Research progress and applications of epigenetic biomarkers in cancer.

Epigenetic changes are heritable changes in gene expression without changes in the nucleotide sequence of genes. Epigenetic changes play an important role in the development of cancer and in the process of malignancy metastasis. Previous studies have shown that abnormal epigenetic changes can be used as biomarkers for disease status and disease prediction. The reversibility and controllability of epigenetic modification changes also provide new strategies for early disease prevention and treatment. In addition, corresponding drug development has also reached the clinical stage. In this paper, we will discuss the recent progress and application status of tumor epigenetic biomarkers from three perspectives: DNA methylation, non-coding RNA, and histone modification, in order to provide new opportunities for additional tumor research and applications.

Advances in Drug Therapy for Metastatic Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a notably poor prognosis. A large number of patients with PDAC develop metastases before they are diagnosed with metastatic pancreatic cancer (mPDAC). For mPDAC, FOLFIRINOX or gemcitabine plus nab-paclitaxel are the current first-line treatments. It is important to note, however, that many patients will fail chemotherapy because of drug resistance. ​Heterogeneous tumors and complex tumor microenvironments are key factors. As a result, clinical researchers are exploring a variety of alternative treatment modalities. Current understanding of the molecular signature and immune landscape of PDAC has motivated the emergence of different targeted and immune-based therapeutic approaches, some of which have shown promising results. The purpose of this review is to discuss the new targets and new drugs for mPDAC in terms of specific pathogenic factors such as metabolic vulnerability, DNA damage repair system, tumor microenvironment and immune system, in order to identify potential vulnerabilities in mPDAC patients and hopefully improve the prognosis of mPDAC patients.

Utilization of 3D printing technology in hepatopancreatobiliary surgery. / 3D打印技术在肝胆胰外科中的应用进展.

The technology of three-dimensional (3D) printing emerged in the late 1970s and has since undergone considerable development to find numerous applications in mechanical engineering, industrial design, and biomedicine. In biomedical science, several studies have initially found that 3D printing technology can play an important role in the treatment of diseases in hepatopancreatobiliary surgery. For example, 3D printing technology has been applied to create detailed anatomical models of disease organs for preoperative personalized surgical strategies, surgical simulation, intraoperative navigation, medical training, and patient education. Moreover, cancer models have been created using 3D printing technology for the research and selection of chemotherapy drugs. With the aim to clarify the development and application of 3D printing technology in hepatopancreatobiliary surgery, we introduce seven common types of 3D printing technology and review the status of research and application of 3D printing technology in the field of hepatopancreatobiliary surgery.

Liver fibrosis and MAFLD: the exploration of multi-drug combination therapy strategies.

In recent years, the prevalence of metabolic-associated fatty liver disease (MAFLD) has reached pandemic proportions as a leading cause of liver fibrosis worldwide. However, the stage of liver fibrosis is associated with an increased risk of severe liver-related and cardiovascular events and is the strongest predictor of mortality in MAFLD patients. More and more people believe that MAFLD is a multifactorial disease with multiple pathways are involved in promoting the progression of liver fibrosis. Numerous drug targets and drugs have been explored for various anti-fibrosis pathways. The treatment of single medicines is brutal to obtain satisfactory results, so the strategies of multi-drug combination therapies have attracted increasing attention. In this review, we discuss the mechanism of MAFLD-related liver fibrosis and its regression, summarize the current intervention and treatment methods for this disease, and focus on the analysis of drug combination strategies for MAFLD and its subsequent liver fibrosis in recent years to explore safer and more effective multi-drug combination therapy strategies.

Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized.

Patients diagnosed with stable coronary artery disease (CAD) are at continued risk of experiencing acute myocardial infarction (AMI). This study aims to unravel the pivotal biomarkers and dynamic immune cell changes, from an immunological, predictive, and personalized viewpoint, by implementing a machine-learning approach and a composite bioinformatics strategy. Peripheral blood mRNA data from different datasets were analyzed, and CIBERSORT was used for deconvoluting human immune cell subtype expression matrices. Weighted gene co-expression network analysis (WGCNA) in single-cell and bulk transcriptome levels was conducted to explore possible biomarkers for AMI, with a particular emphasis on examining monocytes and their involvement in cell-cell communication. Unsupervised cluster analysis was performed to categorize AMI patients into different subtypes, and machine learning methods were employed to construct a comprehensive diagnostic model to predict the occurrence of early AMI. Finally, RT-qPCR on peripheral blood samples collected from patients validated the clinical utility of the machine learning-based mRNA signature and hub biomarkers. The study identified potential biomarkers for early AMI, including CLEC2D, TCN2, and CCR1, and found that monocytes may play a vital role in AMI samples. Differential analysis revealed that CCR1 and TCN2 exhibited elevated expression levels in early AMI compared to stable CAD. Machine learning methods showed that the glmBoost+Enet [alpha=0.9] model achieved high predictive accuracy in the training set, external validation sets, and clinical samples in our hospital. The study provided comprehensive insights into potential biomarkers and immune cell populations involved in the pathogenesis of early AMI. The identified biomarkers and the constructed comprehensive diagnostic model hold great promise for predicting the occurrence of early AMI and can serve as auxiliary diagnostic or predictive biomarkers.