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E-Book 3rd Congress

  • Identification of potential biomarkers with Acute myeloid leukemia based on Bioinformatics analysis
  • Narjes Seddighi,1,* Aidin Darbeh,2
    1. 1- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran 2- Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
    2. 3- Student Research Committee, Faculty of Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran


  • Introduction: Acute myeloid leukemia is a group of heterogeneous clonal diseases that affect the source of myeloid hematopoietic progenitor cells; AML is defined by numerous cytogenetic and molecular heterogeneities. Additionally, AML is characterized by the unchecked growth of myeloid blasts in the peripheral blood and bone marrow in addition to the suppression of regular hematopoiesis. Relapse and refractory disease continue to be major obstacles in the treatment of AML, about 29% of AML patients are expected to survive more than five years. Gene expression profile analysis is a potent research technique that reveals patients' dysregulated genes by integrating data from functional genomics, molecular transcription, and genetics. In this study, we are going to evaluate RNAseq data obtained from TCGA in acute myeloid leukemia and investigate prognostic and protective genes in survival.
  • Methods: First, GEPIA2 (http://gepia2.cancer-pku.cn/) was used to examine the TCGA LAML dataset in order to identify all DEGs linked with LAML among high throughput RNA-Seq data. GEPIA2 is an online program that uses the Genotype-Tissue Expression (GTEx) projects and the TCGA database to evaluate the transcriptional patterns of human malignancies and normal tissues. Genes with a P-value < 0.05 were considered significant; after that, significant genes were divided into 4 groups including up/down prognostic or protective genes according to the logFC and HR. After that, we used cBioPortal (https://www.cbioportal.org/) to evaluate the survival and prognostics of mutation in the significant genes. In the end, a Protein-protein interaction (PPI) network of significant genes associated with AML was constructed with STRING at the Cytoscape software.
  • Results: of 7965 genes acquired from TCGA-RNAseq for AML, 843 genes are considered significant (adjusted P-value < 0.05), of which 666 genes are prognostic and 174 genes are protective. we also use CBioportal to evaluate the effect of mutation on survival in AML patients. According to the result, 8 genes are significantly related to survival including PSMG1, SLC37A1, FAM207A, GPS2, CSTB, CHAF1B, AKAP9, and ABCG1 (p-value and q-value < 0.05). PPI networks were drawn for significant genes and also hub genes.
  • Conclusion: This study identifies hub genes as a promising prognostic, protective, and diagnostic biomarker for acute myeloid leukemia. Further investigations are warranted to identify the therapeutic potential of these genes.
  • Keywords: Acute myeloid leukemia, TCGA, RNAseq, bioinformatics, significant genes