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ObjectivePatients with rheumatoid arthritis (RA) have an increased risk of developing pulp and periapical disease (PAP), but the causal relationship and shared genetic factors between these conditions have not been explored. This study aimed to investigate the bidirectional causal relationship between RA and PAP and to analyze shared genes and pathogenic pathways.MethodsWe utilized GWAS data from the IEU Open GWAS Project and employed five Mendelian randomization methods (MR Egger, weighted median, inverse variance weighted, simple mode, and weighted mode) to investigate the bidirectional causal relationship between RA and PAP. Transcriptome data for RA and irreversible pulpitis (IRP) were obtained from the GEO database. Hub genes were identified through differential analysis, CytoHubba, machine learning (ML), and other methods. The immune infiltration of both diseases was analyzed using the ssGSEA method. Finally, we constructed a regulatory network for miRNAs, transcription factors, chemicals, diseases, and RNA-binding proteins based on the identified hub genes.ResultsRA was significantly associated with an increased risk of PAP (OR = 1.1284, 95% CI 1.0674-1.1929, p < 0.001). However, there was insufficient evidence to support the hypothesis that PAP increased the risk of RA. Integrating datasets and differential analysis identified 84 shared genes primarily involved in immune and inflammatory pathways, including the IL-17 signaling pathway, Th17 cell differentiation, and TNF signaling pathway. Using CytoHubba and three ML methods, we identified three hub genes (HLA-DRA, ITGAX, and PTPRC) that are significantly correlated and valuable for diagnosing RA and IRP. We then constructed a comprehensive regulatory network using the miRDB, miRWalk, ChipBase, hTFtarget, CTD, MalaCards, DisGeNET, and ENCORI databases.ConclusionRA may increase the risk of PAP. The three key genes, HLA-DRA, ITGAX, and PTPRC, have significant diagnostic value for both RA and IRP.

BackgroundTraditional anthropometric measures, including body mass index (BMI), are insufficient for evaluating gallstone risk. This study investigated the association between novel anthropometric indices and gallstone risk among 6,848 participants from the National Health and Nutrition Examination Survey in the United States.MethodsMeasures calculated included weight (WT), BMI, waist circumference (WC), waist-to-height ratio (WtHR), conicity index (CI), A Body Shape Index (ABSI), Body Roundness Index (BRI), Abdominal Volume Index (AVI), and Weight-adjusted Waist Index (WWI). Logistic regression and smooth curve fitting assessed the relationships between these indices and gallstones, complemented by receiver operating characteristic (ROC) curve analysis to evaluate their discriminative power.ResultsThe results indicated significant differences between study groups, with a positive and independent correlation identified between gallstones and all measures except ABSI. Specifically, per 1 SD increase in WC, WT, BMI, WtHR, and AVI was associated with a 57%, 59%, 52%, 53%, and 53% increased risk of gallstones, respectively. Dose-response analysis confirmed a positive correlation between these indices and gallstone risk. ROC analysis highlighted WtHR and BRI as having superior discriminative abilities (AUC = 0.6703). Further, among participants with a BMI < 30 kg/m2, elevated levels of WT, WtHR, CI, BRI, and WWI significantly increased the risk of gallstones (P < 0.001). Likewise, elevated BMI heightened the risk at low levels of WT, WC, WtHR, BRI, AVI, and CI (P < 0.001).ConclusionThis study supports the positive association between various anthropometric indicators and gallstones, recommending that newer anthropometric indices be considered more extensively to enhance gallstone prevention and treatment strategies.

BackgroundLife’s essential’ 8 (LE8) is a newly updated cardiovascular health (CVH) metrics from the American Heart Association, with close relevance to metabolism. Our objective is to explore the association between LE8 scores and incidence of metabolic dysfunction-associated fatty liver disease (MAFLD) and advanced liver fibrosis in American adults.MethodsThis population-based cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018, encompassing adults aged 20 years or older. Validated non-invasive scoring systems were employed to define liver steatosis and advanced liver fibrosis. Multivariable logistic regression and smooth curve fitting techniques were applied to evaluate the associations. All analyses were adjusted for the survey’ complex design parameters and accounted for sample weights.ResultsA total of 11,820 participants were included. A higher LE8 score was found to be inversely associated with the incidence of MAFLD and advanced liver fibrosis, with odds ratios (OR) of 0.64 (95% CI: 0.57–0.71) for MAFLD and 0.75 (95% CI: 0.61–0.92) for advanced liver fibrosis per 1 standard deviation (SD) increase in LE8 score. Similar patterns were found in the relationship between health behaviors/factors score and incidence of MAFLD and advanced liver fibrosis. In subgroup analyses, the interaction test showed that age, education level, marital status, CVD, hypertension and diabetes had a significant impact on the association between LE8 score and MAFLD (all P for interaction < 0.05). Among male, elderly, wealthy, other race, CVD, diabetes and depression participants, the correlation between LE8 score and advanced liver fibrosis was not statistically significant (P > 0.05). Younger participants exhibited a more pronounced negative association between the CVH metric and both MAFLD and advanced life fibrosis.ConclusionLE8 and its subscales score were inversely associated with the presence of MAFLD and advanced liver fibrosis in non-linear patterns. Optimal LE8 score may significantly reduce the risk of liver steatosis and fibrosis.

The RNA methylation of N6 adenosine (m6A) plays a crucial role in various biological processes. Strong evidence reveals that the dysregulation of long non-coding RNAs (lncRNA) brings about the abnormality of downstream signaling in multiple ways, thus influencing tumor initiation and progression. Currently, it is essential to discover effective and succinct molecular biomarkers for predicting colorectal cancer (CRC) prognosis. However, the prognostic value of m6A-related lncRNAs for CRC remains unclear, especially for progression-free survival (PFS). Here, we screened 24 m6A-related lncRNAs in 622 CRC patients and identified five lncRNAs (SLCO4A1-AS1, MELTF-AS1, SH3PXD2A-AS1, H19 and PCAT6) associated with patient PFS. Compared to normal samples, their expression was up-regulated in CRC tumors from TCGA dataset, which was validated in 55 CRC patients from our in-house cohort. We established an m6A-Lnc signature for predicting patient PFS, which was an independent prognostic factor by classification analysis of clinicopathologic features. Moreover, the signature was validated in 1,077 patients from six independent datasets (GSE17538, GSE39582, GSE33113, GSE31595, GSE29621, and GSE17536), and it showed better performance than three known lncRNA signatures for predicting PFS. In summary, our study demonstrates that the m6A-Lnc signature is a promising biomarker for forecasting patient PFS in CRC.

Background: Here, we establish a prognostic signature based on glycosyltransferase-related genes (GTRGs) for head and neck squamous cell carcinoma (HNSCC) patients.Methods: The prognostic signature of GTRGs was constructed via univariate and multivariate Cox analyses after obtaining the expression patterns of GTRGs from the TCGA. A nomogram based on the signature and clinical parameters was established to predict the survival of each HNSCC patient. Potential mechanisms were explored through gene set enrichment analysis (GSEA) and immune cell infiltration, immune checkpoints, immunotherapy, and tumor mutational burden (TMB) analyses. The expression differences and prognostic efficacy of the signature were verified through the gene expression omnibus (GEO) and several online databases.Results: The prognostic signature was constructed based on five glycosyltransferases (PYGL, ALG3, EXT2, FUT2, and KDELC1) and validated in the GSE65858 dataset. The pathways enriched in the high- and low-risk groups were significantly different. The high-risk group had higher tumor purity; lower infiltration of immune cells, such as CD8+ T cells and Tregs; higher cancer-associated fibroblast (CAF) infiltration; lower immune function; and lower checkpoint expression. The signature can also be applied to distinguish whether patients benefit from immunotherapy. In addition, the high-risk group had a higher TMB and more gene mutations, including those in TP53, CSMD1, CDKN2A, and MUC17.Conclusion: We propose a prognostic signature based on glycosyltransferases for HNSCC patients that may provide potential targets and biomarkers for the precise treatment of HNSCC.

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.