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Permanent URI for this collectionhttps://hdl.handle.net/11443/932
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Item Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways(SPRINGER, 2022-01-01) Gultekin, Guldal Inal; Kahraman, Ozlem Timirci; Isbilen, Murat; Durmus, Saliha; Cakir, Tunahan; Yaylim, Ilhan; Isbir, TurgayBackground: The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders. Methods: The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples. Results: Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples. Conclusion: This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches.Item Variant analysis of SARS-CoV-2 strains with phylogenetic analysis and the Coronavirus Antiviral and Resistance Database(FUTURE MEDICINE LTD, 2021-01-01) Sayan, Murat; Arikan, Ayse; Isbilen, MuratAims: This study determined SARS-CoV-2 variations by phylogenetic and virtual phenotyping analyses. Materials \& methods: Strains isolated from 143 COVID-19 cases in Turkey in April 2021 were assessed. Illumina NexteraXT library preparation kits were processed for next-generation ]sequencing. Phylogenetic (neighbor-joining method) and virtual phenotyping analyses (Coronavirus Antiviral and Resistance Database {[}CoV-RDB] by Stanford University) were used for variant analysis. Results: B.1.1.7-1/2 (n = 103, 72\%), B.1.351 (n = 5, 3\%) and B.1.525 (n = 1, 1\%) were identified among 109 SARS-CoV-2 variations by phylogenetic analysis and B.1.1.7 (n = 95, 66\%), B.1.351 (n = 5, 4\%), B.1.617 (n = 4, 3\%), B.1.525 (n = 2, 1.4\%), B.1.526-1 (n = 1, 0.6\%) and missense mutations (n = 15, 10\%) were reported by CoV-RDB. The two methods were 85\% compatible and B.1.1.7 (alpha) was the most frequent SARS-CoV-2 variation in Turkey in April 2021. Conclusion: The Stanford CoV-RDB analysis method appears useful for SARS-CoV-2 lineage surveillance.