Integrated database construction of Fabry disease using artificial intelligence / next generation sequencer

2020 Fiscal Year Final Research Report

• Project/Area Number: 18K07888
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 52050:Embryonic medicine and pediatrics-related
• Research Institution: Jikei University School of Medicine
• Principal Investigator: Kobayashi Hiroshi 東京慈恵会医科大学, 医学部, 教授 (90266619)
• Project Period (FY): 2018-04-01 – 2021-03-31
• Keywords: Fabry病 / AI / 次世代シークエンサー / 循環器系合併症 / CAGE / 予後予測因子

Outline of Final Research Achievements

A group of male patients with Fabry disease were classified according to the presence or absence of cardiac disease. CAGE analysis was performed and genes with significant differences in expression levels between the three groups were extracted. Ten candidate genes with significant differences in expression were selected, and the cardiac MRIs of 23 patients with Fabry disease, excluding the normal group in terms of expression level, were analyzed by AI (segmentation) to detect two genes, CHN1 and COX6CP1, that showed differences between the two groups of patients with Fabry disease with or without cardiac complications. The scatter plot shows an inverse correlation trend, and we are preparing to submit a paper to verify whether the combination or single expression of these two genes can predict the prognosis of cardiac complications in FD.

Free Research Field

遺伝子治療

Academic Significance and Societal Importance of the Research Achievements

ファブリー病で最も死因として多い循環器系合併症の予後予測因子を遺伝子レベルで確認する意味は大きく、統計学的に有意であれば、今後はCAGE法より安価な方法で臨床応用が可能となる。　更に近い将来に遺伝子治療を施行した場合の遺伝子変異の変化を同じ集団で調査し、ウイルスベクターの影響を調査する基礎資料とすることや、遺伝子環境・臨床環境の変化の違いも総合分析し、新しい診療支援システムの構築も視野に入れている。