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) 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.Article Development and Evaluation of 177Lu-Imatinib: Radiolabeling and Cell Culture Studies(Walter de Gruyter GmbH, 2025) Güler, Günnur; Karpuz, M.; Guler, G.; Burak, Z.; Başpainar, Y.; Gundogdu, E.A.; 04.05. Department of Pyhsics; 04. Faculty of Science; 01. Izmir Institute of TechnologyTargeted radiopharmaceuticals offer promising approaches for cancer diagnosis and therapy. This study developed freeze-dried kit formulations of 177Lu-Imatinib (IMT) and evaluated their potential efficacy through in vitro studies. Four formulations (F1-F4) containing IMT and chelating agents were prepared and characterized via Fourier transform infrared (FTIR), ultraviolet spectrum (UV), and thermogravimetric analysis (TGA) to confirm complex formation. Biocompatibility was assessed in NIH-3T3 cells using the MTT assay. Radiolabeling with 177Lu was optimized by varying pH, incubation time, and reactant ratios. Radiochemical purity and stability were analyzed over 7 days using HPLC. Binding affinity and cytotoxicity were evaluated in MCF-7 and NIH-3T3 cells. Spectroscopic analyses confirm successful complex formation. All formulations exhibited >90% viability in NIH-3T3 cells. Optimal radiolabeling conditions (45mg IMT-chelator, pH 5, 60min incubation) yielded >90% efficiency, with stable radiolabeling for 7 days. The 177Lu-IMT-DOTA (F3) formulation showed significantly higher binding and cytotoxic effects in MCF-7 cells compared to controls. The 177Lu-IMT-DOTA (F3) kit demonstrates high radiolabeling efficiency, stability, and selective in vitro cytotoxicity toward breast cancer cells, supporting its potential as a targeted radiopharmaceutical. © 2025 Walter de Gruyter GmbH, Berlin/Boston 2025.