WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Dissecting the Metabolic Landscape of Breast Cancer Subtypes via Elastic Net Modeling and Examining Its Immune Correlates(Walter de Gruyter GmbH, 2026) Ekiz, Hüseyin Atakan; Ekiz, H.A.; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of TechnologyObjectives: Breast cancer is a heterogeneous disease, and the estrogen receptor (ER) status is a key factor in disease classification and treatment planning. While metabolomic profiling has revealed subtype-specific differences, cross-study comparisons have been limited, posing challenges for data extrapolation. This study aims to investigate metabolites that differentiate ER-positive and ER-negative tumors via integrative analyses of multi-omics data. Methods: We jointly analyzed two untargeted metabolomics datasets via elastic net modeling using consistent analysis pipelines tuned for low sample sizes, namely multiple bootstrapping and stability selection. Significant metabolite predictors from two studies were cross-examined to reveal distinctions and commonalities. We also performed differential gene expression analysis using RNA sequencing data from matching samples to link metabolic patterns with transcriptomic signatures and intratumoral immune cell signatures. Results: This study identified unique metabolite signatures in distinct datasets and a limited overlap of discriminating metabolites that can be broadly generalizable for subtyping. Nevertheless, several glycolysis and fatty acid metabolism intermediates exhibited variation depending on the tumor ER status. Consistently, genes related to fatty acid metabolism and glycolysis were enriched in ER-positive and ER-negative tumors respectively. Furthermore, we used multiple immune cell deconvolution algorithms to correlate various immune cell types with the metabolite levels within the tumor microenvironment. Conclusions: Together, these findings highlight the metabolic and immunological diversity of breast cancer and establish a reproducible machine-learning framework for integrating multi-omics data to interrogate tumor complexity. © 2025 the author(s), published by De Gruyter, Berlin/Boston.Editorial A Thin Film Micro-Extraction Based Salivary Metabolomics and Chemometric Strategy for Rapid Lung Cancer Diagnosis(Galenos Publ House, 2025) Özdemir, Durmuş; Basbinar, Yasemin; Goksel, Ozlem; Goksel, Tuncay; Erbas, İlknur; Pelit, Fusun; Ozdemir, Durmus; 01. Izmir Institute of Technology; 04.01. Department of Chemistry; 04. Faculty of ScienceINTRODUCTION: Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide, largely due to the lack of reliable biomarkers for early detection.1 Despite advances in di-agnostic imaging and targeted therapies, the five-year survival rate remains low because most cases are diagnosed at advanced stages. Consequently, the development of sensitive, non-invasive, and cost-effective diagnostic approaches is a major clinical priority. Metabolomics, the comprehensive profiling of small-molecule metabolites, has emerged as a powerful tool for uncovering cancer-associated metabolic alterations, providing insights into tumor biology and facilitating the discovery of novel biomarkers for accurate diagnosis and disease monitoring. Among biological matrices, saliva is a promising diagnostic biofluid because it can be collected non-invasively, is simple to obtain, and reflects systemic and local metabolic changes. Recent studies have demonstrated its potential for detecting various cancers, including lung cancer, highlighting its value for biomarker-based early di-agnosis.2,3 In this study, a novel thin-film microextraction (TFME) technique integrated with liquid chromatography-tandem mass spectrometry (LC-MS/MS) is introduced for the rapid, selective, and reproducible extraction of salivary metabolites. The developed TFME approach offers high throughput, reduced solvent consumption, and enhanced analytical performance, enabling the identification and quantification of key metabolic biomarkers associated with lung cancer. The objective of this workflow is to advance saliva-based metabolomics toward clinical translation, offering a promising avenue for the early and non-invasive diagnosis of lung cancer. MATERIAL AND METHODS: Synthesis of SiO2 Nanoparticles and TFME blade Preparation: SiO2 nanoparticles were synthesized using the Stöber method, followed by post-coating with tetraethyl orthosilicate, centrifugation, wash-ing with ethanol, and drying. The nanoparticles were incorporated into a polyacrylonitrile (PAN) matrix and coated onto steel TFME blades via a controlled dip-coating process to ensure uniform film thick-ness. Participants and Sample Collection: Saliva samples were collected from 40 histopathologically con-firmed lung cancer patients and 38 healthy volunteers following an overnight fast and an oral rinse. Ethical approval and informed consent were obtained (Ege University Ethics Committee, protocol: 15-11.1/46). Saliva samples were centrifuged, diluted (1:2), and stored at -80 °C until analysis. TFME Sampling and Analysis: A 96-well plate system equipped with PAN/SiO2-coated TFME blades was used for metabolite extraction (Figure 1). Blades were immersed in diluted saliva samples and rotated at 850 rpm for 150 minutes to allow analyte adsorption, followed by desorption of analytes in 0.1% formic acid for 30 minutes. Desorbed solutions were spiked with 0.5 µg/mL ornidazole as an internal standard prior to LC-MS/MS analysis. RESULTS: The TFME method was optimized to detect 18 metabolites in pre-treatment saliva samples from lung cancer patients. Chromatographic evaluation demonstrated that the Inertsil 100 column, employing isocratic elution with ornidazole as the internal standard, provided optimal separation effi-ciency and reproducibility. Extraction parameters, including desorption solution type and pH, were optimized; desorption solution type 2 at pH 8-9 yielding the highest metabolite recovery. Analytical validation indicated robust linearity (R2: 0.9841-0.9975), sensitivity (limit of detection: 0.014-0.97 μg/mL; limit of quantification: 0.046-3.20 μg/mL), precision (%relative standard deviation <20%), and accuracy (85-125% for most metabolites). Pathway analysis revealed significant alterations in the me-tabolism of phenylalanine, purine, tyrosine, histidine, and methionine. The Heatmap visualization showed increased levels of proline, hypoxanthine, phenylalanine, and tyrosine in lung cancer pa-tients. receiver operating characteristic curve analysis highlighted these metabolites as potential bi-omarkers, with proline exhibiting the highest diagnostic performance [area under the curve (AUC): 0.946], followed by hypoxanthine (AUC: 0.933) and phenylalanine (AUC: 0.905) CONCLUSION: The findings of this study demonstrate that the TFME approach is a reliable and effi-cient platform for metabolomic profiling in lung cancer. Using pre-treatment saliva samples, the method achieved a sensitivity exceeding 90% for detecting newly diagnosed histopathologically con-firmed patients. Among the metabolites analyzed, proline, hypoxanthine, and phenylalanine showed strong diagnostic potential, consistent with the pathway analyses implicating purine and phenylala-nine metabolism. These results underscore the potential of salivary metabolomics as a non-invasive screening alternative in the absence of validated early lung cancer biomarkers. Additionally, TFME’s high-throughput capacity, cost-effectiveness, and environmental sustainability support its feasibility for routine clinical application.Article Citation - WoS: 1Citation - Scopus: 1Assessment of Thermal and Solvent Stable Spme Fibers for Metabolomics Studies Performed in Living Systems(Elsevier, 2025) Kahremanoglu, Kuebra; Jaroch, Karol; Szeliska, Paulina; Filipiak, Wojciech; Charemski, Bartlomiej; Zuchowska, Karolina; Boyaci, Ezel; 01. Izmir Institute of TechnologySolid phase microextraction (SPME), as a sampling/sample preparation technique, offers unique solutions for the most challenging applications, including metabolomics studies of living systems. However, for global metabolomics it is critical to use an SPME sampler facilitating the extraction of both volatiles and nonvolatiles, which at the same time is compatible with thermal and solvent-assisted desorption. As a promising universal coating, recently hydrophilic-lipophilic balanced (HLB) particles immobilized in PTFE have been introduced as a new SPME sampler to provide a wide-range of analyte coverage and compatibility with solvent and thermal desorption. Thus, making it suitable for both gas and liquid chromatography (GC/LC) based applications. However, its potential in metabolomics has not been investigated to date. In this study, HLB/PTFE SPME fibers were prepared, evaluated with selected polar and non-polar metabolites relevant to biological systems, and validated for cell-line studies. The validation proved that these fibers can extract a wide-range of molecules (LogP: 4.2 to 15.6) with acceptable accuracy (<= 19% RE%) and repeatability (intra-day <= 17% and inter-day 12% RSD%). The LOQ was determined to vary between 150.0 and 500.0 ng/mL. Upon validation, the fibers were used in a proof-of-concept study for extraction of endometabolome and exometabolome of melanoma B16F10 and lung cancer LL2 cell lines. The metabolome studies showed that HLB/ PTFE fibers provide lower coverage, but for some compounds higher extraction efficiency compared to HLB/PAN fibers used in LC-based metabolomics. Fibers also proved suitable for GC-MS analysis, allowing for the detection of 36 volatile organic compounds in the headspace of the cell lines and RPMI medium.Article Citation - WoS: 2Citation - Scopus: 2A Novel Approach Utilizing Rapid Thin-Film Microextraction Method for Salivary Metabolomics Studies in Lung Cancer Diagnosis(Elsevier, 2024) Pelit, Fusun; Özdemir, Durmuş; Ozupek, Nazli Mert; Gul, Merve; Sakrak, Esra; Ocakoglu, Kasim; Goksel, Ozlem; Özdemir, Durmuş; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of TechnologyThis study investigated the potential of targeted salivary metabolomics as a convenient diagnostic tool for lung cancer (LC), utilizing a rapid TFME-based method. It specifically examines TFME blades modified with SiO2 nanoparticles, which were produced using a custom-made coating system. Validation of the metabolite biomarker analysis was performed by these blades using liquid chromatography-tandem mass spectroscopy (LCMS/MS). The extraction efficiencies of SiO2 nanoparticle/polyacrylonitrile (PAN) composite-coated blades were compared for 18 metabolites. Response surface methodology (RSM) was used to optimize the analysis conditions. Linear calibration plots were obtained for all metabolites at concentrations between 0.025 to 4.0 mu g/mL in the presence of internal standard, with correlation coefficients (R-2) ranging from 0.9975 to 0.9841. The limit of detection (LOD) and limit of quantitation (LOQ) were in the range of 0.014 to 0.97 mu g mL(-1) and 0.046 to 3.20 mu gmL(-1), respectively. The %RSD values for all analytes were within the acceptable range (less than 20 %) for the proposed method. The method was applied to the saliva samples of 40 patients with LC and 38 healthy controls. The efficacy of metabolites for LC diagnosis was determined by in silico methods and the results reveal that phenylalanine and purine metabolism metabolites (e.g., hypoxanthine) are of great importance for LC diagnosis. Furthermore, potentially significant biomarker analysis results from the ROC curve data reveal that proline, hypoxanthine, and phenylalanine were identified as potential biomarkers for LC diagnosis.