Exploring the Prognosis-Related Genetic Variation in Gastric Cancer Based on mGWAS.

The use of metabolome genome-wide association studies (mGWAS) has been shown to be effective in identifying functional genes in complex diseases. While mGWAS has been applied to biomedical and pharmaceutical studies, its potential in predicting gastric cancer prognosis has yet to be explored. This study aims to address this gap and provide insights into the genetic basis of GC survival, as well as identify vital regulatory pathways in GC cell progression. Genome-wide association analysis of plasma metabolites related to gastric cancer prognosis was performed based on the Generalized Linear Model (GLM). We used a log-rank test, LASSO regression, multivariate Cox regression, GO enrichment analysis, and the Cytoscape software to visualize the complex regulatory network of genes and metabolites and explored in-depth genetic variation in gastric cancer prognosis based on mGWAS. We found 32 genetic variation loci significantly associated with GC survival-related metabolites, corresponding to seven genes, VENTX, PCDH 7, JAKMIP1, MIR202HG, MIR378D1, LINC02472, and LINC02310. Furthermore, this study identified 722 Single nucleotide polymorphism (SNP) sites, suggesting an association with GC prognosis-related metabolites, corresponding to 206 genes. These 206 possible functional genes for gastric cancer prognosis were mainly involved in cellular signaling molecules related to cellular components, which are mainly involved in the growth and development of the body and neurological regulatory functions related to the body. The expression of 23 of these genes was shown to be associated with survival outcome in gastric cancer patients in The Cancer Genome Atlas (TCGA) database. Based on the genome-wide association analysis of prognosis-related metabolites in gastric cancer, we suggest that gastric cancer survival-related genes may influence the proliferation and infiltration of gastric cancer cells, which provides a new idea to resolve the complex regulatory network of gastric cancer prognosis.

Prognostic Implication of Plasma Metabolites in Gastric Cancer.

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.

Association of job stress, FK506 binding protein 51 (FKBP5) gene polymorphisms and their interaction with sleep disturbance.

Background: Sleep disturbance is an outcome of multiple factors including environmental and genetic influences. Job stress, a complex environmental factor, likely affects sleep quality, significantly reducing the quality of life of workers. Additionally, FK506 binding protein 51 (FKBP5) may be a pathogenic factor for sleep disturbance as it regulates hypothalamic-pituitary-adrenal (HPA) axis activity, where HPA axis has been found to be involved in the regulation mechanism of sleep and stress response. Objectives: The main aim of this study was to investigate the association between job stress and FKBP5 gene polymorphism as well as their interaction with sleep disturbance in Chinese workers; to date, these relationships have not been explored. Methods: This is a cross-sectional study. A total of 675 railway workers (53.8% male) completed a short Effort-Reward Imbalance questionnaire and the Pittsburgh Sleep Quality Index. The SNaPshot single nucleotide polymorphism (SNP) assay was carried out by screening for FKBP5 SNPs in every participant. Generalized multifactor dimensionality reduction (GMDR) was used to identify the strongest G×E interaction combination. Results: The findings showed that job stress was significantly associated with sleep disturbance; specifically, scores on the PSQI subscales (sleep disturbance, sleep medication, and daytime dysfunction) exhibited significant differences between the two job stress groups (X2 = 18.10, p = 0.01). Additionally, the FKBP5 SNP rs1360780-TT (adjusted odds ratio [AOR] = 4.98, 95% confidence interval [CI] = 2.80-8.84) and rs3800373-CC genotype (AOR = 2.06, CI = 1.10-3.86) were associated with an increased risk of sleep disturbance. Job stress and rs1360780 and rs3800373 variants showed a high-dimensional interaction with sleep disturbance as determined by the GMDR model. Conclusion: The FKBP5 gene may increase susceptibility to job stress and result in sleep disturbance, especially in the presence of negative work-related events. These findings contribute to the field of sleep disturbance prevention and treatment.

lncRNA polymorphism affects the prognosis of gastric cancer.

BACKGROUND: Previous studies have found that lncRNA polymorphisms are associated with the prognosis of gastric cancer (GC), but the specific roles of many lncRNA polymorphism sites in gastric cancer are still unclear. Our study aims to deeply explore the relationship between genetic polymorphism of lncRNA and the prognosis of GC. METHODS: The genotypes of candidate SNP locus were detected by Sequenom Mass ARRAY SNP. We deeply analyzed the association of lncRNA polymorphisms with GC prognosis by univariate and multivariate Cox regression, stratified analysis, conjoint analysis, and log-rank test. RESULTS: We found that mutations at rs2579878 and rs10036719 loci reduced the risk of poor prognosis of GC. Stratified analysis showed that rs2795025, rs10036719, and rs12516079 polymorphisms were all associated with tumor prognosis. In addition, conjoint analyses showed that the interaction between these two polymorphic sites (rs2795025 and rs12516079) could increase the risk of poor prognosis. Multivariate analysis also found that the AG/AA genotype of rs10036719 and AG genotype of rs12516079 were independent prognostic factors. Moreover, the high expression of both CCDC26 and LINC02122 were shown to be associated with the poor survival status of GC patients. CONCLUSIONS: We find that the genetic polymorphism of lncRNA plays a role in the development of GC and is closely related to the survival time of patients. It could serve as a predictor of the prognosis of GC.

mGWAS identification of six novel single nucleotide polymorphism loci with strong correlation to gastric cancer.

BACKGROUND: Metabolite genome-wide association studies (mGWAS) are key for understanding the genetic regulation of metabolites in complex diseases including cancers. Although mGWAS has revealed hundreds of metabolomics quantitative trait loci (mQTLs) in the general population, data relating to gastric cancer (GC) are still incomplete. METHODS: We identified mQTLs associated with GC by analyzing genome-wide and metabolome-wide datasets generated from 233 GC patients and 233 healthy controls. RESULTS: Twenty-two metabolites were statistically different between GC cases and healthy controls, and all of them were associated with the risk of gastric cancer. mGWAS analyses further revealed that 9 single nucleotide polymorphisms (SNPs) were significantly associated with 3 metabolites. Of these 9 SNPs, 6 loci were never reported in the previous mGWAS studies. Surprisingly, 4 of 9 SNPs were significantly enriched in genes involved in the T cell receptor signaling pathway. CONCLUSIONS: Our study unveiled several novel GC metabolite and genetic biomarkers, which may be implicated in the prevention and diagnosis of gastric cancer.

The Interactive Effects between Particulate Matter and Heat Waves on Circulatory Mortality in Fuzhou, China.

The interactive effects between particulate matter (PM) and heat waves on circulatory mortality are under-researched in the context of global climate change. We aimed to investigate the interaction between heat waves and PM on circulatory mortality in Fuzhou, a city characterized by a humid subtropical climate and low level of air pollution in China. We collected data on deaths, pollutants, and meteorology in Fuzhou between January 2016 and December 2019. Generalized additive models were used to examine the effect of PM on circulatory mortality during the heat waves, and to explore the interaction between different PM levels and heat waves on the circulatory mortality. During heat waves, circulatory mortality was estimated to increase by 8.21% (95% confidence intervals (CI): 0.32-16.72) and 3.84% (95% CI: 0.28-7.54) per 10 µg/m3 increase of PM2.5 and PM10, respectively, compared to non-heat waves. Compared with low-level PM2.5 concentration on non-heat waves layer, the high level of PM2.5 concentration on heat waves layer has a significant effect on the cardiovascular mortality, and the effect value was 48.35% (95% CI: 6.37-106.89). Overall, we found some evidence to suggest that heat waves can significantly enhance the impact of PM on circulatory mortality.