Background
Acute myeloid leukemia (AML), which is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues, and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers are still incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of AML.
Methods
Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression and AML-related genes in public databases, were employed for investigating potential biomarkers for prognosis of AML.
Results
This study screened a total of 6,383 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expr essed genes are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway.
Conclusions
With the utilization of WGCNA mining, seven gene co-expression modules were identified from TCGA database and there are unreported genes that may as potential driven genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drag targets.