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Takasawa K, Asada K, Kaneko S, Shiraishi K, Machino H, Takahashi S, Shinkai N, Kouno N, Kobayashi K, Komatsu M, Mizuno T, Okubo Y, Mukai M, Yoshida T, Yoshida Y, Horinouchi H, Watanabe SI, Ohe Y, Yatabe Y, Kohno T, Hamamoto R. Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability. Exp Mol Med 2024; 56:646-655. [PMID: 38433247 PMCID: PMC10985003 DOI: 10.1038/s12276-024-01173-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.
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Affiliation(s)
- Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Takaaki Mizuno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yu Okubo
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Masami Mukai
- Division of Medical Informatics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Tatsuya Yoshida
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Hidehito Horinouchi
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yuichiro Ohe
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
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Machino H, Dozen A, Konaka M, Komatsu M, Nakamura K, Ikawa N, Shozu K, Asada K, Kaneko S, Yoshida H, Kato T, Nakayama K, Saloura V, Kyo S, Hamamoto R. Integrative analysis reveals early epigenetic alterations in high-grade serous ovarian carcinomas. Exp Mol Med 2023; 55:2205-2219. [PMID: 37779141 PMCID: PMC10618212 DOI: 10.1038/s12276-023-01090-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/02/2023] [Accepted: 07/06/2023] [Indexed: 10/03/2023] Open
Abstract
High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations and transcription factor dysregulation in HGSOC have not yet been fully elucidated. In this study, we performed integrative omics analyses of a series of stepwise HGSOC model cells originating from human fallopian tube secretory epithelial cells (HFTSECs) to investigate early epigenetic alterations in HGSOC tumorigenesis. Assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) methods were used to analyze HGSOC samples. Additionally, protein expression changes in target genes were confirmed using normal HFTSECs, serous tubal intraepithelial carcinomas (STICs), and HGSOC tissues. Transcription factor motif analysis revealed that the DNA-binding activity of the AP-1 complex and GATA family proteins was dysregulated during early tumorigenesis. The protein expression levels of JUN and FOSL2 were increased, and those of GATA6 and DAB2 were decreased in STIC lesions, which were associated with epithelial-mesenchymal transition (EMT) and proteasome downregulation. The genomic region around the FRA16D site, containing a cadherin cluster region, was epigenetically suppressed by oncogenic signaling. Proteasome inhibition caused the upregulation of chemokine genes, which may facilitate immune evasion during HGSOC tumorigenesis. Importantly, MEK inhibitor treatment reversed these oncogenic alterations, indicating its clinical effectiveness in a subgroup of patients with HGSOC. This result suggests that MEK inhibitor therapy may be an effective treatment option for chemotherapy-resistant HGSOC.
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Affiliation(s)
- Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo, 160-8582, Japan
| | - Mariko Konaka
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Kohei Nakamura
- Genomics Unit, Keio Cancer Center, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo, 160-8582, Japan
| | - Noriko Ikawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama, 930-0152, Japan
| | - Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Hiroshi Yoshida
- Division of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Tomoyasu Kato
- Department of Gynecology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Kentaro Nakayama
- Department of Obstetrics and Gynecology, Shimane University Faculty of Medicine, 89-1 Enyacho, Izumo-shi, Shimane, 693-8501, Japan
| | - Vassiliki Saloura
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Satoru Kyo
- Department of Obstetrics and Gynecology, Shimane University Faculty of Medicine, 89-1 Enyacho, Izumo-shi, Shimane, 693-8501, Japan
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
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Hamamoto R, Takasawa K, Shinkai N, Machino H, Kouno N, Asada K, Komatsu M, Kaneko S. Analysis of super-enhancer using machine learning and its application to medical biology. Brief Bioinform 2023; 24:7084796. [PMID: 36960780 DOI: 10.1093/bib/bbad107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/11/2023] [Accepted: 03/01/2023] [Indexed: 03/25/2023] Open
Abstract
The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.
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Affiliation(s)
- Ryuji Hamamoto
- Division Chief in the Division of Medical AI Research and Development, National Cancer Center Research Institute; a Professor in the Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University and a Team Leader of the Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff in the Medical AI Research and Development, National Cancer Center Research Institute
| | - Norio Shinkai
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff in the Medical AI Research and Development, National Cancer Center Research Institute
| | - Nobuji Kouno
- Department of Surgery, Graduate School of Medicine, Kyoto University
| | - Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff of Medical AI Research and Development, National Cancer Center Research Institute
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff of Medical AI Research and Development, National Cancer Center Research Institute
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute and a Visiting Scientist in the Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project
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4
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Dozen A, Shozu K, Shinkai N, Ikawa N, Aoyama R, Machino H, Asada K, Yoshida H, Kato T, Hamamoto R, Kaneko S, Komatsu M. Tumor Suppressive Role of the PRELP Gene in Ovarian Clear Cell Carcinoma. J Pers Med 2022; 12:jpm12121999. [PMID: 36556220 PMCID: PMC9785654 DOI: 10.3390/jpm12121999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/18/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Ovarian clear cell carcinoma (OCCC) has a poor prognosis, and its therapeutic strategy has not been established. PRELP is a leucine-rich repeat protein in the extracellular matrix of connective tissues. Although PRELP anchors the basement membrane to the connective tissue and is absent in most epithelial cancers, much remains unknown regarding its function as a regulator of ligand-mediated signaling pathways. Here, we obtained sets of differentially expressed genes by PRELP expression using OCCC cell lines. We found that more than 1000 genes were significantly altered by PRELP expression, particularly affecting the expression of a group of genes involved in the PI3K-AKT signaling pathway. Furthermore, we revealed the loss of active histone marks on the loci of the PRELP gene in patients with OCCC and how its forced expression inhibited cell proliferation. These findings suggest that PRELP is not only a molecule anchored in connective tissues but is also a signaling molecule acting in a tumor-suppressive manner. It can serve as the basis for early detection and novel therapeutic approaches for OCCC toward precision medicine.
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Affiliation(s)
- Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Noriko Ikawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Rina Aoyama
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104–0045, Japan
| | - Tomoyasu Kato
- Department of Gynecology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104–0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Correspondence: (S.K.); (M.K.); Tel.: +81-3-3547-5201 (S.K. & M.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Correspondence: (S.K.); (M.K.); Tel.: +81-3-3547-5201 (S.K. & M.K.)
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Shozu K, Kaneko S, Shinkai N, Dozen A, Kosuge H, Nakakido M, Machino H, Takasawa K, Asada K, Komatsu M, Tsumoto K, Ohnuma SI, Hamamoto R. Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer. Clin Epigenetics 2022; 14:147. [PMID: 36371227 PMCID: PMC9656081 DOI: 10.1186/s13148-022-01370-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Proline/arginine-rich end leucine-rich repeat protein (PRELP) is a member of the small leucine-rich proteoglycan family of extracellular matrix proteins, which is markedly suppressed in the majority of early-stage epithelial cancers and plays a role in regulating the epithelial-mesenchymal transition by altering cell-cell adhesion. Although PRELP is an important factor in the development and progression of bladder cancer, the mechanism of PRELP gene repression remains unclear. RESULTS Here, we show that repression of PRELP mRNA expression in bladder cancer cells is alleviated by HDAC inhibitors (HDACi) through histone acetylation. Using ChIP-qPCR analysis, we found that acetylation of lysine residue 5 of histone H2B in the PRELP gene promoter region is a marker for the de-repression of PRELP expression. CONCLUSIONS These results suggest a mechanism through which HDACi may partially regulate the function of PRELP to suppress the development and progression of bladder cancer. Some HDACi are already in clinical use, and the findings of this study provide a mechanistic basis for further investigation of HDACi-based therapeutic strategies.
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Affiliation(s)
- Kanto Shozu
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.267346.20000 0001 2171 836XDepartment of Obstetrics and Gynecology, University of Toyama, Toyama, Japan
| | - Syuzo Kaneko
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Norio Shinkai
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan ,grid.265073.50000 0001 1014 9130Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ai Dozen
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan
| | - Hirofumi Kosuge
- grid.26999.3d0000 0001 2151 536XSchool of Engineering, The University of Tokyo, Tokyo, Japan
| | - Makoto Nakakido
- grid.26999.3d0000 0001 2151 536XSchool of Engineering, The University of Tokyo, Tokyo, Japan
| | - Hidenori Machino
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Ken Takasawa
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Ken Asada
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Masaaki Komatsu
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Kouhei Tsumoto
- grid.26999.3d0000 0001 2151 536XSchool of Engineering, The University of Tokyo, Tokyo, Japan
| | - Shin-Ichi Ohnuma
- grid.83440.3b0000000121901201UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London, EC1V 9EL UK ,grid.5335.00000000121885934Department of Oncology, The Hutchison/MRC Research Center, University of Cambridge, Hills Road, Cambridge, CB2 2XZ UK
| | - Ryuji Hamamoto
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
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6
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Hamamoto R, Koyama T, Kouno N, Yasuda T, Yui S, Sudo K, Hirata M, Sunami K, Kubo T, Takasawa K, Takahashi S, Machino H, Kobayashi K, Asada K, Komatsu M, Kaneko S, Yatabe Y, Yamamoto N. Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information. Exp Hematol Oncol 2022; 11:82. [PMID: 36316731 PMCID: PMC9620610 DOI: 10.1186/s40164-022-00333-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/05/2022] [Indexed: 11/10/2022] Open
Abstract
Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.
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Affiliation(s)
- Ryuji Hamamoto
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Takafumi Koyama
- grid.272242.30000 0001 2168 5385Department of Experimental Therapeutics, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
| | - Nobuji Kouno
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.258799.80000 0004 0372 2033Department of Surgery, Graduate School of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku, Kyoto, 606-8303 Japan
| | - Tomohiro Yasuda
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.417547.40000 0004 1763 9564Research and Development Group, Hitachi, Ltd., 1-280 Higashi-koigakubo, Kokubunji, Tokyo, 185-8601 Japan
| | - Shuntaro Yui
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.417547.40000 0004 1763 9564Research and Development Group, Hitachi, Ltd., 1-280 Higashi-koigakubo, Kokubunji, Tokyo, 185-8601 Japan
| | - Kazuki Sudo
- grid.272242.30000 0001 2168 5385Department of Experimental Therapeutics, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.272242.30000 0001 2168 5385Department of Medical Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
| | - Makoto Hirata
- grid.272242.30000 0001 2168 5385Department of Genetic Medicine and Services, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
| | - Kuniko Sunami
- grid.272242.30000 0001 2168 5385Department of Laboratory Medicine, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
| | - Takashi Kubo
- grid.272242.30000 0001 2168 5385Department of Laboratory Medicine, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
| | - Ken Takasawa
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Satoshi Takahashi
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Hidenori Machino
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Kazuma Kobayashi
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Ken Asada
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Masaaki Komatsu
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Syuzo Kaneko
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.509456.bCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
| | - Yasushi Yatabe
- grid.272242.30000 0001 2168 5385Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan ,grid.272242.30000 0001 2168 5385Division of Molecular Pathology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
| | - Noboru Yamamoto
- grid.272242.30000 0001 2168 5385Department of Experimental Therapeutics, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan
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7
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Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform 2022; 23:6628783. [PMID: 35788277 PMCID: PMC9294421 DOI: 10.1093/bib/bbac246] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/06/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022] Open
Abstract
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rina Aoyama
- Showa University Graduate School of Medicine School of Medicine
| | | | - Ken Asada
- RIKEN Center for Advanced Intelligence Project
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8
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Sakai A, Komatsu M, Komatsu R, Matsuoka R, Yasutomi S, Dozen A, Shozu K, Arakaki T, Machino H, Asada K, Kaneko S, Sekizawa A, Hamamoto R. Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening (supplementary). Biomedicines 2022; 10:biomedicines10030551. [PMID: 35327353 PMCID: PMC8945208 DOI: 10.3390/biomedicines10030551] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 12/10/2022] Open
Abstract
Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem), making it difficult to build trust with medical professionals. Nevertheless, visualizing the internal representation of deep neural networks will increase explanatory power and improve the confidence of medical professionals in AI decisions. We propose a novel deep learning-based explainable representation “graph chart diagram” to support fetal cardiac ultrasound screening, which has low detection rates of congenital heart diseases due to the difficulty in mastering the technique. Screening performance improves using this representation from 0.966 to 0.975 for experts, 0.829 to 0.890 for fellows, and 0.616 to 0.748 for residents in the arithmetic mean of area under the curve of a receiver operating characteristic curve. This is the first demonstration wherein examiners used deep learning-based explainable representation to improve the performance of fetal cardiac ultrasound screening, highlighting the potential of explainable AI to augment examiner capabilities.
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Affiliation(s)
- Akira Sakai
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Correspondence: (M.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Reina Komatsu
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryu Matsuoka
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Tatsuya Arakaki
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Akihiko Sekizawa
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryuji Hamamoto
- Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Correspondence: (M.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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9
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Bolatkan A, Asada K, Kaneko S, Suvarna K, Ikawa N, Machino H, Komatsu M, Shiina S, Hamamoto R. Downregulation of METTL6 mitigates cell progression, migration, invasion and adhesion in hepatocellular carcinoma by inhibiting cell adhesion molecules. Int J Oncol 2022; 60:4. [PMID: 34913069 PMCID: PMC8698744 DOI: 10.3892/ijo.2021.5294] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/29/2021] [Indexed: 12/24/2022] Open
Abstract
RNA modifications have attracted increasing interest in recent years because they have been frequently implicated in various human diseases, including cancer, highlighting the importance of dynamic post‑transcriptional modifications. Methyltransferase‑like 6 (METTL6) is a member of the RNA methyltransferase family that has been identified in many cancers; however, little is known about its specific role or mechanism of action. In the present study, we aimed to study the expression levels and functional role of METTL6 in hepatocellular carcinoma (HCC), and further investigate the relevant pathways. To this end, we systematically conducted bioinformatics analysis of METTL6 in HCC using gene expression data and clinical information from a publicly available dataset. The mRNA expression levels of METTL6 were significantly upregulated in HCC tumor tissues compared to that in adjacent non‑tumor tissues and strongly associated with poorer survival outcomes in patients with HCC. CRISPR/Cas9‑mediated knockout of METTL6 in HCC cell lines remarkably inhibited colony formation, cell proliferation, cell migration, cell invasion and cell attachment ability. RNA sequencing analysis demonstrated that knockout of METTL6 significantly suppressed the expression of cell adhesion‑related genes. However, chromatin immunoprecipitation sequencing results revealed no significant differences in enhancer activities between cells, which suggests that METTL6 may regulate genes of interest post‑transcriptionally. In addition, it was demonstrated for the first time that METTL6 was localized in the cytosol as detected by immunofluorescence analysis, which indicates the plausible location of RNA modification mediated by METTL6. Our findings provide further insight into the function of RNA modifications in cancer and suggest a possible role of METTL6 as a therapeutic target in HCC.
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Affiliation(s)
- Amina Bolatkan
- Department of Diagnostic Imaging and Interventional Oncology, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Kruthi Suvarna
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Noriko Ikawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Shuichiro Shiina
- Department of Diagnostic Imaging and Interventional Oncology, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Department of National Cancer Center Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
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10
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Asada K, Takasawa K, Machino H, Takahashi S, Shinkai N, Bolatkan A, Kobayashi K, Komatsu M, Kaneko S, Okamoto K, Hamamoto R. Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines 2021; 9:biomedicines9111513. [PMID: 34829742 PMCID: PMC8614827 DOI: 10.3390/biomedicines9111513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/06/2021] [Accepted: 10/19/2021] [Indexed: 01/14/2023] Open
Abstract
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Koji Okamoto
- Division of Cancer Differentiation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryuji Hamamoto
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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11
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Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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12
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Kaneko S, Takasawa K, Asada K, Shinkai N, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Komatsu M, Hamamoto R. Epigenetic Mechanisms Underlying COVID-19 Pathogenesis. Biomedicines 2021; 9:1142. [PMID: 34572329 PMCID: PMC8466119 DOI: 10.3390/biomedicines9091142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 12/11/2022] Open
Abstract
In 2019, a novel severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported and was declared a pandemic by the World Health Organization (WHO) in March 2020. With the advancing development of COVID-19 vaccines and their administration globally, it is expected that COVID-19 will converge in the future; however, the situation remains unpredictable because of a series of reports regarding SARS-CoV-2 variants. Currently, there are still few specific effective treatments for COVID-19, as many unanswered questions remain regarding the pathogenic mechanism of COVID-19. Continued elucidation of COVID-19 pathogenic mechanisms is a matter of global importance. In this regard, recent reports have suggested that epigenetics plays an important role; for instance, the expression of angiotensin I converting enzyme 2 (ACE2) receptor, an important factor in human infection with SARS-CoV-2, is epigenetically regulated; further, DNA methylation status is reported to be unique to patients with COVID-19. In this review, we focus on epigenetic mechanisms to provide a new molecular framework for elucidating the pathogenesis of SARS-CoV-2 infection in humans and of COVID-19, along with the possibility of new diagnostic and therapeutic strategies.
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Affiliation(s)
- Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- National Cancer Center Hospital, Department of Endoscopy, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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13
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Komatsu M, Sakai A, Dozen A, Shozu K, Yasutomi S, Machino H, Asada K, Kaneko S, Hamamoto R. Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines 2021; 9:720. [PMID: 34201827 PMCID: PMC8301304 DOI: 10.3390/biomedicines9070720] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/13/2021] [Accepted: 06/18/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.
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Affiliation(s)
- Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Akira Sakai
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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14
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Asada K, Kaneko S, Takasawa K, Machino H, Takahashi S, Shinkai N, Shimoyama R, Komatsu M, Hamamoto R. Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Front Oncol 2021; 11:666937. [PMID: 34055633 PMCID: PMC8149908 DOI: 10.3389/fonc.2021.666937] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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15
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Kaneko S, Mitsuyama T, Shiraishi K, Ikawa N, Shozu K, Dozen A, Machino H, Asada K, Komatsu M, Kukita A, Sone K, Yoshida H, Motoi N, Hayami S, Yoneoka Y, Kato T, Kohno T, Natsume T, von Keudell G, Saloura V, Yamaue H, Hamamoto R. Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential. Cancers (Basel) 2021; 13:cancers13092126. [PMID: 33924956 PMCID: PMC8125448 DOI: 10.3390/cancers13092126] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022] Open
Abstract
Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the genome-wide distribution of histone modifications involved in transcriptional activation. Importantly, we provide evidence that the ChIP-seq datasets obtained from FFPE samples are similar to or even better than the data for corresponding fresh-frozen samples, indicating that FFPE samples are compatible with ChIP-seq analysis. H3K27ac ChIP-seq analyses of 69 FFPE samples using a dual-arm robot revealed that driver mutations in EGFR were distinguishable from pan-negative cases and were relatively homogeneous as a group in lung adenocarcinomas. Thus, our results demonstrate that FFPE samples are an important source for epigenomic research, enabling the study of histone modifications, nuclear chromatin structure, and clinical data.
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Affiliation(s)
- Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Correspondence: (S.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Toutai Mitsuyama
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan;
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (K.S.); (T.K.)
| | - Noriko Ikawa
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
| | - Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Asako Kukita
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo 113-0033, Japan; (A.K.); (K.S.)
| | - Kenbun Sone
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo 113-0033, Japan; (A.K.); (K.S.)
| | - Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (H.Y.); (N.M.)
| | - Noriko Motoi
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (H.Y.); (N.M.)
| | - Shinya Hayami
- Second Department of Surgery, School of Medicine, Wakayama Medical University, Wakayama 641-0011, Japan; (S.H.); (H.Y.)
| | - Yutaka Yoneoka
- Department of Gynecology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (Y.Y.); (T.K.)
| | - Tomoyasu Kato
- Department of Gynecology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (Y.Y.); (T.K.)
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (K.S.); (T.K.)
| | - Toru Natsume
- Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, Tokyo 100-8921, Japan;
- Robotic Biology Institute, Inc., Tokyo 135-0064, Japan
| | | | - Vassiliki Saloura
- Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA;
| | - Hiroki Yamaue
- Second Department of Surgery, School of Medicine, Wakayama Medical University, Wakayama 641-0011, Japan; (S.H.); (H.Y.)
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Correspondence: (S.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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16
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Shozu K, Komatsu M, Sakai A, Komatsu R, Dozen A, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Nakashima A, Sekizawa A, Hamamoto R. Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos. Biomolecules 2020; 10:E1691. [PMID: 33348873 PMCID: PMC7766150 DOI: 10.3390/biom10121691] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 12/17/2022] Open
Abstract
The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segmentation using five-fold cross-validation on 538 ultrasound frames in the four-chamber view (4CV) of 256 normal cases using U-net and DeepLabv3+. MFCY increased the mean values of the intersection over union (IoU) of thoracic wall segmentation from 0.448 to 0.493 for U-net and from 0.417 to 0.470 for DeepLabv3+. These results demonstrated that MFCY improved the segmentation performance of the thoracic wall in fetal ultrasound videos without altering the network structure. MFCY is expected to facilitate the development of diagnostic support models in fetal ultrasound by providing further accurate segmentation of the thoracic wall.
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Affiliation(s)
- Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan;
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Akira Sakai
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-Ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Reina Komatsu
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-Ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
| | - Tatsuya Arakaki
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ryu Matsuoka
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Akitoshi Nakashima
- Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan;
| | - Akihiko Sekizawa
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
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Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel) 2020; 12:E3532. [PMID: 33256107 PMCID: PMC7760590 DOI: 10.3390/cancers12123532] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 11/21/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
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Affiliation(s)
- Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kruthi Suvarna
- Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India;
| | - Masayoshi Yamada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku Tokyo 104-0045, Japan
| | - Kazuma Kobayashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Norio Shinkai
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Masamichi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Shunichi Jinnai
- Department of Dermatologic Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryo Shimoyama
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Akira Sakai
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ken Takasawa
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Amina Bolatkan
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Satoshi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Jun Sese
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Humanome Lab, 2-4-10 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
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Dozen A, Komatsu M, Sakai A, Komatsu R, Shozu K, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Aoki D, Sekizawa A, Hamamoto R. Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules 2020; 10:E1526. [PMID: 33171658 PMCID: PMC7695246 DOI: 10.3390/biom10111526] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/04/2020] [Accepted: 11/05/2020] [Indexed: 12/11/2022] Open
Abstract
Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC.
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Affiliation(s)
- Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Akira Sakai
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Reina Komatsu
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
| | - Tatsuya Arakaki
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ryu Matsuoka
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Daisuke Aoki
- Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;
| | - Akihiko Sekizawa
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Kojima M, Sone K, Oda K, Hamamoto R, Kaneko S, Oki S, Kukita A, Machino H, Honjoh H, Kawata Y, Kashiyama T, Asada K, Tanikawa M, Mori-Uchino M, Tsuruga T, Nagasaka K, Matsumoto Y, Wada-Hiraike O, Osuga Y, Fujii T. The histone methyltransferase WHSC1 is regulated by EZH2 and is important for ovarian clear cell carcinoma cell proliferation. BMC Cancer 2019; 19:455. [PMID: 31092221 PMCID: PMC6521555 DOI: 10.1186/s12885-019-5638-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 04/24/2019] [Indexed: 01/02/2023] Open
Abstract
Background Wolf-Hirschhorn syndrome candidate gene-1 (WHSC1), a histone methyltransferase, has been found to be upregulated and its expression to be correlated with expression of enhancer of zeste homolog 2 (EZH2) in several cancers. In this study, we evaluated the role of WHSC1 and its therapeutic significance in ovarian clear cell carcinoma (OCCC). Methods First, we analyzed WHSC1 expression by quantitative PCR and immunohistochemistry using 23 clinical OCCC specimens. Second, the involvement of WHSC1 in OCCC cell proliferation was evaluated by MTT assays after siRNA-mediated WHSC1 knockdown. We also performed flow cytometry (FACS) to address the effect of WHSC1 on cell cycle. To examine the functional relationship between EZH2 and WHSC1, we knocked down EZH2 using siRNAs and checked the expression levels of WHSC1 and its histone mark H3K36m2 in OCCC cell lines. Finally, we checked WHSC1 expression after treatment with the selective inhibitor, GSK126. Results Both quantitative PCR and immunohistochemical analysis revealed that WHSC1 was significantly overexpressed in OCCC tissues compared with that in normal ovarian tissues. MTT assay revealed that knockdown of WHSC1 suppressed cell proliferation, and H3K36me2 levels were found to be decreased in immunoblotting. FACS revealed that WHSC1 knockdown affected the cell cycle. We also confirmed that WHSC1 expression was suppressed by EZH2 knockdown or inhibition, indicating that EZH2 is upstream of WHSC1 in OCCC cells. Conclusions WHSC1 overexpression induced cell growth and its expression is, at least in part, regulated by EZH2. Further functional analysis will reveal whether WHSC1 is a promising therapeutic target for OCCC. Electronic supplementary material The online version of this article (10.1186/s12885-019-5638-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Machiko Kojima
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Kenbun Sone
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Katsutoshi Oda
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shinya Oki
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Asako Kukita
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Hidenori Machino
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Harunori Honjoh
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yoshiko Kawata
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Tomoko Kashiyama
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Kayo Asada
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Michihiro Tanikawa
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Mayuyo Mori-Uchino
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Tetsushi Tsuruga
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Kazunori Nagasaka
- Department of Obstetrics and Gynecology, Teikyo University School of Medicine, 2-11-1, Kaga, Itabashi-ku, Tokyo, 173 0003, Japan
| | - Yoko Matsumoto
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Osamu Wada-Hiraike
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yutaka Osuga
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Tomoyuki Fujii
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
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20
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Makii C, Oda K, Ikeda Y, Sone K, Hasegawa K, Uehara Y, Nishijima A, Asada K, Koso T, Fukuda T, Inaba K, Oki S, Machino H, Kojima M, Kashiyama T, Mori-Uchino M, Arimoto T, Wada-Hiraike O, Kawana K, Yano T, Fujiwara K, Aburatani H, Osuga Y, Fujii T. MDM2 is a potential therapeutic target and prognostic factor for ovarian clear cell carcinomas with wild type TP53. Oncotarget 2018; 7:75328-75338. [PMID: 27659536 PMCID: PMC5342744 DOI: 10.18632/oncotarget.12175] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 09/02/2016] [Indexed: 01/10/2023] Open
Abstract
MDM2, a ubiquitin ligase, suppresses wild type TP53 via proteasome-mediated degradation. We evaluated the prognostic and therapeutic value of MDM2 in ovarian clear cell carcinoma. MDM2 expression in ovarian cancer tissues was analyzed by microarray and real-time PCR, and its relationship with prognosis was evaluated by Kaplan-Meier method and log-rank test. The anti-tumor activities of MDM2 siRNA and the MDM2 inhibitor RG7112 were assessed by cell viability assay, western blotting, and flow cytometry. The anti-tumor effects of RG7112 in vivo were examined in a mouse xenograft model. MDM2 expression was significantly higher in clear cell carcinoma than in ovarian high-grade serous carcinoma (P = 0.0092) and normal tissues (P = 0.035). High MDM2 expression determined by microarray was significantly associated with poor progression-free survival and poor overall survival (P = 0.0002, and P = 0.0008, respectively). Notably, RG7112 significantly suppressed cell viability in clear cell carcinoma cell lines with wild type TP53. RG7112 also strongly induced apoptosis, increased TP53 phosphorylation, and stimulated expression of the proapoptotic protein PUMA. Similarly, siRNA knockdown of MDM2 induced apoptosis. Finally, RG7112 significantly reduced the tumor volume of xenografted RMG-I clear cell carcinoma cells (P = 0.033), and the density of microvessels (P = 0.011). Our results highlight the prognostic value of MDM2 expression in clear cell carcinoma. Thus, MDM2 inhibitors such as RG7112 may constitute a class of potential therapeutics.
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Affiliation(s)
- Chinami Makii
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Katsutoshi Oda
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Yuji Ikeda
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Kenbun Sone
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Kosei Hasegawa
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Yuriko Uehara
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan.,Division of Genome Science, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Akira Nishijima
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan.,Division of Genome Science, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Kayo Asada
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan.,Division of Genome Science, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Takahiro Koso
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan.,Division of Genome Science, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Tomohiko Fukuda
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Kanako Inaba
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Shinya Oki
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Hidenori Machino
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Machiko Kojima
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Tomoko Kashiyama
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Mayuyo Mori-Uchino
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Takahide Arimoto
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Osamu Wada-Hiraike
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Kei Kawana
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Tetsu Yano
- Department of Obstetrics and Gynecology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Keiichi Fujiwara
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Hiroyuki Aburatani
- Division of Genome Science, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Yutaka Osuga
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Tomoyuki Fujii
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Japan
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21
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Machino H, Iriyama T, Nakayama T, Komatsu A, Nagamatsu T, Osuga Y, Fujii T. A case of a surviving co-twin diagnosed with porencephaly and renal hypoplasia after a single intrauterine fetal death at 21 weeks of gestation in a monochorionic monoamniotic twin pregnancy. Oxf Med Case Reports 2017; 2017:omw096. [PMID: 28116109 PMCID: PMC5241712 DOI: 10.1093/omcr/omw096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 12/10/2016] [Accepted: 12/24/2016] [Indexed: 11/16/2022] Open
Abstract
Monochorionic monoamniotic (MM) twin pregnancy carries a high risk of intrauterine fetal death (IUFD). Single IUFD in an MM twin pregnancy prior to 22 weeks of gestation has been reported to be strongly correlated with double twin demise. To our knowledge, there are no case reports on the natural course of a surviving co-twin in an MM twin pregnancy resulting in live birth after a single IUFD prior to 22 weeks of gestation. Here, we report a case of a surviving co-twin, after a single IUFD at 21 weeks of gestation in a MM twin pregnancy, with an antenatal diagnosis of renal hypoplasia and severe neurological damage leading to porencephaly, and live birth at 36 weeks of gestation.
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Affiliation(s)
- Hidenori Machino
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
| | - Takayuki Iriyama
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
| | - Toshio Nakayama
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
| | - Atsushi Komatsu
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
| | - Takeshi Nagamatsu
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
| | - Yutaka Osuga
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
| | - Tomoyuki Fujii
- Department of Obstetrics and Gynecology , The University of Tokyo Hospital , Tokyo , Japan
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Fujishiro M, Handa Y, Machino H, Miyata K, Nakajima K, Iwahashi C. [Anterior ischemic optic neuropathy in a case of polyarteritis nodosa]. Ryumachi 1998; 38:831-5. [PMID: 10047722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
A 68-year-old male admitted to our hospital because of fever, body weight loss and multiple mononeuropathy. He developed visual loss in his right eye due to anterior ischemic optic neuropathy (AION). Surgical treatment of jejunal perforation was performed and histological examination of jejunal ulcer confirmed the diagnosis of polyarteritis nodosa (PN). Combination therapy of steroid (prednisolone 50 mg/day) and cyclophosphamide (50 mg/day) was started, which saved him from any other ischemic change including that of his left eye. As long as we know, AION occurring in PN is very rare, that is, only 4 cases have been reported in Japan.
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Affiliation(s)
- M Fujishiro
- Department of Internal Medicine, Omiya Red Cross Hospital, Saitama
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Machino H, Kobayashi H, Hayashi K, Tawara Y, Ito M, Kishimoto S. Nitric oxide is involved in the inhibitory action of cholecystokinin octapeptide (CCK-OP) on proximal colonic motility. Regul Pept 1997; 69:47-52. [PMID: 9163582 DOI: 10.1016/s0167-0115(97)02128-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
To determine whether nitric oxide (NO) is a possible mediator in the inhibitory action of CCK-octapeptide (CCK-OP) on circular muscle contractions of the rat proximal colon, contractile activities of the circular muscle were recorded in the proximal colon of unrestrained conscious rats in the fasting state using a miniature strain gauge force transducer and an implantable telemetry system. Regular and rhythmic phasic contractions were observed during the fasted condition, similar to the myoelectric migrating complex seen in intestinal contractions of the fasting dog. These phasic contractions were almost completely inhibited after intraperitoneal (i.p.) administration of CCK-OP at a dose of 15 microg/kg body weight. N(omega)-nitro-arginine, methyl ester (L-NAME), at doses of 20 and 200 mg/kg i.p. administered prior to i.p. injection of CCK-OP, prevented the inhibitory action on the fasting phasic contractions. The degree of prevention was dose-dependent. 100 mg/kg body weight i.p. injection of L-arginine inhibited the circular muscle contractions. The same dose of D-arginine had no action on contractions of the circular muscle of the proximal colon in the fasted rat. From these data, we conclude that NO is one possible mediator in the inhibitory mechanism of CCK-OP on smooth muscle motor activity of the rat proximal colon in vivo.
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Affiliation(s)
- H Machino
- First Department of Internal Medicine, Hiroshima University School of Medicine, Minami-ku, Japan
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Tokumo H, Ishida K, Komatsu H, Machino H, Morinaka K. External biliary jejunal drainage through a percutaneous endoscopic gastrostomy for tube-fed patients with obstructive jaundice. J Clin Gastroenterol 1997; 24:103-5. [PMID: 9077728 DOI: 10.1097/00004836-199703000-00013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
We describe a new procedure, which can help patients with obstructive jaundice improve their quality of life (QOL). Although percutaneous transhepatic biliary drainage (PTBD) can relieve jaundice, the procedure has some disadvantages. Percutaneous endoscopic gastrostomy (PEG) is a useful method for providing nutritional support to patients unable to swallow. We have combined these two techniques. We used the combination ofa 20-F catheter and a 9-F jejunal catheter for PEG. The PTBD catheter and the 9-F jejunal catheter are connected outside the patient's body. Externally drained bile from the PTBD catheter can flow back into the jejunum, and the opening between the 20-F catheter and the 9-F jejunal catheter is used for tube feeding. This procedure was adopted in a patient. Since the procedure, the patient's nutritional status and daily living activities have improved. We conclude that the procedure is useful for tube-fed patients with obstructive jaundice.
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Affiliation(s)
- H Tokumo
- Third Department of Internal Medicine, Hiroshima General Hospital, Hatsukaichi-city, Japan
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25
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Affiliation(s)
- S Kishimoto
- Department of Medicine, Hiroshima University, Japan
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Kishimoto S, Fujimura J, Machino H, Shimamoto T, Kobayashi H, Shimizu S, Haruma K, Kajiyama G, Sakurai K, Yamasaki K. Therapeutic effects of oral rebamipide and in combination with cimetidine on experimental gastritis in rats. Res Commun Chem Pathol Pharmacol 1992; 78:259-77. [PMID: 1335596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The present study evaluated the therapeutic effects of rebamipide alone and in combination with cimetidine on experimental gastritis established by the administration of 5 mM sodium taurocholate (TCA) for 6 months in rats. Morphological and biochemical analyses were performed to determine the effects of rebamipide, administered alone or in combination with cimetidine, on chronic, atrophic and erosive gastritis. Rebamipide and cimetidine were administered to rats as a dietary admixture for 4 weeks after withdrawal of TCA. Rebamipide dose-dependently reduced the total length of the erosion, normalized the mucosal thickness and increased the number of parietal and total cells, and tended to reduce interstitial infiltration of inflammatory cells and proliferation of collagenous fibers. Moreover, histochemical and biochemical studies also showed rebamipide to be effective. Rebamipide increased the PAS-positive mucus and normalized the reduced gastric mucosal SOD activity. The therapeutic effect of rebamipide on the experimental gastritis was enhanced by the combined use of cimetidine. These results suggest that rebamipide has a therapeutic effect on chronic atrophic erosive gastritis induced by TCA, and that the mechanism of the therapeutic effect is partially due to the increase in PAS-positive mucus and gastric mucosal SOD activity. Furthermore, the enhanced effect of the combination therapy of rebamipide with cimetidine was considered to be due to the actions of both cimetidine and rebamipide.
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Affiliation(s)
- S Kishimoto
- Department of Medicine, Hiroshima University School of Medicine, Japan
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27
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Fujimura J, Haruma K, Hata J, Yamanaka H, Machino H, Yoshihara M, Sumii K, Kishimoto S, Kajiyama G. [New approach to evaluate duodenogastric reflux by color Doppler]. Nihon Shokakibyo Gakkai Zasshi 1992; 89:1472. [PMID: 1513050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- J Fujimura
- First Department of Internal Medicine, Hiroshima University School of Medicine
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Machino H, Shiraishi S, Miki Y. Comparisons of in vivo and in vitro photosensitivities and DNA repair in fibroblast and keratinocyte cells. Arch Dermatol Res 1986; 279:125-9. [PMID: 3566336 DOI: 10.1007/bf00417533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Minimal erythema dose of ultraviolet light correlated significantly with the UV-sensitivity of fibroblast cells from 5 patients with xeroderma pigmentosum, 13 patients with keratinocytic neoplasms of the skin, and 21 control subjects, but not with that of cells from 6 patients with photosensitive dermatitis. In unscheduled DNA synthesis after UV irradiation, the number of grains per nucleus was much less in keratinocytes than in fibroblasts, but the relative dose-response relationship was similar. This indicates that keratinocytes can also be used in vitro UV-sensitivity studies.
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Machino H, Miki Y, Teramoto T, Shiraishi S, Sasaki MS. Cytogenetic studies in a patient with porokeratosis of Mibelli, multiple cancers and a forme fruste of Werner's syndrome. Br J Dermatol 1984; 111:579-86. [PMID: 6498091 DOI: 10.1111/j.1365-2133.1984.tb06628.x] [Citation(s) in RCA: 21] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
A 49-year-old man with extensive porokeratosis of Mibelli (PM) developed a squamous cell carcinoma and several carcinomas-in-situ within the lesional skin. The patient also had diabetes mellitus and a short stature with a prematurely aged appearance. The patient's father and two siblings also had PM. The patient died from metastatic squamous cell carcinoma, and at autopsy an adenocarcinoma of the descending colon was also found. Fibroblasts cultured from both the PM-affected and unaffected skin showed chromosomal abnormalities and a decreased lifespan. Cellular sensitivity to ultraviolet rays measured by unscheduled DNA synthesis and colony-forming ability were within normal limits. An association with a forme fruste of Werner's syndrome was suspected.
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Abstract
A Japanese girl, 2 years, 8 months of age, with palmoplantar keratosis and dendritic corneal opacities, showed increased tyrosine levels in the plasma, urine, and cerebrospinal fluid. The mental and physical growth was not retarded. The hepatorenal functions were within normal limits. Electron microscopically, the epidermal keratinocytes showed increased tonofibrils and no structures suggestive of tyrosine crystals. Cytosol and mitochondrial tyrosine aminotransferase (TAT) activities of the liver were greatly decreased, while p-hydroxyphenyl pyruvate oxidase (p-HPPO) activity was not decreased. The plasma tyrosine levels were controlled for 3 years with low phenylalanine-tyrosine diet.
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Abstract
Thirty-one patients with Bowen's disease were studied in a restricted district of Namikata and its neighborhood of Ehime, Japan. Nineteen were alive and 12 were dead; the youngest living patient was 52 years of age. Invasive skin cancers were found in 10 patients and internal malignancies in 10, including 7 patients with pulmonary cancers. Palmoplantar keratosis was present in 25 patients and raindrop-type pigment anomalies in 15. Neutron activation analysis of hair showed only slightly higher arsenic values in patients with Bowen's disease than those in normal controls, though the differences were statistically significant at p less than 0.05. A possible arsenic exposure 43 years previously was considered responsible for the occurrence of neoplasms, though the arsenic route and amount were not determined. Bowen's disease started within 10 years, invasive skin cancers after 20 years, and pulmonary cancers after 30 years following the suspected arsenic exposure.
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Okada T, Iwao E, Kawatsu T, Yamada T, Matsuda K, Machino H, Miki Y. [A case of disseminated, cutaneous deep candidiasis associated with Hodgkin's disease (author's transl)]. Nihon Hifuka Gakkai Zasshi 1979; 89:1031-9. [PMID: 317116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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