Taketo Kato
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Lecturer
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About
Taketo Kato is a Lecturer at Nagoya University in Japan. His research primarily focuses on thoracic surgery, particularly the molecular mechanisms of tumor progression, novel therapeutic strategies in mesothelioma, and the implications of robotic surgery for mediastinal tumors. He has authored numerous studies on postoperative outcomes in lung cancer and the applicability of machine learning in predicting surgical risks.
Recent Grants
Grant: Close
細胞外小胞の革新的サーフェスオーム解析を用いた肺癌早期診断バイオマーカーの開発
Open Date: 2022-04-01
Close Date: 2024-03-31
Grant: Close
Investigation of the molecular behavior and the therapeutic strategy regarding thymoma
Open Date: 2016-04-01
Close Date: 2019-03-31
Articles (14)
Year:
2025
Year:
2025
Early-stage lung adenocarcinoma (LUAD) patients remain at substantial risk for recurrence and disease-related death, highlighting the unmet need of biomarkers for the assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. To identify circulating miRNAs useful for predicting recurrence in early-stage LUAD, we performed miRNA microarray analysis with pools of pretreatment plasma samples from patients with stage I LUAD who developed recurrence or remained recurrence-free during the follow-up period. Subsequent validation in 85 patients with stage I LUAD resulted in the development of a circulating miRNA panel comprising miR-23a-3p, miR-320c, and miR-125b-5p and yielding an area under the curve (AUC) of 0.776 in predicting recurrence. Furthermore, the three-miRNA panel yielded an AUC of 0.804, with a sensitivity of 45.8% at 95% specificity in the independent test set of 57 stage I and II LUAD patients. The miRNA panel score was a significant and independent factor for predicting disease-free survival (p < 0.001, hazard ratio [HR] = 1.64, 95% confidence interval [CI] = 1.51–4.22) and overall survival (p = 0.001, HR = 1.51, 95% CI = 1.17–1.94). This circulating miRNA panel is a useful noninvasive tool to stratify early-stage LUAD patients and determine an appropriate treatment plan with maximal efficacy.
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2024
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2024
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2024
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2024
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2024
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2024
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2024
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2024
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2024
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2023
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2023
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2023
Collaborators (6)
Ayumu Taguchi
The University of Texas MD Anderson Cancer Center
Shota Nakamura
Lecturer
Nagoya University
Makoto Ishii
Professor
Nagoya University
Koji Kawaguchi
Associate Professor
Mie University Hospital
Toyofumi Chen-Yoshikawa
Professor
Nagoya University Graduate School of Medicine
Ryo Emoto
Kyoto University
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