A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers

Pharmacological p38 MAPK inhibitor SB203580 enhances AML stem cell line KG1a chemosensitivity to daunorubicin by promoting late apoptosis, cell growth arrest in S-phase, and miR-328-3p upregulation

Acute myeloid leukaemia (AML) is characterized by uncontrolled proliferation of myeloid progenitor cells and impaired maturation, leading to immature cell accumulation in the bone marrow and bloodstream, resulting in hematopoietic dysfunction. Chemoresistance, hyperactivity of survival pathways, and miRNA alteration are major factors contributing to treatment failure and poor outcomes in AML patients. This study aimed to investigate the impact of the pharmacological p38 mitogen-activated protein kinase (MAPK) inhibitor SB203580 on the chemoresistance potential of AML stem cell line KG1a to the therapeutic drug daunorubicin (DNR). KG1a and chemosensitive leukemic HL60 cells were treated with increasing concentrations of DNR. Cell Titer-Glo®, flow cytometry, phosphokinase and protein arrays, Western blot technology, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were employed for assessment of cell viability, half-maximal inhibitory concentration (IC50) determination, apoptotic status detection, cell cycle analysis, apoptosis-related protein and gene expression monitoring. Confocal microscopy was used to visualize caspase and mitochondrial permeability transition pore (mPTP) activities. Exposed at various incubation times, higher DNR IC50 values were determined for KG1a cells than for HL60 cells, confirming KG1a cell chemoresistance potential. Exposed to DNR, late apoptosis induction in KG1a cells was enhanced after SB203580 pretreatment, defined as the combination treatment. This enhancement was confirmed by increased cleavage of poly(ADP-ribose) polymerase, caspase-9, caspase-3, and augmented caspase-3/-7 and mPTP activities in KG1a cells upon combination treatment, compared to DNR. Using phosphokinase and apoptosis protein arrays, the combination treatment decreased survival Akt phosphorylation and anti-apoptotic Bcl-2 expression levels in KG1a cells while increasing the expression levels of the tumor suppressor p53 and cyclin-dependent kinase inhibitor p21, compared to DNR. Cell cycle analysis revealed KG1a cell growth arrest in G2/M-phase caused by DNR, while combined treatment led to cell growth arrest in S-phase, mainly associated with cyclin B1 expression levels. Remarkably, the enhanced KG1a cell sensitivity to DNR after SB203580 pretreatment was associated with an increased upregulation of miR-328-3p and slight downregulation of miR-26b-5p, compared to DNR effect. Altogether, these findings could contribute to the development of a new therapeutic strategy by targeting the p38 MAPK pathway to improve treatment outcomes in patients with refractory or relapsed AML.

Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia

BACKGROUND

Acute Myeloid Leukemia (AML) generally has a relatively low survival rate after treatment. There is an urgent need to find new biomarkers that may improve the survival prognosis of patients. Machine-learning tools are more and more widely used in the screening of biomarkers.

METHODS

Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), lrFuncs, IdaProfile, caretFuncs, and nbFuncs models were used to screen key genes closely associated with AML. Then, based on the Cancer Genome Atlas (TCGA), pan-cancer analysis was performed to determine the correlation between important genes and AML or other cancers. Finally, the diagnostic value of important genes for AML was verified in different data sets.

RESULTS

The survival analysis results of the training set showed 26 genes with survival differences. After the intersection of the results of each machine learning method, DNM1, MEIS1, and SUSD3 were selected as key genes for subsequent analysis. The results of the pan-cancer analysis showed that MEIS1 and DNM1 were significantly highly expressed in AML; MEIS1 and SUSD3 are potential risk factors for the prognosis of AML, and DNM1 is a potential protective factor. Three key genes were significantly associated with AML immune subtypes and multiple immune checkpoints in AML. The results of the verification analysis show that DNM1, MEIS1, and SUSD3 have potential diagnostic value for AML.

CONCLUSION

Multiple machine learning methods identified DNM1, MEIS1, and SUSD3 can be regarded as prognostic biomarkers for AML.