Single-cell N6-methyladenosine-related genes function within the tumor microenvironment to affect the prognosis and treatment sensitivity in patients with gastric cancer.

BACKGROUND: Gastric cancer (GC) ranks fifth for morbidity and third for mortality worldwide. The N6-methyladenosine (m6A) mRNA methylation is crucial in cancer biology and progression. However, the relationship between m6A methylation and gastric tumor microenvironment (TME) remains to be elucidated. METHODS: We combined single-cell and bulk transcriptome analyses to explore the roles of m6A-related genes (MRG) in gastric TME. RESULTS: Nine TME cell subtypes were identified from 23 samples. Fibroblasts were further grouped into four subclusters according to different cell markers. M6A-mediated fibroblasts may guide extensive intracellular communications in the gastric TME. The m6A-related genes score (MRGs) was output based on six differentially expressed single-cell m6A-related genes (SCMRDEGs), including GHRL, COL4A1, CAV1, GJA1, TIMP1, and IGFBP3. The protein expression level was assessed by immunohistochemistry. We identified the prognostic value of MRGs and constructed a nomogram model to predict GC patients' overall survival. MRGs may affect treatment sensitivity in GC patients. CONCLUSION: Our study visualized the cellular heterogeneity of TME at the single-cell level, revealed the association between m6A mRNA modification and intracellular communication, clarified MRGs as an independent risk factor of prognosis, and provided a reference for follow-up treatment.

Combined short-term exposure to meteorological, pollution factors and pertussis in different groups from Jining, China.

Background: Previous studies have typically explored daily lagged relationships among pertussis and meteorology, with little assessment of effect and interaction among pollutants mixtures. Methods: Our researchers collected pertussis cases data from 2017-2022 as well as meteorological and contaminative factors for the Jining region. First, we reported the application of the Moving Epidemic Method (MEM) to estimate epidemic threshold and intensity level. Then we developed a Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR) model to assess single, multiple effects and interaction of meteorological and pollution factors on pertussis cases for different sex, delayed and epidemic threshold groups. Results: There has been a yearly upward trend in the incidence of pertussis in Jining regions. High prevalence threshold years were in 2018-2019, the epidemic peak was mainly concentrated in 32 weeks. Totally, pertussis infections disease was separately 2.1% (95% confidence Interval (CI) = 1.3, 2.8) and 1.1% (95% CI = 0.3, 1.9) higher per decile increase in temperature and sulphur dioxide (SO2). And pertussis infections disease was 1.1% lower per decile increase in humidity. In the different stratified analyses, air pressure was a strong negative effect in males and in the lagged 11-20 days group, with 7.3 and 14.7%, respectively. Sulphur dioxide had a relatively weak positive effect in males, females and the group after 20 days lag, ranging from 0.5 to 0.6%. The main positive effectors affecting the onset of disease at low and high threshold levels were ozone (O3) and SO2, respectively, while the negative effectors were SO2 and carbon monoxide (CO), respectively. Conclusions: This is the first mathematically based study of seasonal threshold of pertussis in China, which allows accurate estimation of epidemic level. Our findings support that short-term exposure to pollutants is the risk factor for pertussis. We should concentrate on pollutants monitoring and effect modeling.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

Background: The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Methods: Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. Results: We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Conclusion: Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.