The University of Tokyo · 의학
켄불 소네 교수의 연구실은 종양 생물학과 에피제네틱스의 교차 분야에서 활동하며, 특히 히스톤 변형과 에피제네틱 조절 단백질이 암 발생 및 진행에 미치는 영향을 중심으로 연구하고 있습니다. 주로 염색체 손상 복구와 관련된 히스톤 H2AX의 인산화 및 메틸화 조절 메커니즘을 규명하고 있으며, 특히 SUV39H2, EZH2, SETD8 등의 히스톤 메틸트랜스퍼라제가 난소암, 자궁내막암 등에서 어떻게 기능하는지 분석하고 있습니다. 또한 인공지능 기반 의료 영상 진단 기술을 활용한 자궁내막암 조기 진단 시스템 개발도 함께 진행하고 있어, 기초 생물학과 임상 응용을 융합한 연구를 펼치고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The presence of phosphorylated histone H2AX (γ-H2AX) is associated with the local activation of DNA-damage repair pathways. Although γ-H2AX deregulation in cancer has previously been reported, the molecular mechanism involved and its relationship with other histone modifications remain largely unknown. Here we find that the histone methyltransferase SUV39H2 methylates histone H2AX on lysine 134. When H2AX was mutated to abolish K134 methylation, the level of γ-H2AX became significantly reduced.
Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to detect the regions affected by endometrial cancer automatically from hysteroscopic images. In this study, 177 patients (60 with normal endometrium, 21 with uterine myoma, 60 with endometrial polyp, 15 wit
The histone methyltransferase EZH2, a key epigenetic modifier, is known to be associated with human tumorigenesis. However, the physiological importance of EZH2 and its clinical relevance in endometrial cancer remain unclear. Hence, in the present study, we investigated the expression and function of EZH2 in endometrial cancer. In a quantitative real-time PCR analysis of 11 endometrial cancer cell lines and 52 clinical endometrial cancer specimens, EZH2 was significantly overexpressed in cancer
With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduc
Endometrial cancer is one of the most frequently diagnosed gynecological malignancies worldwide. However, its prognosis in advanced stages is poor, and there are only few available treatment options when it recurs. Epigenetic changes in gene function, such as DNA methylation, histone modification, and non-coding RNA, have been studied for the last two decades. Epigenetic dysregulation is often reported in the development and progression of various cancers. Recently, epigenetic changes in endomet
The histone methyltransferase SETD8, which methylates the lysine 20 of histone H4 (H4K20), is reportedly involved in human carcinogenesis along with nonhistone proteins such as p53. However, its expression profiles and functions in the context of high-grade serous ovarian carcinoma (HGSOC) are still unknown. The purpose of this study was to investigate the role of SETD8 in HGSOC. We performed quantitative real-time PCR and immunohistochemistry to detect the expression of SETD8 in HGSOC samples a
The histone methyltransferase SET domain-containing protein 8 (SETD8), which methylates histone H4 lysine 20 (H4K20) and non-histone proteins such as p53, plays key roles in human carcinogenesis. Our aim was to determine the involvement of SETD8 in endometrial cancer and its therapeutic potential and identify the downstream genes regulated by SETD8 via H4K20 methylation and the p53 signaling pathway. We examined the expression profile of SETD8 and evaluated whether SETD8 plays a critical role in