Development of a diagnostic system for early detection of ovarian cancer targeting glycoproteins in exosomes

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K16865
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 56040:Obstetrics and gynecology-related
• Research Institution: Tokai University
• Principal Investigator: Hayashi Masaru 東海大学, 医学部, 助教 (80631837)
• Project Period (FY): 2022-04-01 – 2024-03-31
• Keywords: 卵巣癌 / 子宮内膜症 / エクソソーム / 糖蛋白 / 糖ペプチド / 深層学習

Outline of Final Research Achievements

We have developed (patented) an ovarian cancer detection AI that determines cancer and non-cancer from the peak values of approximately 2000 glycopeptides obtained by degrading blood glycoproteins. In this study, we aimed to create the above detection AI with glycopeptides from glycoproteins in exosomes to increase the specificity and sensitivity of the ovarian cancer detection AI, but the glycopeptides from glycoproteins in exosomes purified from 1000 μL of serum proved to be difficult to analyze due to the small amount.

Free Research Field

婦人科腫瘍/卵巣癌/早期発見

Academic Significance and Societal Importance of the Research Achievements

エクソソームは細胞外小胞であり、蛋白やRNAなど、起源細胞に由来する多くの分子を含み、疾患バイオマーカーの貯蔵庫と報告されている。我々はエクソソーム中の糖蛋白、そこから得た糖ペプチドに注目し我々の開発した卵巣癌検知AIへの応用を試みたが、残念ながらエクソソーム中の糖蛋白からの糖ペプチドは微量であり解析が困難であった。今後、エクソソーム中の糖蛋白を詳細に解析できる方法が開発されることを期待したい。