Construction and validation of a m6A RNA methylation and ferroptosis-related prognostic model for pancreatic cancer by integrated bioinformatics analysis.

Background: Both N6-methyladenosine (m6A) ribonucleic acid (RNA) methylation and ferroptosis regulators are demonstrated to have significant effects on the malignant clinicopathological characteristics of pancreatic adenocarcinoma (PAAD) patients. However, the currently available clinical indexes are not sufficient to predict precise prognostic outcomes pf PAAD patients accurately. This study aims to examine the clinicopathologic features of m6A RNA methylation and ferroptosis regulators in predicting the outcomes of different types of cancer. Methods: As the foundation for this research, the differentially expressed genes (DEGs) between PAAD tissues and adjacent normal tissues were first identified. Next, dimensional reduction analysis (DCA) based on m6A RNA methylation regulators and ferroptosis regulators were performed and DEGs between good/poor prognosis PAAD patient clusters were identified. DEGs were then screened by Cox analysis, and finally a risk signature was established by least absolute shrinkage and selection operator (LASSO) analyses. The prediction model based on risk score was further evaluated by a validation set from Gene Expression Omnibus (GEO) database. Results: In total, 4 m6A RNA methylation regulator genes and 29 ferroptosis regulator genes were found to have close causal relationships with the prognosis of PAAD, and a risk score with 3 m6A methylation regulators (i.e., IGF2BP2, IGF2BP3, and METTL16) and 4 ferroptosis regulators (i.e., ENPP2, ATP6V1G2, ITGB4, and PROM2) was constructed and showed to be highly involved in PAAD progression and could serve as effective markers for prognosis with AUC value equaled 0.753 in training set and 0.803 in validation set. Conclusions: The combined prediction model, composed of seven regulators of m6A methylation and ferroptosis, in this study more effectively reflects the progression and prognosis of PAAD than previous single genome or epigenetic analysis. Our study provides a broader perspective for the subsequent establishment of prognostic models and the patients may benefit from more precision management.

Derivation and validation of a prediction model for neonate unplanned rehospitalization in a tertiary center in China.

BACKGROUND: To construct and externally validate a prediction model for neonate unplanned rehospitalization within 31 days of discharge. METHODS: A retrospective study was performed in the Department of Neonatology of the Children's Hospital of Fudan University. A binominal regression method was applied to construct and validate the prediction model. Analysis was performed on a total of 11,116 neonates with an index admission between 11/1/2016 and 12/31/2018. Neonates admitted from 11/1/2016 to 1/31/2018 were used for the selection of prognostic variables and construction of the model. Model validation was then performed with neonates admitted from 2/1/2018 to 12/31/2018. RESULTS: The rehospitalization rate for neonates was 3.27% (373/11,116). A total of 512 neonates were enrolled for the construction of the prediction model. Gestational age (GA), NICU length of stay (LOS), nonmedical order discharge and younger maternal age were strongly correlated with rehospitalization. By incorporating these 4 strong risk factors, we constructed a model to predict neonate unplanned rehospitalization within 31 days of discharge. The formula was turned into a nomogram for use in clinical practice. The nomogram has a total score of 180, with a predicted risk from 0 to 100%. Neonates are at high risk for rehospitalization if they have a total score greater than 39 points, according to the cutoff point established by the Youden index. The model was shown to have good discriminatory ability, with area under the receiver operating characteristic curves of 0.68 and 0.65 in the model construction and validation datasets, respectively. A total of 39 points is the cutoff for follow-up. CONCLUSIONS: The model is able to predict neonate unplanned rehospitalization well. A total score greater than 39 indicates that follow-up is necessary.

The AKR1B1 inhibitor epalrestat suppresses the progression of cervical cancer.

Cervical cancer is the leading cause of cancer-related death among women worldwide. Identifying an effective treatment with fewer side effects is imperative, because all of the current treatments have unique disadvantages. Aldo-keto reductase family 1 member B1 (AKR1B1) is highly expressed in various cancers and is associated with tumor development, but has not been studied in cervical cancer. In the current study, we used CRISPR/Cas9 technology to establish a stable HeLa cell line with AKR1B1 knockout. In vitro, AKR1B1 knockout inhibited the proliferation, migration and invasion of HeLa cells, providing evidence that AKR1B1 is an innovative therapeutic target. Notably, the clinically used epalrestat, an inhibitor of aldose reductases, including AKR1B1, had the same effect as AKR1B1 knockout on HeLa cells. This result suggests that epalrestat could be used in the clinical treatment of cervical cancer, a prospect that undoubtedly requires further research. Moreover, aiming to determine the underlying regulatory mechanism of AKR1B1, we screened a series of differentially regulated genes (DEGs) by RNA sequencing and verified selected DEGs by quantitative RT-PCR. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs revealed a correlation between AKR1B1 and cancer. In summary, epalrestat inhibits the progression of cervical cancer by inhibiting AKR1B1, and thus may be a new drug for the clinical treatment of cervical cancer.