1.A genetic variant in the immune-related gene ERAP1 affects colorectal cancer prognosis
Danyi ZOU ; Yimin CAI ; Meng JIN ; Ming ZHANG ; Yizhuo LIU ; Shuoni CHEN ; Shuhui YANG ; Heng ZHANG ; Xu ZHU ; Chaoqun HUANG ; Ying ZHU ; Xiaoping MIAO ; Yongchang WEI ; Xiaojun YANG ; Jianbo TIAN
Chinese Medical Journal 2024;137(4):431-440
Background::Findings on the association of genetic factors and colorectal cancer (CRC) survival are limited and inconsistent, and revealing the mechanism underlying their prognostic roles is of great importance. This study aimed to explore the relationship between functional genetic variations and the prognosis of CRC and further reveal the possible mechanism.Methods::We first systematically performed expression quantitative trait locus (eQTL) analysis using The Cancer Genome Atlas (TCGA) dataset. Then, the Kaplan-Meier analysis was used to filter out the survival-related eQTL target genes of CRC patients in two public datasets (TCGA and GSE39582 dataset from the Gene Expression Omnibus database). The seven most potentially functional eQTL single nucleotide polymorphisms (SNPs) associated with six survival-related eQTL target genes were genotyped in 907 Chinese CRC patients with clinical prognosis data. The regulatory mechanism of the survival-related SNP was further confirmed by functional experiments.Results::The rs71630754 regulating the expression of endoplasmic reticulum aminopeptidase 1 ( ERAP1) was significantly associated with the prognosis of CRC (additive model, hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.08-1.88, P = 0.012). The results of dual-luciferase reporter assay and electrophoretic mobility shift assay showed that the A allele of the rs71630754 could increase the binding of transcription factor 3 (TCF3) and subsequently reduce the expression of ERAP1. The results of bioinformatic analysis showed that lower expression of ERAP1 could affect the tumor immune microenvironment and was significantly associated with severe survival outcomes. Conclusion::The rs71630754 could influence the prognosis of CRC patients by regulating the expression of the immune-related gene ERAP1. Trial Registration::No. NCT00454519 (https://clinicaltrials.gov/)
2.Construction and validation of a prediction model of aspiration risk of acute poisoning patients during gastric lavage
Shuoni ZHANG ; Junjie WANG ; Bo ZHANG ; Xuelan LIU
Chinese Journal of Nursing 2024;59(17):2100-2107
Objective To analyze the influencing factors of aspiration risk in patients with acute poisoning during gastric lavage,and to build and validation a prediction model of aspiration risk in patients with acute poisoning during gastric lavage.Methods Through literature search and analysis,the risk factors of aspiration during gastric lavage was summarized in patients with acute poisoning.A retrospective study was conducted on patients with acute poisoning in the emergency department of a tertiary A general hospital in Ningbo from January 2020 to June 2023.Through R 4.2.1 and Python 3.11 programming language,the random forest,logistic regression,extreme gradient boosting tree and gradient boosting decision tree algorithms in machine learning were used to establish a prediction model of aspiration risk during gastric lavage in patients with acute poisoning and carry out internal verification.The prediction effects of the 4 prediction models were evaluated by confusion matrix,calibration curve,receiver operating characteristic curve,area under curve,Kolmogorov-Smirnov value,accuracy,precision,recall rate and F1 score,and the best model was selected.Results The modeling results of the 4 machine learning algorithms show that the area under the curve of the Random Forest,Logistic Regression,Extreme Gradient Boosting Tree,and Gradient Boosting Decision Tree algorithms are 0.954(0.934~0.974),0.878(0.843~0.913),0.910(0.880~0.939),and 0.917(0.889~0.945),respectively.The internal validation results show that the area under the curve of the random forest,logistic regression,extreme gradient boosting tree,and gradient boosting decision tree algorithms are 0.910(0.864~0.955),0.877(0.824~0.931),0.849(0.790~0.908),and 0.873(0.819~0.928),respectively.Age,state of consciousness,D-dimer and the time of absorption of poison are the 3 characteristics that are particularly prominent in the order of importance of the influencing factors of aspiration during gastric lavage in patients with acute poisoning.Conclusion Among the 4 prediction models,random forest model has better prediction effect,with good discrimination ability for the risk of aspiration during gastric lavage in patients with acute poisoning,and it is convenient for clinical use,which can provide references for medical staff to take preventive treatment and care.

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