Objective: To assess causal links between senescence-related genes and non-alcoholic fatty liver disease (NAFLD) using summary-data Mendelian randomization (SMR) and colocalization analyses. Methods: Our study examined the relationship between senescence and NAFLD by integrating DNA methylation, gene expression, and protein quantitative trait loci (mQTL, eQTL, and pQTL) data. Summary statistics for NAFLD were sourced from a previous study (discovery) and the FinnGen database (replication), with additional cohorts for nonalcoholic steatohepatitis and liver fibrosis. Genetic variants near senescence-related genes were used as instrumental variables to assess causal relationships. Colocalization analysis was performed to confirm shared causal variants and liver-specific eQTL data were used for validation. Furthermore, we validated findings using cell and mouse models of NAFLD. Cell models were treated with oleic acid, and NAFLD mice were induced using a high-fat diet. Results: We identified 40 mQTLs, 9 eQTLs, and 3 pQTLs significantly linked to NAFLD in the discovery cohort. Multi-omics data highlighted three genes—S100A6, ENDOG, and TP53I3—as potential causal contributors. Notably, S100A6 was confirmed at both the methylation sites (cg24155129 and cg01910639) and gene expression levels, with methylation at these CpG sites significant regulating its expression. Liver-specific validation revealed that ENDOG expression was negatively associated with NAFLD, consistent with findings in blood. Finally, differential expression of all three genes was confirmed in cell models, with S100A6 and ENDOG further validated in a mouse model. Conclusion: Our findings suggest that S100A6, ENDOG, and TP53I3 are associated with NAFLD, providing insights for further research into the disease’s underlying etiology.

Objective: To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI). Methods: A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue. The 93 patients were randomly divided into training and validation groups in 7:3 ratio. The imaging data of patients, including CT and MRI, were collected and processed using 3D Slicer to delineate the region of interest (ROI) for each tumor. Radiomics features were extracted from CT and MRI of patients using Python. Lasso regression analysis was used to select optimal radiomics features for MVI in the training group. The optimal radiomics features of CT and MRI were selected to establish the prediction model. The predictive performance of the model was evaluated using the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results: After univariate and multivariate analyses, it was found that tumor diameter was significantly different between the MVI positive and negative groups. After extracting 2153 imaging phenotyping features from the CT and MRI images of the 93 patients using Python, ten standardized coefficient non-zero imaging phenotyping features were finally determined by Lasso regression analysis in the CT and MRI images. A comprehensive predictive model with clinical variable and optimal radiomics features was established. The area under the curve (AUC) of the training group was 0.916 (95%CI: 0.843–1.000), sensitivity: 95.2%, specificity: 79.2%. In the validation group, the predictive model diagnosed MVI with AUC = 0.816 (95%CI: 0.642–0.990), sensitivity: 84.2%, and specificity: 75.0%. Conclusion: The joint model that integrated the optimal radiomics features with clinical variables has good diagnostic performance for MVI of HCC and specific clinical applicability.

Background and Aims: Systemic therapy is mainly recommended for advanced hepatocellular carcinoma (HCC). Considering the variety of treatments available for HCC, there is a need to understand their relative benefits and risks, especially for the newly approved combination of immune checkpoint inhibitors and vascular endothelial growth factor inhibitors represented by atezolizumab in combination with bevacizumab. A reticulated meta-analysis was used to evaluate the efficacy and safety of atezolizumab-bevacizumab combination therapy compared with other first-line systemic therapies for the treatment of patients advanced HCC. Methods: PubMed, The Cochrane Library, Web of Science, and Embase databases were searched from the time of library construction to 01 December 2022, and the data were extracted and analyzed using Stata16.0 for Meta-analysis. The data were extracted separately, and a meta-analysis was performed using the software Stata16.0. Results: 16 clinical studies with 8,779 subjects were identified from 13,417 records and were used to build the evidence network for all trials. TThe combination therapy of atezolizumab and bevacizumab has the advantage of prolonging the OS of patients when treating advanced HCC [HR=5.71, 95%CI (4.30, 7.12), p<0.05] Also, the combination therapy has the advantage of prolonging the patient‘s progression free survival [HR=1.60, 95%CI (0.89, 2.49), p<0.05]. Conclusions: Atezolizumab-bevacizumab combination therapy can improve clinical outcomes such as OS and PFS in patients with advanced HCC.

Objective: To explore a more effective and practical comprehensive index for differentiating benign from malignant obstructive jaundice by analyzing the clinical data of patients with benign obstructive jaundice (BJ) group and malignant obstructive jaundice (MJ) group. Methods: A retrospective analysis was conducted on the clinical data of 339 patients with obstructive jaundice. The cases were divided into two data sets: training cohort and validation cohort. The cases were divided into two groups: malignant and benign obstructive jaundice group. Logistic regression analysis was used to build a prediction model for judging the nature of obstructive jaundice, and the prediction model was verified using the validation cohort. Results: Multivariate analysis revealed that CEA, TBIL, and NLR were independent factors in malignant obstructive jaundice. A comprehensive index for differentiating benign from malignant obstructive jaundice was established based on these indicators. The sensitivity, specificity, and receiver operating characteristic curve of this model for differentiating benign from malignant obstructive jaundice were 79.57%, 93.26%, and 0.920, respectively. Conclusion: The prediction model based on the comprehensive index of CEA, TBIL, and NLR has a higher accuracy in differentiating malignant obstructive jaundice.

Alcohol-related liver disease (ALD) is a worldwide burden. Cuproptosis has been shown to play a key role in the development of several diseases. However, the role and mechanisms of cuproptosis in ALD remain unclear. The RNA-sequencing data of ALD liver samples were downloaded from the Gene Expression Omnibus (GEO) database. Bioinformatical analyses were performed using the R data package. We then identified key genes through multiple machine learning methods. Immunoinfiltration analyses were used to identify different immune cells in ALD patients and controls. The expression levels of key genes were further verified. We identified three key cuproptosis-related genes (CRGs) (DPYD, SLC31A1, and DBT) through an in-depth analysis of two GEO datasets, including 28 ALD samples and eight control samples. The area under the curve (AUC) value of these three genes combined in determining ALD was 1.0. In the external datasets, the three key genes had AUC values as high as 1.0 and 0.917, respectively. Nomogram, decision curve, and calibration curve analyses also confirmed these genes’ ability to predict the diagnosis. These three key genes were found to be involved in multiple pathways associated with ALD progression. We confirmed the mRNA expression of these three key genes in mouse ALD liver samples. Regarding immune cell infiltration, the numbers of B cells, CD8 (+) T cells, NK cells, T-helper cells, and Th1 cells were significantly lower in ALD patient samples than in control liver samples. Single sample gene set enrichment analysis (ssGSEA) was then used to estimate the immune microenvironment of different CRG clusters and CRG-related gene clusters. In addition, we calculated CRG scores through principal component analysis (PCA) and selected Sankey plots to represent the correlation between CRG clusters, gene clusters, and CRG scores. Finally, the three key genes were confirmed in mouse ALD liver samples and liver cells treated with ethanol. We first established a prognostic model for ALD based on 3 CRGs and robust prediction efficacy was confirmed. Our investigation contributes to a comprehensive understanding of the role of cuproptosis in ALD, presenting promising avenues for the exploration of therapeutic strategies.