Transcription factor 7 like 2 promotes metastasis in hepatocellular carcinoma via NEDD9-mediated activation of AKT/mTOR signaling pathway.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system, and the exact mechanism of HCC is still unclear. Transcription factor 7 like 2 (TCF7L2) plays a pivotal role in cell proliferation and stemness maintenance. However, the exact mechanism of TCF7L2 in HCC remains unclear. METHODS: Clinical samples and public databases were used to analyze the expression and prognosis of TCF7L2 in HCC. The function of TCF7L2 in HCC was studied in vitro and in vivo. ChIP and luciferase assays were used to explore the molecular mechanism of TCF7L2. The relationship between TCF7L2 and NEDD9 was verified in HCC clinical samples by tissue microarrays. RESULTS: The expression of TCF7L2 was upregulated in HCC, and high expression of TCF7L2 was associated with poor prognosis of HCC patients. Overexpression of TCF7L2 promoted the metastasis of HCC in vitro and in vivo, while Knockdown of TCF7L2 showed the opposite effect. Mechanically, TCF7L2 activated neural precursor cell expressed developmentally downregulated protein 9 (NEDD9) transcription by binding to the -1522/-1509 site of the NEDD9 promoter region, thereby increasing the phosphorylation levels of AKT and mTOR. The combination of TCF7L2 and NEDD9 could distinguish the survival of HCC patients. CONCLUSIONS: This study demonstrated that TCF7L2 promotes HCC metastasis by activating AKT/mTOR pathway in a NEDD9-dependent manner, suggesting that potential of TCF7L2 and NEDD9 as prognostic markers and therapeutic targets for HCC.

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation.

BACKGROUND: Acute kidney injury (AKI) is a common complication after liver transplantation (LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach. METHODS: A total of 493 patients with donation after cardiac death LT (DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes (KDIGO). The clinical data of patients with AKI (AKI group) and without AKI (non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The incidence of AKI was 35.7% (176/493) during the follow-up period. Compared with the non-AKI group, the AKI group showed a remarkably lower survival rate (P < 0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval (CI): 0.794-0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models (P < 0.001). CONCLUSIONS: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

The Role of Ischemia/Reperfusion Injury in Early Hepatic Allograft Dysfunction.

Liver transplantation (LT) is the only available curative treatment for patients with end-stage liver disease. Early allograft dysfunction (EAD) is a life-threatening complication of LT and is thought to be mediated in large part through ischemia/reperfusion injury (IRI). However, the underlying mechanisms linking IRI and EAD after LT are poorly understood. Most previous studies focused on the clinical features of EAD, but basic research on the underlying mechanisms is insufficient, due, in part, to a lack of suitable animal models of EAD. There is still no consensus on definition of EAD, which hampers comparative analysis of data from different LT centers. IRI is considered as an important risk factor of EAD, which can induce both damage and adaptive responses in liver grafts. IRI and EAD are closely linked and share several common pathways. However, the underlying mechanisms remain largely unclear. Therapeutic interventions against EAD through the amelioration of IRI is a promising strategy, but most approaches are still in preclinical stages. To further study the mechanisms of EAD and promote collaborations between LT centers, optimized animal models and unified definitions of EAD are urgently needed. Because IRI and EAD are closely linked, more attention should be paid to the underlying mechanisms and the fundamental relationship between them. Ischemia/reperfusion-induced adaptive responses may play a crucial role in the prevention of EAD, and more preclinical studies and clinical trials are urgently needed to address the current limitation of available therapeutic interventions.

Establishment and validation of a cholesterol metabolism-related prognostic signature for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) represents the most important type of liver cancer, the 5-year survival rate for advanced HCC is 2%. The heterogeneity of HCC makes previous models fail to achieve satisfactory results. The role of Cholesterol-based metabolic reprogramming in cancer has attracted more and more attention. In this study, we screened cholesterol metabolism-related genes (CMRGs) based on a systematical analysis from TCGA and GEO database. Then, we constructed a prognostic signature based on the screened 5 CMRGs: FDPS, FABP5, ANXA2, ACADL and HMGCS2. The clinical value of the five CMRGs was validated by TCGA database and HPA database. HCC patients were assigned to the high-risk and low-risk groups on the basis of median risk score calculated by the five CMRGs. We evaluated the signature in TCGA database and validated in ICGC database. The results revealed that the prognostic signature had good prognostic performance, even among different clinicopathological subgroups. The function analysis linked CMRGs with KEGG pathway, such as cell adhesion molecules, drug metabolism-cytochrome P450 and other related pathways. In addition, patients in the high-risk group exhibited characteristics of high TP53 mutation, high immune checkpoints expression and high immune cell infiltration. Furthermore, based on the prognostic signature, we identified 25 most significant small molecule drugs as potential drugs for HCC patients. Finally, a nomogram combined risk score and TNM stage was constructed. These results indicated our prognostic signature has an excellent prediction performance. This study is expected to provide a potential diagnostic and therapeutic strategies for HCC.

Combination of Early Allograft Dysfunction and Protein Expression Patterns Predicts Outcome of Liver Transplantation From Donation After Cardiac Death.

Early allograft dysfunction (EAD) after liver transplantation (LT) accompanies poor prognosis. This study aims to explore the relationship between pretransplant intrahepatic proteins and the incidence of EAD, and the value of combined EAD and protein profiles for predicting recipient and graft survival prognosis. Liver biopsy specimens of 105 pretransplant grafts used for LT were collected and used for immunohistochemistry analysis of 5 proteins. And matched clinical data of donor, recipient, transplantation, and prognosis were analyzed. The incidence of EAD was 41.9% (44/105) in this cohort. Macrovesicular steatosis (P = 0.016), donor body mass index (P = 0.013), recipients' pretransplant serum creatinine (P = 0.036), and intrahepatic expression of heme oxygenase 1 (HO1) (P = 0.015) and tumor necrosis factor α (TNF-α) (P = 0.039) were independent predictors of EAD. Inferior graft and recipient prognosis were observed in patients who experienced EAD (P = 0.028 and 0.031) or received grafts with higher expression of sirtuin 1 (P = 0.005 and 0.013). The graft and recipient survival were worst in patients with both EAD and high expression of sirtuin 1 (P = 0.001 and 0.004). In conclusion, pretransplant intrahepatic expression of HO1 and TNF-α are associated with the incidence of EAD. The combination of EAD and EAD-unrelated proteins showed superiority in distinguishing recipients with worse prognosis.

NEAT1/hsa-miR-372-3p axis participates in rapamycin-induced lipid metabolic disorder.

Rapamycin is a crucial immunosuppressive regimen for patients that have undergone liver transplantation (LT). However, one of the major side effects of rapamycin include metabolic disorders such as dyslipidemia, and the mechanism remains unknown. This study aims to explore the biomolecules that are responsible for rapamycin-induced dyslipidemia and the control strategies that can reverse the lipid metabolism disorder. In this study, data collected from LT patients, cell and mouse models treated with rapamycin were analyzed. Results showed an increase of triglycerides (TGs) induced by rapamycin. MicroRNAs (miRNAs) play important roles in many vital biological processes including TG metabolism. hsa-miR-372-3p was filtered using RNA sequencing and identified as a key regulator in rapamycin-induced TGs accumulation. Using bioinformatics and experimental analyses, target genes of hsa-miR-372-3p were predicted. These genes were alkylglycerone phosphate synthase (AGPS) and apolipoprotein C4 (APOC4), which are reported to be involved in TG metabolism. LncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) was also identified as an upstream regulatory factor of hsa-miR-372-3p. From the results of this study, NEAT1/hsa-miR-372-3p/AGPS/APOC4 axis plays a vital role in rapamycin-disruption of lipid homeostasis. Therefore, targeting this axis is a potential therapeutic target combating rapamycin-induced dyslipidemia after LT.

Delivery of microRNA-33 Antagomirs by Mesoporous Silica Nanoparticles to Ameliorate Lipid Metabolic Disorders.

Lipid metabolic disorders have become a major global public health concern. Fatty liver and dyslipidemia are major manifestations of these disorders. Recently, MicroRNA-33 (miR-33), a post-transcriptional regulator of genes involved in cholesterol efflux and fatty acid oxidation, has been considered as a good therapeutic target for these disorders. However, the traditional methods of gene therapy impede their further clinical transformation into a mature treatment system. To counter this problem, in this study we used mesoporous silica nanoparticles (MSNs) as nanocarriers to deliver miR-33 antagomirs developing nanocomposites miR-MSNs. We observed that the hepatocellular uptake of miR-33 antagomirs increased by ∼5 times when they were delivered using miR-MSNs. The regulation effects of miR-MSNs on miR-33 and several genes involved in lipid metabolism were confirmed in L02 cells. In a high-fat diet fed mice, miR-33 intervention via miR-MSNs lowered the serum triglyceride levels remarkably by 18.9% and reduced hepatic steatosis. Thus, our results provide a proof-of-concept for a potential strategy to ameliorate lipid metabolic disorders.

Trypsin-catalyzed multicomponent reaction: A novel and efficient one-pot synthesis of thiazole-2-imine derivatives.

The first Trypsin from porcine pancreas catalyzed a novel one-pot three-component reaction of α-bromoketone, primary alkylamines, and phenylisothiocyanate for the synthesis of thiazole-imine derivatives with high yields (up to 98%) in a short time under mild conditions. The results revealed that Trypsin exhibited excellent catalytic activity and great tolerance for broad substrates. This Trypsin-catalyzed three component convergent method provides a novel strategy for the synthesis of thiazole-2-imine derivatives and expands the promiscuous functions of enzymes in organic synthesis.