Competing interests D.D.D.C., S.Y.S. and A.C. are listed as inventors on patents filed that are related to this method. D.D.D.C. received research funding from Pfizer and Nektar therapeutics not related to this project.

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment. K E Y W O R D S bioinformatical analysis, differentially expressed genes (DEGs), hepatocellular carcinoma (HCC), pathways J Cell Biochem. 2019;120:10069-10081.wileyonlinelibrary.com/journal/jcb

Ex vivo liver resection combined with autotransplantation is a recently introduced approach to cure end‐stage hepatic alveolar echinococcosis (HAE), which is considered unresectable by conventional radical resection due to echinococcal dissemination into the crucial intrahepatic conduits and adjacent structures. This article aims discuss the manipulation details and propose reasonable indications for this promising technique. All patients successfully underwent liver autotransplantation with no intraoperative mortality. The median weight of the autografts was 636 g (360‐1300 g), the median operation time was 12.5 hours (9.4‐19.5 hours), and the median anhepatic phase was 309 minutes (180‐ 460 minutes). Intraoperative blood loss averaged 1800 mL (1200‐6000 mL). Postoperative complications occurred in 13 patients during hospitalization; 5 patients experienced postoperative complications classified as Clavien‐Dindo grade III or higher, and 2 patients died of intraabdominal bleeding and acute cerebral hemorrhage, respectively. Twenty‐nine patients were followed for a median of 14.0 months (3‐42 months), and no HAE recurrence was detected. The technique requires neither an organ donor nor any postoperative immunosuppressant, and the success of the treatment relies on meticulous preoperative assessments and precise surgical manipulation.

The current clinical classification of primary liver cancer is unable to efficiently predict the prognosis of combined hepatocellular cholangiocarcinoma (cHCC). Accurate satellite nodules (SAT) and microvascular invasion (MVI) prediction in cHCC patients is very important for treatment decision making and prognostic evaluation. The aim of this work was to explore important factors affecting the prognosis of cHCC patients after liver resection and to develop preoperative nomograms to predict SAT and MVI in cHCC patients. The nomogram was developed using the data from 148 patients who underwent liver resection for cHCC patients at our hospital between January 2006 and December 2014. Based on the results of the multivariate analysis, a nomogram integrating all significant independent factors affecting overall survival and recurrence-free survival was constructed to predict the prognosis of cHCC. Next, risk factors for SAT and MVI were evaluated with logistic regression. Blood signatures were established using the LASSO regression, and then, we combined the clinical risk factors and blood signatures of the patients to establish predictive models for SAT and MVI. The C-index of the nomogram for predicting survival was 0.685 (95% CI, 0.638 to 0.732), which was significantly higher than the C-index for other liver cancer classification systems.