The University of Tokyo · Medicine
Professor Kenbun Sone's research lab focuses on the molecular mechanisms of epigenetic regulation in gynecological cancers, particularly endometrial and ovarian carcinomas. The lab investigates the roles of histone-modifying enzymes—such as SUV39H2, EZH2, and SETD8—in DNA damage response, tumorigenesis, and cancer progression, with an emphasis on lysine methylation of histones and its functional consequences. Additionally, the lab integrates artificial intelligence and deep learning techniques to develop automated diagnostic systems for gynecological malignancies using medical imaging and omics data.
Figures are computed from collected data and may differ slightly.
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
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