OBJECTIVES: Previous studies have demonstrated that the metals cadmium and arsenic exhibit estrogen-like effects and may influence the occurrence and development of gynecological tumors. This study aims to explore the association between urinary cadmium and arsenic levels and the prevalence of gynecologic cancers using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: Data from female participants in NHANES 2003-2018 were analyzed. Using R software, datasets (DEMO, BMX, etc.) were merged, and complete cases were retained by intersecting row names, yielding a total of 2 999 participants. After applying strict exclusion criteria, 2 802 participants were included: 83 with gynecologic cancer (cancer group) and 2 719 without (control group). Demographic, reproductive health, and urinary cadmium and arsenic data were collected. Binary Logistic regression models were employed to assess associations between urinary cadmium and arsenic levels and gynecologic cancer risk. RESULTS: High urinary cadmium and arsenic levels were risk factors for gynecologic cancers, with odds ratios (ORs) of 1.623 (95% CI 1.217 to 2.166) and 1.003 (95% CI 1.001 to 1.005), respectively. After propensity score matching (PSM), the trend remained; cadmium was still a statistically significant risk factor with an OR of 2.182 (95% CI 1.343 to 3.545), while arsenic's association, though not statistically significant, still trended toward risk (OR=1.004, 95% CI 0.999 to 1.009). Subgroup analyses showed that both cadmium and arsenic were risk factors for ovarian cancer (OR=1.745, 95% CI 1.178 to 2.586 and OR=1.005, 95% CI 1.002 to 1.008, respectively); these associations persisted after PSM. Additionally, cadmium increased the risk of endometrial cancer (OR=1.617, 95% CI 1.109 to 2.356). CONCLUSIONS Exposure to cadmium and arsenic is associated with an increased risk of ovarian and endometrial cancers. These findings suggest that reducing environmental exposure to heavy metals such as cadmium and arsenic may help prevent certain gynecologic cancers.
Humans ; Female ; Cadmium/urine* ; Arsenic/urine* ; Genital Neoplasms, Female/urine* ; Middle Aged ; Nutrition Surveys ; Adult ; Risk Factors ; Environmental Exposure/adverse effects* ; Aged

Background and Aims:Pancreatic ductal adenocarcinoma(PDAC)is a highly malignant digestive system tumor with an inferior prognosis,and its early diagnosis and treatment remain significant challenges.In recent years,RNA methylation modifications(such as m6A and m5C)have attracted considerable attention for their roles in tumor development;however,their regulatory mechanisms and clinical significance in PDAC remain unclear.This study was conducted to identify prognosis-related long noncoding RNAs(lncRNAs)associated with m6A and m5C in PDAC,construct a reliable prognostic prediction model,and explore their relationship with the tumor immune microenvironment.Methods:Based on RNA-seq data from the TCGA-PDAC cohort,differentially expressed lncRNAs(DElncRNAs)related to m6A and m5C were identified through differential expression analysis and Pearson correlation analysis.The samples were randomly divided into a training set(n=89)and a validation set(n=89).Key DElncRNAs were selected using LASSO-Cox regression to construct a prognostic model,and patients were categorized into high-and low-risk groups based on risk scores.Kaplan-Meier survival analysis,ROC curves,and multivariate Cox regression were used to evaluate the model's predictive performance.Furthermore,CIBERSORT and ESTIMATE scores were used to analyze immune cell infiltration characteristics and tumor microenvironment(TME)differences between the high-and low-risk groups.Results:To construct the prognostic model,four m6A-and m5C-related DElncRNAs(LINC00857,LINC02038,TSPOAP1-AS1,and TRPC7-AS1)were identified.Patients in the high-risk group had significantly lower overall survival than those in the low-risk group(P<0.05),and the risk score was an independent prognostic factor for PDAC(HR=1.551,95%CI=1.297-1.854,P<0.001).ROC curve analysis showed that the risk score model exhibited high predictive efficiency in both the training and validation sets(AUC values for 1,3,and 5 years:0.766,0.875,0.879;0.685,0.711,0.792,respectively).Immune analysis revealed increased infiltration of M0 macrophages with lower TME scores in the high-risk group(all P<0.05),suggesting an immunosuppressive microenvironment.Conclusion:This study successfully established a PDAC prognostic model based on m6A-and m5C-related DElncRNAs and confirmed its independent predictive value.High-risk patients exhibited M0 macrophage enrichment and immunosuppressive microenvironment characteristics,possibly contributing to poor prognosis.

Background and Aims:Pancreatic ductal adenocarcinoma(PDAC)is a highly malignant digestive system tumor with an inferior prognosis,and its early diagnosis and treatment remain significant challenges.In recent years,RNA methylation modifications(such as m6A and m5C)have attracted considerable attention for their roles in tumor development;however,their regulatory mechanisms and clinical significance in PDAC remain unclear.This study was conducted to identify prognosis-related long noncoding RNAs(lncRNAs)associated with m6A and m5C in PDAC,construct a reliable prognostic prediction model,and explore their relationship with the tumor immune microenvironment.Methods:Based on RNA-seq data from the TCGA-PDAC cohort,differentially expressed lncRNAs(DElncRNAs)related to m6A and m5C were identified through differential expression analysis and Pearson correlation analysis.The samples were randomly divided into a training set(n=89)and a validation set(n=89).Key DElncRNAs were selected using LASSO-Cox regression to construct a prognostic model,and patients were categorized into high-and low-risk groups based on risk scores.Kaplan-Meier survival analysis,ROC curves,and multivariate Cox regression were used to evaluate the model's predictive performance.Furthermore,CIBERSORT and ESTIMATE scores were used to analyze immune cell infiltration characteristics and tumor microenvironment(TME)differences between the high-and low-risk groups.Results:To construct the prognostic model,four m6A-and m5C-related DElncRNAs(LINC00857,LINC02038,TSPOAP1-AS1,and TRPC7-AS1)were identified.Patients in the high-risk group had significantly lower overall survival than those in the low-risk group(P<0.05),and the risk score was an independent prognostic factor for PDAC(HR=1.551,95%CI=1.297-1.854,P<0.001).ROC curve analysis showed that the risk score model exhibited high predictive efficiency in both the training and validation sets(AUC values for 1,3,and 5 years:0.766,0.875,0.879;0.685,0.711,0.792,respectively).Immune analysis revealed increased infiltration of M0 macrophages with lower TME scores in the high-risk group(all P<0.05),suggesting an immunosuppressive microenvironment.Conclusion:This study successfully established a PDAC prognostic model based on m6A-and m5C-related DElncRNAs and confirmed its independent predictive value.High-risk patients exhibited M0 macrophage enrichment and immunosuppressive microenvironment characteristics,possibly contributing to poor prognosis.

Objective: To evaluate the preventive effect of implementing the free influenza vaccination policy on school absence among primary and secondary school students, so as to provide a reference for formulating and adjusting vaccination strategies. Methods: Among primary and secondary school students aged 6 to 18 in Longgang District, Shenzhen, they were divided into a vaccinated group (265 996 students) and an unvaccinated group (122 513 students) according to their influenza vaccination history during November 2023. Propensity score matching was used to conduct a 1∶1 match between the two groups to balance covariates. The number of absences per month was set as the dependent variable to construct a difference in differences model, and Poisson regression was employed to analyze the overall and multi time point effects. Results: Vaccination against influenza was associated with low rate of absenteeism among primary and secondary school students, with an overall preventive effect of 26.52% (95% CI = 23.47% -29.45%). The preventive effects in November (the month of vaccination) and December 2023, January and March 2024 were 42.12%, 40.12%, 30.33% and 20.91%, respectively. The preventive effect of the influenza vaccine on absenteeism among primary school students (26.39%) was not significantly different from that among secondary school students ( 27.97% ) ( P >0.05). The regression coefficient for class vaccination rates ranged from 0.998 to 0.999 ( P <0.01), indicating that for every 10% increase in influenza vaccination rates, absenteeism could be reduced by 1.5% to 2.2%. Conclusion Implementing free influenza vaccination for primary and secondary school students might help to reduce the risk of absenteeism, yielding significant socioeconomic benefits.