Construction of a novel prognostic scoring model for HBV-ACLF liver failure based on dynamic data.

Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC.

Background: Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC).

Methods: HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA.