Developing DNA methylation-based prognostic biomarkers of acute myeloid leukemia.
Changgang SunJing ZhuangChao ZhouLijuan LiuCun LiuHuayao LiMinzhang ZhaoGongxi LiuChanggang SunPublished in: Journal of cellular biochemistry (2018)
Acute myeloid leukemia (AML) is a heterogeneous clonal neoplasm characterized by complex genomic alterations. The incidence of AML increases with age, and most cases experience serious illness and poor prognosis. To explore the relationship between abnormal DNA methylation and the occurrence and development of AML based on the Gene Expression Database (GEO), this study extracted data related to methylation in AML and identified a methylated CpG site that was significantly different in terms of expression and distribution between the primary cells of AML patients, and hematopoietic stem/progenitor cells from normal bone marrow. To further investigate the differences caused by the dysfunction of methylation sites, bioinformatics analysis was used to screen methylation-related biomarkers, and the potential prognostic genes were selected by univariate and multivariate Cox proportional hazards regressions. Finally, five independent prognostic indicators were identified. In addition, these results provide new insight into the molecular mechanisms of methylation.
Keyphrases
- acute myeloid leukemia
- dna methylation
- poor prognosis
- genome wide
- gene expression
- allogeneic hematopoietic stem cell transplantation
- long non coding rna
- bioinformatics analysis
- bone marrow
- copy number
- end stage renal disease
- ejection fraction
- newly diagnosed
- mesenchymal stem cells
- induced apoptosis
- risk assessment
- prognostic factors
- chronic kidney disease
- low grade
- emergency department
- big data
- oxidative stress
- cell cycle arrest
- deep learning
- transcription factor
- machine learning
- signaling pathway
- risk factors
- acute lymphoblastic leukemia
- drug induced
- patient reported
- genome wide identification