Hepatocellular carcinoma (HCC) is the third most common malignant tumor of the digestive system. Plasma cell tumor heterotopic gene 1 (PVT1) is an intergenic long non-coding RNA that is aberrantly expressed in different cancers. Myocardin related transcription factor A (MKL1) is a transcriptional coactivator of serum response factor that has been shown to promote cancer cell migration and invasion. In this study, we investigated the relationship between PVT1 and MKL1 as a novel regulatory mechanism underlying HCC progression. We used HepG2 and Cos‑7 cell lines. Transfection experiments with miR-3619-5p mimics/inhibitor, PVT1, siRNA-PVT1, MKL1, or siRNA-MKL1 were performed. RNA and protein levels were analyzed by quantitative reverse transcription PCR and Western blot, respectively. Cell migration was assessed by Transwell assay. Luciferase assays, RNA-FISH, RNA immunoprecipitation, and chromatin immunoprecipitation assays were performed to confirm the interaction between PVT1, miR-3619-5p, and MKL1 in HCC cells. Overexpression of PVT1 was positively correlated with MKL1 upregulation, which promoted HepG2 cell migration. miR-3619-5p inhibited MKL1 expression in HCC cells by acting on its 3' UTR. Furthermore, PVT1 promoted MKL1 expression and migration in HCC cells by directly binding to miR-3619-5p. In a positive feedback loop, MKL1 could activate PVT1 transcription by binding to the CArG box in the promoter region. Our findings may provide a basis for the development of novel targeted therapies in HCC.
Objective
Acute kidney injury (AKI) is a common and life-threatening complication of liver failure. The purpose of this study is to construct a nomogram and online calculator to predict the development of hospital-acquired acute kidney injury (HA-AKI) in patients with acute-on-chronic liver failure (ACLF), which may contribute to the prognosis of ACLF.
Methods
574 ACLF patients were evaluated retrospectively. AKI was defined by criteria proposed by International Club of Ascites (ICA) and divided into community-acquired and hospital-acquired AKI (CA-AKI and HA-AKI). The difference between CA-AKI and HA-AKI, factors associated with development into and recovered from AKI periods. The risk factors were identified and nomograms were developed to predict the morbidity of HA-AKI in patients with ACLF.
Results
Among 574 patients, 217(37.8%) patients had AKI, CA-AKI and HA-AKI were 56 (25.8%) and 161 (74.2%) respectively. The multivariate logistic regression model (KP-AKI) for predicting the occurrence of HA-AKI were age, gastrointestinal bleeding, bacterial infections, albumin, total bilirubin, blood urea nitrogen and prothrombin time. The AUROC of the KP-AKI in internal and external validations were 0.747 and 0.759, respectively. Among 217 AKI patients, 81(37.3%), 96(44.2%) and 40(18.4%) patients were with ICA-AKI stage progression, regression and fluctuated in-situ, respectively. The 90-day mortality of patients with AKI was 55.3% higher than non-AKI patients 21.6%. The 90-day mortality of patients with progression of AKI was 88.9%, followed by patients with fluctuated in-situ 40% and regression of AKI 33.3%.
Conclusions
The nomogram constructed by KP-AKI can be conveniently and accurately in predicting the development of HA-AKI, and AKI can increase the 90-day mortality significantly in ACLF patients.
Trial registration Chinese clinical trials registry: ChiCTR1900021539.
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