Depciuch, JoannaJakubczyk, PawelPaja, WieslawPancerz, KrzysztofWosiak, AgnieszkaKula-Maximenko, MonikaGuleken, Zozanİşletme / Business Administration2024-05-252024-05-25202331549-96341549-964210.1016/j.nano.2023.1026572-s2.0-85146322598https://doi.org/10.1016/j.nano.2023.102657https://hdl.handle.net/20.500.14517/1255Guleken, Zozan/0000-0002-4136-4447; Inal Gultekin, Guldal/0000-0002-8313-6119; Wosiak, Agnieszka/0000-0001-6124-1236; Jakubczyk, Paweł/0000-0002-5353-7915; Saribal, Devrim/0000-0003-3301-3708; Paja, Wieslaw/0000-0002-6446-036X; Paja, Wieslaw/0000-0002-6446-036X; Tarhan, Nevzat/0000-0002-6810-7096Colorectal 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.eninfo:eu-repo/semantics/closedAccessColon cancerICAM-1CEACAM-1Raman spectroscopyMachine learningArticleQ2Q148WOS:00097566540000136646194