New Fiberoptic Choledochoscopy-guided Percutaneous Transhepatic Choledochoscope Lithotomy Combined with Dual-frequency Laser Lithotripsy for the Treatment of Intractable Hepatolithiasis.

Objective: To investigate the clinical effects of new fiberoptic cholangioscopy-guided percutaneous transhepatic choledochoscope lithotomy (PTCSL) combined with dual-frequency laser lithotripsy for the treatment of intractable hepatolithiasis. Methods: Eighty patients with intractable hepatolithiasis who received treatment in the Second Hospital Affiliated to Zhejiang University School of Medicine from December 2020 to December 2022 were grouped according to the surgical methods. Forty-two patients who received hepatectomy were divided into the control group, 38 patients who received new fiber-optic choledochoscope-guided PTCSL combined with dual-frequency laser lithotripsy were divided into the observation group, and the treatment results of the two groups were compared. Results: The operation time, postoperative pain duration, and hospital stay of the observation group were significantly shorter than those of the control group, and intraoperative bleeding was significantly lower than that of the control group, with statistically significant differences (P < .05). Seven days after surgery, the total bilirubin (TBIL), alanine aminotransferase (ALT), and albumin (ALB) levels in the observation group were significantly lower than those in the control group (P < .05). The stone residual rate of 7.89% in the observation group was significantly lower than that of 26.19% in the control group (χ2=4.625, P < .05). The difference in biliary bleeding rate between the two groups was not statistically significant (χ2=0.427, P > .05). Conclusion: Overall results of new fiber-optic choledochoscope-guided PTCSL combined with dual-frequency laser lithotripsy for the treatment of intractable hepatolithiasis.

Construction and validation of a prognostic model for hepatocellular carcinoma: Inflammatory ferroptosis and mitochondrial metabolism indicate a poor prognosis.

Background: An increasing number of innovations have been discovered for treating hepatocellular carcinoma (HCC or commonly called HCC) therapy, Ferroptosis and mitochondrial metabolism are essential mechanisms of cell death. These pathways may act as functional molecular biomarkers that could have important clinical significance for determining individual differences and the prognosis of HCC. The aim of this study was to construct a stable and reliable comprehensive model of genetic features and clinical factors associated with HCC prognosis. Methods: In this study, we used RNA-sequencing (fragments per kilobase of exon model per million reads mapped value) data from the Cancer Genome Atlas (TCGA) database to establish a prognostic model. We enrolled 104 patients for further validation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analyses (KEGG) analysis were used for the functional study of differentially expressed genes. Pan-cancer analysis was performed to evaluate the function of the Differentially Expressed Genes (DEGs). Thirteen genes were identified by univariate and least absolute contraction and selection operation (LASSO) Cox regression analysis. The prognostic model was visualized using a nomogram. Results: We found that eight genes, namely EZH2, GRPEL2, PIGU, PPM1G, SF3B4, TUBG1, TXNRD1 and NDRG1, were hub genes for HCC and differentially expressed in most types of cancer. EZH2, GRPEL2 and NDRG1 may indicate a poor prognosis of HCC as verified by tissue samples. Furthermore, a gene set variation analysis algorithm was created to analyze the relationship between these eight genes and oxidative phosphorylation, mitophagy, and FeS-containing proteins, and it showed that ferroptosis might affect inflammatory-related pathways in HCC. Conclusion: EZH2, GRPEL2, NDRG1, and the clinical factor of tumor size, were included in a nomogram for visualizing a prognostic model of HCC. This nomogram based on a functional study and verification by clinical samples, shows a reliable performance of patients with HCC.

miR-18a-5p Targets FBP1 to Promote Proliferation, Migration, and Invasion of Liver Cancer Cells and Inhibit Cell Apoptosis.

Liver cancer is one of the most aggressive malignant tumors. It is significant to understand the molecular mechanism of liver cancer cells to develop new treatment plans. Studies have identified that FBP1 serves as a cancer inhibitor gene. To research the effect mechanism of FBP1 in liver cancer cells, bioinformatics analysis was performed to study its expression in liver cancer tissue. Survival analysis was also performed. Moreover, starBase database was applied to predict upstream regulatory genes of FBP1. Dual-luciferase assay was performed to testify their targeted relationship. The mRNA and protein expression levels of FBP1 in liver cancer cells were detected by qRT-PCR and western blot, respectively. Cell viability was analyzed by CCK-8 assay. The migratory and invasive abilities of cells were analyzed by Transwell assay. The apoptosis of liver cancer cells was detected by flow cytometry. The results showed that the expression of FBP1 was downregulated in liver cancer tissue and cells. FBP1 low expression was correlated with the poor prognosis of patients. miR-18a-5p could inhibit FBP1 expression. Overexpression of FBP1 could inhibit the progression of liver cancer cells and promote cell apoptosis. Overexpressing miR-18a-5p could promote the progression of liver cancer cells and inhibit cell apoptosis. However, overexpressing FBP1 simultaneously could reverse the effect. miR-18a-5p and FBP1 are expected to be candidates for liver cancer treatment.

Screening Hub Genes of Hepatocellular Carcinoma Based on Public Databases.

Tumor recurrence and metastasis often occur in HCC patients after surgery, and the prognosis is not optimistic. Hence, searching effective biomarkers for prognosis of is of great importance. Firstly, HCC-related data was acquired from the TCGA and GEO databases. Based on GEO data, 256 differentially expressed genes (DEGs) were obtained firstly. Subsequently, to clarify function of DEGs, clusterProfiler package was used to conduct functional enrichment analyses on DEGs. Protein-protein interaction (PPI) network analysis screened 20 key genes. The key genes were filtered via GEPIA database, by which 11 hub genes (F9, CYP3A4, ASPM, AURKA, CDC20, CDCA5, NCAP, PRC1, PTTG1, TOP2A, and KIFC1) were screened out. Then, univariate Cox analysis was applied to construct a prognostic model, followed by a prediction performance validation. With the risk score calculated by the model and common clinical features, univariate and multivariate analyses were carried out to assess whether the prognostic model could be used independently for prognostic prediction. In conclusion, the current study screened HCC prognostic gene signature based on public databases.