Abstract
BackgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.MethodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.ResultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.ConclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.
Highlights
We aimed to explore the molecular mechanism of Acute myeloid leukemia (AML) using a comprehensive bioinformatics analysis, and construct a clinical model to identify survival associated hub genes of AML patients
We used the limma package to screen out differentially expressed genes (DEGs) from 154 AML samples and 69 non-leukemia samples
In order to further analyze DEGs, we explored the functional variation between the two groups using the cluster Profiler package
Summary
Allogeneic stem cell transplantation is a suitable method to reduce the risk of recurrence of AML, and increase the risk of serious complications. Proteomics and bioinformatics analysis methods have been used to develop new personalized treatment strategies, study of the functions of related biomolecules, and collection of information on emerging trends in genome matching of clinical data are effective methods to improve the prognosis of patients (Wang et al, 2015; Bret et al, 2016; Cai and Levine, 2019). Acute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use
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