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논문 기본 정보

자료유형
학술저널
저자정보
Saba Manoochehrabadi (Department of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran) Morteza Talebi (Department of Medical Genetics and Molecular Biology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran) Hossein Pashaiefar (Cell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Hematology and Cell Therapy, Research Institute for Oncology, Tehran University of Medical Sciences, Tehran, Iran) Soudeh Ghafouri‑Fard (Department of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran) Mohammad Vaezi (Cell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Hematology and Cell Therapy, Research Institute for Oncology, Tehran University of Medical Sciences, Tehran, Iran) Mir Davood Omrani (Department of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran) Mohammad Ahmadvand (Cell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Hematology and Cell Therapy, Research Institute for Oncology, Tehran University of Medical Sciences, Tehran, Iran)
저널정보
대한혈액학회 Blood Research Blood Research Vol.59 No.1
발행연도
2024.3
수록면
4 - 4 (1page)
DOI
10.1007/s44313-024-00002-0

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Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with an unfavorable outcome. The present research aimed to identify novel biological targets for AML diagnosis and treatment. In this study, we performed an in-silico method to identify antisense RNAs (AS-RNAs) and their related co-expression genes. GSE68172 was selected from the AML database of the Gene Expression Omnibus and compared using the GEO2R tool to find DEGs. Antisense RNAs were selected from all the genes that had significant expression and a survival plot was drawn for them in the GEPIA database, FOXD2-AS1 was chosen for further investigation based on predetermined criteria (logFC ≥|1| and P < 0.05) and its noteworthy association between elevated expression level and a marked reduction in the overall survival (OS) in patients diagnosed with AML. The GEPIA database was utilized to investigate FOXD2- AS1-related co-expression and similar genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene ontology (GO) function analysis of the mentioned gene lists were performed using the DAVID database. The protein–protein interaction (PPI) network was then constructed using the STRING database. Hub genes were screened using Cytoscape software. Pearson correlation analysis was conducted using the GEPIA database to explore the relationship between FOXD2-AS1 and the hub genes. The transcription of the selected coding and non-coding genes, including FOXD2-AS1, CDC45, CDC20, CDK1, and CCNB1, was validated in 150 samples, including 100 primary AML non-M3 blood samples and 50 granulocyte colony stimulating factor (G-CSF)-mobilized healthy donors, using quantitative Real-Time PCR (qRT-PCR). qRT-PCR results displayed significant upregulation of lnc-FOXD2-AS1, CDC45, and CDK1 in primary AML non-M3 blood samples compared to healthy blood samples (P = 0.0032, P = 0.0078, and P = 0.0117, respectively). The expression levels of CDC20 and CCNB1 were not statistically different between the two sets of samples (P = 0.8315 and P = 0.2788, respectively). We identified that AML patients with upregulation of FOXD2-AS1, CDK1, and CDC45 had shorter overall survival (OS) and Relapse-free survival (RFS) compared those with low expression of FOXD2-AS1, CDK1, and CDC45. Furthermore, the receiver operating characteristic (ROC) curve showed the potential biomarkers of lnc -FOXD2-AS1, CDC45, and CDK1 in primary AML non-M3 blood samples. This research proposed that the dysregulation of lnc-FOXD2-AS1, CDC45, and CDK1 can contribute to both disease state and diagnosis as well as treatment. The present study proposes the future evolution of the functional role of lnc-FOXD2-AS1, CDC45, and CDK1 in AML development.
#Acute myeloid leukemia #Bioinformatics #Lnc -FOXD2-AS1 #CDC45 #CDK1 #CDC20 and CCNB1

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