Background: This study aimed to determine serum Bisphenol A (BPA) concentrations in patients with dilated cardiomyopathy (DCM) as well as the association between serum BPA and several hormonal parameters in DCM patients compared with a healthy control group. Materials and methods: Eighty-eight DCM patients and 88 age- and gender-matched healthy controls were included. Serum BPA levels and several hormonal parameters (including total testosterone (T), sex hormone-binding globulin (SHBG) and estradiol (E2) were measured by using corresponding ELISA Kits. The free androgen index (FAI) was calculated by the formula: total T in nmol/L × 100/SHBG in nmol/L. Results: BPA levels in the total DCM group were significantly higher compared with that in the controls (6.9 ± 2.7 ng/mL vs. 3.8 ± 1.9 ng/mL, p < 0.001). Significant difference was also observed in SHBG and FAI between DCM patients and controls, (76.9 ± 30.9 nM/L vs. 41.0 ± 15.6 nM/L and 2.9 ± 3.5 vs.5.3 ± 2.6, respectively, both of p < 0.001). Similar trends were observed in the male and female subgroup. Mean T level was lower in DCM group than in control group (540.8 ± 186.0 pg/mL vs. 656.3 ± 112.9 pg/mL, p < 0.001). Linear regression analysis has shown that increasing serum BPA levels were statistically significantly associated with increased SHBG levels. However, no statistical difference was noted for E2. Conclusion: Our findings firstly demonstrated that BPA exposure increased in DCM patients compared with that in healthy controls, while FAI and T levels decreased. SHBG presented a positive association with BPA. It is concluded that hormone disorder induced by BPA exposure might be an environmental factor in the pathology of DCM.

Background: Allograft Inflammatory Factor 1 (AIF-1) is a member of the allograft inflammatory factor gene family and plays an essential role in the occurrence and development of malignant tumors. However, little is known about the expression pattern, predictive value, and biological function of AIF-1 across cancers. Materials and Methods: We first analyzed AIF-1 expression across cancers based on data from public databases. Univariate Cox regression and Kaplan-Meier analyses were used to explore the predictive value of AIF-1 expression in various cancers. Moreover, gene set enrichment analysis (GSEA) was applied to determine the cancer hallmarks associated with AIF-1 expression. Spearman correlation analysis was performed to investigate the association between AIF-1 expression and tumor microenvironment scores, immune cell infiltration, immune-related genes, TMB, MSI, and DNA methyltransferases. Results: AIF-1 expression was upregulated in most cancer types and exhibited prognosis-predictive ability. AIF-1 expression was positively correlated with immune infiltrating cells and immune checkpoint-related genes in most cancers. Additionally, the promoter methylation level of AIF-1 was different in distinct tumors. High methylation levels of AIF-1 were associated with a worse prognosis in UCEC and melanoma, whereas they were associated with a better prognosis in GBM, KIRC, OV, and UVM. Finally, we found that AIF-1 was significantly highly expressed in KIRC tissues. Functionally, silencing AIF-1 dramatically decreased proliferation, migration, and invasion abilities. Conclusion: Our results reveal that AIF-1 acts as a robust tumor biomarker and is closely correlated with tumor immune infiltration. Furthermore, AIF-1 may function as an oncogene and promote tumor progression in KIRC.

Cardiorenal syndrome (CRS) is a complex clinical condition characterized by the simultaneous dysfunction of the heart and kidneys. The atherogenic index of plasma (AIP), calculated as the logarithm of the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C), has emerged as a potential biomarker for cardiovascular risk. This study investigates the association between AIP and CRS, aiming to explore the potential linkage between AIP and CRS. Data were sourced from the National Health and Nutrition Examination Survey spanning 2005-2018, involving 35,365 participants after applying exclusion criteria. The primary exposure variable was AIP, categorized into quartiles, while the primary outcome variable was CRS, defined by the coexistence of cardiovascular disease (CVD) and chronic kidney disease (CKD). Statistical analyses, considering sample weights, included ANOVA, Chi-square tests, logistic regression models, and restricted cubic spline (RCS) analysis to examine nonlinear relationships. The weighted logistic regression analysis showed a positive correlation between AIP and CRS across all models. In the fully adjusted model, the highest AIP quartile had a significantly increased odds ratio (OR) for CRS (Q4: OR = 1.62; 95% CI: 1.21-2.15). RCS analysis confirmed a positive correlation between AIP and CRS, with TG positively and HDL-C negatively correlated with CRS. Subgroup analysis indicated a significant interaction with hypertension, showing a stronger association in non-hypertensive individuals. Higher AIP levels are associated with an increased prevalence of CRS, with a notable hypertension-specific interaction indicating a higher effect in individuals without hypertension.

Background: Snail family transcriptional repressor 2 (SNAI2) is a transcription factor that induces epithelial to mesenchymal transition in neoplastic epithelial cells. It is closely related to the progression of various malignancies. However, the significance of SNAI2 in human pan-cancer is still largely unknown.Methods: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were taken to examine the SNAI2 expression pattern in tissues and cancer cells. The link between SNAI2 gene expression levels and prognosis, as well as immune cell infiltration, was investigated using the Kaplan-Meier technique and Spearman correlation analysis. We also explored the expression and distribution of SNAI2 in various tumor tissues and cells by the THPA (Human Protein Atlas) database. We further investigated the relationship between SNAI2 expression levels and immunotherapy response in various clinical immunotherapy cohorts. Finally, the immunoblot was used to quantify the SNAI2 expression levels, and the proliferative and invasive ability of pancreatic cancer cells was determined by colony formation and transwell assays.Results: We discovered heterogeneity in SNAI2 expression in different tumor tissues and cancer cell lines by exploring public datasets. The genomic alteration of SNAI2 existed in most cancers. Also, SNAI2 exhibits prognosis predictive ability in various cancers. SNAI2 was significantly correlated with immune-activated hallmarks, cancer immune cell infiltrations, and immunoregulators. It’s worth noting that SNAI2 expression is significantly related to the effectiveness of clinical immunotherapy. SNAI2 expression was also found to have a high correlation with the DNA mismatch repair (MMR) genes and DNA methylation in many cancers. Finally, the knockdown of SNAI2 significantly weakened the proliferative and invasive ability of pancreatic cancer cells.Conclusion: These findings suggested that SNAI2 could be used as a biomarker in human pan-cancer to detect immune infiltration and poor prognosis, which provides a new idea for cancer treatment.

Objective: Numerous single nucleotide polymorphisms (SNPs) have been identified as genetic contributors to atrial fibrillation (AF). The aim of this study was to investigate the effects of genome-wide N⁶-methyladenosine (m⁶A)-SNPs on AF. Method: m⁶A-SNPs were identified by analysis of raw data from published AF GWAS datasets and the list of m⁶A-SNPs from the m⁶AVar database. Expression quantitative trait loci (eQTL) analysis was conducted to evaluate the effects of m⁶A-SNPs on gene expression. The expression of linked genes was validated in three independent AF-associated gene expression datasets (GSE14975, GSE108660 and GSE2240). Results: A total of 1429 (6.2%) unique m⁶A-SNPs that were significantly associated with AF were identified. Seventeen m⁶A-SNPs in 14 genes reached genome-wide significance. Eight m⁶A-SNPs demonstrated eQTL signals. Four m⁶A-SNPs (rs383692, rs3211105, rs1061259 and rs1152582) exhibited strong cis-eQTL signals associated with the gene expression levels of SMIM8, JMJD1C and SYNE2. SYNE2 and SMIM8 had differential gene expression levels between the AF and sinus rhythm groups. In addition, SYNE2 expression was uniformly downregulated in AF samples compared with normal control samples in the three datasets. Conclusions: Our results provide the first demonstration that m⁶A-SNPs are strongly associated with AF, and extend understanding of m⁶A modification as a potential biological pathway underlying AF.