Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC).

Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese sentences. Nevertheless, the traditional text data augmentation ignores the semantics between words in sentences, besides, it has limitations in alleviating the problem of the diversity of augmented sentences. In this paper, a novel medical data augmentation (MDA) is proposed for NLP tasks, which combines the medical knowledge graph with text data augmentation to generate augmented data. Experiments on the named entity recognition task and relational classification task demonstrate that the MDA can significantly enhance the efficiency of the deep learning models compared to cases without augmentation.

Background: Mesenchymal cells, including hepatic stellate cells (HSCs), fibroblasts (FBs), myofibroblasts (MFBs), and vascular smooth muscle cells (VSMCs), are the main cells that affect liver fibrosis and play crucial roles in maintaining tissue homeostasis. The dynamic evolution of mesenchymal cells is very important but remains to be explored for researching the reversible mechanism of hepatic fibrosis and its evolution mechanism of hepatic fibrosis to cirrhosis. Methods: Here, we analysed the transcriptomes of more than 50,000 human single cells from three cirrhotic and three healthy liver tissue samples and the mouse hepatic mesenchymal cells of two healthy and two fibrotic livers to reconstruct the evolutionary trajectory of hepatic mesenchymal cells from a healthy to a cirrhotic state, and a subsequent integrative analysis of bulk RNA sequencing (RNA-seq) data of HSCs from quiescent to active (using transforming growth factor β1 (TGF-β1) to stimulate LX-2) to inactive states. Results: We identified core genes and transcription factors (TFs) involved in mesenchymal cell differentiation. In healthy human and mouse livers, the expression of NR1H4 and members of the ZEB families (ZEB1 and ZEB2) changed significantly with the differentiation of FB into HSC and VSMC. In cirrhotic human livers, VSMCs transformed into HSCs with downregulation of MYH11, ACTA2, and JUNB and upregulation of PDGFRB, RGS5, IGFBP5, CD36, A2M, SOX5, and MEF2C. Following HSCs differentiation into MFBs with the upregulation of COL1A1, TIMP1, and NR1H4, a small number of MFBs reverted to inactivated HSCs (iHSCs). The differentiation trajectory of mouse hepatic mesenchymal cells was similar to that in humans; however, the evolution trajectory and proportion of cell subpopulations that reverted from MFBs to iHSCs suggest that the mouse model may not accurately reflect disease progression and outcome in humans. Conclusions: Our analysis elucidates primary genes and TFs involved in mesenchymal cell differentiation during liver fibrosis using scRNA-seq data, and demonstrated the core genes and TFs in process of HSC activation to MFB and MFB reversal to iHSC using bulk RNA-seq data of human fibrosis induced by TGF-β1. Furthermore, our findings suggest promising targets for the treatment of liver fibrosis and provide valuable insights into the molecular mechanisms underlying its onset and progression.