Prediction of gastric cancer by machine learning integrated with mass spectrometry-based N-glycomics
Yazarlar (5)
Karabük Üniversitesi, Türkiye
Karabük Üniversitesi, Türkiye
Karabük Üniversitesi, Türkiye
Doç. Dr. Hacı Mehmet KAYILI
Karabük Üniversitesi, Türkiye
Hacettepe Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Analyst |
| Dergi ISSN | 0003-2654 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2023 |
| Cilt No | 148 |
| Sayı | 9 |
| Sayfalar | 2073 / 2080 |
| DOI Numarası | 10.1039/d2an02057b |
| Makale Linki | http://dx.doi.org/10.1039/d2an02057b |
| Özet |
| Early and accurate diagnosis of gastric cancer is vital for effective and targeted treatment. It is known that glycosylation profiles differ in the cancer tissue development process. This study aimed to profile the N-glycans in gastric cancer tissues to predict gastric cancer using machine learning algorithms. The (glyco-) proteins of formalin-fixed parafilm embedded (FFPE) gastric cancer and adjacent control tissues were extracted by chloroform/methanol extraction after the conventional deparaffinization step. The N-glycans were released and labeled with a 2-amino benzoic (2-AA) tag. The MALDI-MS analysis of the 2-AA labeled N-glycans was performed in negative ionization mode, and fifty-nine N-glycan structures were determined. The relative and analyte areas of the detected N-glycans were extracted from the obtained data. Statistical analyses identified significant expression levels of 14 different N-glycans in gastric cancer tissues. The data were separated based on the physical characteristics of N-glycans and used to test in machine-learning models. It was determined that the multilayer perceptron (MLP) was the most appropriate model with the highest sensitivity, specificity, accuracy, Matthews correlation coefficient, and f1 scores for each dataset. The highest accuracy score (96.0 ± 1.3) was obtained from the whole N-glycans relative area dataset, and the AUC value was determined as 0.98. It was concluded that gastric cancer tissues could be distinguished from adjacent control tissues with high accuracy using mass spectrometry-based N-glycomic data. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 16 |
| SCOPUS | 17 |
| Google Scholar | 18 |