Person: DOĞAN, BERKCAN
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DOĞAN
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BERKCAN
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Publication BRCA variations risk assessment in breast cancers using different artificial intelligence models(Mdpi, 2021-11-08) Şentürk, Niyazi; Tuncel, Gülten; Doğan, Berkcan; Aliyeva, Lamiya; Dündar, Mehmet Sait; Özemri Sağ, Şebnem; Mocan, Gamze; Temel, Şehime Gülsün; Dündar, Munis; Ergoren, Mahmut Çerkez; DOĞAN, BERKCAN; ALIYEVA, LAMIYA; ÖZEMRİ SAĞ, ŞEBNEM; TEMEL, ŞEHİME GÜLSÜN; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı.; Bursa Uludağ Üniversitesi/Sağlık Bilimleri Enstitüsü/Translasyonel Tıp Anabilim Dalı.; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Histoloji ve Embriyoloji Anabilim Dalı.; 0000-0001-8061-8131; CCG-4609-2022 ; AAH-8355-2021 ; AAD-5249-2020; AAG-8385-2021Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.Publication Hsa-miR-584-5p as a novel candidate biomarker in Turkish men with severe coronary artery disease(Springer, 2019-12-21) Çoban, Neslihan; Pirim, Dilek; Erkan, Ayçan Fahri; Doğan, Berkcan; Ekici, Berkay; PİRİM, DİLEK; DOĞAN, BERKCAN; Bursa Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Moleküler Biyoloji ve Genetik Bölümü; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı; 0000-0002-0522-9432; 0000-0001-8061-8131; HTP-6233-2023; AAD-5249-2020; ABA-4957-2020Coronary artery disease (CAD) is still the preliminary cause of mortality and morbidity in the developed world. Identification of novel predictive and therapeutic biomarkers is crucial for accurate diagnosis, prognosis and treatment of the CAD. The aim of this study was to detect novel candidate miRNA biomarker that may be used in the management of CAD. We performed miRNA profiling in whole blood samples of angiographically confirmed Turkish men with CAD and non-CAD controls with insignificant coronary stenosis. Validation of microarray results was performed by qRT-PCR in a larger cohort of 62 samples. We subsequently assessed the diagnostic value of the miRNA and correlations of miRNA with clinical parameters. miRNA-target identification and network analyses were conducted by Ingenuity Pathway Analysis (IPA) software. Hsa-miR-584-5p was one of the top significantly dysregulated miRNA observed in miRNA microarray. Men-specific down-regulation (p = 0.040) of hsa-miR-584-5p was confirmed by qRT-PCR. ROC curve analysis highlighted the potential diagnostic value of hsa-miR-584-5p with a power area under the curve (AUC) of 0.714 and 0.643 in men and in total sample, respectively. The expression levels of hsa-miR-584-5p showed inverse correlation with stenosis and Gensini scores. IPA revealed CDH13 as the only CAD related predicted target for the miRNA with biological evidence of its involvement in CAD. This study suggests that hsa-miR-584-5p, known to be tumor suppressor miRNA, as a candidate biomarker for CAD and highlighted its putative role in the CAD pathogenesis. The validation of results in larger samples incorporating functional studies warrant further research.Publication Clinical and molecular evaluation of MEFV gene variants in the Turkish population: a study by the National Genetics Consortium(Springer Heidelberg, 2022-01-31) Dundar, Munis; Fahrioglu, Umut; Yildiz, Saliha Handan; Bakir-Gungor, Burcu; Temel, Sehime Gulsun; Akin, Haluk; Artan, Sevilhan; Cora, Tulin; Sahin, Feride Iffet; Dursun, Ahmet; Sezer, Ozlem; Gurkan, Hakan; Erdogan, Murat; Gunduz, C. Nur Semerci; Bisgin, Atil; Ozdemir, Ozturk; Ulgenalp, Ayfer; Percin, E. Ferda; Yildirim, Malik Ejder; Tekes, Selahaddin; Bagis, Haydar; Yuce, Huseyin; Duman, Nilgun; Bozkurt, Gokay; Yararbas, Kanay; Yildirim, Mahmut Selman; Arman, Ahmet; Mihci, Ercan; Eraslan, Serpil; Altintas, Zuhal Mert; Aymelek, Huri Sema; Ruhi, Hatice Ilgin; Tatar, Abdulgani; Ergoren, Mahmut Cerkez; Cetin, G. Ozan; Altunoglu, Umut; Caglayan, Ahmet Okay; Yuksel, Berrin; Ozkul, Yusuf; Saatci, Cetin; Kenanoglu, Sercan; Karasu, Nilgun; Dundar, Bilge; Ozcelik, Firat; Demir, Mikail; Siniksaran, Betul Seyhan; Kulak, Hande; Kiranatlioglu, Kubra; Baysal, Kubra; Kazimli, Ulviyya; Akalin, Hilal; Dundar, Ayca; Boz, Mehmet; Bayram, Arslan; Subasioglu, Asli; Colak, Fatma Kurt; Karaduman, Neslihan; Gunes, Meltem Cerrah; Kandemir, Nefise; Aynekin, Busra; Emekli, Rabia; Sahin, Izem Olcay; Ozdemir, Sevda Yesim; Onal, Muge Gulcihan; Senel, Abdurrahman Soner; Poyrazoglu, Muammer Hakan; Kisaarslan, Ayse Nur Pac; Gursoy, Sebnem; Baskol, Mevlut; Calis, Mustafa; Demir, Huseyin; Zararsiz, Gozde Erturk; Erdogan, Mujgan Ozdemir; Elmas, Muhsin; Solak, Mustafa; Ulu, Memnune Sena; Thahir, Adam; Aydin, Zafer; Atasever, Umut; Sag, Sebnem Ozemri; Aliyeva, Lamiya; Alemdar, Adem; Dogan, Berkcan; Erguzeloglu, Cemre Ornek; Kaya, Niyazi; Ozkinay, Ferda; Cogulu, Ozgur; Durmaz, Asude; Onay, Huseyin; Karaca, Emin; Durmaz, Burak; Aykut, Ayca; Cilingir, Oguz; Aras, Beyhan Durak; Gokalp, Ebru Erzurumluoglu; Arslan, Serap; Temena, Arda; Haziyeva, Konul; Kocagil, Sinem; Bas, Hasan; Susam, Ezgi; Keklikci, Ali Riza; Sarac, Elif; Kocak, Nadir; Nergiz, Suleyman; Terzi, Yunus Kasim; Dincer, Selin Akad; Baskin, Esra Sidika; Genc, Gunes Cakmak; Bahadir, Oguzhan; Sanri, Aslihan; Yigit, Serbulent; Tozkir, Hilmi; Yalcintepe, Sinem; Ozkayin, Nese; Kiraz, Aslihan; Balta, Burhan; Gonen, Gizem Akinci; Kurt, E. Emre; Ceylan, Gulay Gulec; Ceylan, Ahmet Cevdet; Erten, Sukran; Bozdogan, Sevcan Tug; Boga, Ibrahim; Yilmaz, Mustafa; Silan, Fatma; Kocabey, Mehmet; Koc, Altug; Cankaya, Tufan; Bora, Elcin; Bozkaya, Ozlem Giray; Ercal, Derya; Ergun, Mehmet Ali; Ergun, Sezen Guntekin; Duman, Yesim Sidar; Beyazit, Serife Busra; Uzel, Veysiye Hulya; Em, Serda; Cevik, Muhammer Ozgur; Eroz, Recep; Demirtas, Mercan; Firat, Cem Koray; Kabayegit, Zehra Manav; Altan, Mustafa; Mardan, Lamiya; Sayar, Ceyhan; Tumer, Sait; Turkgenc, Burcu; Karakoyun, Hilal Keskin; Tunc, Betul; Kuru, Seda; Zamani, Aysegul; Geckinli, Bilgen Bilge; Ates, Esra Arslan; Clark, Ozden Altiok; Toylu, Asli; Coskun, Mert; Nur, Banu; Bilge, Ilmay; Bayramicli, Oya Uygur; Emmungil, Hakan; Komesli, Zeynep; Zeybel, Mujdat; Gurakan, Figen; Tasdemir, Mehmet; Kebudi, Rejin; Karabulut, Halil Gurhan; Tuncali, Timur; Kutlay, Nuket Yurur; Kahraman, Cigdem Yuce; Onder, Nerin Bahceciler; Beyitler, Ilke; Kavukcu, Salih; Tulay, Pinar; Tosun, Ozgur; Tuncel, Gulten; Mocan, Gamze; Kale, Hamdi; Uyguner, Zehra Oya; Acar, Aynur; Altinay, Mert; Erdem, Levent; TEMEL, ŞEHİME GÜLSÜN; ÖZEMRİ SAĞ, ŞEBNEM; ALIYEVA, LAMIYA; ALEMDAR, ADEM; DOĞAN, BERKCAN; Ergüzeloğlu, Cemre Örnek; Kaya, Niyazi; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı.; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Histoloji ve Embriyoloji Anabilim Dalı.; Bursa Uludağ Üniversitesi/Sağlık Bilimleri Enstitüsü/Translasyonel Tıp Anabilim Dalı.; 0000-0001-8061-8131; AAG-8385-2021; AAH-8355-2021; CCG-4609-2022; HIZ-7332-2022; AAD-5249-2020; EXQ-7887-2022; FEL-0562-2022Familial Mediterranean fever (FMF) is a monogenic autoinflammatory disorder with recurrent fever, abdominal pain, serositis, articular manifestations, erysipelas-like erythema, and renal complications as its main features. Caused by the mutations in the MEditerranean FeVer (MEFV) gene, it mainly affects people of Mediterranean descent with a higher incidence in the Turkish, Jewish, Arabic, and Armenian populations. As our understanding of FMF improves, it becomes clearer that we are facing with a more complex picture of FMF with respect to its pathogenesis, penetrance, variant type (gain-of-function vs. loss-of-function), and inheritance. In this study, MEFV gene analysis results and clinical findings of 27,504 patients from 35 universities and institutions in Turkey and Northern Cyprus are combined in an effort to provide a better insight into the genotype-phenotype correlation and how a specific variant contributes to certain clinical findings in FMF patients. Our results may help better understand this complex disease and how the genotype may sometimes contribute to phenotype. Unlike many studies in the literature, our study investigated a broader symptomatic spectrum and the relationship between the genotype and phenotype data. In this sense, we aimed to guide all clinicians and academicians who work in this field to better establish a comprehensive data set for the patients. One of the biggest messages of our study is that lack of uniformity in some clinical and demographic data of participants may become an obstacle in approaching FMF patients and understanding this complex disease.Publication Different perspectives on translational genomics in personalized medicine(Galenos Publ House, 2022-12-01) Çelik, Hale Göksever; Küçükkaya, Reyhan Diz; Acar, Ece Gümüşoğlu; Günel, Tuba; Doğan, Berkcan; DOĞAN, BERKCAN; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı.; Bursa Uludağ Üniversitesi/Tıp Fakültesi/İç Hastalıkları Anabilim Dalı.; 0000-0001-8061-8131; 0000-0002-5162-3262; 0000-0001-5814-7118; 0000-0003-3807-0330; A-8941-2018; AAD-5249-2020Personalized medicine is a relatively new and interesting concept in the medical and healthcare industries. New approaches in current research have supported the search for biomarkers, based on the genomic, epigenomic and proteomic profile of individuals, using new technological tools. This perspective involves the potential to determine optimal medical interventions and provide the optimal benefit-risk balance for treatment, whilst it also takes a patient's personal situation into consideration. Translational genomics, a subfield of personalized medicine, is changing medical practice, by facilitating clinical or non-clinical screening tests, informing diagnoses and therapeutics, and routinely offering personalized health-risk assessments and personalized treatments. Further research into translational genomics will play a critical role in creating a new approach to cancer, pharmacogenomics, and women's health. Our current knowledge may be used to develop new solutions that can be used to minimize, improve, manage, and delay the symptoms of diseases in real-time and maintain a healthy lifestyle. In this review, we define and discuss the current status of translational genomics in some special areas including integration into research and health care.Publication Metabolic pathways of potential mirna biomarkers derived from liquid biopsy in epithelial ovarian cancer(Spandidos, 2023-04-01) Gümüşoğlu-Acar, Ece; Günel, Tuba; Hosseini, Mohammad Kazem; Doğan, Berkcan; Tekarslan, Efnan Elif; Gürdamar, Berk; Çevik, Nazife; Sezerman, Uğur; Topuz, Samet; Aydınlı, Kılıç; DOĞAN, BERKCAN; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı; AAD-5249-2020Epithelial ovarian cancer (EOC) is the type of OC with the highest mortality rate. Due to the asymptomatic nature of the disease and few available diagnostic tests, it is mostly diagnosed at the advanced stage. Therefore, the present study aimed to discover predictive and/or early diagnostic novel circulating microRNAs (miRNAs or miRs) for EOC. Firstly, microarray analysis of miRNA expression levels was performed on 32 samples of female individuals: Eight plasma samples from patients with pathologically confirmed EOC (mean age, 45 (30-54) years), eight plasma samples from matched healthy individuals (HIs) (mean age, 44 (30-65) years), eight EOC tissue samples (mean age, 45 (30-54) years) and eight benign ovarian (mean age, 35 (17-70) years) neoplastic tissue samples A total of 31 significantly dysregulated miRNAs in serum and three miRNAs in tissue were identified by microarray. The results were validated using reverse transcription-quantitative PCR on samples from 10 patients with pathologically confirmed EOC (mean age, 47(30-54) years), 10 matched His (mean age, 40(26-65) years], 10 EOC tissue samples (mean age, 47(30-54) years) and 10 benign ovarian neoplastic tissue samples (mean age, 40(17-70) years). The 'Kyoto Encyclopedia of Genes and Genomes' (KEGG) database was used for target gene and pathway analysis. A total of three miRNAs from EOC serum (hsa-miR-1909-5p, hsa-miR-885-5p and hsa-let-7d-3p) and one microRNA from tissue samples (hsa-miR-200c-3p) were validated as significant to distinguish patients with EOC from HIs. KEGG pathway enrichment analysis showed seven significant pathways, which included 'prion diseases', 'proteoglycans in cancer', 'oxytocin signaling pathway', 'hippo signaling pathway', 'adrenergic signaling in cardiomyocytes', 'oocyte meiosis' and 'thyroid hormone signaling pathway', in which the validated miRNAs served a role. This supports the hypothesis that four validated miRNAs, have the potential to be a biomarker of EOC diagnosis and target for treatment.