Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Identification of Potential Dual HDAC6 and HSP90 Inhibitors for the Treatment of Cancer Using Molecular Docking, Molecular Dynamics and MM/PBSA Studies: A Comprehensive In Silico Study(Bentham Science Publ Ltd, 2026) Yucel, Muhsin Samet; Akcok, IsmailBackground Histone deacetylase 6 (HDAC6) and heat shock protein 90 (Hsp90) are crucial therapeutic targets in cancer research with their interconnected roles in regulating protein homeostasis and cellular processes. The interaction of these proteins within the cytosolic complex plays a critical role in regulating cancer cell survival and progression. Notably, current studies highlight that the simultaneous inhibition of HDAC6 and Hsp90 can produce synergistic effects and offer a promising therapeutic potential for combating malignant cancers.Objective The objective of this study was to explore potential compounds that can inhibit both HDAC6 and Hsp90 proteins.Methods In this study, a number of in-silico computational techniques were employed. A total of 791 molecules, sharing at least 30% similarity with previously identified four HDAC inhibitors, were obtained from the ZINC15 database and subjected to docking on HDAC6 and Hsp90 proteins. The top eight ligands demonstrating the best binding scores against both targets, with panobinostat and ganetespib serving as reference compounds for HDAC6 and Hsp90, respectively, were selected for further analysis. Subsequently, ADME prediction and molecular dynamics simulations were conducted on the selected ligands.Results A detailed molecular docking, molecular dynamics simulations and ADME studies have revealed that ZINC27653366 exhibited the highest inhibitory potential against both Hsp90 and HDAC6 target proteins, making it the most promising inhibitor.Conclusion In conclusion, although additional in vitro and in vivo studies are required for the validation, in silico evaluation of ZINC27653366 may position it as a promising candidate for the treatment of different types of cancers.Article G-S a Prior Biological Knowledge-Based Pattern Detection and Enrichment Framework for Multi-Omics Data Integration(MDPI, 2025-11-29) Unlu Yazici, Miray; Bakir-Gungor, Burcu; Yousef, MalikThe rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases.Article Toward the Design of New Α-Carboline Derivatives Against Anaplastic Lymphoma Kinase (Alk): A Comprehensive in Silico Approach(Wiley-VCH Verlag GmbH, 2025-11) Sari, Ceyhun; Akcok, IsmailAfter the first description of anaplastic lymphoma kinase (ALK) in an anaplastic large cell lymphoma cell line as a nucleophosmin (NPM) fusion partner, ALK and its various fusion partners have been implicated in numerous cancers such as non-small cell lung cancer (NSCLC), anaplastic large cell lymphoma (ALCL), neuroblastoma, and rhabdomyosarcoma. In the last decade, several compounds targeting ALK have been developed and approved by the Food and Drug Administration (FDA). Despite the advances of generations of ALK inhibitors, a recent study highlighted that around half of the ALK-positive NSCLC patients will go through disease progression in response to first-line alectinib, which is a second-generation ALK inhibitor. In this study, we aimed to propose a novel alpha-carboline compound targeting the ALK tyrosine kinase domain to be used against various types of cancer in which ALK fusion proteins may be involved. In this regard, we designed more than 200 alpha-carboline derivatives and investigated their binding properties against ALK tyrosine kinase by using in silico protocols consisting of molecular docking studies, molecular dynamics simulations, MM/PBSA binding free energy calculation, and essential dynamics analysis. Considering the obtained results, we developed two promising candidates, compounds 208 & 209 with -9.05 and -9.80 binding energies, respectively, which demonstrated improved binding profiles over the course of a 300 ns simulation.Article Citation - WoS: 1Citation - Scopus: 2Tomatidine, a Steroidal Alkaloid, Synergizes With Cisplatin to Inhibit Cell Viability and Induce Cell Death Selectively on FLT3-ITD+ Acute Myeloid Leukemia Cells(Humana Press inc, 2024-07-11) Ayvaz, Havva Berre; Yenigul, Munevver; Akcok, Emel Basak Gencer; Gencer Akçok, Emel BaşakBackgroundAcute Myeloid Leukemia (AML) is a hematological cancer that frequently presents with a range of side effects and drug resistance during anticancer drug treatment. The current study aims to achieve increased efficacy by combining lower doses of cisplatin with increasing concentrations of tomatidine in AML cells to increase efficacy.MethodsAnti-proliferative effects of single and combination of cisplatin and tomatidine were assessed via MTT cell viability assay. The Annexin V/Propidium Iodide Double Staining method was used to measure the apoptotic effects of combined tomatidine and cisplatin treatment. Then, Western Blot analysis was performed to measure Poly (ADP-ribose) polymerase (PARP) and Caspase-3 protein expression levels.ResultsCisplatin treatment with lower concentrations displayed high cytotoxic effects on AML cells, compared with tomatidine. The combination of the Inhibitory Concentration (IC) 20 value of cisplatin and increasing doses of tomatidine exhibited a significant decrease in cell viability relative to single treatments. The combination index analysis revealed a mild synergistic effect of cisplatin IC20 and varying tomatidine doses. The apoptosis induced when cisplatin was combined with 500 mu M tomatidine by almost 20%, while the percentage of apoptosis in combination with 1 mM tomatidine was measured by 50% for both cell lines. The upregulation of proapoptotic cleaved-PARP (3.2 and 1.08-fold for THP-1 and MOLM-13, respectively) and downregulation in Caspase-3 (0.23 and 0.13-fold for THP-1 and MOLM-13, respectively) was detected.ConclusionsTogether, the study indicated that when tomatidine combined with cisplatin on AML cell lines, a combinatorial anti-proliferative and apoptotic effect is observed. The combination of cisplatin with tomatidine may be a promising approach.Book Part Sustainable Strategies for Cancer Phytomedicine: Balancing Efficacy and the Environment Responsibility(Springer Nature, 2025) Sari, Sibel; Saylan, DemetCancer is a complex disease, with approximately six million new cases reported annually. Despite the numerous treatment strategies employed worldwide, the severity of cancer continues to increase. Conventional cancer treatments, such as surgery, radiotherapy, and chemotherapy, are standard practices, but their clinical success is constrained by toxic side effects leading to damage to healthy tissues, unavoidable off-target effects, and significant cancer recurrence resulting from incomplete surgical removal. Therefore, interest in alternative therapies sourced primarily from natural products is increasing. The popularity of phytomedicine in cancer treatment approaches is increasing because of its efficacy, affordability, accessibility, and minimal adverse effects. Additionally, green chemistry approaches can be used to synthesize a wide array of anticancer drugs with various chemical structures, enhancing their therapeutic efficacy while minimizing or eliminating side effects and toxicity. The enhanced efficacy of cancer medicines made from plants is achieved via molecular innovations that support precise targeting. Various drug delivery systems that aim to reduce environmental pollution while reducing waste can be optimized for better results in therapy through nanotechnology. The delivery of effective cancer therapies while preserving the environment for generations to come is the objective of this approach, which includes green chemistry, sustainable production, and molecular developments. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 3Citation - Scopus: 4Rapamycin and Niacin Combination Induces Apoptosis and Cell Cycle Arrest Through Autophagy Activation on Acute Myeloid Leukemia Cells(Springer, 2024-12-23) Subay, Lale Beril; Akcok, Emel Basak Gencer; Akcok, Ismail; Gencer Akçok, Emel BaşakBackgroundAcute myeloid leukemia (AML) is a heterogeneous hematological malignancy caused by disorders in stem cell differentiation and excessive proliferation resulting in clonal expansion of dysfunctional cells called myeloid blasts. The combination of chemotherapeutic agents with natural product-based molecules is promising in the treatment of AML. In this study, we aim to investigate the anti-cancer effect of Rapamycin and Niacin combination on THP-1 and NB4 AML cell lines.Methods and ResultsThe anti-proliferative effects of Rapamycin and Niacin were determined by MTT cell viability assay in a dose- and time-dependent manner. The combination indexes were calculated by isobologram analysis. Furthermore, apoptosis was investigated by Annexin-V/Propidium Iodide(PI) double staining and cell cycle distribution was measured by PI staining. The expression levels of autophagy-related proteins were detected by western blotting. The combination of Rapamycin and Niacin synergistically decreased cell viability of AML cell lines. The combination treatment induced the apoptotic cell population of THP-1 and NB4 by 4.9-fold and 7.3-fold, respectively. In THP-1 cells, the cell cycle was arrested at the G2/M phase by 10% whereas the NB4 cells were accumulated at the G0/G1 phase. The combination treatment decreased Akt and p-Akt expression. Besides, the ATG7 expression was reduced by combination treatment on THP-1 cells. Similarly, the ATG5 level was downregulated in NB4 cells. The level of LC3B-II/LC3B-I, which is an indicator of autophagy flux, was upregulated in THP-1 and NB4 cells.ConclusionAlthough further studies are required, the combination of Rapamycin and Niacin combats cell proliferation by inducing cellular apoptosis, cell cycle arrest and autophagy activation.Article Citation - Scopus: 302Molecular Mechanisms of Drug Resistance and Its Reversal in Cancer(Taylor and Francis Ltd healthcare.enquiries@informa.com, 2015-03-11) Kartal Yandim, Melis; Adan Gökbulut, Aysun; Baran, Yusuf; Adan-Gokbulut, Aysun; Kartal-Yandim, MelisChemotherapy is the main strategy for the treatment of cancer. However, the main problem limiting the success of chemotherapy is the development of multidrug resistance. The resistance can be intrinsic or acquired. The resistance phenotype is associated with the tumor cells that gain a cross-resistance to a large range of drugs that are structurally and functionally different. Multidrug resistance arises via many unrelated mechanisms, such as overexpression of energy-dependent efflux proteins, decrease in uptake of the agents, increase or alteration in drug targets, modification of cell cycle checkpoints, inactivation of the agents, compartmentalization of the agents, inhibition of apoptosis and aberrant bioactive sphingolipid metabolism. Exact elucidation of resistance mechanisms and molecular and biochemical approaches to overcome multidrug resistance have been a major goal in cancer research. This review comprises the mechanisms guiding multidrug resistance in cancer chemotherapy and also touches on approaches for reversing the resistance. © 2017 Elsevier B.V., All rights reserved.Article Citation - WoS: 26Citation - Scopus: 48Metabolic Imaging Based Sub-Classification of Lung Cancer(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Bicakci, Mustafa; Ayyildiz, Oguzhan; Aydin, Zafer; Basturk, Alper; Karacavus, Seyhan; Yilmaz, BulentLung cancer is one of the deadliest cancer types whose 84% is non-small cell lung cancer (NSCLC). In this study, deep learning-based classification methods were investigated comprehensively to differentiate two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The study used 1457 F-18-FDG PET images/slices with tumor from 94 patients (88 men), 38 of which were ADC and the rest were SqCC. Three experiments were carried out to examine the contribution of peritumoral areas in PET images on subtype classification of tumors. We assessed multilayer perceptron (MLP) and three convolutional neural network (CNN) models such as SqueezeNet, VGG16 and VGG19 using three kinds of images in these experiments: 1) Whole slices without cropping or segmentation, 2) cropped image portions (square subimages) that include the tumor and 3) segmented image portions corresponding to tumors using random walk method. Several optimizers and regularization methods were used to optimize each model for the diagnostic classification. The classification models were trained and evaluated by performing stratified 10-fold cross validation, and F-score and area-under-curve (AUC) metrics were used to quantify the performance. According to our results, it is possible to say that inclusion of peritumoral regions/tissues both contributes to the success of models and makes segmentation effort unnecessary. To the best of our knowledge, deep learning-based models have not been applied to the subtype classification of NSCLC in PET imaging, therefore, this study is a significant cornerstone providing thorough comparisons and evaluations of several deep learning models on metabolic imaging for lung cancer. Even simpler deep learning models are found promising in this domain, indicating that any improvement in deep learning models in machine learning community can be reflected well in this domain as well.Book Part Citation - Scopus: 1Measurement of Autophagic Activity in Cancer Cells With Flow Cytometric Analysis Using Cyto-Id Staining(Humana Press Inc., 2024) Şansaçar, Merve; Gencer Akçok, Emel BaşakAutophagy is an evolutionarily conserved process providing the energy that cells need to survive, especially in stress situations, through catabolic processes. Considering the dual role of autophagy in cancer cells depending on the cellular context, it is crucial to comprehend the effect of drug candidates put forward to prevent cancer through the autophagy pathway. The CYTO-ID® Autophagy Detection Kit allows a rapid, specific and quantitative measurement of autophagic activity at the cellular level using a 488 nm-excitable green fluorescent detection reagent via flow cytometer. In this chapter, we present the CYTO-ID® Autophagy Detection method with a stepwise protocol to monitor the autophagy flux after the application of any compound to suspension cancer cell lines with flow cytometric analysis. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 13Citation - Scopus: 13Ethacrynic Acid and Cinnamic Acid Combination Exhibits Selective Anticancer Effects on K562 Chronic Myeloid Leukemia Cells(Springer, 2022-05-18) Yenigul, Munevver; Akcok, Ismail; Gencer Akcok, Emel BasakBackground Despite the recent advances in chemotherapy, the outcomes and the success of these treatments still remain insufficient. Novel combination treatments and treatment strategies need to be developed in order to achieve more effective treatment. This study was designed to investigate the combined effect of ethacrynic acid and cinnamic acid on cancer cell lines. Methods The anti-proliferative effect of ethacrynic acid and cinnamic acid was investigated by MTT cell viability assay in three different cancer cell lines. Combination indexes were calculated using CompuSyn software. Apoptosis was assessed by flow cytometric Annexin V-FITC/PI double-staining. The effect of the inhibitors on cell cycle distribution was measured by propidium iodide staining. Results The combination treatment of ethacrynic acid and cinnamic acid decreased cell proliferation significantly, by 63%, 75% and 70% for K562, HepG2 and TFK-1 cells, respectively. A 5.5-fold increase in the apoptotic cell population was observed after combination treatment of K562 cells. The population of apoptotic cells increased by 9.3 and 0.4% in HepG2 and TFK-1 cells, respectively. Furthermore, cell cycle analysis shows significant cell cycle arrest in S and G2/M phase for K562 cells and non-significant accumulation in G0/G1 phase for TFK-1 and HepG2 cells. Conclusions Although there is a need for further investigation, our results suggest that the inhibitors used in this study cause a decrease in cellular proliferation, induce apoptosis and cause cell cycle arrest.