N6-methyladenosine (m6A) is the most abundant posttranscriptional modification of mRNA in eukaryotes. Recent evidence suggests that dysregulated m6A-associated proteins and m6A modifications play a pivotal role in the initiation and progression of diseases such as cancer. Here, we identified that IGF2BP3 is specifically overexpressed in acute myeloid leukemia (AML), a subtype of leukemia associated with poor prognosis and high genetic risk. IGF2BP3 is required for maintaining AML cell survival in an m6A-dependent manner, and knockdown of IGF2BP3 dramatically suppresses the apoptosis, reduces the proliferation, and impairs the leukemic capacity of AML cells in vitro and in vivo. Mechanistically, IGF2BP3 interacts with RCC2 mRNA and stabilizes the expression of m6A-modified RNA. Thus, we provided compelling evidence demonstrating that the m6A reader IGF2BP3 contributes to tumorigenesis and poor prognosis in AML and can serve as a target for the development of cancer therapeutics.

Acute myeloid leukemia (AML) is a clonal malignant proliferative blood disorder with a poor prognosis. Ferroptosis, a novel form of programmed cell death, holds great promise for oncology treatment, and has been demonstrated to interfere with the development of various diseases. A range of genes are involved in regulating ferroptosis and can serve as markers of it. Nevertheless, the prognostic significance of these genes in AML remains poorly understood. Transcriptomic and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate Cox analysis was performed to identify ferroptosis-related genes with prognostic value, and the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate Cox regression analysis were utilized to optimize gene selection from the TCGA cohort (132 samples) for model construction. Tumor samples from the GEO database (136 samples and 104 samples) were used as validation groups to estimate the predictive performance of the risk model. Finally, an eight-gene prognostic signature (including CHAC1, CISD1, DPP4, GPX4, AIFM2, SQLE, PGD, and ACSF2) was identified for the prediction of survival probability and was used to stratify AML patients into high- and low-risk groups. Survival analysis illustrated significantly prolonged overall survival and lower mortality in the low-risk group. The area under the receiver operating characteristic curve demonstrated good results for the training set (1-year: 0.846, 2-years: 0.826, and 3-years: 0.837), which verified the accuracy of the model for predicting patient survival. Independent prognostic analysis indicated that the model could be used as a prognostic factor (p ≤ 0.001). Functional enrichment analyses revealed underlying mechanisms and notable differences in the immune status of the two risk groups. In brief, we conducted and validated a novel ferroptosis-related prognostic model for outcome prediction and risk stratification in AML, with great potential to guide individualized treatment strategies in the future.

Objective: Acute myeloid leukemia (AML) is a malignant clonal disorder. Despite enormous progress in its diagnosis and treatment, the mortality rate of AML remains high. The aim of this study was to identify prognostic biomarkers by using the gene expression profile dataset from public database, and to improve the risk-stratification criteria of survival for patients with AML. Materials and methods: The gene expression data and clinical parameter were acquired from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. A total of 856 differentially expressed genes (DEGs) were obtained from the childhood AML patients classified into first complete remission (CR1) group (n=791) and not CR group (n=249). We performed a series of bioinformatics analysis to screen key genes and pathways, further comprehending these DEGs through Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Results: Six genes (SLC17A7, MSX2, CDC26, MSLN, CTSZ and DEFA3) identified by univariate, Kaplan-Meier survival and multivariate Cox regression analyses were used to develop the prognostic model. Further analysis showed that the survival estimations in the high-risk group had an increased risk of death compared with the low-risk group based on the model. The area under the curve of the receiver operator characteristic curve in the prognostic model for predicting the overall survival was 0.729, confirming good prognostic model. We also performed a nomogram to provide an individual patient with the overall probability, and internal validation in the TARGET cohort. Conclusion: We identified a six-gene prognostic signature for risk-stratifying in patients with childhood AML. The risk classification model can be used to predict CR markers and may assist clinicians in providing realize the individualized treatment in this patient population.

Purpose: Acute myeloid leukaemia (AML) is a common haematological disease in adults. The overall survival (OS) remains unsatisfactory. It is critical to identify potential prognostic biomarkers and develop a nomogram that predicts overall survival in patients with AML. Patients and Methods: We used gene expression dataset and clinical data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify differential expression analysis, survival analysis, and prognostic value of IGHD gene family (IGHDs) in AML patients. A risk score model was built through Lasso analysis and multivariate Cox regression. We also developed a nomogram and evaluated its accuracy with Harrell’s Harmony Index (C-index) and calibration curve. Last, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database was used for external validation. Results: IGHD1-20 mRNA expression level was an independent prognostic factor for patients with AML by multivariate analysis. After Lasso analysis and multivariate Cox regression, we constructed a 3-gene model (IGHD1-1, IGHD1-20, IGHD3-16) associated with OS in AML. Risk score and age were validated as independent risk factors for prognosis and were used to build a nomogram. The C index and calibration curve results show that its ability to predict 1-year, 3-year and 5-year overall survival is accurate. Conclusion: The mRNA level of IGHDs was increased in AML patients. IGHD1-20 was an independent risk factor for OS in AML patients. The IGHDs risk model (IGHD1-1, IGHD1-20, IGHD3-16) relates to the OS of AML patients. The nomogram, including risk score and age, can conveniently and effectively predict the overall survival rate of patients.

Inherited anemia continues to pose a significant public health concern on a global scale, owing to its extensive geographical prevalence, substantial patient population, and profound ramifications. Here, we investigated detailed information on inherited anemias (including thalassemias, thalassemias trait, sickle cell disease, sickle cell trait, G6PD deficiency, and G6PD trait) for the period 1990–2019 from the Global Burden of Disease study. Over the course of three decades, there has been a persistent rise in the incidence of inherited anemias worldwide, culminating in a total of 44,896,026 incident cases in 2019. However, the prevalence of inherited anemias has exhibited a consistent downward trend over successive years. Significantly, these inherited anemias primarily impact females, exhibiting a male-to-female ratio of 1:1.88. Among males, the most prevalent inherited anemia is G6PD deficiency, whereas G6PD trait prevails among females. The incidence rates of inherited anemias and their temporal trend exhibited significant variations across different regions, with Central Sub-Saharan Africa displaying the highest incidence rates and Central Latin America experiencing the most substantial decline. The findings of this study suggest a significant correlation between the Socio-Demographic index (SDI) and incidence rates of inherited anemias, particularly in regions with lower SDI levels such as Africa and South Asia. These results contribute valuable insights for the analysis of global trends in the burden of inherited anemias.

Background: Alternative splicing (AS), a crucial post-transcriptional regulatory mechanism in expanding the coding capacities of genomes and increasing the diversity of proteins, still faces various challenges in the splicing regulation mechanism of acute myeloid leukemia (AML) and microenvironmental changes. Results: A total of 27,833 AS events were detected in 8337 genes in 178 AML patients, with exon skip being the predominant type. Approximately 11% of the AS events were significantly related to prognosis, and the prediction models based on various events demonstrated high classification efficiencies. Splicing factors correlation networks further altered the diversity of AS events through epigenetic regulation and clarified the potential mechanism of the splicing pathway. Unsupervised cluster analysis revealed significant correlations between AS and immune features, molecular mutations, immune checkpoints and clinical outcome. The results suggested that AS clusters could be used to identify patient subgroups with different survival outcomes in AML, among which C1 was both associated with good outcome in overall survival. Interestingly, C1 was associated with lower immune scores compared with C2 and C3, and favorable-risk cytogenetics was rarely distributed in C2, but much more common in C1. Conclusions: This study revealed a comprehensive landscape of AS events, and provides new insight into molecular targeted therapy and immunotherapy strategy for AML.