Identification and validation of a prognostic 8-gene signature for acute myeloid leukemia

Identification and validation of BATF as a prognostic biomarker and regulator of immune cell infiltration in acute myeloid leukemia

Background

Basic leucine zipper ATF-like transcription factor (BATF) is a nuclear basic leucine zipper protein affiliated with the AP-1/ATF superfamily. Previous research has confirmed that BATF expression plays a significant role in the tumour microenvironment. However, the associations between BATF expression and prognoses in acute myeloid leukaemia (AML) patients and their immunological effects remain unclear.

Methods

Genomic and clinical AML data were got from the TCGA (TCGA-LAML) and GEO (GSE37642) databases for the subsequent analysis. The expression levels of BATF in AML patients were assessed using GEPIA, and the results were verified by qRT-PCR and Western blotting. In the meantime, the prognostic value of BATF was evaluated using univariate and multivariate analyses, receiver operating characteristic (ROC) curve (AUC) analysis, and Kaplan-Meier (KM) survival analysis. EdU, colony formation, and CCK-8 assays were employed to evaluate the proliferation of cells. Moreover, we detected the association of BATF expression with drug sensitivity through database analysis and in vivo experiments. To further investigate the mechanism of action of BATF in AML, RNA sequencing (RNA-seq) analysis was performed, followed by pathway enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Meanwhile, we detected the connection between BATF expression and the proportions of immune cells via flow cytometry in C1498 mouse model of AML. Finally, we investigated the association between BATF expression and cell-cell communication within the AML cell population using single-cell sequencing.

Results

In this study, we thoroughly investigated the role of BATF in AML. First, we observed a significant elevation in the expression of BATF in patients and cells in AML. Further analysis revealed an association between high BATF expression and poor prognosis in AML. Additionally, BATF expression was found to promote the proliferative capacity of AML cells. Moreover, the results showed that the expression level of BATF dramatically affected the effect of chemotherapy in AML patients. We also discovered that BATF expression could activate multiple immune-related pathways, altering the proportions of CD8+T cells and NK cells, suggesting that BATF may be a regulator of immune cell infiltration. Finally, there were differences in receptor ligand pairs between AML cells with high and low expression of BATF and immune cells.

Conclusion

Bioinformatics analysis and experimental verification revealed that BATF expression could alter the proportions of CD8+T cells and NK cells in AML and affect drug sensitivity, making it a potential treatment target for AML.

Unlocking reproducible transcriptomic signatures for acute myeloid leukaemia: Integration, classification and drug repurposing

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, which lead to various findings in transcriptomic research. This study addresses these challenges by integrating 34 datasets, including 26 control groups, 6 prognostic datasets and 2 single-cell RNA sequencing (scRNA-seq) datasets to identify 10,000 AML-related genes (ARGs). We focused on genes with low variability and high consistency and successfully discovered 191 AML signatures (ASs). Leveraging machine learning techniques, specifically the XGBoost model and our custom framework, we classified AML subtypes with both scRNA-seq and bulk RNA-seq data, complementing the ELN2022 classification approach. Our research also identified promising treatments for AML through drug repurposing, with solasonine showing potential efficacy for high-risk AML patients, supported by molecular docking and transcriptomic analyses. To enhance reproducibility and customizability, we developed CSAMLdb, a user-friendly database platform. It facilitates the reuse and personalized analysis of nearly all results obtained in this research, including single-gene prognostics, multi-gene scoring, enrichment analysis, machine learning risk assessment, drug repositioning analysis and literature abstract named entity recognition. CSAMLdb is available at http://www.csamldb.com.

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

METHOD

This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods.

RESULTS

Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques.

CONCLUSIONS

This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

A 4-gene prognostic index for enhancing acute myeloid leukaemia survival prediction

Despite advancements in utilizing genetic markers to enhance acute myeloid leukaemia (AML) outcome prediction, significant disease heterogeneity persists, hindering clinical management. To refine survival predictions, we assessed the transcriptome of non-acute promyelocytic leukaemia chemotherapy-treated AML patients from five cohorts (n = 975). This led to the identification of a 4-gene prognostic index (4-PI) comprising CYP2E1, DHCR7, IL2RA and SQLE. The 4-PI effectively stratified patients into risk categories, with the high 4-PI group exhibiting TP53 mutations and cholesterol biosynthesis signatures. Single-cell RNA sequencing revealed enrichment for leukaemia stem cell signatures in high 4-PI cells. Validation across three cohorts (n = 671), including one with childhood AML, demonstrated the reproducibility and clinical utility of the 4-PI, even using cost-effective techniques like real-time quantitative polymerase chain reaction. Comparative analysis with 56 established prognostic indexes revealed the superior performance of the 4-PI, highlighting its potential to enhance AML risk stratification. Finally, the 4-PI demonstrated to be potential marker to reclassified patients from the intermediate ELN2017 category to the adverse category. In conclusion, the 4-PI emerges as a robust and straightforward prognostic tool to improve survival prediction in AML patients.

Single-cell transcriptomic profiling reveals immune cell heterogeneity in acute myeloid leukaemia peripheral blood mononuclear cells after chemotherapy

PURPOSE

Acute myeloid leukaemia (AML) is a heterogeneous disease characterised by the rapid clonal expansion of abnormally differentiated myeloid progenitor cells residing in a complex microenvironment. However, the immune cell types, status, and genome profile of the peripheral blood mononuclear cell (PBMC) microenvironment in AML patients after chemotherapy are poorly understood. In order to explore the immune microenvironment of AML patients after chemotherapy, we conducted this study for providing insights into precision medicine and immunotherapy of AML.

METHODS

In this study, we used single-cell RNA sequencing (scRNA-seq) to analyse the PBMC microenvironment from five AML patients treated with different chemotherapy regimens and six healthy donors. We compared the cell compositions in AML patients and healthy donors, and performed gene set enrichment analysis (GSEA), CellPhoneDB, and copy number variation (CNV) analysis.

RESULTS

Using scRNA-seq technology, 91,772 high quality cells of 44,950 PBMCs from AML patients and 46,822 PBMCs from healthy donors were classified as 14 major cell clusters. Our study revealed the sub-cluster diversity of T cells, natural killer (NK) cells, monocytes, dendritic cells (DCs), and haematopoietic stem cell progenitors (HSC-Prog) in AML patients under chemotherapy. NK cells and monocyte-DCs showed significant changes in transcription factor expression and chromosome copy number variation (CNV). We also observed significant heterogeneity in CNV and intercellular interaction networks in HSC-Prog cells.

CONCLUSION

Our results elucidated the PBMC single-cell landscape and provided insights into precision medicine and immunotherapy for treating AML.

Vacuolar ATPase Is a Possible Therapeutic Target in Acute Myeloid Leukemia: Focus on Patient Heterogeneity and Treatment Toxicity

Vacuolar ATPase (V-ATPase) is regarded as a possible target in cancer treatment. It is expressed in primary acute myeloid leukemia cells (AML), but the expression varies between patients and is highest for patients with a favorable prognosis after intensive chemotherapy. We therefore investigated the functional effects of two V-ATPase inhibitors (bafilomycin A1, concanamycin A) for primary AML cells derived from 80 consecutive patients. The V-ATPase inhibitors showed dose-dependent antiproliferative and proapoptotic effects that varied considerably between patients. A proteomic comparison of primary AML cells showing weak versus strong antiproliferative effects of V-ATPase inhibition showed a differential expression of proteins involved in intracellular transport/cytoskeleton functions, and an equivalent phosphoproteomic comparison showed a differential expression of proteins that regulate RNA processing/function together with increased activity of casein kinase 2. Patients with secondary AML, i.e., a heterogeneous subset with generally adverse prognosis and previous cytotoxic therapy, myeloproliferative neoplasia or myelodysplastic syndrome, were characterized by a strong antiproliferative effect of V-ATPase inhibition and also by a specific mRNA expression profile of V-ATPase interactome proteins. Furthermore, the V-ATPase inhibition altered the constitutive extracellular release of several soluble mediators (e.g., chemokines, interleukins, proteases, protease inhibitors), and increased mediator levels in the presence of AML-supporting bone marrow mesenchymal stem cells was then observed, especially for patients with secondary AML. Finally, animal studies suggested that the V-ATPase inhibitor bafilomycin had limited toxicity, even when combined with cytarabine. To conclude, V-ATPase inhibition has antileukemic effects in AML, but this effect varies between patients.

Human VDAC pseudogenes: an emerging role for VDAC1P8 pseudogene in acute myeloid leukemia

BACKGROUND

Voltage-dependent anion selective channels (VDACs) are the most abundant mitochondrial outer membrane proteins, encoded in mammals by three genes, VDAC1, 2 and 3, mostly ubiquitously expressed. As 'mitochondrial gatekeepers', VDACs control organelle and cell metabolism and are involved in many diseases. Despite the presence of numerous VDAC pseudogenes in the human genome, their significance and possible role in VDAC protein expression has not yet been considered.

RESULTS

We investigated the relevance of processed pseudogenes of human VDAC genes, both in physiological and in pathological contexts. Using high-throughput tools and querying many genomic and transcriptomic databases, we show that some VDAC pseudogenes are transcribed in specific tissues and pathological contexts. The obtained experimental data confirm an association of the VDAC1P8 pseudogene with acute myeloid leukemia (AML).

CONCLUSIONS

Our in-silico comparative analysis between the VDAC1 gene and its VDAC1P8 pseudogene, together with experimental data produced in AML cellular models, indicate a specific over-expression of the VDAC1P8 pseudogene in AML, correlated with a downregulation of the parental VDAC1 gene.

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection

In this current era, biomedical big data handling is a challenging task. Interestingly, the integration of multi-modal data, followed by significant feature mining (gene signature detection), becomes a daunting task. Remembering this, here, we proposed a novel framework, namely, three-factor penalized, non-negative matrix factorization-based multiple kernel learning with soft margin hinge loss (3PNMF-MKL) for multi-modal data integration, followed by gene signature detection. In brief, limma, employing the empirical Bayes statistics, was initially applied to each individual molecular profile, and the statistically significant features were extracted, which was followed by the three-factor penalized non-negative matrix factorization method used for data/matrix fusion using the reduced feature sets. Multiple kernel learning models with soft margin hinge loss had been deployed to estimate average accuracy scores and the area under the curve (AUC). Gene modules had been identified by the consecutive analysis of average linkage clustering and dynamic tree cut. The best module containing the highest correlation was considered the potential gene signature. We utilized an acute myeloid leukemia cancer dataset from The Cancer Genome Atlas (TCGA) repository containing five molecular profiles. Our algorithm generated a 50-gene signature that achieved a high classification AUC score (viz., 0.827). We explored the functions of signature genes using pathway and Gene Ontology (GO) databases. Our method outperformed the state-of-the-art methods in terms of computing AUC. Furthermore, we included some comparative studies with other related methods to enhance the acceptability of our method. Finally, it can be notified that our algorithm can be applied to any multi-modal dataset for data integration, followed by gene module discovery.

Integrated bioinformatic analysis of mitochondrial metabolism-related genes in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a common hematologic malignancy characterized by poor prognoses and high recurrence rates. Mitochondrial metabolism has been increasingly recognized to be crucial in tumor progression and treatment resistance. The purpose of this study was to examined the role of mitochondrial metabolism in the immune regulation and prognosis of AML.

Methods

In this study, mutation status of 31 mitochondrial metabolism-related genes (MMRGs) in AML were analyzed. Based on the expression of 31 MMRGs, mitochondrial metabolism scores (MMs) were calculated by single sample gene set enrichment analysis. Differential analysis and weighted co-expression network analysis were performed to identify module MMRGs. Next, univariate Cox regression and the least absolute and selection operator regression were used to select prognosis-associated MMRGs. A prognosis model was then constructed using multivariate Cox regression to calculate risk score. We validated the expression of key MMRGs in clinical specimens using immunohistochemistry (IHC). Then differential analysis was performed to identify differentially expressed genes (DEGs) between high- and low-risk groups. Functional enrichment, interaction networks, drug sensitivity, immune microenvironment, and immunotherapy analyses were also performed to explore the characteristic of DEGs.

Results

Given the association of MMs with prognosis of AML patients, a prognosis model was constructed based on 5 MMRGs, which could accurately distinguish high-risk patients from low-risk patients in both training and validation datasets. IHC results showed that MMRGs were highly expressed in AML samples compared to normal samples. Additionally, the 38 DEGs were mainly related to mitochondrial metabolism, immune signaling, and multiple drug resistance pathways. In addition, high-risk patients with more immune-cell infiltration had higher Tumor Immune Dysfunction and Exclusion scores, indicating poor immunotherapy response. mRNA-drug interactions and drug sensitivity analyses were performed to explore potential druggable hub genes. Furthermore, we combined risk score with age and gender to construct a prognosis model, which could predict the prognosis of AML patients.

Conclusion

Our study provided a prognostic predictor for AML patients and revealed that mitochondrial metabolism is associated with immune regulation and drug resistant in AML, providing vital clues for immunotherapies.

Establishment of tumor protein p53 mutation-based prognostic signatures for acute myeloid leukemia

PURPOSE

The tumor protein p53 gene (TP53) mutations are associated with poor prognosis of patients with acute myeloid leukemia (AML). This study aimed to establish TP53 mutation-based prognostic risk signatures.

PATIENTS AND METHODS

The transcriptomes and clinical characteristics of AML patients were acquired from The Cancer Genome Atlas database, including 11 TP53-mutant samples and 114 TP53-wildtype samples. Differentially expressed mRNAs and long non-coding RNAs (lncRNA) in TP53-mutant samples were identified. Weighted gene correlation network analysis was performed to generate survival-associated co-expression modules. LASSO regression analysis was conducted to build mRNA- and lncRNA-based prognostic risk signatures. Kaplan-Meier curve analysis and multivariate regression analysis were carried out to assess the prognostic values of the risk signatures. Receiver operating characteristic (ROC) analysis was performed to evaluate the accuracy of the signatures.

RESULTS

Based on the co-expression modules, a 5-mRNA risk signature and a 13-lncRNA risk signature were constructed to predict the overall survival for AML patients. Kaplan-Meier curves revealed that the high-risk patients had significantly shorter overall survival than the low-risk patients. ROC analysis yielded 1-, 3-, and 5-year AUCs of 0.681, 0.783, and 0.827 for mRNA signature and 0.85, 0.835, and 0.908 for lncRNA signature. Multivariate regression analysis revealed that both mRNA (HR = 1.45, P< 0.001) and lncRNA (HR = 1.19, P< 0.001) risk scores were independent prognostic factors for AML patients.

CONCLUSION

We provided a potential patients stratification tool for AML prognosis prediction and management, which established by effective TP53 mutation-related gene signatures.

Evidence of Omics, Immune Infiltration, and Pharmacogenomics for BATF in a Pan-Cancer Cohort

Background: Cytotoxic CD8+ T-cell exhaustion is the major barrier for immunotherapy in tumors. Recent studies have reported that the basic leucine zipper activating transcription factor–like transcription factor (BATF) is responsible for countering cytotoxic CD8+ T-cell exhaustion. Nevertheless, the expression and roles of BATF in tumors have been poorly explored.

Methods: In the present study, we conducted a multi-omics analysis, including gene expression, methylation status, DNA alterations, pharmacogenomics, and survival status based on data from the Cancer Genome Atlas (TCGA) database to discern expression patterns and prognostic roles of BATF in tumors. We also explored potential roles of BATF in a pan-cancer cohort by performing immune infiltration, Gene Ontology (GO) enrichment, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In vitro assay was also performed to explore roles of BATF in tumor cells.

Results: We found that BATF was aberrantly upregulated in 27 types of tumors with respect to the corresponding normal tissues. Abnormal BATF expression in tumors predicted survival times of patients in a tissue-dependent manner. The results of GO, immune infiltration, and KEGG analysis revealed that increased BATF expression in tumors participated in modulating immune cell infiltration via immune-related pathways. BATF expression could also predict immunotherapeutic and chemotherapy responses in cancers. Moreover, knockdown of BATF suppresses tumor cell viability.

Conclusion: Our present study reports the vital roles of BATF in tumors and provides a theoretical basis for targeting BATF therapy.

GPX1-associated prognostic signature predicts poor survival in patients with acute myeloid leukemia and involves in immunosuppression

OBJECTIVE

Treatment of acute myeloid leukemia (AML) remains a challenge. It is urgent to understand the microenvironment to improve therapy and prognosis.

METHODS

Bioinformatics methods were used to analyze transcription expression profile of AML patient samples with complete clinical information from UCSC Xena TCGA-AML datasets and validate with GEO datasets. Western blot, qPCR, RNAi and CCK8 assay were used to assay the effect of GPX1 expression on AML cell viability and the expression of genes of interest.

RESULTS

Our analyses revealed that highly expressed GPX1 in AML patients links to unfavorable prognosis. GPX1 expression was positively associated with not only fraction levels of myeloid-derived suppressor cells (MDSCs), monocytes and T cell exhaustion, the expression levels of MDSC markers, MDSC-promoting CCR2 and immune inhibitory checkpoints (TIM3/Gal-9, SIRPα and VISTA), but also negatively with low fraction levels of CD4+ and CD8+ T cells. Silencing GPX1 expression reduced AML cell viability and CCR2 expression. Moreover, GPX1-targetd kinases were PKC family, SRC family, SYK and PAK1, which promote AML progression and the resistance to therapy. Furthermore, Additionally, GPX1-associated prognostic signature (GPS) is an independent risk factor with high area under curve (AUC) values of receiver operating characteristic (ROC) curves. High risk group based on GPS enriched not only with endocytosis which transfers mitochondria to favor AML cell survival in response to chemotherapy, but also NOTCH, WNT and TLR signaling which promote therapy resistance.

CONCLUSION

Our results revealed the significant involvement of GPX1 in AML immunosuppression via and provided a prognostic signature for AML patients.

Establishment of Prognosis Model in Acute Myeloid Leukemia Based on Hypoxia Microenvironment, and Exploration of Hypoxia-Related Mechanisms

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic neoplasm with poor survival outcomes. However, the routine clinical features are not sufficient to accurately predict the prognosis of AML. The expression of hypoxia-related genes was associated with survival outcomes of a variety of hematologic and lymphoid neoplasms. We established an 18-gene signature-based hypoxia-related prognosis model (HPM) and a complex model that consisted of the HPM and clinical risk factors using machine learning methods. Both two models were able to effectively predict the survival of AML patients, which might contribute to improving risk classification. Differentially expressed genes analysis, Gene Ontology (GO) categories, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to reveal the underlying functions and pathways implicated in AML development. To explore hypoxia-related changes in the bone marrow immune microenvironment, we used CIBERSORT to calculate and compare the proportion of 22 immune cells between the two groups with high and low hypoxia-risk scores. Enrichment analysis and immune cell composition analysis indicated that the biological processes and molecular functions of drug metabolism, angiogenesis, and immune cell infiltration of bone marrow play a role in the occurrence and development of AML, which might help us to evaluate several hypoxia-related metabolic and immune targets for AML therapy.

High Expression of CLEC11A Predicts Favorable Prognosis in Acute Myeloid Leukemia

Background

Acute myeloid leukemia (AML) is a heterogeneous disease of the hematopoietic system, for which identification of novel molecular markers is potentially important for clinical prognosis and is an urgent need for treatment optimization.

Methods

We selected C-type lectin domain family 11, member A (CLEC11A) for study via several public databases, comparing expression among a variety of tumors and normal samples as well as different organs and tissues. To investigated the relationship between CLEC11A expression and clinical characteristics, we derived an AML cohort from The Cancer Genome Atlas (TCGA); we also investigated the Bloodspot and HemaExplorer databases. The Kaplan-Meier method and log-rank test were used to evaluate the associations between CLEC11A mRNA expression, as well as DNA methylation, and overall survival (OS), event-free survival (EFS), and relapse-free survival (RFS). DNA methylation levels of CLEC11A from our own 28 de novo AML patients were assessed and related to chemotherapeutic outcomes. Bioinformatics analysis of CLEC11A was carried out using public databases.

Results

Multiple public databases revealed that CLEC11A expression was higher in leukemia. The TCGA data revealed that high CLEC11A expression was linked with favorable prognosis (OS p-value = 2e-04; EFS p-value = 6e-04), which was validated in GSE6891 (OS p-value = 0; EFS p-value = 0; RFS p-value = 2e-03). Methylation of CLEC11A was negatively associated with CLEC11A expression, and high CLEC11A methylation level group was linked to poorer prognosis (OS p-value = 1e-02; EFS p-value = 2e-02). Meanwhile, CLEC11A hypermethylation was associated with poor induction remission rate and dismal survival. Bioinformatic analysis also showed that CLEC11A was an up-regulated gene in leukemogenesis.

Conclusion

CLEC11A may be used as a prognostic biomarker, and could do benefit for AML patients by providing precise treatment indications, and its unique gene pattern should aid in further understanding the heterogeneous AML mechanisms.

Wnt Signaling in Leukemia and Its Bone Marrow Microenvironment

Leukemia is an aggressive hematologic neoplastic disease. Therapy-resistant leukemic stem cells (LSCs) may contribute to the relapse of the disease. LSCs are thought to be protected in the leukemia microenvironment, mainly consisting of mesenchymal stem/stromal cells (MSC), endothelial cells, and osteoblasts. Canonical and noncanonical Wnt pathways play a critical role in the maintenance of normal hematopoietic stem cells (HSC) and LSCs. In this review, we summarize recent findings on the role of Wnt signaling in leukemia and its microenvironment and provide information on the currently available strategies for targeting Wnt signaling.