Novel Gene Signature Reveals Prognostic Model in Acute Myeloid Leukemia

BACKGROUND: Acute 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. METHODS: To 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. RESULTS: Three 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. CONCLUSION: This 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.