| Project/Area Number |
18K07019
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 49020:Human pathology-related
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| Research Institution | Kagoshima University |
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Principal Investigator |
Higashi Michiyo 鹿児島大学, 医歯学域鹿児島大学病院, 准教授 (60315405)
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| Co-Investigator(Kenkyū-buntansha) |
横山 勢也 鹿児島大学, 医歯学域医学系, 助教 (20569941)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 病理学 / 膵胆道腫瘍 / ムチン抗原 / 予後因子 / 機械学習 / MUC1 / MUC4 / MUC / methylation / prognosis / 膵胆道系腫瘍 / 病理 / 膵胆管系腫瘍 / 早期診断 / 治療効果判定 / ムチン |
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| Outline of Final Research Achievements |
We had reported that expression profiles of mucins (MUC1, MUC2 and MUC4) are closely associated with biological behavior of human pancreatobiliary neoplasms. We evaluated the methylation status of MUC1, MUC2, and MUC4 promoter regions in pancreatic tissue samples from patients with various pancreatic lesions using methylation-specific electrophoresis. Then, integrating these results and clinicopathological features, we used support vector machine-, neural network-, and multinomial-based methods to develop a prognostic classifier.Significant differences were identified between the positive- and negative-prediction classifiers of patients in 5-year overall survival in the cross-validation test. Multivariate analysis revealed that these prognostic classifiers were independent prognostic factors analyzed by not only neoplastic tissues but also non-neoplastic tissues.
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| Academic Significance and Societal Importance of the Research Achievements |
膵切除検体を用いて各種ムチンの免疫染色結果とDNAメチル化の解析結果が相関していることを示した。またそれらが予後と密接に相関していることを示した。特に腫瘍部のみならず非腫瘍部の組織を用いた解析でも、予後予測因子となり得ることを示した。これは、腫瘍が直接得られない場合でも、非腫瘍部の組織が得られれば予後予測が可能になる可能性を示している。低侵襲の検査や処置ですむ可能性があり、患者負担の軽減のみならず、医療者の負担や医療資源のコスト減にも繋がる可能性がある。
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