PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Evaluation of HOTAIR, HOXD8, HOXD9, HOXD11 Gene Expression Levels in Turkish Patients With Acute and Chronic Myeloid Leukemia: A Single Center Experience(Cellular and Molecular Biology Association, 2024-11-27) Saraymen, Esma; Erdem, Yakut; Akalin, Hilal Ünlü; Taşçıoğlu, Nazife; Saraymen, Berkay; Celik, Serhat; Özkul, Yusuf T.Homeobox (HOX) transcript antisense RNA (HOTAIR) and HOX genes are reported to be more expressed in various cancers in humans in recent studies. The role of HOTAIR and HOXD genes in acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) is not well known. In this study, expression levels of HOXD8, HOXD9 and HOXD11 from HOXD gene family and HOTAIR were determined from peripheral blood samples of 30 AML and 30 CML patients and 20 healthy volunteers by quantitative Real Time PCR. We determined that the expression levels of HOXD9 and HOXD11 in the AML patients were significantly lower than the control group (p<0.001 and p=0.002, respectively). There was no significant difference in the expression levels of HOTAIR and HOXD8 when compared to the control group. In the CML patients there was a significant increase in the expression level of HOTAIR when compared to the control group (p=0.002). The expression levels of HOXD9 and HOXD11 were found to be significantly lower than the control group (p<0.001). Our study showed that HOTAIR may not be a biomarker in the diagnosis and is not significantly correlated with the clinicopathological prognostic characteristics of AML. Additionally; it can be said that HOTAIR is oncogenic by suppressing the expression of HOXD9 and HOXD11 but not HOXD8 in CML patients. The expression profiles of HOTAIR may be a potential biomarker in the diagnosis of CML patients in predicting and monitoring drug resistance. © 2025 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 4Computational Detection of Pre-MicroRNAs(Humana Press Inc., 2021-08-26) Saçar Demirci, Müşerref DuyguMicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed. © 2021 Elsevier B.V., All rights reserved.