Periodontitis is a chronic and multifactorial inflammatory disease. However, existing medications often lack sufficient therapeutic effects. The aim is to identify potential biomarkers and efficient therapeutic targets using Mendelian randomisation (MR) and single-cell analysis. MR analysis was conducted based on the cis-expression quantitative trait loci (cis-eQTLs) extracted from the eQTLGen Consortium and genome-wide association study (GWAS) data of periodontitis sourced from the Gene Lifestyle Interactions in Dental Endpoints (GLIDE) consortium (17,353 cases, 28,210 controls). Subsequently, colocalisation analysis was employed to detect whether genes and periodontitis shared the same casual variant. Finally, enrichment analysis, protein-protein interaction (PPI) networks, drug prediction, phenome-wide association study (PheWAS), molecular docking, and single-cell analysis were conducted to validate the significance of target genes. Fourteen drug targets were significant related with periodontitis in MR analysis. Following the colocalisation and summary-data-based MR (SMR) analysis, 3 targets (S100A12, S100A9, and S100A8) were classified into tier 1 with strong evidence, 6 therapeutic targets (ADAM12, ADHFE1, BLK, HEBP1, SERPINE2, and TEK) were classified into tier 2 with moderate evidence, and 5 therapeutic targets (LY86, MMEL1, S100B, SPP1, and TRIB3) were classified into tier 3 with convincing evidence. PheWAS analysis showed that only TEK and SPP1 in tier 2 may induce side effects, including cardiometabolic and oncological issues. Molecular docking demonstrated strong binding between drugs and their respective protein targets. In the single-cell analysis, 5 target genes (HEBP1, LY86, S100A8, S100A9, and S100A12) exhibited enrichment in monocytes, while BLK and LY86 were primarily enriched in B cells. The study identified 14 potential therapeutic targets for periodontitis. Among these, 3 therapeutic targets (S100A12, S100A9, and S100A8) demonstrated robust and well-supported results. Drugs designed to target these genes have a higher possibility of success in clinical trials, which are hopeful for prioritising periodontitis drug development.

Dental caries is a localized deterioration of hard tissues caused by acidic by-products of bacterial fermentation of dietary carbohydrates. However, except for fluoride, there were few studies focusing on the direct association between other trace elements. This study aimed to explore how other trace elements affect dental caries. Data from 10,426 individuals were collected from the National Health and Nutrition Examination Survey (NHANES) during the 2010–2016 cycle. The sample was restricted to individuals aged 20 years or older who had complete clinical oral examination data, and who had also completed blood (N = 10,086), serum (N = 5,152), or urine tests (N = 5,297). These three groups were weighted to represent multi-year data. The effects of 20 trace elements on dental caries experience were investigated using quartiles of continuous variables and weighted multivariate logistic regression models. The analysis revealed significant differences in trace elements (lead, cadmium, mercury, molybdenum, tin, and tungsten) between individuals with no dental caries experience and those with dental caries experience (P < 0.05). Adjusted results from the weighted multivariate logistic regression model showed that the odds ratios (OR) for following trace elements were significantly positively associated with dental caries experience: lead (OR = 1.0105; 95% CI 1.0030–1.0181; P = 0.0077), cobalt (OR = 1.0048; 95% CI 1.001–1.0087; P = 0.0154), cesium (OR = 1.0028; 95% CI 1.0011–1.0046; P = 0.0027), and arsenic (OR = 1.0002; 95% CI 1-1.0004; P = 0.0244), while iron (OR = 0.9974; 95% CI 0.9954–0.9994; P = 0.0116) showed the negative association. The results of this cross-sectional study conducted on the U.S. population identified a statistically significant positive association between dental caries and trace elements, including lead, cobalt, cesium, and arsenic, while a negative association was observed with iron.

Objective This study aims to explore shared key genes between head and neck neoplasm (HNN) and aging. Methods Using single-cell RNA sequencing data of peripheral blood from HNSCC patients, aging individuals, and healthy controls, we identified cross-group co-expressed, downregulated cell subpopulations as core targets. Integrated pseudotime trajectory analysis and intercellular communication modeling were employed to investigate the dynamic evolution and functional interaction patterns of these subpopulations. Differentially expressed genes were identified, followed by Mendelian randomization (MR) analysis to assess their causal associations with HNN. Co-localization analysis was performed using GWAS data for HNN and expression quantitative trait loci (eQTL) datasets. Key genes were further subjected to metabolic pathway enrichment analysis. Results T cell subsets were found to be represented in both HNN and aging. Among them, CD4_naive T cells were down-regulated in both groups, leading to the identification of 24 differentially expressed genes. MR studies have shown that CCR, LEF1, NOSIP and FHIT have causal relationships with HNN. In the validation phase, however, only FHIT was retained, for which co-localization analysis revealed limited evidence of a shared causal variant between the GWAS and eQTL signals (H4 = 0.01). The metabolic enrichment highlighted metabolic pathways associated with these genes. Conclusions This study identified CD4_naive T cells down-regulation as a shared feature of HNN and aging and highlighted FHIT as potential molecular links. These findings may provide novel insights into the intersection of aging and tumorigenesis based on MR and single-cell analysis, offering potential targets for combined therapeutic strategies.

Background: Melanoma, a type of skin cancer that can spread to other parts of the body, currently lacks highly precise individualized treatment options. Methods: We performed multi-omics integration on The Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort to identify melanoma molecular subtypes. The identified genes were validated in independent meta cohorts from GEO, followed by transcriptome-wide association study (TWAS) validation using Genotype-Tissue Expression (GTEx) and UK Biobank datasets. Additionally, we analyzed machine learning-driven signature (CMLS) development, tumor microenvironment characteristics, immunotherapy response, and potential therapeutic targets. Finally, single-cell and spatial transcriptomics provided further biological insights and the pathomechanisms. Result: Our study identified two distinct molecular subtypes of SKCM using multimodal data integration with the MOVICS package: Cancer Subtype 1 (CS1) and CS2. CS2 showed a better prognosis and was enriched in immune-suppressive pathways such as WNT–β signaling, while CS1 exhibited higher activation of the PI3K pathway and DNA repair mechanisms, along with greater tumor invasiveness. TWAS analysis results combined the findings from TCGA-SKCM and the meta-cohort, identifying six significant prognostic-related genes (SPRGs). The CMLS prognostic model, based on SPRGs ( CAP2 , SELL , and LAPTM5 as risk factors and GZMA , FCER1G , and LYZ as protective factors), stratified patients into high-group (poorer survival) and low-risk groups. Single-cell and spatial transcriptomic analyses further validated CMLS prognostic results, highlighting distinct tumor microenvironment interactions and progression trajectories. Conclusion: Identifications of molecular subtypes and CMLS represent a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice foundation for personalized therapies.

Objective: The purpose was to investigate the shared key genes between chronic periodontitis (CP) and aging. Materials and Methods: First, we collected single‐cell RNA sequencing (scRNA‐seq) data of peripheral blood samples from patients with CP, aging, and healthy control (HC) and confirmed the cross‐group coexpressed and downregulated cell subsets as the core research objects. Subsequently, we explored the dynamic evolutionary rules and functional interaction patterns of these cell subsets by integrating pseudotime trajectory analysis and intercellular communication modeling. Then, we screened the differentially expressed gene targets and adopted mendelian randomization (MR) analysis to clarify causal association between aging and CP susceptibility. Finally, we performed colocalization analysis by integrating data from CP genome‐wide association studies (GWAS) and expression quantitative trait locus datasets and conducted metabolic pathway enrichment analysis on the key genes obtained. Results: Subsets of T cells were found to account for a significant proportion in both CP patients and aging individuals. CD8_CM cells showed a downward trend in these two groups, screening of 16 differentially expressed genes. MR indicated that GZMK, KLRG1, and LYAR had causal relationships and were retained in the validation phase, and colocalization analysis yielded limited evidence supporting the existence of shared causal variants. Furthermore, metabolic enrichment analysis delineated the metabolic pathways functionally associated with these genes. Conclusion: This study found the downregulation of CD8_CM cells is a shared feature of CP and aging, and pointed out that GZMK, KLRG1 and LYAR may be the molecular links, offering an innovative perspective on elucidating the intersection mechanisms underlying aging and CP.