The Role of Neutrophils as a Driver in Hepatic Ischemia-Reperfusion Injury and Cancer Growth.

The innate immune system plays an essential role in the response to sterile inflammation and its association with liver ischemia and reperfusion injury (IRI). Liver IRI often manifests during times of surgical stress such as cancer surgery or liver transplantation. Following the initiation of liver IRI, stressed hepatocytes release damage-associated molecular patterns (DAMPs) which promote the infiltration of innate immune cells which then initiate an inflammatory cascade and cytokine storm. Upon reperfusion, neutrophils are among the first cells that infiltrate the liver. Within the liver, neutrophils play an important role in fueling tissue damage and tumor progression by promoting the metastatic cascade through the formation of Neutrophil Extracellular Traps (NETs). NETs are composed of web-like DNA structures containing proteins that are released in response to inflammatory stimuli in the environment. Additionally, NETs can aid in mediating liver IRI, promoting tumor progression, and most recently, in mediating early graft rejection in liver transplantation. In this review we aim to summarize the current knowledge of innate immune cells, with a focus on neutrophils, and their role in mediating IRI in mouse and human diseases, including cancer and transplantation. Moreover, we will investigate the interaction of Neutrophils with varying subtypes of other cells. Furthermore, we will discuss the role and different treatment modalities in targeting Neutrophils and NETs to prevent IRI.

The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis.

Introduction: Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods: The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system. Results: 363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan-Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38-4.80), 2 points: 3.91 (2.11-7.24), and 3 points: 5.09 (2.70-9.58), p < 0.01). Conclusion: Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.