Person: TURANLI, BESTE
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TURANLI
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BESTE
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Publication Metadata only Transcriptomic-Guided Drug Repositioning Supported by a New Bioinformatics Search Tool: geneXpharma(MARY ANN LIEBERT, INC, 2017) TURANLI, BESTE; Turanli, Beste; Gulfidan, Gizem; Arga, Kazim YalcinDrug repositioning is an innovative approach to identify new therapeutic indications for existing drugs. Drug repositioning offers the promise of reducing drug development timeframes and costs, and because it involves drugs that are already in the clinic, it might remedy some of the drug safety challenges traditionally associated with drug candidates that are not yet available in the clinic. The gene-by-drug interactions are an important dimension of optimal drug repositioning and development strategies. While gene-by-drug interactions have been curated and presented in various databases, novel bioinformatics tools and approaches are timely, and required with a specific focus to support drug positioning. We report, in this study, the design of a public web-accessible transcriptomic-/gene expression-guided pharmaceuticals search tool, geneXpharma (www.genexpharma.org). GeneXpharma is a public platform with user-centric interface that provides statistically evaluated gene expressions and their drug interactions for 48 diseases under seven different disease categories. GeneXpharma is designed and organized to generate hypotheses on druggable genome within the disease-gene-drug triad and thus, help repositioning of drugs against diseases. The search system accommodates various entry points using drugs, genes, or diseases, which then enable researchers to extract drug repurposing candidates and readily export for further evaluation. Future developments aim to improve the geneXpharma algorithm, enrich its content, and enhance the website interface through addition of network visualizations and graphical display items. Bioinformatics search tools can help enable the convergence of drug repositioning and gene-by-drug interactions so as to further optimize drug development efforts in the future.Publication Metadata only Differential Protein Interactome in Esophageal Squamous Cell Carcinoma Offers Novel Systems Biomarker Candidates with High Diagnostic and Prognostic Performance(MARY ANN LIEBERT, INC, 2021) TURANLI, BESTE; Gulfidan, Gizem; Beklen, Hande; Sinha, Indu; Kucukalp, Fulya; Caloglu, Buse; Esen, Ipek; Turanli, Beste; Ayyildiz, Dilara; Arga, Kazim Yalcin; Sinha, RaghuEsophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.Publication Metadata only Systems biomarkers in psoriasis: Integrative evaluation of computational and experimental data at transcript and protein levels(ELSEVIER SCIENCE BV, 2018) TURANLI, BESTE; Sevimoglu, Tuba; Turanli, Beste; Bereketoglu, Ceyhun; Arga, Kazim Yalcin; Karadag, Ayse SerapPsoriasis is a complex autoimmune disease with multiple genes and proteins being involved in its pathogenesis. Despite the efforts performed to understand mechanisms of psoriasis pathogenesis and to identify diagnostic and prognostic targets, disease-specific and effective biomarkers were still not available. This study is compiled regarding clinical validation of computationally proposed biomarkers at gene and protein expression levels through qRT-PCR and ELISA techniques using skin biopsies and blood plasma. We identified several gene and protein clusters as systems biomarkers and presented the importance of gender difference in psoriasis. A gene cluster comprising of P13, IRF9, IFIT1 and NMI were found as positively correlated and differentially co-expressed for women, whereas SUB1 gene was also included in this cluster for men. The differential expressions of IRF9 and NMI in women and SUB1 in men were validated at gene expression level via qRT-PCR. At protein level, PI3 was abundance in disease states of both genders, whereas PC4 protein and WIF1 protein were significantly higher in healthy states than disease states of male group and female group, respectively. Regarding abundancy of PI3 and WIF1 proteins in women, and PI3 and PC4 in men may be assumed as systems biomarkers at protein level.Publication Metadata only Acute myeloid leukemia: New multiomics molecular signatures and implications for systems medicine diagnostics and therapeutics innovation(2022-07-01) ARĞA, KAZIM YALÇIN; TURANLI, BESTE; YILMAZ, BETÜL; Kelesoglu N., Kori M., TURANLI B., ARĞA K. Y., Yilmaz B., Duru O. A.Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.