Association of long non-coding RNA candidates with inflammation in endometriosis
2022
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Advisor: Prof. Dr. Ayşe Evrim Bayrak
Abstract (EN)
Endometriosis is a gynecological disease frequently seen in women of reproductive age, characterized by the presence and proliferation of endometrial tissue outside the uterine cavity. Long non-coding RNAs (lncRNAs), a class of molecules over 200 nucleotides in length, play important roles in a variety of biological processes. In this study, it was aimed to analyse the expressions of lncRNAs in leukocytes and tissues of endometriosis patients and to determine their relationship with the disease. For this purpose, tissue (n=23) and peripheral blood (n=28) were collected from endometriosis patients (n=32). Peripheral blood (n=27) and normal ovarian tissue (n=2) were taken from healthy women as a control group. In addition, peripheral blood (n=20) was taken from the patients in the 1st month follow-up. Expression levels of candidate lncRNAs in these samples were determined by quantitative RT-PCR and their association with endometriosis was statistically evaluated. In this study, in the endometriosis patient group compared to the controls; BAT5 expression increased 3-fold in leukocytes and 20.70-fold in tissues. MALAT1 expression increased 4.3 times in leukocytes and 1.21 times in tissues. In addition, it was found that UBOX5 expression decreased 1.08 times in leukocytes and increased 5.35 times in tissues. In addition, in the first-month follow-up patients after the operation, compared to their preoperative status; in leukocytes, BAT5 expression decreased 2.07-fold, MALAT1 expression 2.64-fold, while UBOX5 expression increased 2.02-fold. In ROC analysis, it was determined that candidate lncRNAs could be candidate biomarkers for the diagnosis of endometriosis, with varying expression levels in leukocytes. In conclusion, it has been shown that selected lncRNAs in this study might be associated with inflammation in the pathogenesis of endometriosis and have the potential to be a biomarker for diagnosis.
Author
Dr. Hülya Aydınlı
Institution
How to Cite
Hülya Aydınlı (Master Thesis). Association of long non-coding RNA candidates with inflammation in endometriosis, 2022, İstanbul University.
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