Identification of ferroptosis related genes and pathways in prostate cancer cells under erastin exposure.

BACKGROUND: Few studies are focusing on the mechanism of erastin acts on prostate cancer (PCa) cells, and essential ferroptosis-related genes (FRGs) that can be PCa therapeutic targets are rarely known. METHODS: In this study, in vitro assays were performed and RNA-sequencing was used to measure the expression of differentially expressed genes (DEGs) in erastin-induced PCa cells. A series of bioinformatic analyses were applied to analyze the pathways and DEGs. RESULTS: Erastin inhibited the expression of SLC7A11 and cell survivability in LNCaP and PC3 cells. After treatment with erastin, the concentrations of malondialdehyde (MDA) and Fe2+ significantly increased, whereas the glutathione (GSH) and the oxidized glutathione (GSSG) significantly decreased in both cells. A total of 295 overlapping DEGs were identified under erastin exposure and significantly enriched in several pathways, including DNA replication and cell cycle. The percentage of LNCaP and PC3 cells in G1 phase was markedly increased in response to erastin treatment. For four hub FRGs, TMEFF2 was higher in PCa tissue and the expression levels of NRXN3, CLU, and UNC5B were lower in PCa tissue. The expression levels of SLC7A11 and cell survivability were inhibited after the knockdown of TMEFF2 in androgen-dependent cell lines (LNCaP and VCaP) but not in androgen-independent cell lines (PC3 and C4-2). The concentration of Fe2+ only significantly increased in TMEFF2 downregulated LNCaP and VCaP cells. CONCLUSION: TMEFF2 might be likely to develop into a potential ferroptosis target in PCa and this study extends our understanding of the molecular mechanism involved in erastin-affected PCa cells.

The relationship between trajectories of renal oxygen saturation and acute kidney injury: a prospective cohort study with a secondary analysis.

BACKGROUND: Acute kidney injury (AKI) is a major postoperative consequence, affecting prognosis of older patients. Effective prediction or intervention to predict or prevent the incidence of AKI is currently unavailable. AIMS: Dynamic changes of renal tissue oxygen saturation (RSO2) during surgery process are understudied and we intended to explore the distinct trajectories and associations with postoperative AKI. METHODS: This was a secondary analysis including data for older patients who underwent open hepatectomy surgery with informed consent. Latent class mixed models (LCMM) method was conducted to generate trajectories of intraoperative renal tissue RSO2 through different time points. The primary outcome was postoperative 7-day AKI. The univariate and multivariate regression analysis were performed to identify the relationship between distinct trajectories of renal tissue RSO2 and the risk of AKI. Meanwhile, the prediction efficacy of renal tissue RSO2 at different time points was compared to find potential intervention timing. RESULTS: Postoperative AKI occurred in 14 (15.2%) of 92 patients. There are two distinct renal tissue RSO2 trajectories, with 44.6% generating "high-downwards" trajectory and 55.4% generating "consistently-high" trajectory. Patients with "high-downwards" trajectory had significantly higher risk of postoperative AKI than another group (Unadjusted OR [Odds Ratio] = 3.790, 95% CI [Confidence Interval]: 1.091-13.164, p = 0.036; Adjusted OR = 3.973, 95% CI 1.020-15.478, p = 0.047, respectively). Predictive performance was 71.4% sensitivity and 60.3% specificity for "high-downwards" trajectory of renal tissue RSO2 to identify AKI. Furthermore, the renal tissue RSO2 exhibited the lowest level and the best results in terms of the sensitivity during the hepatic occlusion period, may be considered as a "time of concern". CONCLUSIONS: Older patients undergoing hepatectomy may show high-downwards trajectory of renal tissue RSO2, indicating a higher risk of AKI, and the lowest level was identified during the hepatic occlusion period. These findings may help to provide potential candidates for future early recognition of deterioration of kidney function and guide interventions.

Development and validation of a prediction model to predict major adverse cardiovascular events in elderly patients undergoing noncardiac surgery: A retrospective cohort study.

BACKGROUND AND AIMS: Current existing predictive tools have limitations in predicting major adverse cardiovascular events (MACEs) in elderly patients. We will build a new prediction model to predict MACEs in elderly patients undergoing noncardiac surgery by using traditional statistical methods and machine learning algorithms. METHODS: MACEs were defined as acute myocardial infarction (AMI), ischemic stroke, heart failure and death within 30 days after surgery. Clinical data from 45,102 elderly patients (≥65 years old), who underwent noncardiac surgery from two independent cohorts, were used to develop and validate the prediction models. A traditional logistic regression and five machine learning models (decision tree, random forest, LGBM, AdaBoost, and XGBoost) were compared by the area under the receiver operating characteristic curve (AUC). In the traditional prediction model, the calibration was assessed using the calibration curve and the patients' net benefit was measured by decision curve analysis (DCA). RESULTS: Among 45,102 elderly patients, 346 (0.76%) developed MACEs. The AUC of this traditional model was 0.800 (95% CI, 0.708-0.831) in the internal validation set, and 0.768 (95% CI, 0.702-0.835) in the external validation set. In the best machine learning prediction model-AdaBoost model, the AUC in the internal and external validation set was 0.778 and 0.732, respectively. Besides, for the traditional prediction model, the calibration curve of model performance accurately predicted the risk of MACEs (Hosmer and Lemeshow, p = 0.573), the DCA results showed that the nomogram had a high net benefit for predicting postoperative MACEs. CONCLUSIONS: This prediction model based on the traditional method could accurately predict the risk of MACEs after noncardiac surgery in elderly patients.

TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and their related relationships when searching for papers. To address these issues, a database on tea risk substances and their research relationships was constructed. These data were correlated by knowledge mapping techniques, and a Neo4j graph database centered on tea risk substance research was constructed, containing 4189 nodes and 9400 correlations (e.g. research category-PMID, risk substance category-PMID, and risk substance-PMID). This is the first knowledge-based graph database that is specifically designed for integrating and analyzing risk substances in tea and related research, containing nine main types of tea risk substances (including a comprehensive discussion of inclusion pollutants, heavy metals, pesticides, environmental pollutants, mycotoxins, microorganisms, radioactive isotopes, plant growth regulators, and others) and six types of tea research papers (including reviews, safety evaluations/risk assessments, prevention and control measures, detection methods, residual/pollution situations, and data analysis/data measurement). It is an essential reference for exploring the causes of the formation of risk substances in tea and the safety standards of tea in the future. Database URL http://trsrd.wpengxs.cn.