Shilin Chen

Background: Gastric cancer (GC) is a leading cause of cancer death in China, particularly in rural high-incidence areas. miRNA-related SNPs may interact with diet to influence GC risk. We therefore examined miRNA-related SNPs in COPS2, CTSF, and NT5E, together with their cognate miRNAs, and assessed their interactions with dietary factors in overall, cardia, and non-cardia GC. Methods: A 1:1 matched case-control study included 550 GC cases and 550 controls from a high-incidence county in Fujian, China. Eight miRNA-related SNPs were genotyped, and seven conforming to Hardy–Weinberg equilibrium were included in the final analysis. Conditional logistic regression and generalized multifactor dimensionality reduction (GMDR) were used to assess associations and interactions. Results: CTSF rs4576 was associated with increased overall and non-cardia GC risk. NT5E rs6913634 was associated with increased overall and cardia GC risk, whereas miR-30a rs2222722 was associated with increased risk of cardia GC only. GMDR identified hard-food preference, pickled vegetable intake, and rs4576 as the best interaction model for overall GC. A positive additive interaction was observed between rs4576 and hard-food preference (RERI = 1.65, 95% CI: 0.049–3.26). G-allele carriers with hard-food preference or pickled vegetable intake had markedly elevated joint risks. Conclusions: miRNA-related polymorphisms in CTSF and NT5E and in miR-30a showed subsite-related association patterns in this high-risk Chinese population; however, without formal between-subsite heterogeneity tests, these should be interpreted as observed patterns rather than established subtype-specific differences. Additionally, the preliminary positive additive interaction between rs4576 and hard-food preference points to a potential gene–diet interaction that warrants validation in independent populations before it can inform dietary prevention.

Background/Objectives: Ambient fine particulate matter (PM_2.5) poses significant health risks to older adult populations, yet the specific contributions of its chemical constituents, particularly non-phthalate and non-organophosphate ester compounds, remain poorly understood. This study aimed to elucidate the mechanistic links between PM_2.5-bound ester exposures and metabolic pathway alterations in elderly individuals. Methods: A total of 258 elderly residents aged 60 years or older from Fuzhou, China, were recruited. Personal PM_2.5 exposure was monitored over 72 h using UPAS V2 samplers, with chemical components analyzed via gas chromatography–mass spectrometry (GC–MS). Plasma metabolomic profiling was conducted using liquid chromatography–mass spectrometry (LC–MS), and metabolic pathway enrichment was performed using MetaboAnalyst 5.0. Linear regression models adjusted for covariates (age, sex, BMI, lifestyle factors) assessed associations between ester exposures and metabolite abundance. Results: The mean PM_2.5 concentration was 38.06 μg/m³, with ester compounds dominating the chemical composition. Twenty high-concentration non-target esters were prioritized for analysis. PM_2.5 ester exposure was associated with alterations in key metabolic pathways, including steroid biosynthesis, glycolysis/gluconeogenesis, glycerophospholipid metabolism, and purine/pyrimidine metabolism. When interpreted alongside prior epidemiological evidence, these alterations represent putative links to increased risks of insulin resistance, cardiovascular dysfunction, and metabolic syndrome—relationships that require confirmation in prospective cohort studies and controlled toxicological experiments. Conclusions: Putatively annotated PM_2.5-bound ester compounds, particularly non-regulated subclasses, are associated with systemic metabolic alterations in older adults, coincident with perturbations in steroid and lipid metabolism. While these findings are exploratory and hypothesis-generating, they highlight the need to incorporate specific ester profiles into PM_2.5 risk assessments and develop targeted interventions for vulnerable aging populations.

Background: Gastric cancer remains a leading cause of cancer-related deaths worldwide. Although significant progress has been made in clinical diagnosis and treatment, the molecular mechanisms underlying gastric cancer have not yet been fully elucidated. To address this, this study employs a multi-omics approach to systematically analyze the molecular characteristics of gastric cancer. Methods: This case–control study enrolled 218 GC patients and 218 healthy controls, and adopted a multi-omics strategy combining inductively coupled plasma mass spectrometry (ICP-MS), element-related genome-wide association study (eGWAS), and untargeted metabolomics to explore the element-gene-metabolite regulatory axis in GC. Results: A total of nine plasma differential elements associated with gastric cancer were identified, with a combined diagnostic accuracy of 0.918. Specifically, elements such as Fe, Co, and Li showed significant correlations with 63 genes involved in key signaling pathways, including MAPK, SMAD, and Wnt. Genome-wide association studies (GWAS) revealed that gastric cancer-related genes were significantly enriched in cancer-associated pathways and signaling cascades such as Rap1. Metabolomic analysis further demonstrated that 20 elements in the gastric cancer cohort correlated with 94 metabolites, predominantly enriched in pyrimidine and glutathione metabolism pathways. Conclusions: These nine plasma differential elements showed high combined diagnostic efficacy and were associated with genes and metabolites enriched in cancer-related signaling, metabolic reprogramming, and DNA damage response pathways. Together, these findings suggest potential multi-level associations among plasma elemental alterations, genetic variation, and metabolic dysregulation in GC, providing candidate circulating biomarkers and mechanistic clues for future investigation.

(1) Background: This study investigated the characteristics and influencing factors of exposure to fine particulate matter (PM_2.5) and its chemical composition among elderly residents, with the aim of revealing potential differences in exposure. (2) Methods: A total of 258 elderly individuals were monitored for 72 h through individual, indoor, and outdoor PM_2.5 measurements. Concentrations were determined, and non-targeted components were analyzed by gas chromatography-mass spectrometry (GC-MS). Through Spearman correlation analysis, generalized linear model, and linear regression to explore the influencing factors. (3) Results: The individual PM_2.5 concentration was higher than both the indoor and outdoor concentrations. A total of 20,962 compounds were detected in personal PM_2.5 samples, 6794 in indoor PM_2.5 samples, among which 4285 compounds were shared between the two sample types. The components were mainly esters, aromatic compounds, and amines. PM_2.5 concentration was correlated with age, housing area, humidifier use, and second-hand smoke exposure. Chemical composition is related to outdoor pollution, furniture material, and daily behavior. (4) Conclusions: The individual PM_2.5 concentration is higher than the environmental concentration, and its chemical composition overlaps with the indoor and outdoor environment, which is jointly affected by demography, living conditions, and daily behavior.

Background: This study was performed to evaluate the early warning value of wastewater-based epidemiology (WBE) in monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its correlation with population-level coronavirus disease 2019 (COVID-19) infection trends. Methods: Wastewater samples from Fuzhou’s Sewage Treatment Plant A were concentrated via membrane filtration and quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Viral load data were integrated with sentinel hospital positivity rates and respiratory outpatient visits from 11 city hospitals. Stratified cross-correlation lag analysis was performed by gender, age, and hospital type. Results: Using the lowest single-day genome concentration as a proxy for daily SARS-CoV-2 levels was advantageous. Wastewater viral concentrations correlated positively with clinical cases, with peaks preceding reports by 0 to 17 days. Stratified analysis further indicated that women, older adults, and individuals from general hospitals were more sensitive to changes in wastewater viral loads, showing stronger correlations between infection trends and wastewater signals. Conclusions: Wastewater surveillance of SARS-CoV-2 can effectively predict COVID-19 infection trends and offers a scientific basis for stratified and targeted interventions. The findings underscore the value of WBE as an early warning tool in public health surveillance.