Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Acute pancreatitis (AP) is a leading cause of gastrointestinal-related hospital admissions with significant morbidity and mortality. Although the underlying pathophysiology of AP is rather complex, which greatly limits the treatment options, more and more studies have revealed that infiltrating immune cells play a critical role in the pathogenesis of AP and determine disease severity. Thus, immunomodulatory therapy targeting immune cells and related inflammatory mediators is expected to be a novel treatment modality for AP which may improve the prognosis of patients. Cells of the innate immune system, including macrophages, neutrophils, dendritic cells, and mast cells, represent the majority of infiltrating cells during AP. In this review, an overview of different populations of innate immune cells and their role during AP will be discussed, with a special focus on neutrophils and macrophages.

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

Acute pancreatitis (AP) is a disease characterized by local and systemic inflammation with an increasing incidence worldwide. Receptor-interacting serine/threonine protein kinase 3 (RIPK3), mixed-lineage kinase domain-like protein (MLKL), and innate immune cell macrophages have been reported to be involved in the pathogenesis of AP. However, the mechanisms by which RIPK3 and MLKL regulate pancreatic injury, as well as the interactions between injured pancreatic acinar cells and infiltrating macrophages in AP, remain poorly defined. In the present study, experimental pancreatitis was induced in C57BL/6J, Ripk3^-/- and Mlkl^-/- mice by cerulein plus lipopolysaccharide in vivo, and primary pancreatic acinar cells were also isolated to uncover cellular mechanisms during cerulein stimulation in vitro. The results showed that MLKL and its phosphorylated protein p-MLKL were upregulated in the pancreas of the mouse AP model and cerulein-treated pancreatic acinar cells, independent of its canonical upstream molecule Ripk3, and appeared to function in a cell death-independent manner. Knockout of Mlkl attenuated AP in mice by reducing the polarization of pancreatic macrophages toward the M1 phenotype, and this protective effect was partly achieved by reducing the secretion of CXCL10 from pancreatic acinar cells, whereas knockout of Ripk3 did not. In vitro neutralization of CXCL10 impaired the pro-M1 ability of the conditioned medium of cerulein-treated pancreatic acinar cells, whereas in vivo neutralization of CXCL10 reduced the polarization of pancreatic macrophages toward M1 and the severity of AP in mice. These findings suggested that targeting the MLKL-CXCL10-macrophage axis might be a promising strategy for the treatment of AP.