MiRNA polymorphisms affect the prognosis of gastric cancer: insights from Xianyou, Fujian.

Introduction: Gastric cancer, characterized by high incidence and substantial disease burden, has drawn continuous attention regarding its occurrence and prognosis. Genetics plays a crucial role in influencing the prognosis of gastric cancer, and single nucleotide polymorphisms are closely associated with the occurrence, development, and prognosis of this malignant tumor. Our study aims to conduct survival analysis on patients carrying different single nucleotide polymorphisms, exploring the relationship between miRNA single nucleotide polymorphisms and the prognosis of gastric cancer. Methods: Genetic data from 344 patients in Xianyou, Fujian, formed the basis of our study. We delineated the survival rate and median survival time, utilizing the log-rank test and COX regression analysis as statistical tools. Results: Upon stratifying the data by sex or operation, it was discerned that the GG genotype at MSH2 rs17502941 independently posed a heightened risk for gastric cancer. Other stratification analyses suggested that the subsequent single nucleotide polymorphisms were correlated with patient prognosis: rs17502941, rs884225, rs1468063, rs7143252, and rs2271738. Discussion: The outcomes of this study strongly suggest that miRNA polymorphisms significantly influence the survival time of gastric cancer patients and can serve as effective predictors for the prognosis of gastric cancer.

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.

Development of statistical regression and artificial neural network models for estimating nitrogen, phosphorus, COD, and suspended solid concentrations in eutrophic rivers using UV-Vis spectroscopy.

River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet-visible (UV-Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions. To address this, a framework integrating desktop and in situ UV-Vis spectrometers was developed to establish reliable conversion models. The absorption spectra obtained from a desktop spectrometer were utilized to create models for estimating nitrate-nitrogen (NO3-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). We validated these models using the absorption spectra obtained from an in situ spectrometer. Partial least squares regression (PLSR) employing selected wavelengths and principal component regression (PCR) employing all wavelengths demonstrated high accuracy in estimating NO3-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH4-N concentration using all wavelengths. Due to the dominance of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and PCR methods utilizing all wavelengths effectively estimated TP and SS, respectively. The determination coefficients (R2) of all the calibrated models exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were within 0.4. Our approach shows excellent efficiency and potential in establishing reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This approach eliminates the need for time-consuming and uncertain in situ absorption spectrum measurements during model setup, which may be affected by fluctuating natural and anthropogenic environmental conditions.

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.