BET inhibitor JQ1 induces apoptosis of ovarian and endometrial endometrioid carcinoma cells by downregulating .

Although ovarian endometrioid carcinoma (OEC), frequently associated with endometrial endometrioid carcinoma (EEC), is often diagnosed at an early stage, the prognosis remains poor. The development of new, effective drugs to target these cancers is highly desirable. The bromodomain and extra-terminal domain (BET) family proteins serve a role in regulating transcription by recognizing histone acetylation, which is implicated in several types of cancer.

The efficacy and safety of lenvatinib plus pembrolizumab in vulnerable patients with metastatic or recurrent endometrial cancer: a single institution experience.

Background: Effective management with second-line therapy with the lenvatinib + pembrolizumab regimen for patients with advanced endometrial cancer is necessary.

Methods: This retrospective study enrolled patients with endometrial cancer treated with the lenvatinib + pembrolizumab regimen. We evaluated progression-free survival (PFS), overall survival (OS), safety for patients non-eligible for the KEYNOTE775 trial, aged ≥65 years, or with ECOG performance status 1-2.

Inhibition of protein arginine methyltransferase 6 activates interferon signaling and induces the apoptosis of endometrial cancer cells via histone modification.

Histone modification, a major epigenetic mechanism regulating gene expression through chromatin remodeling, introduces dynamic changes in chromatin architecture. Protein arginine methyltransferase 6 (PRMT6) is overexpressed in various types of cancer, including prostate, lung and endometrial cancer (EC). Epigenome regulates the expression of endogenous retrovirus (ERV), which activates interferon signaling related to cancer.

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources.

Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas.

Identification of target cells of human papillomavirus 18 using squamocolumnar junction organoids.

Human papillomavirus 18 (HPV18) is a highly malignant HPV genotype among high-risk HPVs, characterized by the difficulty of detecting it in precancerous lesions and its high prevalence in adenocarcinomas. The cellular targets and molecular mechanisms underlying its infection remain unclear. In this study, we aimed to identify the cells targeted by HPV18 and elucidate the molecular mechanisms underlying HPV18 replication.

Evolution of a surgical system using deep learning in minimally invasive surgery (Review).

Recently, artificial intelligence (AI) has been applied in various fields due to the development of new learning methods, such as deep learning, and the marked progress in computational processing speed. AI is also being applied in the medical field for medical image recognition and omics analysis of genomes and other data. Recently, AI applications for videos of minimally invasive surgeries have also advanced, and studies on such applications are increasing.

Application of organoid culture from HPV18-positive small cell carcinoma of the uterine cervix for precision medicine.

Background: Small cell carcinoma of the uterine cervix (SCCC) is a rare and highly malignant human papillomavirus (HPV)-associated cancer in which human genes related to the integration site can serve as a target for precision medicine. The aim of our study was to establish a workflow for precision medicine of HPV-associated cancer using patient-derived organoid.

Methods: Organoid was established from the biopsy of a patient diagnosed with HPV18-positive SCCC.

Cells with stem-like properties are associated with the development of HPV18-positive cervical cancer.

The cellular origins of cervical cancer and the histological differentiation of human papillomavirus (HPV)-infected cells remain unexplained. To gain new insights into the carcinogenesis and histological differentiation of HPV-associated cervical cancer, we focused on cervical cancer with mixed histological types. We conducted genomic and transcriptomic analyses of cervical cancers with mixed histological types.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models.

Histone arginine methyltransferase CARM1 selective inhibitor TP-064 induces apoptosis in endometrial cancer.

Histone modification is the key epigenetic mechanism that regulates gene expression. Coactivator-associated arginine methyltransferase 1 (CARM1) is an arginine methyltransferase that catalyzes dimethylation of histone H3 (H3R17) at arginine 17. Lately, it has been suggested that CARM1 is associated with human carcinogenesis, and the CARM1-selective inhibitor, TP-064, has been shown to be a potential therapeutic agent for multiple myeloma.

Application of artificial intelligence in gynecologic malignancies: A review.

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 introduce and review these studies.

Targeting Epigenetic Regulators for Endometrial Cancer Therapy: Its Molecular Biology and Potential Clinical Applications.

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.