Identification of potential biomarkers and molecular mechanisms involve in AML prognosis using Bioinformatics approaches
Identification of potential biomarkers and molecular mechanisms involve in AML prognosis using Bioinformatics approaches
Sara Parizan,1,*
1. Department of cell and molecular biology, faculty of chemistry, university of kashan, kashan, Iran.
Introduction: Acute myeloid leukemia is a form of acute leukemia, which is characterized by an increase in the number of abnormal white blood cells [1]. The incidence rate of AML is 4.3 per 100,000 in the US (United States). The most common therapies for patients with AML are chemotherapy and allogeneic stem cell transplantation but most older patients show poor prognosis and survival [2]. Therefore it is expected that advancing therapeutic strategies, can affect patient outcomes, especially for elderly patients. After many years, the microarray is one of the recent advanced techniques which can analyze a large number of samples for cancer research and provides new insight to treat various diseases [4]. In this study, we try to identify potential biomarkers of AML, by comparing AML and normal samples using bioinformatics tools to enhance therapeutic strategies for patients with AML.
Methods: The microarray dataset was downloaded from the GEO database and differentially expressed genes were screened using R packages analysis. Additionally, functional enrichment analyses were performed based on David online tool, to gain the main molecular mechanisms associated with DEGs. Then for further analysis of DEGs, the protein-protein interaction (PPI) network was generated using the STRING database and Cytoscape software.
Results: A total of 459 DEGs including 125 up-regulated and 334 down-regulated DEGs were screened between AML/ normal samples. The result of the KEGG pathway analysis shows that DEGs were mainly associated with Hematopoietic cell linage and Transcriptional misregulation in cancer. The result of GO term analysis indicated that DEGs were significantly associated with B cell activation and immune effector process. Furthermore, ten hub genes were identified through PPI network analysis which among them TNF, FLT3, and CDC44 show the highest degree of connectivity.
Conclusion: A total of 459 DEGs including 125 up-regulated and 334 down-regulated DEGs were screened between AML/ normal samples. The result of the KEGG pathway analysis shows that DEGs were mainly associated with Hematopoietic cell linage and Transcriptional misregulation in cancer. The result of GO term analysis indicated that DEGs were significantly associated with B cell activation and immune effector process. Furthermore, ten hub genes were identified through PPI network analysis which among them TNF, FLT3, and CDC44 show the highest degree of connectivity.