PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - WoS: 5Citation - Scopus: 6Genetic Variants in Genes Correlated to the PI3K/AKT Pathway: The Role of ARAP3, CDH5, KIF and RELN Primary Lymphedema(International Society of Lymphology, 2024-08-28) Dundar, Mehmet Sait; Belanová, I.; Bonetti, Gabriele; Gelanová, V.; Kozáčiková, R.; Vešelényiová, Dominika; Donato, Kevin; Michelini, S.Genetic anomalies affecting lymphatic development and function can lead to lymphatic dysfunction, which could manifest as lymphedema- Understanding the signaling pathways governing lymphatics function is crucial for developing targeted diagnostic and therapeutic interventions. This study aims to characterize genetic variants in genes involved in the PUKIAKT signaling pathway, which plays a critical role in lymphangiogenesis. 408 patients diagnosed with primary lymphedema were sequenced usinga next-generation sequencing (NGS) gene panel composed of 28 diagnostic genes and 71 candidate genes. The analysis revealed six variants in genes RFLN, ARAP3,CDHS and K1F11. Five of these variants have never been reported in the literature. All these genes have been correlated to lymphatic activity and are involved in the P13K/AKT pathway. As the P13K/AKT signaling pathway plays an essential role in lymphangiogenesis and lymphatic function, genetic variants in genes correlated to this pathway could lead to lymphedema. Our findings underscore the potential of the P13K/AKT pathway in lymphedema pathogenesis, supporting the role of RELN,ARAP3,CDH5,and KIF11 as diagnostic and therapeutic targets. © 2024 Elsevier B.V., All rights reserved.Article Evaluation of HOTAIR, HOXD8, HOXD9, HOXD11 Gene Expression Levels in Turkish Patients With Acute and Chronic Myeloid Leukemia: A Single Center Experience(Cellular and Molecular Biology Association, 2024-11-27) Saraymen, Esma; Erdem, Yakut; Akalin, Hilal Ünlü; Taşçıoğlu, Nazife; Saraymen, Berkay; Celik, Serhat; Özkul, Yusuf T.Homeobox (HOX) transcript antisense RNA (HOTAIR) and HOX genes are reported to be more expressed in various cancers in humans in recent studies. The role of HOTAIR and HOXD genes in acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) is not well known. In this study, expression levels of HOXD8, HOXD9 and HOXD11 from HOXD gene family and HOTAIR were determined from peripheral blood samples of 30 AML and 30 CML patients and 20 healthy volunteers by quantitative Real Time PCR. We determined that the expression levels of HOXD9 and HOXD11 in the AML patients were significantly lower than the control group (p<0.001 and p=0.002, respectively). There was no significant difference in the expression levels of HOTAIR and HOXD8 when compared to the control group. In the CML patients there was a significant increase in the expression level of HOTAIR when compared to the control group (p=0.002). The expression levels of HOXD9 and HOXD11 were found to be significantly lower than the control group (p<0.001). Our study showed that HOTAIR may not be a biomarker in the diagnosis and is not significantly correlated with the clinicopathological prognostic characteristics of AML. Additionally; it can be said that HOTAIR is oncogenic by suppressing the expression of HOXD9 and HOXD11 but not HOXD8 in CML patients. The expression profiles of HOTAIR may be a potential biomarker in the diagnosis of CML patients in predicting and monitoring drug resistance. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4CCPred: Global and Population-Specific Colorectal Cancer Prediction and Metagenomic Biomarker Identification at Different Molecular Levels Using Machine Learning Techniques(Elsevier Ltd, 2024-11) Bakir-Güngör, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, MalikColorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.