ABSTRACT
BTB and CNC homology 1 (BACH1) regulates biological processes, including energy metabolism and oxidative stress. Insufficient liver regeneration after hepatectomy remains an issue for surgeons. The Pringle maneuver is widely used during hepatectomy and induces ischemia/reperfusion (I/R) injury in hepatocytes. A rat model of two-thirds partial hepatectomy with repeated I/R treatment was used to simulate clinical hepatectomy with Pringle maneuver. Delayed recovery of liver function after hepatectomy with the repeated Pringle maneuver in clinic and impaired liver regeneration in rat model were observed. Highly elevated lactate levels, along with reduced mitochondrial complex III and IV activities in liver tissues, indicated that the glycolytic phenotype was promoted after hepatectomy with repeated I/R. mRNA expression profile analysis of glycolysis-related genes in clinical samples and further verification experiments in rat models showed that high BACH1 expression levels correlated with the glycolytic phenotype after hepatectomy with repeated I/R. BACH1 overexpression restricted the proliferative potential of hepatocytes stimulated with HGF. High PDK1 expression and high lactate levels, together with low mitochondrial complex III and IV activities and reduced ATP concentrations, were detected in BACH1-overexpressing hepatocytes with HGF stimulation. Moreover, HO-1 expression was downregulated, and oxidative stress was exacerbated in the BACH1-overexpressing hepatocytes with HGF stimulation. Cell experiments involving repeated hypoxia/reoxygenation revealed that reactive oxygen species accumulation triggered the TGF-ß1/BACH1 axis in hepatocytes. Finally, inhibiting BACH1 with the inhibitor hemin effectively restored the liver regenerative ability after hepatectomy with repeated I/R. These results provide a potential therapeutic strategy for impaired liver regeneration after repeated I/R injury.
ABSTRACT
Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683-0.948) in the differentiation subgroup, 0.922 (0.785-1) in the CK7 subgroup, 0.762 (0.618-0.906) in the Ki67 subgroup, and 0.849 (0.667-1) in the p53 mutation status subgroup. Confusion matrices and waterfall plots highlighted the good performance of the models. Comprehensive evaluation was carried out using SHapley Additive exPlanations plots, which revealed notable contributions from wavelet filter features. This study highlights the potential of ultrasound-based radiomics, specifically the importance of peritumoral tissue analysis, for predicting HCC characteristics. The results warrant further validation of peritumoral tissue radiomics in larger, multicenter studies.
