Identification of Key Genes and Pathways Associated with RUNX1 Mutations in Acute Myeloid Leukemia Using Bioinformatics Analysis

Med Sci Monit. 2018 Oct 5:24:7100-7108. doi: 10.12659/MSM.910916.

Abstract

BACKGROUND RUNXl plays a key regulatory role in the process of hematopoiesis and is a common target for multiple chromosomal translocations in human acute leukemia. Mutations of RUNX1 gene can lead to acute leukemia and affect the prognosis of AML patients. We aimed to identify pivotal genes and pathways involved in RUNX1-mutated patients of with acute myeloid leukemia (AML) and to explore possible molecular markers for novel therapeutic targets of the disease. MATERIAL AND METHODS The RNA sequencing datasets of 151 cases of AML were obtained from the Cancer Genome Atlas database. Differentially expressed genes (DEGs) were identified using edgeR of the R platform. PPI (protein-protein interaction) network clustering modules were analyzed with ClusterONE, and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses for modules were performed. RESULTS A total of 379 genes were identified as DEGs. The KEGG enrichment analysis of DEGs showed significantly enriched pathways in cancer, extracellular matrix (ECM)-receptor interaction pathway, and cyclic adenosine monophosphate (cAMP) signaling pathway. The top 10 genes ranked by degree were PRKACG, ANKRD7, RNFL7, ROPN11, TEX14, PRMT8, OTOA, CFAP99, NRXN1, and DMRT1, which were identified as hub genes from the protein-protein interaction network (PPI). Statistical analysis revealed that RUNX1-mutated patients with AML had a shorter median survival time (MST) with poor clinical outcome and an increased risk of death when compared with those without RUNX1 mutations. CONCLUSIONS DEGs and pathways identified in the present study will help understand the molecular mechanisms underlying RUNX1 mutations in AML and develop effective therapeutic strategies for RUNX1-mutation AML.

MeSH terms

  • Biomarkers, Tumor / genetics
  • Biomarkers, Tumor / metabolism
  • Cluster Analysis
  • Computational Biology / methods
  • Core Binding Factor Alpha 2 Subunit / genetics*
  • Core Binding Factor Alpha 2 Subunit / metabolism
  • Databases, Genetic
  • Gene Expression Profiling / methods
  • Gene Expression Regulation, Neoplastic
  • Gene Regulatory Networks
  • Humans
  • Leukemia, Myeloid, Acute / genetics*
  • Leukemia, Myeloid, Acute / metabolism
  • Mutation*
  • Protein Interaction Maps
  • Sequence Analysis, RNA / methods
  • Signal Transduction / genetics
  • Transcriptome / genetics

Substances

  • Biomarkers, Tumor
  • Core Binding Factor Alpha 2 Subunit
  • RUNX1 protein, human