Development of a diagnostic method for prostate cancer malignancy using intracellular localization of tumor suppressor ATBF1 as a biomarker

2023 Fiscal Year Final Research Report

• Project/Area Number: 16K11024
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Urology
• Research Institution: Nagoya City University
• Principal Investigator: Kawaguchi Makoto 名古屋市立大学, 医薬学総合研究院(医学), 研究員 (50204699)
• Co-Investigator(Kenkyū-buntansha): 三浦 裕 至学館大学, 健康科学部, 教授 (90285198)
• Project Period (FY): 2016-04-01 – 2024-03-31
• Keywords: ATBF1 / SNP / 脳梗塞 / 肺血栓塞栓症 / 川崎病

Outline of Final Research Achievements

The expression levels of ATBF1 can classify the malignancy of various human cancers and can be used for the marker to differentiate the prognosis. In breast cancer, the higher the mRNA expression level, the better the prognosis. However, in bladder cancer, in order to predict the long-term prognosis based on the mRNA expression level alone, it was necessary to perform immunohistochemical analysis to differentiate between the nucleus and the cytoplasm. Because ATBF1 is a huge protein of 400 kDa, and there was no commercially available antibody that could reliably differentiate between the intracellular localization of the nucleus and the cytoplasm. Therefore, we estimated the antigen-presenting region from the primary structure and purified all of them as anti-ATBF1 antibodies. We also succeeded in creating a monoclonal antibody that targets its specific amino acid. As a result of creating these specific antibodies, it became possible to automatically recognize intranuclear ATBF1.

Free Research Field

泌尿器科学

Academic Significance and Societal Importance of the Research Achievements

癌の悪性度を分類して診断することは、それぞれのがん患者に対する治療法を考える上で極めて重要な診断になる。病理組織学的にはHE染色による悪性度を分類が現在も主流である。しかし、膀胱癌においてはATBF1の核と細胞質の鑑別を免疫組織学的に診断することで、10年予後まで極めて正確に予測できることを、本研究成果として達成することができた。私たちが開発した核と細胞質の細胞内局在と確実に鑑別できる抗体を用いることにより、画像解析により核内ATBF1であることを自動的に認識できるようになった。このことは臨床現場で癌の悪性度を判断するための重要な客観的データとして実用化されることが期待できる。