visualized with a Zeiss LSM510 Axiovert 200 M laser-scanning confocal microscope.2+]i were determined at 488 nm excitation/530 nm emission and 543 nm excitation/560 nm emission using a confocal microscope . Isolated nuclei were loaded as reported24. The membrane-impermeant Ca2+ dye Calcium Green Dextran  was loaded into the nucleoplasm, while the membrane permeant Ca2+ dye Fluo-4 AM  was loaded into the nuclear envelope. Ca2+ imaging in single isolated nuclei was performed using Nikon confocal microscopy.Hepatocytes infected with adenovirus regarding GCaMP6m  and NLS-R-GECO for 24 h were subsequently serum-starved for 16 h in Medium 199, after which changes in [Ca25, and cADPR was measured by the cycling method described previously26.NAD and ADPR levels were measured using LC-MS/MS as described previouslyt test and one-way ANOVA followed by Tukey’s multiple comparison test. Data are represented as the mean ± standard error of the mean (SEM). The number of independent experiments and information about the statistical details and methods are shown in the relevant figure legends. A value of P < 0.05 was considered significant.Statistical analyses were performed using GraphPad Prism 8. Data were analyzed by Student’s unpaired 27. Although nCD38 has been found to regulate nuclear Ca2+ homeostasis27, the role of nCD38 in physiological functions remains unknown. First, we verified the existence of CD38 in the nucleus of primary hepatocytes. Confocal microscopy using anti-CD38 antisera revealed perinuclear immunofluorescence with ring-like labeling around the nucleus, which co-localized with Lamin B1  but not CD38 knockout (Cd38−/−) mice  experiments  to activate Cx43. Treating the nucleoplast with 1-oleoyl-2-acetyl-sn-glycerol (OAG), a synthetic diacylglycerol analog that induces Cx43 phosphorylation, resulted in reduced NAD+ levels while increasing ADPR levels  lagged a few seconds behind glucagon treatment when compared to cytosolic Ca2+ levels, which increased immediately following exposure to glucagon, indicating that nuclear Ca2+ signals follow Ca2+ fluctuations in the cytosol  completely abolished glucagon-induced Ca2+ signals, including the initial signals, in the cytosol and the nucleus, but XeC only inhibited the sustained Ca2+ signal and not the initial Ca2+ signal in both the cytosol and nucleus in Cd38+/+ primary hepatocytes  generated by CD38. We then sought to determine which Ca2+ second messengers are involved in glucagon-induced Ca2+ changes by using various antagonistic analogs. Glucagon-induced sustained Ca2+ signals in the cytosol and the nucleus were blocked by treatment with either XeC or 8-Br-cADPR   only inhibited sustained Ca2+ signals in the nucleus  following Ca2+ store depletion by both IP3 and cADPR. As expected, glucagon-induced, sustained nuclear, and cytosolic Ca2+ signals were completely abolished under Ca2+-free conditions and by the SOCE inhibitor SKF 96365  mice 39. Nuclear PtdIns-PLC acts as positive feedback for nuclear Ca2+ signaling40. Thus, we investigated whether the increase in the nuclear Ca2+ signal with glucagon is affected by nuclear PLC activation. First, we identified the expression of PLCβ1, β3, β4, δ1, δ3, δ4, γ1, and γ2 isoforms in mouse primary hepatocytes Prms Fig. . PLCδ1 a 2+ Fig. . These fnts Fig. . In cont5. We also confirmed an increase in plasma glucagon levels in the fasting condition when compared to fed WT mice  displayed increased hepatic insulin signaling and reduced expression of gluconeogenic enzymesck1 Fig. . Our datck1 Fig. . Given t46. Moreover, PKA mediates the phosphorylation of IP3R to increase cytosolic Ca2+, leading to an increase in calcineurin activity and the subsequent dephosphorylation of the CREB coactivator CRTC25. Based on our results, ADPR-mediated nuclear Ca2+ signaling is essential for CaMKII/CaMKIV/CREB phosphorylation, and nuclear Ca2+ signals are a determinant factor, converging multiple upstream signals, including cytosolic Ca2+ signaling, in gluconeogenic gene transcription in response to glucagon. This conclusion is supported by findings that the upregulation of ADPR-mediated Ca2+ signaling results in increased gluconeogenesis in diabetes.Glucagon regulates glucose homeostasis via a cAMP/PKA-dependent signaling pathway that results in the phosphorylation of CREB, which in turn activates the transcription of gluconeogenic enzymes2+ influx through TRPM2 activated by ADPR, leading to activation of the CaMKII/CaMKIV/CREB pathway for gluconeogenesis. We suggest that nCD38 plays a critical role in glucose homeostasis under physiological conditions such as fasting.In summary, this study has identified the essential role of nCD38 in glucagon-treated primary hepatocytes (Fig. All data needed to evaluate the conclusions in the paper are present in the main text or the supplementary materials.Supplementary information


N6-methyladenosine (m6A) has been confirmed to function critically in acute myeloid leukemia (AML) progression. Hitherto, the subtyping and prognostic predictive significance of m6A-correlated genes in AML is unclear.From The Cancer Genome Atlas (TCGA-LAML), Therapeutically Applicable Research to Generate Effective Treatments (TARGET-AML) and Gene Expression Omnibus  databases, we collected the sequencing data of AML patients. The batch effect was removed via limma package for TCGA-LAML and TARGET-AML, and the aggregated samples were AML cohorts. Samples in the AML cohort identified m6A models in AML by consensus clustering based on 23-m6A-related modulators. M6A-related differentially expressed genes (m6ARDEGs) influencing the overall survival (OS) of AML were determined by performing differential expression analysis and univariate COX analysis, and consensus-based clustering was utilized to access AML molecular subtypes. LASSO and multivariate COX analyses were performed to obtain the optimized m6ARDEGs to construct the m6A Prognostic Risk Score (m6APR_Score). Whether the model was robust was evaluated according to Kaplan–Meier (K-M) and receiver operator characteristic (ROC) curves. Further, the abundance of immune cell infiltration was explored in different m6A modification patterns and molecular subtypes and m6APR_Score groupings. Finally, nomogram was constructed to predict OS in AML. Quantitative real-time polymerase chain reaction (RT-qPCR) and cell counting kit-8 (CCK-8) assay were used to validate the genes in m6APR_Score in AML cells.The m6A models  and molecular subtypes  were identified in the AML cohort, exhibiting different prognosis and immunoreactivity. We recognized novel prognostic biomarkers of AML such as CD83, NRIP1, ACSL1, METTL7B, OGT, and C4orf48. AML patients were grouped into high-m6APR_Score and low-m6APR_Score groups, with the later group showing a better prognosis than former one. Both the AML cohort and the validation cohort GSE71014 demonstrated excellent prediction. Finally, the nomogram accurately predicted the survival of patients suffering from AML. Further, the decision curves showed that both nomogram and m6APR_Score showed excellent prediction. It was confirmed in vitro experiments that mRNA expressions of NRIP1, ACSL1, METTL7B and OGT were elevated, while CD83 and C4orf48 mRNA expressions downregulated in AML cells. A significant increase in the viability of U937 and THP-1 cell lines after inhibition of CD83, while siMETTL7B had contrast results.Our study demonstrated that m6APR_Score and CD83, NRIP1, ACSL1, METTL7B, OGT, and C4orf48 potentially provided novel and promising prognostic support for AML patients.The online version contains supplementary material available at 10.1186/s12920-023-01629-1. Acute myeloid leukemia (AML) refers to a type of scarce blood disorder of adult origin in the bone marrow, and the incidence of AML known with age predisposition, with a progressive increase in incidence with age . AlthougN6-methyladenosine (m6A) is the commonest form of RNA methylation modification in cells in the molecular level, and aberrant m6A modification directly affected the central law process , 8. AccuConsidering the potential worth of m6A in AML, we identified different m6A models in AML as well as molecular subtypes. Differentially expressed genes affected by m6A were further established, and then prognostic prediction models and nomogram were constructed as m6A signatures. In addition, this study also explored the different m6A models as well as immune cell infiltration differences in molecular subtypes, which further extended the clinical value of m6A-related prognostic signatures in AML.https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) database, and 132 primary tumor samples were maintained after screening. The expression files TARGET-AML 156 of AML samples  were acquired through TARGET (https://ocg.cancer.gov/programs/target) database. To minimize the experimental error, the TCGA-LAML and TARGET data were processed to remove the batch effect using the removeBatchEffect function of the limma package ([https://www.ncbi.nlm.nih.gov/geo/) database for 104 AML patients. we used the AML cohort as the training set and GSE71014 as the validation set.In this study, the RNA-Seq dataset  TCGA-LAML of AML was uploaded through TCGA  with km algorithm and 1-Spearman correlation was performed to identify m6A models of samples in the AML cohort based on the 23-m6A modulators included 8 writers , 11 readers , and 2 erasers   with km .p < 0.01). Based on the expression data of these m6ARDEGs, consensus clustering analysis was performed on samples from the AML cohort to identify m6A-associated molecular subtypes in AML.DEGs in different m6A models (|log2FC|> 1 & FDR < 0.05) were identified using the limma package, which we defined as m6A-related DEGs (m6ARDEGs). Univariate COX analysis was performed on m6ARDEGs to obtain m6ARDEGs that could influence AML prognosis  acted as external validation dataset (OHSC-AML dataset).LASSO and multivariate COX regression analyses were conducted on m6ARDEGs capable of influencing AML prognosis to construct an AML prognostic model , and theDifferences in OS of patients from different m6APR_Score subgroups were assessed by K-M analysis. The prognostic predictive performance of m6APR_Score was evaluated using ROC curve. Finally, whether the model was robust was validated in the validation set GSE71014.https://cibersort.stanford.edu/) [The relative abundance of 22 immune cell species in tumor tissue was quantified using the CIBERSORT algorithm (rd.edu/) . The scord.edu/) .For different m6A models and m6A subtypes, h.all.v7.5.1.symbols.gmt was downloaded from the HALLMARK database to obtain the biological signaling pathways contained therein , and thehttps://rdrr.io/cran/rms/). Finally, the robustness of m6APR_Score and nomogram in predicting AML prognosis was evaluated by drawing decision curves.Univariate and multivariate COX analyses were performed combining m6APR_Score, clinical traits of AML samples to identify significant independent factors for AML prognosis. Subsequently, nomogram predicting AML 1-year, 3-year and 5-year survival risk were generated using the rms package by combining m6APR_Score and prognostically significant independent factors, and the prediction accuracy of the nomogram was analyzed using calibration curves (GM12878 (BNCC360167), THP-1 (BNCC358410) and U937 (BNCC359322) cells were purchased from Beijing Bena Biotechnology Co. . Cells were cultured in DEME F-12 medium. Transfection of the negative control (NC), CD83 siRNA and METTL7B siRNA  was conducted by applying Lipofectamine 2000 . GGGGCAAAATGGTTCTTTCGACG (CD83-si) and ACCCAAATCCCCACTTTGAGAAG (METTL7B-si) were the target sequences for CD83 siRNA and METTL7B siRNA.The total RNA from GM12878, THP-1 and U937 cell lines  was extracted using TRIzol reagent. cDNA was created from 500 ng of RNA using the HiScript II SuperMix . By applying the SYBR Green Master Mix, RT-qPCR was carried out in ABI 7500 System . 45 cycles of 94 °C for 10 min, 94 °C for 10 s, and 60 °C for 45 s each comprised the PCR amplification conditions. Table The CCK-8  was conducted to measure cell viability. In 96-well plates, cells from various treatments were grown at a density of 1 × 103 cells per well. Solution CCK-8 was used. Using a microplate reader, the OD 450 values of each well were determined during a 2-h incubation at 37 °C .https://www.r-project.org/, R version 4.2.2). Data pre-processing via Sangerbox platform . The unsupervised clustering algorithm was performed to cluster the samples in the AML cohort into three subtypes   , m6AM2 vs m6AM1_m6AM3 (m6AM2 group) and m6AM3 vs m6AM1_m6AM2 (m6AM3 group). Totally 131 m6ARDEGs were identified in m6AM1-3 (FDR < 0.05 & |log2FC|> 1). Univariate COX screened 23 m6ARDEGs that were significantly and significantly associated with OS in AML (01) Fig. A. The exp < 0.0001). CD4 memory resting, Plasma cells, B cells naive, T cells, T.cells.CD8 were richer in cluster B (p < 0.0001)  Fig. A. MCP-co01) Fig. B. In inc01) Fig. C.Fig. 5IThe prognostic model assessed via m6APR_Score was established in the AML cohort based on 23-m6ARDEGs. LASSO and multivariate Cox regression were determined to dig out optimal prognostic m6ARDEGs, overall, CD83, NRIP1, ACSL1, METTL7B, OGT and C4orf48 were dug out as optimal prognostic m6ARDEGs  T cell targeted therapy was proposed based on the report by Shrestha and colleagues that CD83 was a novel target against graft-versus-host disease in AML patients undergoing bone marrow hematopoietic stem cell transplantation . GrasediOur research showed that the three m6A clusters, three m6A gene clusters, and m6Ascore were strongly linked with the relative abundance of 22 immune cell infiltration. It is now understood that the m6A alteration is crucial for controlling the immunological response . B-cellsOverall, we ascertained the different m6A models in AML based on the 23-m6A modulators. Finally, a 6-gene prognostic model was created, and the model was highly robust. However, there remained shortcomings in this study. Firstly, this study was conducted using data from earlier studies in public databases for sublicensing, and there was a lack of clinical cohorts to validate the robustness of the model. Second, although we ascertained 6 m6A-associated prognostic genes, we did not further explore the specific biomolecular mechanisms of these genes in relation to m6A modification in AML in both in vivo and in vitro assays. Further investigation of the relationship between these genes and AML and m6A modification is necessary.Our study constructed a 6-gene prognostic predictive model for predicting prognosis in AML, which constructed an m6A signature that could also serve as an independent prognostic factor for AML compared to traditional clinicopathological prognostic factors. We also preliminarily identified different m6A models in AML and their association with the immune landscape. This study provided new scientific evidence for effective and accurate clinical prediction of AML prognosis, and m6A modification patterns in AML might also help medical research practitioners to better understand AML disease mechanisms.Additional file 1: Supplementary Figure 1. Identification of m6A models. (A) CDF curves in consensus clustering (B) Clustering consistency at k = 2-10 (C) Heatmap of sample consistency for optimal clustering groupings.Additional file 2: Supplementary Figure 2. Identification of the m6A molecular subtypes. (A) CDF curves in consensus clustering (B) Clustering consistency at k = 2-10 (C) Heatmap of sample consistency for optimal clustering groupings.Additional file 3: Supplementary Figure 3. m6APR_Score independence analysis and evaluation of Nomogram predictive performance. (A) 1-year, 3-year, 5-year calibration curves for Nomogram (B) Decision curves for Nomogram, m6APR_Score.Additional file 4.Additional file 5.


The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for selecting exact clutter atoms in this paper. The simulation results show that MH-STAP can estimate the clutter subspace more accurately than the traditional algorithm; PSO-STAP and GWO-STAP showed better clutter suppression performance in four MH-STAP methods. To search for more accurate clutter atoms, PSO and GWO are combined to improve the method’s capacity for global optimization. Meanwhile, the fitness function is improved by using prior knowledge of the clutter distribution. The simulation results show that the improved PSO-GWO-STAP algorithm provides excellent clutter suppression performance, which solves the off-grid problem better than does single MH-STAP. Space-time adaptive processing (STAP) is an effective technique to suppress clutter received by airborne radar and enhance its ability to detect moving targets ,2,3. HowRecent research has increasingly explored the integration of deep learning with STAP. The CNN-STAP method establishes a mapping relationship between low-resolution and high-resolution clutter angle-Doppler spectra, subsequently utilizing the CNN output to select main clutter component atoms for the reconstruction of the clutter covariance matrix (CCM) . An enhaTo solve the off-grid problem in SR-STAP, the reduced-dimension local search clutter subspace estimation STAP (RD-LSCSE-STAP) algorithm was introduced in . The RD A novel approach, the gridless STAP algorithm based on particle swarm optimization (PSO-STAP), was proposed in . This alMeta-heuristic algorithms (MH) are effective methods to solve global optimization problems, which mainly simulate natural phenomena to achieve the optimal solution. They are categorized into evolutionary computation (EC) and swarm intelligence (SI) algorithms . Within In this study, several meta-heuristic algorithms  are employed to address the off-grid problem in SR-STAP. These algorithms select accurate atoms by maximizing a suitable fitness function in a gridless spatial-Doppler profile. The findings indicate that meta-heuristic algorithms significantly reduce the off-grid effect in SR-STAP. Specifically, PSO and GWO, as SI algorithms, construct more precise clutter subspaces compared to other algorithms. Furthermore, building on these findings, the PSO-GWO-STAP algorithm is developed, amalgamating the strengths of PSO and GWO. Concurrently, the fitness function is improved to boost the algorithm’s performance under certain conditions. The results demonstrate that the PSO-GWO-STAP algorithm outperforms the PSO-STAP algorithm, showing its suitability for SR-STAP applications.The rest of this paper is organized as follows. T and H represent transposition and conjugate transposition operations, respectively. The symbol 0l norm and 2l norm, respectively. Notation: Boldface uppercase letters represent matrices, and boldface lowercase letters represent vectors. M × N elements is depicted in θ and φ denote the azimuth and pitch angles, respectively. ψ is defined as the cone angle, and pθ signifies the angle between the antenna plane and the direction of the flight velocity V. The altitude of the platform is denoted by H. Moreover, the radar operates at a wavelength of λ, with an array spacing of d and a pulse repeat frequency of rf, and it transmits K pulses in each coherent processing interval (CPI).The geometric model of an airborne radar system employing a uniform planar array (UPA) consisting of lth range cell can be expressed as:cN represents the number of independent clutter units and N refers to the white noise received by the radar. S is the space-time steering vector, and To streamline computational demands, the system can be equivalently represented as an N-element equally spaced linear array . The spas = ρs NN and d = ρd KN grid points along the spatial and temporal axes, respectively, where d, ρsρ > 1 determine the resolution of the plane. Assuming all clutter units are precisely located on these grid points, Formula (1) can be reformulated as:Φ is the NK × sNdN overcomplete dictionary composed of space-time steering vectors. 0l norm and 2l norm of the vector, respectively, and In SR-STAP, the spatial-Doppler plane is uniformly dispersed into Meta-heuristic algorithms typically commence with multiple candidate solutions as initial values. These algorithms calculate the objective function value based on these initial values and update the candidate solutions through various methods, eventually progressing to the next iteration cycle. Theoretically, meta-heuristic algorithms are expected to locate the global optimum over time. However, it is acknowledged that no single algorithm is universally effective for all problem types. Consequently, although the basic PSO-STAP method has been proposed, four meta-heuristic algorithms  are employed to address the off-grid problem. Their optimization performances in relation to SR-STAP are compared in this section.F in the mutation operation and crossover rate CR in the crossover operation [cP and mutation probability mP [In the PSO algorithm, particles are characterized by two attributes: position and velocity. The velocity of a particle determines its position in the subsequent iteration. Particles follow the current optimal particle to search in the space . GWO exeperation . The staility Pm .k represents the number of external iterations. The fitness function is crucial for determining the optimal solution in meta-heuristic algorithms. Fewer space-time vectors are expected to be selected to estimate the clutter subspace for SR-STAP methods. Therefore, an appropriate space-time steering vector can be selected by maximizing the fitness function in each iteration. The basic fitness function formula is as follows:cP = 0.7 and mP = 0.2 for GA, and F = 0.5 and CR = 0.9 for DE. The parameters of PSO are consistent with [A previous study has established that the typical population size for PSO ranges between 20 and 50 . Consequent with . The gen(1)k = 0, Initialization: (2)Optimization: obtain the optimal space-time steering vector (3)k = k + 1, Update: (4)Judgment: When the criterion Then, the weight vector of STAP is calculated by:In this letter, the performances of four algorithms applied to SR-STAP are evaluated and compared with the traditional algorithm under the condition of UPA with the parameters in the η = 0.8. The global meshless search characteristic of the four meta-heuristic algorithms resulted in atoms being predominantly located on the actual clutter ridge, as illustrated in The first experiment focused on comparing the distributions of atoms in GA-STAP, DE-STAP, PSO-STAP, GWO-STAP, and LMSSE-STAP . As shown in Among the four MH-STAP algorithms, PSO and GWO exhibited the most rapid decline in residual power, followed by DE, with GA showing the slowest rate of decline. When the number of iterations exceeded 33, the residual powers of the five algorithms converged to a similar level. Reaching the threshold requires 25 iterations for PSO-STAP and GWO-STAP, 26 for DE-STAP, 27 for GA-STAP, and 29 for LMSSE-STAP. This indicates that meta-heuristic algorithms require fewer atoms to represent the clutter subspace than LMSSE-STAP.The third experiment is set to evaluate the improvement factor (IF) of the five algorithms in the same situation, which is defined as:K is the external number of iterations, not pulses in CPI (search for the collection of clutter atoms), P is the population size, T is the number of internal iterations , and D is the dimension of the data. With the same population size and number of internal iterations, the SI algorithms (PSO and GWO) generally require fewer external iterations than the EC algorithms (DE and GA) to reach the threshold, which means Finally, the computational complexities of the four kinds of STAP based on meta-heuristic algorithms are analyzed. The time complexity of MH-STAP can be approximatively considered as In conclusion, compared with the traditional method, meta-heuristic algorithms can vastly improve the performance of SR-STAP with certain increases in the amount of calculation. PSO-STAP and GWO-STAP show better clutter suppression and less computational complexity, making them promising prospects for SR-STAP.The observations indicate that both the PSO and GWO algorithms demonstrate exceptional efficacy in SR-STAP. In general, PSO may converge on local optimal solutions, and GWO can maintain the balance between local and global search . TherefoC using the following formula:As shown in pθ = 15°, C is to reduce the possibility of selecting particles outside the clutter distribution region, so as to construct a more accurate clutter subspace and improve the convergence performance of the algorithm.The expression for the new fitness function is as follows:x is the position of the particle; t is the iteration number; T is the maximum number of iterations, and In PSO, the formula for a particle to update its velocity and position is as follows:t is the number of current iterations. T is the maximum number of iterations.In GWO, the course of the hunting process is expressed as follows:As previously mentioned, the PSO algorithm has its own drawbacks. Therefore, PSO and GWO are combined to form a new optimizer to obtain better clutter suppression performance. The PSO-GWO algorithm process is as follows:P, boundary conditions, and maximum number of iterations T, and randomly generate two populations (P1 for PSO and P2 for GWO). In other words, initialize parameters including the particle position Step 1: Set the population size Step 2: The fitness value of each particle and wolf is calculate using (12). According to the results, the α wolf current position Step 3: Update Step 4: Update Step 5: Sort Step 6: Update t reaches the set maximum number T, and the final Step 7: Steps 3 to 6 are repeated until the number of iterations The improved PSO-GWO-STAP is a method to obtain the optimal space-time steering vectors using the PSO-GWO algorithm; the process of the proposed model is shown in P) and iteration times (T).This subsection first analyzes the impact of the improved fitness function and the PSO-GWO algorithm on clutter suppression performance in SR-STAP. The improvement factor (IF) is regarded as the main evaluation parameter, and the results are shown in It can be seen that compared with the single MH-STAP method, both the improved fitness function (IFF) and the PSO-GWO algorithm can improve the performance of SR-STAP. With the increase of the algorithm population size and the number of iterations, the performance improvement brought by the improved fitness function becomes limited, but the PSO-GWO algorithm continues to demonstrate its advantages. The reason for this phenomenon is that with the increase in computational complexity, the search capability of the algorithm is strengthened, allowing for better solutions without external constraints. Moreover, the most critical aspect of MH-STAP is to accurately find the clutter atom subset, which is limited by the search capability of the chosen meta-heuristic algorithm. A single meta-heuristic algorithm can improve its search capability by increasing the population size and iteration number, and it is highly likely to fall into local optima. The technique of algorithm hybridization can achieve global optimization under limited conditions. Therefore, PSO-GWO-STAP can obtain a more accurate clutter subspace than PSO-STAP, and its performance improvement is not affected by increases in population and iterations in this case.The time complexity of the PSO-GWO-STAP algorithm can be considered as As shown in Furthermore, the actual target detection capability of the proposed PSO-GWO-STAP method is verified with mountain-top data. In In this study, an improved PSO-GWO-STAP algorithm is proposed for airborne radar to solve the off-grid problem of SR-STAP. First, we apply meta-heuristic algorithms  to SR-STAP to achieve meshless searching. Simulation and experimental results show that, compared with the traditional method, MH-STAP methods can more accurately find atoms on a spatial-Doppler profile. Furthermore, PSO-STAP and GWO-STAP show better clutter suppression performance than the other methods. Then, the fitness function is improved based on the prior information of the clutter distribution, which can improve the performance of MH-STAP under certain conditions. To obtain more excellent performance, PSO-GWO-STAP is created by combining GWO and PSO. GWO will transmit information to PSO in each iteration to improve the global optimization ability. The results show that the improved PSO-GWO-STAP algorithm can construct a more accurate clutter subspace than single MH-STAP. The limitation of the proposed method is that it consumes certain computing resources while improving the performance. In the future, we will research a low-computational-complexity algorithm which can achieve a greater improvement in clutter suppression performance for SR-STAP. Meanwhile, the proposed new fitness function can play a limited role only if it meets certain conditions, we will also research on a more universal function based prior information to further improve performance.


Acute myeloid leukemia is a rare blood cancer that develops from the clonal expansion of malignant myeloid precursor cells located in the bone marrow. Despite a relatively low incidence in the general population as compared to other cancers, it is one of the most common types of hematological cancers in adults. In recent years, various genome-wide association studies have been conducted to examine how genetic variation impacts disease risk and clinical outcomes in patients diagnosed with acute leukemias. Overall, in the field of cancer research, numerous genome-wide studies have been performed, however, there is a lack of studies specifically focused on patients diagnosed with acute myeloid leukemia. This review provides a summary of the recent genome-wide studies conducted in acute myeloid leukemia, focusing on challenges and limitations associated with research of this heterogenous and rare disease.Acute myeloid leukemia (AML) is a clonal hematopoietic disease that arises from chromosomal and genetic aberrations in myeloid precursor cells. AML is one of the most common types of acute leukemia in adults; however, it is relatively rare overall, comprising about 1% of all cancers. In the last decade or so, numerous genome-wide association studies (GWAS) have been conducted to screen between hundreds of thousands and millions of variants across many human genomes to discover genetic polymorphisms associated with a particular disease or phenotype. In oncology, GWAS has been performed in almost every commonly occurring cancer. Despite the increasing number of studies published regarding other malignancies, there is a paucity of GWAS studies for AML. In this review article, we will summarize the current status of GWAS in AML. Acute myeloid leukemia (AML) is a heterogenous hematologic malignancy characterized by the unregulated clonal expansion of abnormal myeloid progenitor cells in bone marrow. Immature leukemic cells accumulate in the bone marrow and blood, eventually infiltrating other organs, which disrupt production of normal blood cells. As the disease progresses, weakness, infection, bleeding and a myriad of other complications occur in the absence of treatment. AML can be categorized as de novo or secondary AML, with the latter subtype attributed to progression of other diseases or being a result of treatment with radiation or cytotoxic agents (known as therapy-related AML). Even with recent advancements in medical technology, treatment of patients diagnosed with AML remains challenging, with high rates of relapse and refractory disease contributing to dismal outcomes. Optimizing chemotherapy regimens to reduce adverse drug reactions and improve tolerability while attaining maximum therapeutic effect remains an unmet need. Although it is the most common category of acute leukemia in adults, AML is considered a rare disease, having an approximate incidence rate in the United States of 4.1 per 100,000 men and women per year ,3,4.The numerous chromosomal and genetic abnormalities make AML challenging to treat. Current treatment approaches utilize an integrated approach whereby phenotypic and genetic data are used to stratify patients into risk groups at diagnosis and during treatment. Although risk stratification has improved prognostication, a more thorough understanding of AML will likely contribute to more personalized approaches to treatment, with the overarching goal being improving survival outcomes and maintaining a durable remission. Consistent with the polygenic nature of complex diseases, hundreds of genes with recurring mutations that detrimentally affect hematopoiesis have been identified; however, it has been shown that a small number of recurrent mutations in driver genes are attributed to those portions of the genome that act as coding regions ,6,7. WitInitially, early studies focused on heritability within families that contained clusters of AML, which identified a few highly penetrant germline mutations that confer higher risk ,9,10. MoThe lack of definitive findings from family-based linkage studies and other epidemiological reports underscore the polygenic nature of AML. Thus, GWAS-based approaches can facilitate the discovery of novel genetic variants in genes of biological relevance to AML. The premise of GWAS is based on the “common disease—common variation” hypothesis and uses a case–control study design . The effAn extensive literature search of the electronic databases PubMed and EMBASE was conducted to identify peer-reviewed scientific studies (published on or before 6 January 2023) that reported results from GWAS on AML disease risk and/or clinical outcomes using the search terms “acute myeloid leukemia”, “AML”, “genetic studies”, “genome wide association studies”, “GWAS” and “gene polymorphisms”. The searches were limited to studies of humans and written in the English language. In addition to studies reporting GWAS, we included one study that conducted a haplotype-based association analysis in AML, as well as another study that tested selected SNPs from a published GWAS.Using computer database retrieval, 333 articles were identified from PUBMED and EMBASE, of which 186 duplicate studies were removed. After following the exclusion criteria, 22 records were retrieved for further examination and an additional 15 articles were removed because results were not reported solely in relation to AML-diagnosed patients. Therefore, seven studies met the criteria for inclusion in this review .n = 80, 62 with t-AML and 18 with t-MDS) and validation cohorts  [p < 0.001. Of these cases, for five SNPs, minor allele was associated with significant risk of t-AML and for 10 SNPs, major allele was associated with significant risk of t-AML. Of note, three SNPs  were completely absent in cases but occurred with a frequency 6–8% in controls. These 15 SNPs were genotyped in independent validation cohorts (70 cases and 95 controls), and after exclusion of five SNPs due to linkage disequilibrium (LD) or minor allele frequency (<0.1 in cases and control), 10 SNPs were further analyzed. Though none of SNPs were validated at the pre-defined significance thresholds, two SNPs (rs1394384 and rs1381392) showed a trend towards significance. Comparing SNPs within both discovery and validation cohorts, six SNPs  were significant at p < 0.05 in both cohorts.Study 1  : Given th t-MDS) . The conASIC2) gene, which encodes an amiloride-sensitive cation channel that belongs to the DEG/ENaC superfamily and was protective of risk to t-AML. SNP rs1199098 was located in ~2 KB upstream of the inositol–polyphosphate multikinase (IPMK) gene, and the authors concluded the relevance of IPMK due to its interactions with AKT kinase and Wnt/beta-catenin signaling [IPMK has also been implicated in transcriptional regulation and inositide metabolism in the nucleus and is, thus, a regulator of nuclear signaling pathways [Given prior exposure to alkylating agents has been associated with acquired lesions in either or both chromosome 5 and 7, the authors conducted a subset analysis in patients from both cohorts who had these genetic aberrations. In this homogenous subset, three SNPs, namely rs1394384, rs1381392 and rs1199098, were validated as potential markers associated with t-AML. Of these SNPs, rs1394384 was found in the intron of the Acid Sensing Ion Channel Subunit 2  was genotyped using the Affymetrix SNP6.0 Array. The control dataset consisted of non-AML patients derived from the International HapMap Project (n = 218) [p < 1 × 10−5 and mapped to 591 genes. Using a novel candidate gene approach to prioritize haplotypes [RUNX1, JAK1, PDGFRA and FGFR2. The Runt-related transcription factor 1 (RUNX1) gene is well-characterized in CBF-AML and other hematologic malignancies, and it is known to impact hematopoiesis [JAK1) gene is a member of the JAK/STAT signaling pathway and involved in the regulation of various cellular processes, including cell growth and differentiation, as well as immune responses. It has been implicated in the pathogenesis of several types of leukemia, including acute lymphoblastic leukemia (ALL) and AML [PDGFRA) gene is more commonly associated with other types of AML, such as the subset of AML with eosinophilia and abnormalities of chromosome 5 or 7 [FGFR2) gene is not typically altered in CBF-AML, mutations in FGFR2 have been associated with many other malignancies [FGFR2 locus have been previously reported in multiple myeloma and myeloproliferative disorders [Study 2  : A genomn = 218) . Approxiplotypes , four hiopoiesis . The Jan and AML . The plae 5 or 7 . Althouggnancies . Moreoveisorders .RUNX1, which is the primary susceptibility gene associated with CBF-AML in this study, is not surprising, given that the genotype data are enriched in patients with CBF-AML. However, its association with the other prioritized genes is intriguing. Due to the molecular heterogeneity in AML, genetic abnormalities can vary between individual cases of leukemia, and there may be rare cases where JAK1, PDGFRA and FGFR2 abnormalities are present in CBF-AML. Nonetheless, the presence of mutations or aberrations in these genes is not a typical feature of CBF-AML. Of note, no other studies used GWHAS in AML to investigate the way in which genetic variation impacts risk of AML.The presence of p < 1 × 10−4 suggestive significance threshold, the primary SNP, i.e., rs721947, which was located on chromosome 12, was most strongly associated with cytarabine resistance. It is located downstream of NEDD1 Gamma-Tubulin Ring Complex Targeting Factor (NEDD1) gene and situated within a long intergenic non-coding RNA region. Another SNP, i.e., rs1376041, is located in an area close to the exon/intron junction of the Adhesion G Protein-Coupled Receptor G1 (ADGRG1) gene . The minor allele A for rs1376041 was associated with resistance to cytarabine. ADGRG1 has been shown to participate in cancer-cell proliferation and associated with inferior results in AML [IGF1R) gene, which is expressed in human leukemia cells and was previously shown to be associated with AML [IGF1R is one of the phosphorylated receptor tyrosine kinases present in AML cases, and it has been linked to mutant RAS-positive AML, having the potential therapeutic benefits of dual suppression of IGF1R and RAS signaling [ADGRG1 and IGF1R, satisfied all three criterion and were chosen for in vitro mechanistic studies. Small inhibitory RNA (si-RNA)-mediated knockdown of ADGRG1 and IGF1R in AML cell lines confirmed the observations that primary AML cells with knock down resulted in increased apoptosis and, thus, sensitivity to cytarabine. This study utilized the most comprehensive collection of in vitro cytarabine chemosensitivity data from pediatric AML patients. However, the small sample size warrants additional research to confirm these results, as well as in-depth functional studies to establish the underlying mechanism that causes cytarabine resistance.Study 3  : Resistas in AML ,43. Anotwith AML . IGF1R iignaling . To enhap < 10 × 10−6 (none of the variants achieved genome-wide significance). To validate the markers with the lowest p-values, an independent cohort of German cases and controls was used. Chromosomal loci 19q13, 19p13 and 13q22 were selected for validation due to the presence of SNPs with lowest overall p-values and representation of two or more variants below suggestive significance levels. The two SNPs located on chromosome 19 were validated using a third independent cohort, and SNP rs57706619 and the 19q13 SNP rs75797233 showed significant association with AML. Next, a meta-analysis combining all the cohorts confirmed the association between rs75797233  and AML risk.Study 4  : To evalBICRA) gene. BIRCA is a candidate tumor suppressor gene for glioma and has been implicated in transcriptional regulation in colorectal cancer [BICRA expression in whole blood. Furthermore, chromatin immunoprecipitation (ChIP) sequencing data confirmed the presence of a GATA2 transcription factor binding site in this region, as well as that the presence of the risk allele repressed a critical residue at the GATA2 binding site. Using quantitative reverse transcription polymerase chain reaction (PCR) and ChIP quantitative PCR, it was shown that cell lines heterozygous for the rs75797233 risk allele had significantly reduced BICRA expression and reduced GATA2 binding to the locus. These results suggest that rs75797233 may be associated with AML risk through regulating BICRA expression through GATA2; however, in-depth mechanistic studies are needed to establish the role of BICRA expression in the development of AML. Although this past study was one of the largest AML disease risk GWAS conducted, the genome-wide association in BICRA was not reported in other independent cohorts of AML patients [B3GNT3) gene. The third SNP, i.e., rs2039647, was only significant in one of the three cohorts and mapped to chromosome 13q22. This haplotype block included the long non-coding RNA LINC00402 and KLF Transcription Factor 12 (KLF12) gene, which encodes a protein that is a member of the Krüppel-like zinc finger protein family and has been broadly associated with various other cancers types.SNP rs75797233 is located 13KB from BRD4 Interacting Chromatin Remodeling Complex Associated Protein . Within the discovery cohort, genome-wide significant (p < 5 × 10−8) susceptibility loci were observed in CN-AML and included SNP rs3997854 located in Major Histocompatibility Complex, Class II, DQ Alpha 2 (HLA-DQA2) gene (Chr 6p21.32) and rs75391980 (Chr 4q22.3). Of note, a few other SNPs reached significance at a suggestive threshold of (p < 10−6), including rs4674579, rs2621279, rs13183143, rs11481 and rs6077414.Study 5  : This man = 977 cases, of which 465 were CN-AML, and n = 3728 controls) and meta-analysis confirmed the presence of rs4930561 in Lysine Methyltransferase 5B (KMT5B) gene on Chr 11q13.2 in all-AMLregardless of subtypes and rs3916765 in HLA-DQ2 (Chr 6p21.32) in CN-AML. Further meta-analysis of SNPs common to all four cohorts identified rs10789158 in Cache Domain Containing 1 (CACHD1) at Chr 1p31.1 risk locus in a consistent direction for all-AML and rs17773014 in the Aldo-Keto Reductase Family 1 Member B (AKR1B1) gene at Chr 7q33 for CN-AML. These results remained consistent when analysis was performed to correct any underlying effects of population ancestry by re-examining cases and controls of British and German origin present in the four GWAS cohorts. An attempt to test these variants in a cohort of 767 AML patients from the UK, Germany and Hungary did not find any association between the variants and relapse-free or overall survival in all-AML or CN-AML patients (n = 369). SNP rs4930561 was reported to be significantly associated with risk of AML, regardless of subtype. The variant is located in the KMT5B gene on chromosome 11q13.2 and a member of the histone–lysine methyltransferases (KMTs) family, which are essential in many tightly regulated cellular processes. Dysregulation of KMTs has been associated with many cancers [KMT5B expression did not show significant association with the lead SNP.Validation of a fourth independent cohort  was not found to have a significant association with AML in this study. One of the reasons for this finding might be its rarity and the fact that it was only imputed to a high enough quality in three GWAS studies. These data bolster the existing evidence of genetic and biological diversity in AML and highlight the need for collaborative studies to increase statistical accuracy and aid in the discovery of genetic risk loci that are specific to AML sub-types.The second genome-wide significant susceptibility locus for CN-AML was identified at Chr 6q21.32 in gnancies ,51,52. EIRF4) gene was associated with an increased risk of de novo AML, de novo MDS and therapy-related MDS, but not therapy-related AML. IRF4 is a transcription factor involved in T and B lymphocyte differentiation. SNP rs12203592 is implicated in IRF4 regulation via interactions with the chromatin loop [Study 6  : This latin loop . Though ZNF230); rs9290663, in potassium calcium-activated channel subfamily M regulatory beta subunit 2 (KCNMB2); and rs11155133, in non-coding RNA gene (LOC102723724), were associated with a higher risk of developing AML, whereas rs10873876 in ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 3 (ST6GALNAC3) was associated with s lower risk of developing AML.Study 7  : Though ZNF360 was associated with a higher risk of M2-AML in cases with AML1/ETO fusion but not in cases without fusion, suggesting a subtype-specific impact of the SNP. ST6GALNAC SNP showed an inconsistent association relative to previous report; this lack of association was speculated to be due to the different study populations: ALL vs. AML compared to European vs. Chinese patients. Further investigation of the SNP in LOC102723724 using in silico databases predicted that A to G base-change might affect the binding of C/EBP; however, current evidence of this underlying mechanism warrants further research. While not an independent GWAS in AML, the objective of this replication study was to replicate findings from two previous GWAS studies on acute leukemias. However, ALL vs. AML differences, sample size limitations, and potential gene–environment interactions limited efforts at validation of the previous findings.Among these primary SNPs, rs2191566 in While GWAS studies have been successful in other complex diseases, there has been a relative lack of such studies in relation to AML. This issue is partly due to challenges and limitations associated with conducting GWAS studies in AML. The rarity of the disease, the heterogeneity of AML subtypes, and the large and well-characterized cohorts required to identify statistically significant associations are some of these challenges. Additionally, AML is a complex disease in which both genetic and environmental factors contribute to disease risk, making it difficult to distinguish the relative contribution of each type of factor.This article highlights findings from a handful of GWAS studies conducted in relation to AML. Overall, there are several limitations common to the majority of the studies included in this review. Other than one small study performed in pediatric AML patients, all studies were conducted in predominantly adult patient cohorts. Age is associated with distinct genetic and molecular AML characteristics ; as suchDue to the rarity of AML cases, insufficient sample sizes, which reduce statistical power, make it challenging to detect significant associations. Thus, combining diverse AML cohorts or including MDS patients in genetic studies can introduce several challenges and limitations. AML and MDS are heterogeneous diseases with multiple subtypes and varying genetic profiles. Combining patients with different clinical features can introduce confounding factors that may impact the interpretation of genetic associations. Variability in treatment response, survival outcomes, and disease progression can mask or distort the true genetic associations within each disease entity. Furthermore, grouping diverse subtypes together can limit researchers’ ability to identify subtype-specific genetic alterations and their functional consequences. While combining AML with MDS patients certainly increases cohort size, it further expands the heterogeneity and limits the power to detect risk alleles; thus, it would be recommended to conduct separate genetic studies for AML and MDS whenever possible. It is important to carefully consider and address the inherent heterogeneity and potential confounding factors when interpreting and generalizing the results.Another common feature of these studies has been the focus on effects of SNPs. These factors highlight the need to modify current approaches by performing GWAS studies in addition to establishing cohorts of larger sample size and integrating cases with other omics data, as well as established somatic cytogenetic and genetic lesions. Further integration of top hits to create polygenic risk scores may increase researchers’ ability to estimate the cumulative contribution of AML susceptibility loci to disease risk.https://gdc.cancer.gov/, accessed on 11 June 2023), which is a platform that allows researchers to access and analyze genomic and clinical data from numerous cancer studies. In summary, data sharing is essential for maximizing the scientific value of research data, and efforts to promote and facilitate data sharing in AML research should be continued and expanded.In recent years, there has been growing awareness of the importance of data sharing in scientific research, and several initiatives have been launched to promote and facilitate data sharing in genetics and genomics research. For example, the Global Alliance for Genomics and Health (GA4GH) is an international organization that aims to promote data sharing in genomics research through the development of standardized tools, policies and frameworks . These iDespite the success of GWAS studies in other complex diseases, there is paucity of such studies in AML, which highlights the unmet need for comprehensive and collaborative initiatives to enhance identification of risk alleles and development of polygenic scores that are associated with AML disease or treatment outcomes, especially in specific cytogenetic subgroups. One major limitation is that the majority of studies have been performed in patients with predominantly European ancestry, thus warranting the creation of future studies that consider patients from other racial/ethnic groups. Overall, genome-wide studies in AML offer opportunities to advance our understanding of the disease, improve patient outcomes through facilitating personalized medicine approaches.


To investigate the correlation between postoperative limb/component alignments and clinical/functional outcomes following medial unicondylar knee arthroplasty (mUKA).Inclusion criteria included peer-reviewed English- or German-language publications assessing postoperative limb or implant alignment and clinical outcomes of mUKA. Methodological Index for Non-Randomized Studies (MINORS) was used to assess article quality. A total of 2767 knees from 2604 patients were evaluated. Significant correlations were observed between postoperative limb/component alignments and clinical/functional outcomes after mUKA. Inferior outcomes were associated with lower placement and excessive valgus alignment of the tibia component (> 3°). A recommended external rotation of 4°-5° was identified for the tibia component, with specific cut-off values for the femoral and tibia components. Optimal outcomes in mUKA were associated with a varus coronal limb alignment. The tibia implant component performed well within a specific alignment range. An exact external rotation value was recommended for the tibia component, while internal rotation correlated negatively with the femoral component.IV (level IV retrospective case series were included).The online version contains supplementary material available at 10.1186/s40634-023-00655-3. Unicondylar knee arthroplasty (UKA) is a well-established treatment option for osteoarthritis and osteonecrosis, specifically targeting either the lateral or the medial compartments of the knee joint . Long-teTraditionally, patients undergoing medial UKA (mUKA) have a preoperative varus phenotype and overall varus limb alignment. However, recent research challenges this notion, suggesting a more nuanced understanding of knee phenotypes and alignments; Hirschmann et al. analyzed 308 non-osteoarthritic knees and identified 43 different knee phenotypes, with functional and anatomical alignment targets observed in varying proportions . FurtherIt is well known that undercorrection may contribute to increased polyethylene wear, while overcorrection may lead to osteoarthritis in the lateral knee compartment , 29. AddThe objectives of this systematic review are twofold: to identify significant correlations between different postoperative limb/component alignments and clinical/functional outcomes in mUKA, and to examine whether specific postoperative coronal limb or coronal/axial component alignments yield superior clinical and/or functional outcomes compared to alternative alignments. It is hypothesized that significant correlations between postoperative limb/component alignment and clinical/functional outcomes exist in mUKA. However, it is presumed that no single postoperative limb/component alignment strategy can unequivocally be proven superior in terms of clinical/functional outcomes.A systematic literature search adhering to PRISMA guidelines  was condAfter removing duplicates and collecting all relevant articles, the studies were screened based on inclusion criteria using the title and abstract. Inclusion criteria encompassed English- or German-language publications in peer-reviewed journals that assessed the clinical and/or functional outcomes of medial unicondylar knee arthroplasty (mUKA) based on postoperative overall limb or component alignment. Studies unrelated to mUKA, pertaining to lateral unicondylar knee arthroplasty (lUKA), sagittal alignment, revision arthroplasty or failure rates were excluded. Only full-text articles with available numeric data  were considered.Next, the selected articles were independently reviewed for eligibility through full-text analysis by two authors. The same authors then manually screened the reference lists for additional articles meeting the inclusion criteria but not covered by the original search terms. In case of uncertainty regarding inclusion a third author was consulted. The endpoints of the included studies comprised postoperative limb and component alignments, various clinical and functional scores, and patient-reported outcome measures (PROMs) such as the Knee Society Score (KSS), Oxford Knee Score (OKS), Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMasters Universities Arthritis Index (WOMAC), and Forgotten Joint Score (FJS).The methodological quality of the included studies was independently assessed by two authors using the Methodological Index for Non-Randomized Studies (MINORS) for non-randomized comparative and non-comparative clinical intervention studies . The maxRelevant information such as title, author, year of publication, study design, level of evidence, number of knees, follow-up period, patient demographics, clinical and functional outcome scores, and radiological outcomes were extracted from the selected publications by one author into a Microsoft Excel spreadsheet.p < 0.05 was considered statistically significant for data interpretation.Continuous variables were described using means and standard deviations or means and ranges, while categorical variables were reported as absolute and relative frequencies. A significance level of The initial literature search yielded 215 publications, of which 12 met the inclusion criteria  were inconsistent, with varying optimal intervals for KSS and OKS scores reported across studies Table . HoweverWhen examining the tibia component, higher values of external rotation were linked to lower KSS and OKS scores Table . Ng et aThe main finding of this review is the presence of significant correlations between different limb/component alignments and clinical/functional outcomes following mUKA. Specifically, the 1º-4º HKA alignment was reported as an optimal range for the coronal alignment of the knee, resulting in superior functional and clinical outcomes compared to other analyzed intervals. However, findings regarding the coronal alignment of the femoral component are somewhat heterogeneous and ambiguous, with different values presented by multiple authors as yielding the best outcomes. On the other hand, when discussing the coronal alignment of the tibia component, the analyzed studies predominantly suggest the -2.5º —5º (varus) interval as generating the best clinical and functional outcomes. Furthermore, worse outcomes have been observed for patients with a tibia component positioned lower than the intercondylar eminence and the lateral joint, and/or in excessive valgus alignment relative to the lower limb axis. In terms of the axial alignment of the implant, a clear recommended interval of external rotation for the tibia component has been identified as 4º-5º. Cut-off values for external rotation of the tibia and femoral components have also been determined, with 10º-12º for the femoral component and 8º-9º for the tibia component. Additionally, a negative correlation has been observed between internal rotation of the tibia component and the femoral component. In conclusion, the study has achieved its aims and partially confirmed the hypothesis.One interesting finding is the debatable reliability of the Knee Society Score (KSS) in calculating clinical outcomes, particularly the clinical/objective KSS. Several included studies question the deduction of KSS scores for varus or valgus limb alignments that fall outside the recommended intervals, such as 5º-10º valgus FTA for the 1989 KSS or 2º-10º valgus for the 2011 KSS , 16, 30.This systematic review is the first to analyze both limb and implant component alignments following mUKA from the perspective of clinical and functional outcomes. While Riviere et al. conducted a systematic review on limb alignment in mUKA ; their fHowever, this study has several limitations. One drawback is the lack of differentiation between fixed and mobile bearing medial UKA. The risks associated with overstuffing the medial compartment in mobile bearing UKA, as highlighted by Smith et al. , were noDespite these limitations, the reported results should provide guidance to orthopedic surgeons and improve the understanding of mUKA as a valid option for reducing knee pain and restoring functionality in patients with isolated medial osteoarthritis. However, future studies with higher levels of evidence and larger cohorts are needed. An international conversation should also be initiated regarding the criteria used in the KSS to award or deduct points for knee alignment. Additionally, given the existence of two KSS scores (1989 and 2011) in circulation, it is recommended that orthopedic surgeons reach a consensus and recommend the use of only one KSS while discontinuing the other. This will help prevent the dissemination of heterogeneous and contradictory results in the scientific community.Optimal outcomes in mUKA were associated with a varus coronal limb alignment. The tibia implant component performed well within a specific alignment range. An exact external rotation value was recommended for the tibia component, while internal rotation correlated negatively with the femoral component.Additional file 1.


Formal training in how to mentor is not generally available to students, postdoctoral fellows, or junior faculty. We provide here a framework to develop as a mentor, using the GREAT model. This includes giving opportunities and opening doors; reaching out to help students identify their strengths and reach their goals; encouraging them by serving as a positive example; advising each mentee as an individual; and training them for independent thinking. In this personal view, we expand on each of these steps to illustrate how to develop a personalized mentoring style of your own. By combining these approaches, you as a mentor can work with your mentees to develop an effective and productive mentoring relationship.NEW & NOTEWORTHY We provide here a framework to develop as a mentor, using the GREAT model. This includes giving opportunities and opening doors; reaching out to help students identify their strengths and reach their goals; encouraging them by serving as a positive example; advising each mentee as an individual; and training them for independent thinking. In physiology and other biomedical research and education disciplines, a mentor serves to guide their mentees on how to identify and succeed at their career goals . MentorsThere are different models of mentoring, including the traditional mentor-mentee model where the mentee serves as an apprentice learning from the advisor. Other models include casual or informal mentoring; peer mentoring, where colleagues collaborate to mentor each other; reverse mentoring , where someone more junior provides guidance and new knowledge to a more senior colleague; shadow mentoring, where a mentor provides dedicated and unbiased mentorship but does not receive any formal recognition for mentoring; and distance monitoring, where mentor and mentee are located in different places and the mentor and the mentee are in touch virtually to render help in the mentee’s personal and career development –8.Any of these models requires intentional mentoring, which refers to mentoring that is deliberate, planned, and purposeful . Intenti1) they are enthusiastic and demonstrate strong compassion and selflessness; 2) they act as career guides; 3) they commit time and effort; 4) they support and practice balance; and 5) they are role models who establish a legacy for others to follow and emulate : “Are you saying…?” or “What I hear you say is….” This assures the mentee that you are listening and demonstrates that you are sincerely interested in them. Active listening also provides an opportunity to clarify any miscommunication or misunderstandings. When communication is open, this allows the mentor an opportunity to learn from their mentees; if you are not learning, this may mean that your mentees are not comfortable adding their ideas to the discussion.Regardless of the obvious benefits of effective mentoring, mentor training is not systematically taught or required during training and development, and most mentors end up mentoring along the same style in which they were mentored. In the worst-case scenario, the mentoring style is negative and the mentor is absent, abusive, or unequal in treatment across mentees, whether intentional or not; the mentor does not align with the mentee in terms of personality or ability to provide career and psychosocial support; or the mentor does not share goals or expectations . As negaThe first step in setting up a successful mentor and mentee partnership is to develop a mentoring plan. The best mentoring plans are specific, provide opportunities for the mentee to grow, and use continuous effective communication to provide feedback. The mentoring plan should include activities in which both mentor and mentee will engage so that responsibility is shared and clearly stated. Mapping a mentoring road map involves self-reflection and honesty on the part of the trainee, as well as establishing a trusting environment by the mentor, to establish, maintain, and advance the relationship with both sides committing to the effort , 19.1) give opportunities and open doors; 2) reach out to help students identify their strengths and reach their goals; 3) encourage them by serving as a positive support network; 4) advise each mentee according to individual personality, career interests, and individual goals; and 5) train them for independent thinking. Developing better mentoring skills will facilitate the development of individual mentee training plans that are realistic and achievable. We provide here a template for developing or enhancing mentoring skills, using the GREAT model to The most important step to developing an effective mentoring relationship is to help the mentee clarify their long-range career goals and needs , 20, 21.In graduate school and postdoctoral fellowship training, we are not given formal training in mentoring. For those who transition to industry or follow the academic path, there is usually not a formal system in place to develop mentoring skills. For this reason, it is usually left up to the individual to seek training in mentoring. The good news is that there are a number of resources available to help you, in addition to the other references cited in this perspective. Resources range from literature to formal classes to certificate programs in mentoring and coaching. We encourage you not to take on this challenge alone and not rely to only on mentoring as you were mentored. Receiving formal and informal feedback is also important, as is continual self-assessment and adjusting to improve.Take time for self-reflection and filter the mentoring practices you found to be most beneficial from those that were not instrumental or perhaps were detrimental to your own career growth. Breaking the cycle of bad mentorship starts when one refuses to treat their trainees how they were trained. One way to assess this is to keep track of the trainees who stay in contact with you, as this is a direct reflection of the importance they give to your advice. Likewise, if you had the benefit of an excellent mentor, consider which aspects of their mentoring style you found most effective and would like to emulate. Learning to mentor is like learning to write or teach: the more thinking, planning, and practice you put into it, the more likely you will end up being good at it.The best mentors provide equal part knowledge and equal part sponsorship to their trainees . For resFor any successful career, resilience and strong coping mechanisms are requirements and giving positive examples of ways to handle rejection is necessary. As a sponsor, mentors help to foster the independence of their trainees, promote their professional development, teach them to network, and advocate to propel their success. One example of helping your mentees develop resilience is by establishing a team culture of positivity. For manuscript decisions where the journal has left the door open for revision (no matter how major a revision is required), taking a positive tone of congratulating the first author will develop a mindset in them that fosters current and future success. When the manuscript is rejected by the journal, letting them feel bad for a night and then coming in the next day with a plan will allow them to acknowledge the rejection without allowing it to take control. Creating a positive feedback loop through effective mentoring will also promote cultural change in our future leaders, which is a particularly important aspect for creating a more diverse scientific workforce , 25. A dAnother component to building resilience and effective coping strategies is to actively celebrate success. As physiologists and biomedical researchers and educators, we are trained to focus on the negative and what needs to be fixed, often glossing over successes as the expected outcome. Taking time to celebrate and acknowledge success will counterbalance the times when rejection is the result of our efforts. Ask yourself periodically when was the last time the team celebrated a success. Providing a positive environment is especially important for women and minority groups underrepresented in science who are already facing systemic biases. Mentors who understand that differences in the system exist can purposefully align their advising strategies to ways that best support the individual student , 27. UsiIn summary, we provide here a framework for developing a plan to develop into a GREAT mentor. As you develop and practice your skills, you will want to periodically self-assess using strategies detailed above. Mentoring success metrics include mentees who attain their milestones, obtain the necessary competencies to define career options and goals, are provided with enough examples to handle future experiences, and are supplied with opportunities that allow them to grow. Basically, the mentee feels prepared for their future if the mentor pairing was successful. Another useful self-assessment for examining how good a mentor you are can be found in Lee et al. . This inWe hope that you find the GREAT model useful for adopting in your mentoring programs not only in science, technology, engineering, and math (STEM)-related disciplines but also in various health professions programs.We acknowledge funding from the Alzheimer’s Association under AD Strategic Fund Award ABA-23–975038 (J.S.D.); from the National Institutes of Health under award numbers SC1GM139814 (A.M.S.) and R25GM116727 (L.W.); from the US Department of Energy under award number DE-EM0005266 (A.R.); and from Biomedical Laboratory Research and Development Service of the Veterans Affairs Office of Research and Development under award number 5I01BX000505 (M.L.L.).The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.American Journal of Physiology-Heart and Circulatory Physiology. Given her role, she was not involved in the peer review of this article outside of being an author and had no access to information regarding its peer review. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.M. L. Lindsey is editor-in-chief of M.L.L. and L.W. conceived and designed research; M.L.L. and L.W. drafted manuscript; J.S.D., A.M.S., A.R., M.L.L., and L.W. edited and revised manuscript; J.S.D., A.M.S., A.R., M.L.L., and L.W. approved final version of manuscript.


Although anthracyclines are the first-line chemotherapy drugs for treating non-M3 acute myeloid leukaemia (AML), their efficacy remains limited. It is important to identify factors that influence the efficacy of anthracyclines against AML. Mitochondrial apoptosis-related genes play significant roles in the pathogenesis, treatment, and prognosis of AML.BCL2L11, CASP8, TP63, TP53BP2, PLAUR, SOD2, BNIP3L, and MMP9, were screened out. Then, DNA from 279 patients with AML and 321 healthy individuals were extracted and the contributions of single nucleotide polymorphisms (SNPs) within these genes to the patient’s chemotherapy response, susceptibility to AML, and overall survival were investigated.We utilized the CRISPR/Cas9 screening system to find AML anthracyclines resistance related genes and several mitochondrial apoptosis-related genes, such as PLAUR gene was associated with an increase in complete remission after anthracycline-based induction chemotherapy. rs4880 in SOD2 was associated with the response to the second course of chemotherapy, whereas rs3789068 in BCL2L11 was associated with susceptibility to AML.Our findings indicated that SNP rs4251864 in the Our results about the association of SNPs in mitochondrial apoptosis-related genes with the response to anthracycline-based chemotherapy in AML provide an important reference for predicting the treatment outcomes in patients with this disease. Acute myeloid leukaemia (AML), a heterogeneous myeloid malignancy, is characterised by the uncontrolled clonal proliferation of undifferentiated myeloid cells. Clinically, induction chemotherapy is needed as soon as possible to induce remission to save the life of patients with this disease. Although anthracycline- and cytarabine-based chemotherapy regimens are still the first-line treatments for non-M3 AML, the complete remission (CR) rates resulting from these drugs are only approximately 70–80% , 2. PrimTo more accurately explore the bioindicators that affect the activity of anthracyclines against AML, we performed clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-based screening for candidate genes after the exposure of THP-1 cells to the anthracycline drug idarubicin (IDA). Then, we used functional pathway enrichment analysis to identify candidate molecules related to the mitochondrial apoptosis pathway, which has been shown to be involved in the pathogenesis, treatment, and prognosis of AML , 4. The ABCB1 gene were found to predict the outcome of chemotherapy in AML patients  in AML patients are considered to be one of the important reasons for individual sensitivity to chemotherapy drugs , 11. Sompatients , 14. Morpatients . In thisIn total, 279 patients with newly diagnosed AML (non-M3) were recruited from the Department of Hematology, Qilu Hospital of Shandong University from June 2011 to December 2021. All patients  were diagnosed in accordance with the World Health Organization classification system and National Comprehensive Cancer Network (NCCN) guideline criteria , 17. TheFor CRISPR/Cas9 screening with the CRISPR-Pool™ SAM vector , a CRISPR screening library was used, which contained 70,290 single guide RNAs (sgRNAs) targeting 23,430 genes and control genes, with an average of three sgRNAs per gene. THP-1 cells (a human AML monocytic cell line) were first transfected with the MS2-P65-HSF-Hygro lentivirus and then screened using 500 μg/mL hygromycin B . Subsequently, these cells were infected with the sgRNA-dCas9-VP64-blasticidin lentivirus at a multiplicity of infection of approximately 0.25 and coverage rate of 300×, and the cells were cultured for 2 days. Then, the cells were treated with 4 μg/mL cysticercosin  for 72 h and cultured for 5 days to maintain a 300× coverage until the screening assay. After establishing day 0 as the baseline for sequencing, the THP-1 cells were divided into two groups: one group was grown in the absence of drug treatment as the untreated control, whereas the other group was grown in the continuous presence of low-dose IDA (3 ng/mL). The screening process lasted for 7 days. The two groups of cells were collected and used for high-throughput sgRNA sequencing, and the genes with large differences in sgRNA expression levels between the two cell groups were screened Figure 1A commercial DNA extraction kit  was used to extract genomic DNA from bone marrow-derived mononuclear cells. The concentration and purity of the extracted DNA were detected using a spectrophotometer. The SNPs of the selected genes are listed in Total RNA was extracted from mononuclear cells using the TRIzol reagent , and converted into cDNA using the PrimeScript RT Reagent Kit Perfect Real Time . Quantitative PCR was performed on a LightCycler 480II Real-Time PCR system  following the standard protocol. At the end of the reactions, the cycle threshold (CT) was measured and the mean CT of three independent replicates was calculated. GAPDH was used as a reference gene.P-value of greater than 0.05 and a minor allele frequency of greater than 5% were included in the study. To determine the relationships of the SNP phenotype or allele frequency with the susceptibility to AML, risk stratification, CR, relapse, and OS, the following statistical methods were used: the chi-square test or Fisher’s exact test for preliminary screening; univariate and multivariate binary logistic regression analyses for determining the odds ratios (ORs) with 95% confidence intervals (95%CI) and P-values adjusted for sex and age; Kaplan–Meier curves for estimating OS; and Cox regression analysis for evaluating the prognostic factors of AML. SPSS 20.0 software  was used for all statistical analyses. Statistical significance was considered on the basis of either a two-tailed P-value of less than 0.05 or a false discovery rate (FDR) q-value of less than 0.05.For all SNPs, tests for deviation from Hardy–Weinberg equilibrium (HWE) were performed using Pearson’s goodness-of-fit chi-square test. SNPs with an HWE The THP-1 cells that had been treated with IDA for 7 days and the untreated control cells were harvested for high-throughput sgRNA sequencing. Genes with highly variable sgRNA expression levels between the two cell groups were considered to potentially contribute to IDA resistance. Firstly, we conducted functional pathway enrichment analysis of the differentially expressed genes and found that most were concentrated in the mitochondrial apoptosis-related pathways. Then, eight of the most promising genes in these pathways were screened out Figure 1BCL2L11, CASP8, TP63, and TP53BP2) screened by CRISPR/Cas9 were increased in both resistant cell lines, while the expression levels of the four sensitive genes  were decreased in resistant cell lines  gene and susceptibility to AML in both co-dominant and recessive models . Univariate logistic regression analysis also confirmed the association between rs4251864 and susceptibility to AML in the co-dominant model (P = 0.015). After adjusting for sex and age and FDR correction, the recessive GA genotype of rs4251864 was found to be significantly associated with susceptibility to AML, with an increase in CR (P = 0.015)  Table 4.SOD2) gene and susceptibility to refractory AML under the co-dominant and recessive models . Univariate logistic regression analysis also indicated an association between rs8031 and susceptibility to refractory AML in both the models . The co-dominant AT and recessive TT genotypes of rs8031 were significantly associated with susceptibility to refractory AML after adjusting for sex and age . The results were statistically significant after FDR correction  were divided into the CR and non-CR groups. After the first course of chemotherapy, 161 patients received a second chemotherapy course, whereas the remaining 19 patients either refused chemotherapy or succumbed to the disease. At the end of the second chemotherapy course, 129 patients achieved CR, whereas the remaining 32 patients did not. Preliminary screening with the chi-square test or Fisher’s exact test revealed a significant correlation between the rs8031 SNP in the superoxide dismutase 2 (rrection Table 5.BCL2L11) gene , rs724710 in BCL2L11 , rs798755 in the tumour protein P53 binding protein 2 (TP53BP2) gene (co-dominant model), and rs4251864 in PLAUR  were significantly correlated with susceptibility to AML (P < 0.05). Univariate logistic regression analysis showed associations of the following SNPs with susceptibility to AML: rs3789068 in BCL2L11 under the co-dominant, dominant, and allele models (P < 0.05); rs724710 in BCL2L11 under the allele model (P = 0.027); and rs4251864 in PLAUR under the dominant and allele models . All afore-mentioned univariate logistic regression analyses were adjusted for sex and age. However, after FDR correction, the co-dominant GG genotype and G alleles of rs3789068 in BCL2L11 were found to be associated with susceptibility to AML (q < 0.05) , where the relationship between each locus and AML was analyzed. Preliminary screening with the chi-square test or Fisher’s exact test showed that the SNPs rs3789068 in the Bcl-2-like protein 11 ( < 0.05) Table 6.P > 0.05)    4. The oP < 0.001). Additionally, patients with a white blood cell count (WBC) count higher than 100 × 109 had significantly lower OS (median = 85 days) than those with a WBC count lower than 100 × 109 . Patients who did not receive allogeneic haematopoietic stem cell transplantation  had significantly lower OS (mean = 538 ± 49.716 days) than those who received treatment . Similarly, patients who did not receive chemotherapy also had significantly lower OS (median = 56 days) than those who received treatment . Additionally, OS was significantly lower in patients with adverse risk stratification (median = 213 days) than in those with favourable stratification .Patients aged 60 years or older had significantly lower OS (median = 198 days) than those in the younger age group , age greater than 60 years , WBC count higher than 100 × 109/L , no chemotherapy , and no allo-HSCT  were independent prognostic risk factors for AML  , is mainly responsible for the expression of cellular plasmin, which promotes cell extravasation and tissue invasion. Studies have found that PLAUR is closely related to the occurrence and prognosis of a variety of malignant tumours  SOD2 itself has a dichotomous effect of both promoting and inhibiting the tumour; and (2) only a small number of patients did not respond to the second course of chemotherapy. Therefore, the influence of the SOD2 gene polymorphism on the response to AML chemotherapy still needs to be confirmed in a study with a larger sample size.Additionally, we found an association between a the body . Althougthe body . Thereforeported , 29. OneGG genotype and dominant GA/GG genotypes of rs3789068 in BCL2L11 were found to be significantly associated with susceptibility to AML. As a member of the BCL2 family, BCL2L11 plays a key role in regulating apoptosis in B- and T-cell homeostasis (BCL2L11 can bind to anti-apoptotic Bcl-2-like proteins and plays an important role in the initiation of apoptosis (BCL2. Therefore, single nucleotide mutations in BCL2L11 may break the homeostasis of haematopoietic cells and lead to haematological malignancy. The role of rs3789068 in inducing susceptibility to haematological malignancies has been reported. In a study involving 5330 patients with B-cell non-Hodgkin’s lymphoma and 6260 healthy individuals, rs3789068 was found to be associated with an increased risk of the disease (BCL2L11 gene SNP with susceptibility to AML.Although many studies on the relationship between SNPs and AML prognosis have been carried out, there are few reports on that between SNPs and susceptibility to AML. In our study, the co-dominant eostasis , 31. BCLpoptosis . However disease . These sIn this study, we innovatively combined CRISPR/Cas9 screening and SNP analysis to study the correlation between SNP sites and AML chemotherapy response, which greatly improves the reliability of the results and will be the biggest advantage of our study. Meanwhile, the molecular and cellular mechanisms of the identified genes that influence drug sensitivity worth for further study. In addition, owing to the heterogeneity of the AML patient population and the small number of patients in this study, the results should be further confirmed by studies with larger sample sizes.In this study, we proved that rs4251864 in PLAUR or rs4880 in SOD2 were associated with the response to first-induction chemotherapy or second-course chemotherapy of AML containing anthracyclines, respectively. The results suggested that the mitochondrial apoptosis-related pathways represented by PLAUR and SOD2 are very important for anthracycline sensitivity. Their SNPs can be used as predictors for the response to chemotherapy in AML patients. And we plan to further investigate the molecular and cellular mechanisms of the identified genes that influence drug sensitivity in future studies.The data presented in the study are deposited in the GEO repository, accession number GSE235688.This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Qilu Hospital, Shandong University. We obtained informed consent from the patients and controls involved in the study.CJ, TS, and GM designed the study; GM performed the research, analyzed the data, and drafted the manuscript. ML, YX, YW, YM, MJ, JZ, and JY performed the research and evaluated the data. All authors contributed to the article and approved the submitted version.