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Polycomb repressive complex 2 (PRC2) acts as an epigenetic repressor by depositing repressive H3K27me3 marks, but how it is regulated and directed to specific genes remains unknown. PRC2 is now found to bind at low levels to many gene promoters, including active ones devoid of H3K27me3, and the EZH2 catalytic subunit binds directly to nascent transcripts. EZH2 is the catalytic subunit of PRC2, a central epigenetic repressor essential for development processes in vivo and for the differentiation of embryonic stem cells (ESCs) in vitro . The biochemical function of PRC2 in depositing repressive H3K27me3 marks is well understood, but how it is regulated and directed to specific genes before and during differentiation remains unknown. Here, we report that PRC2 binds at low levels to a majority of promoters in mouse ESCs, including many that are active and devoid of H3K27me3. Using in vivo RNA-protein cross-linking, we show that EZH2 directly binds the 5′ region of nascent RNAs transcribed from a subset of these promoters and that these binding events correlate with decreased H3K27me3. Our findings suggest a molecular mechanism by which PRC2 senses the transcriptional state of the cell and translates it into epigenetic information.

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

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.

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.

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.

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of “explainable AI.”

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. Ovarian cancer: The significance of epigenetic changes Analyzing epigenetic effects on gene activity, which are due to changes other than mutations in the primary DNA sequence of genes, reveals new insights into the most lethal form of ovarian cancers. Epigenetic effects are due to chemical modifications of DNA, RNA and associated proteins that can influence the activity of genes and the proteins they code for. Researchers in Japan led by Hidenori Machino and Ryuji Hamamoto at the RIKEN Center for Advanced Intelligence Project, Tokyo, analyzed epigenetic alterations in a series of human cells modelling stages of high-grade serous ovarian carcinoma (HGSOC). They identified significant changes in the activity of several specific genes, RNAs and proteins. The insights prompted the researchers to undertake cell-based studies that suggested drugs inhibiting key cell regulatory enzymes known as MEK proteins could be used as new treatments.

Mismatch repair (MMR)-deficient cancers are characterized by microsatellite instability (MSI) and hypermutation. However, it remains unclear how MSI and hypermutation arise and contribute to cancer development. Here, we show that MSI and hypermutation are triggered by replication stress in an MMR-deficient background, enabling clonal expansion of cells harboring ARF/p53-module mutations and cells that are resistant to the anti-cancer drug camptothecin. While replication stress-associated DNA double-strand breaks (DSBs) caused chromosomal instability (CIN) in an MMR-proficient background, they induced MSI with concomitant suppression of CIN via a PARP-mediated repair pathway in an MMR-deficient background. This was associated with the induction of mutations, including cancer-driver mutations in the ARF/p53 module, via chromosomal deletions and base substitutions. Immortalization of MMR-deficient mouse embryonic fibroblasts (MEFs) in association with ARF/p53-module mutations was ~60-fold more efficient than that of wild-type MEFs. Thus, replication stress-triggered MSI and hypermutation efficiently lead to clonal expansion of cells with abrogated defense systems. Mismatch repair (MMR)-deficient cancers are characterized by microsatellite instability (MSI) and hypermutation. Here authors reveal a mechanism by which replication stress induces MSI and associated induction of mutations in vitro.

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.

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI‐equipped software as a medical device (AI‐SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data‐driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI‐based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology. This review article investigates the current state of artificial intelligence (AI) in the field of cancer research. After first providing a historical perspective on AI and its introduction into medicine, this article described recent and current advances in AI‐based approaches and devices for medical imaging and omics analysis, particularly in the field of oncology, highlighting both approved systems and systems under development. Finally, this article critically reviewed the current state of AI research in medicine and described the main limitations and potential approaches to overcome them.