Phosphorylation of RasGRP1 by Shc3 prevents RasGRP1 degradation and contributes to Ras/c-Jun activation in hepatocellular carcinoma.

Ras guanine nucleotide-releasing protein 1 (RasGRP1), a Ras activator, is upregulated in hepatocellular carcinoma (HCC) and other kinds of cancer and is associated with the poor prognosis of patients. However, little is known about the underlying regulatory mechanisms of RasGRP1 in the context of cancer. Here, we report that RasGRP1 physically interacted with the adaptor protein Src homolog and collagen homolog 3 (Shc3). Moreover, RasGRP1 C-terminus domain (aa 607-797) bound to the central collagen-homology 1 (CH1) domain of Shc3. Subsequently, Shc3 enhanced the RasGRP1 tyrosine phosphorylation rate and stability by inhibiting its ubiquitination. Notably, the phosphorylation-mimicking mutants of RasGRP1, RasGRP1 Y704A, and Y748A, rescued the phosphorylation and ubiquitination levels of RasGRP1 in HCC cells. Further investigation showed that the RasGRP1 and Shc3 interaction induced activation of Ras and c-Jun, resulting in cell proliferation in vitro. Moreover, the regulation of Shc3/RasGRP1/Ras/c-Jun signal transduction was confirmed in vivo using the subcutaneous xenograft mouse model. Thus, we propose that continuous Shc3 overexpression may be a possible mechanism for maintaining RasGRP1 stability and that persistent activation of Ras/c-Jun signaling through the interaction of RasGRP1 and Shc3 is a key event increasing cell proliferation. Our findings suggest that the interaction of RasGRP1 and Shc3 plays an important role in HCC tumorigenesis and suggests the potential clinical usage of novel biomarkers and therapeutic targets in HCC.

Occurrence and influencing factors of cyclosporine A on the kidney injury following allogeneic hematopoietic stem cell transplantation: A systematic review and meta-analysis.

OBJECTIVE: Whether cyclosporine A (CsA) is a risk factor of kidney injury after allogeneic hematopoietic stem cell transplantation (allo-HSCT) has not been determined. We aim to comprehensively review the correlation and influencing factors between CsA and kidney injury in patients following allo-HSCT. METHODS: We searched PubMed, Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CNKI, VIP, Wanfang and CBM Database from inception to March 2022. Two researchers independently conducted literature screening, data extraction and quality assessment. Qualitative and quantitative methods were combined to analyze the data. RESULTS: We included a total of 30 studies. Meta-analyses of total incidence of kidney injury related to CsA was 37.0% [95% CI (25.4%, 48.6%); n = 15]. The proportion of CsA-related acute kidney injury to total acute kidney injury following allo-HSCT was 59.7% [95% CI (49.1%, 70.3%); n = 9]. One study found that AKI had a significant association with CsA in multivariate analysis [RR = 6.173; 95% CI (4.032, 9.434)]. With respect to cyclosporine combination and nephrotoxicity, 6/9 studies demonstrated that the concomitant medications for CsA (especially aminoglycoside antibiotics and amphotericin B) had negative effect on kidney functions related to CsA in allo-HSCT patients. No consensus was reached for "dose of CsA", "duration of CsA use", "comorbidities" and "CsA levels" across studies. CONCLUSIONS: CsA may be a risk factor for kidney injury in patients following allo-HSCT, especially the concomitant use of CsA and nephrotoxic medications.

Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation.

BACKGROUND: This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation. METHODS: Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C0 . PCR was used to determine the donors' and recipients' CYP3A5 genotypes. Multivariate stepwise regression analysis and stepwise elimination covariates were used for covariates selection. Thirteen machine learning algorithms were applied for the development of prediction models. APE, the ratio of the APE ≤3 ng ml-1 and ideal rate (the proportion of the predicted value with a relative error of 30% or less) were used to evaluate the predictive performance of the model. RESULTS: A total of 163 infant patients were included in this study. In the case of the optimal combination of covariates, the Ridge model had the lowest APE, 2.01 (0.85, 3.35 ng ml-1 ). The highest ratio of the APE ≤3 ng ml-1 was the LAR model (71.77%). And the Ridge model showed the highest ideal rate (55.05%). For the Ridge model, GRWR was the most important predictor. CONCLUSIONS: Compared with other ML models, the Ridge model had good predictive performance and potential clinical application.