Sönmez, Deniz

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Deniz SÖNMEZ
Sonmez, Deniz
Sönmez, Deniz
Sonmez Deniz
Deniz Sonmez
SONMEZ Deniz
Sönmez Deniz
SÖNMEZ Deniz
Deniz SONMEZ
Sönmez, D.
Deniz Sönmez
Sönmez D.
D., Sönmez
Sonmez D.
Deniz S.
Sonmez, D.
Deniz, Sönmez
Job Title
Dr. Öğr. Üyesi
Email Address
deniz.sonmez@okan.edu.tr
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0000-0002-2044-670X
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3

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3

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    Article
    Citation Count: 3
    Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer
    (Elsevier, 2023) Depciuch, Joanna; Jakubczyk, Pawel; Paja, Wieslaw; Pancerz, Krzysztof; Wosiak, Agnieszka; Kula-Maximenko, Monika; Guleken, Zozan; İşletme / Business Administration
    Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELISA method. Raman spectra showed higher Raman intensities at 1447 cm-1 1560 cm-1, 1665 cm-1, and 1769 cm-1, which originated from CH2 proteins and lipids, amide II and amide I, and C_O lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm-1 and 1306 cm-1 can be used as spectroscopy markers of colon cancer. (c) 2023 Published by Elsevier Inc.
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    Article
    Citation Count: 31
    Assessment of structural protein expression by FTIR and biochemical assays as biomarkers of metabolites response in gastric and colon cancer
    (Elsevier, 2021) Guleken, Zozan; Bulut, Huri; Gultekin, Guldal Inal; Arikan, Soykan; Yaylim, Ilhan; Hakan, Mehmet Tolgahan; Depciuch, Joanna; İşletme / Business Administration; Fizyoloji / Physiology
    Colon and gastric cancers are the widespread benign types of cancers which are synchronous and metachronous neoplasms. In terms of the progression and progress of the disease, metabolic processes and differentiation in protein structures have an important role in for treatment of the disease. In this study we proposed to investigate the metabolic process and the differentiation of protein secondary structure among colon and gastric cancer as well as healthy controls using biochemistry and Fourier Transform InfraRed spectroscopy (FTIR) methods. For this purpose, we measured blood serum of 133 patients, which were conducted upon oncology department (45 colon cancer, 45 gastric cancer and 43 control individuals). The obtained spectroscopic results and biochemical assays showed significant reduction in the amount of functional groups in cancer groups contrary with total protein measurements and structure of protein differences between colon and gastric cancers. Differentiations were visible in serum levels of CEA, CA-125, CA-15-3, CA-19-9 AFP (Alpha fetoprotein) of gastric and colon cancer patients as well as in amide III and secondly described amide I regions. Our findings suggest that amide I bonds in colon cancer cells can be helpful in diagnosis of colon cancer. Indeed, our results showed that metabolic processes were higher in gastric cancer group than in colon cancer. Hence, FTIR spectroscopy and curve-fitting analysis of amide I profile can be successfully applied as tools for identifying quantitative and qualitative changes of proteins in human cancerous blood serum. However, what is very important, in PCA analysis we see, that the scatter plot of PC1 (variability 80%) and PC2 (variability 15%) show that the data related to the control and two cancer groups are clustered together with different magnitudes and directions.
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    Article
    Citation Count: 12
    An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker
    (Elsevier Ireland Ltd, 2023) Guleken, Zozan; Jakubczyk, Pawel; Paja, Wieslaw; Pancerz, Krzysztof; Wosiak, Agnieszka; Yaylim, Ilhan; Depciuch, Joanna; İşletme / Business Administration
    Background and Objective: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca. Methods: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evalu-ate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and EL ISA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery ( n = 26) and healthy ( n = 44) were comrpised in this study. Results: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm -1 was observed and, the Raman intensity of amide III, II, I, and CH2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm -1, as well as between 2700 and 30 0 0 cm -1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm -1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm. Conclusions: The obtained results suggest, that Raman shifts at 1302 and 1306 cm -1 could be spectro-scopic markers of gastric cancer.(c) 2023 Elsevier B.V. All rights reserved.