Emerging evidence indicate that cancer-associated fibroblasts (CAFs) affect tumor progression by reshaping the tumor microenvironment. Neutrophils are prominent components of solid tumors and important in cancer progression. Whether the phenotype and function of neutrophils in hepatocellular carcinoma (HCC) are influenced by CAFs is not well understood. Herein, we investigated the effect of HCC-derived CAFs (HCC-CAFs) on the neutrophils and explored the biological role of this effect. We found that HCC-CAFs induced chemotaxis of neutrophils and protected them from spontaneous apoptosis. Neutrophils were activated by the conditioned medium from HCC-CAFs with increased expression of CD66b, PDL1, IL8, TNFa, and CCL2, and with decreased expression of CD62L. HCC-CAF-primed neutrophils impaired T-cell function through the PD1/PDL1 signaling pathway. We revealed that HCC-CAFs induced the activation of STAT3 pathways in neutrophils, which are essential for the survival and function of activated neutrophils. In addition, we demonstrated that HCC-CAF-derived IL6 was responsible for the STAT3 activation of neutrophils. Collectively, our results suggest that HCC-CAFs regulate the survival, activation, and function of neutrophils within HCC through an IL6–STAT3–PDL1 signaling cascade, which presents a novel mechanism for the role of CAFs in remodeling the cancer niche and provides a potential target for HCC therapy.

Background/Aims: Systemic inflammatory response (SIR) is widely considered as a preoperative risk factor for hepatocellular carcinoma (HCC) outcomes. The neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR), two of the prognostic indices, have been investigated in post-therapeutic recurrence and survival of HCC. Here, we quantify the prognostic value of these two biomarkers and evaluate their consistency in different HCC therapies. Methods: A systematic review of electronic database of the Web of Science, Embase, PubMed and the Cochrane Library was conducted to search for associations between the NLR and PLR in the blood and clinical outcomes of HCC. Overall survival (OS) and recurrence-free survival (RFS) were the primary outcomes, and hazard ratios (HRs) and 95% confidence intervals (95% CIs) were explored as effect measures. Subgroup analyses were performed to explore the heterogeneity of different therapies. Results: A total of 24 articles comprising 6318 patients were included in the meta-analysis. Overall, the pooled outcomes revealed that a high NLR before treatment predicted a poor OS (HR: 1.54, 95% CI: 1.34 to 1.76, p<0.001) and poor RFS (HR: 1.45, 95% CI: 1.16 to 1.82, p=0.001). Moreover, an increased PLR predicted a poor OS (HR: 1.63, 95% CI: 1.34 to 1.98, p<0.001) and earlier HCC recurrence (HR: 1.52, 95% CI: 1.21 to 1.91, p<0.001). In addition, both the NLR and PLR were identified as independent risk factors for predicting OS and RFS in HCC patients in a subgroup analysis of different treatment types, including curative or palliative therapy; however, these results were not found in the sorafenib subgroup due to limited clinical research. Conclusion: An increased NLR or PLR indicated poor outcomes for patients with HCC. The NLR and PLR may be considered as reliable and inexpensive biomarkers for making clinical decisions regarding HCC treatment.

Mesenchymal stem cells (MSCs) have been reported to exert therapeutic effects on immunoregulation, tissue repair, and regeneration from the bench to the bedside. Increasing evidence demonstrates that extracellular vesicles (EVs) derived from MSCs could contribute to these effects and are considered as a potential replacement for stem cell‐based therapies. However, the efficacy and underlying mechanisms of EV‐based treatment in hepatic ischemia‐reperfusion injury (IRI) remain unclear. Here, we demonstrated that human umbilical cord MSC‐EVs (huc‐MSC‐EVs) could protect against IRI‐induced hepatic apoptosis by reducing the infiltration of neutrophils and alleviating oxidative stress in hepatic tissue in vivo. Meanwhile, huc‐MSC‐EVs reduced the respiratory burst of neutrophils and prevented hepatocytes from oxidative stress‐induced cell death in vitro. Interestingly, we found that the mitochondria‐located antioxidant enzyme, manganese superoxide dismutase (MnSOD), was encapsulated in huc‐MSC‐EVs and reduced oxidative stress in the hepatic IRI model. Knockdown of MnSOD in huc‐MSCs decreased the level of MnSOD in huc‐MSC‐EVs and attenuated the antiapoptotic and antioxidant capacities of huc‐MSC‐EVs, which could be partially rescued by MnSOD mimetic manganese (III) 5,10,15,20‐tetrakis (4‐benzoic acid) porphyrin (MnTBAP). In summary, these findings provide new clues to reveal the therapeutic effects of huc‐MSC‐EVs on hepatic IRI and evaluate their preclinical application.—Yao, J., Zheng, J., Cai, J., Zeng, K., Zhou, C., Zhang, J., Li, S., Li, H., Chen, L., He, L., Chen, H., Fu, H., Zhang, Q., Chen, G., Yang, Y., Zhang, Y. Extracellular vesicles derived from human umbilical cord mesenchymal stem cells alleviate rat hepatic ischemia‐reperfusion injury by suppressing oxidative stress and neutrophil inflammatory response. FASEB J. 33, 1695–1710 (2019). http://www.fasebj.org

Purpose Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively. Methods In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models. Results Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923–0.973) and 0.980 (95% CI 0.959–0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797–0.947) and 0.906 (95% CI 0.821–0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027). Conclusion The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.