Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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) Kus, M.E.; Ekiz, H.A.Objectives: 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.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, EzelSolid 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.