PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article Citation - WoS: 44Hesperidin Promotes Programmed Cell Death by Downregulation of Nongenomic Estrogen Receptor Signalling Pathway in Endometrial Cancer Cells(Elsevier Ltd., 2018) Cincin, Zeynep Birsu; Kıran, Bayram; Baran, Yusuf; Çakmakoğlu, BediaEndometrial carcinoma (EC) is the most common malignant gynecologic tumor in women. EC is thought to be caused by increasing estrogen levels relative to progesterone in the body. Hesperidin (Hsd), a biologically active flavonoid, could be extracted from Citrus species. It has been recently shown that Hsd could exert anticarcinogenic properties in different cancer types. However, the effects of Hsd and its molecular mechanisms on EC remain unclear. In this study, the antiproliferative, apoptotic and genomic effects of Hsd in EC and its underlying mechanisms were identified. We found that Hsd significantly suppressed the proliferation of EC cells in dose and time dependent manner. Mechanistic studies showed that Hsd could contribute apoptosis by inducing externalization of phosphatidyl serine (PS), caspase-3 activity and loss of mitochondrial membrane (MMP). Furthermore, we examined that Hsd could also significantly upregulate the expression of proapoptotic Bax subgroup genes (Bax and Bik) while downregulating the anti-apoptotic protein Bcl-2 in EC cell lines. According to GO enrichment and KEGG pathway analysis of differentially expressed genes in Hsd treated EC cells, we identified that Hsd could promote cell death via downregulation of estrogen receptor I (ESRI) that was directly related to ERK/MAPK pathway. Taken together, our study first showed that Hsd could be an antiestrogenic compound that could modulate nongenomic estrogen receptor signaling through inhibition of EC cell growth. Our findings may provide us a novel growth inhibitory agent for EC treatment after verifying its molecular mechanism with in vivo studies.Article Citation - WoS: 16Citation - Scopus: 18Computational and Bioinformatics Methods for Microrna Gene Prediction(Humana Press, 2014) Allmer, JensMicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.Article Citation - WoS: 37Citation - Scopus: 46Computational Methods for Microrna Target Prediction(Humana Press, 2014) Hamzeiy, Hamid; Yousef, Malik; Allmer, JensMicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.