Person: TURANLI, BESTE
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TURANLI
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BESTE
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Publication Metadata only Differential interactome based drug repositioning unraveled potential therapeutics for colorectal cancers(2022-01-19) TURANLI, BESTE; ÖZBEK SARICA, PEMRA; YILMAZ, BETÜL; ARĞA, KAZIM YALÇIN; BEKLEN H., Arslan S., GULFİDAN G., TURANLI B., ÖZBEK SARICA P., YILMAZ B., ARĞA K. Y.Publication Metadata only Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks(ELSEVIER SCIENCE INC, 2022) TURANLI, BESTE; Gulfidan, Gizem; Soylu, Melisa; Demirel, Damla; Erdonmez, Habib Burak Can; Beklen, Hande; Sarica, Pemra Ozbek; Arga, Kazim Yalcin; Turanli, BesteThe identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different bio-logical levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was per -formed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.Publication Metadata only Determination of candidate biomarkers through differential interactome in colorectal adenocarcinoma(2019-10-17) TURANLI, BESTE; ÖZBEK SARICA, PEMRA; YILMAZ, BETÜL; ARĞA, KAZIM YALÇIN; BEKLEN H., GULFİDAN G., TURANLI B., ÖZBEK SARICA P., YILMAZ B., ARĞA K. Y.Colorectal cancer is one of the most lethal types of cancers common in both men and women. According to the data base published by the International Agency for Research on Cancer, colorectal cancer is the third most common type of cancer found in Turkey and worldwide [1]. The high heterogeneity of colorectal cancer leads to difficulties explaining the biology and behavior of this cancer. The aim of this study is to identify prognostic biomarkers and potential therapeutics for colorectal cancer using the protein interactions differentiated among healthy and tumor groups. Among this purpose at first stage, the differential protein-protein interactions (dPPIs) were identified by using “Differential Interactome” algorithm[2] which is published by our research group. Two independent data sets were obtained from “The Cancer Genome Atlas (TCGA)” containing 644 tumor samples and 51 normal samples and “Gene Expression Omnibus (GEO)” containing 32 tumor samples and 32 normal samples. As a result of differential interactome analysis, significant dPPIs were determined (2434 dPPIs in GEO data set, 1619 dPPIs in TCGA data set) and highly interacting protein modules were identified. Principal Component Analysis for diagnostic purpose and Kaplan-Meier analysis for prognostic purpose were performed for each module. 16 modules were determined significant having diagnostic potential while 6 modules were found having prognostic potential. In addition common significant dPPIs in both data sets were observed in point of drug repositioning and 6 dPPIs and 13 drug targets for these interactions were identified. By using molecular dynamics simulations root mean square deviation (RMSD) and root mean square fluctuation were taken as performance metrics to perform further investigation in vitro cell culture. This study will shed light on the identification of specific biomarkers and drug targets for early detection, disease progression, and accurate treatment, helping to understand some systems of colorectal cancer by preventing high mortality.