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BACKGROUND: Endometrial cancer (EC) is one of the gynecologic malignancy cancer with increasing incidence and mortality rates, partly due to aging populations and genetic risks. This study explores the associations between biological age accelerations (BAA) and risk of incident EC and assesses the joint effect of genetic factor and BAA. MATERIALS AND METHODS: Based on the UK Biobank cohort, 132,315 women participants were included for primary analysis and 124,119 white participants for genetic risk analysis. Biological age(BA) was calculated using the Klemera-Doubal (KDM) and PhenoAge method based on clinical biomarkers. We calculated two metrics for BAA (including KDM residual and PhenoAge residual) using residual analysis, comparing them against chronological age. The risk of incident EC was evaluated using multivariable Cox proportional-hazards models, adjusting for relevant covariates. Polygenic risk scores (PRS) were computed from known EC-associated SNPs. RESULTS: Both KDM and PhenoAge residual, were significantly associated with increased EC risk. In fully adjusted models, the highest tertile of KDM and PhenoAge residual was significantly associated with incident EC compared with the lowest group, with HRs of 1.278 (P=0.0044) and 1.424 (P<0.0001), repectively. The population-attributable fractions were 7.84% for KDM residual (P=0.0044), 9.78% for PhenoAge residual (P=0.0005), and 8.47% for genetic risk (P=0.0005). Additionally, joint associations of BAA and genetic risk with incident EC was evaluated. Compared with low genetic risk and low BAA, high genetic risk and high BAA was significantly associated with the incidence of EC with HRs of up to 2.172 (95% CI 1.592-2.963) for KDM and 2.226 (95% CI 1.640-3.022) for PhenoAge. Overall, higher levels of PhenoAge residual were consistently associated with an increased risk of incident EC, regardless of genetic risk. CONCLUSION: BAA and genetics both enhance the risk of incident EC. The effect of the PhenoAge residual is greater than that of the investigated genes, which in turn is greater than that of the KDM residual. These findings highlight the importance of considering both BAA and genetic factors in EC prevention.
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The impairment of blood-brain barrier (BBB) integrity is the pathological basis of hemorrhage transformation and vasogenic edema following thrombolysis and endovascular therapy. There is no approved drug in the clinic to reduce BBB damage after acute ischemic stroke (AIS). Glial growth factor 2 (GGF2), a recombinant version of neuregulin-1ß that can stimulates glial cell proliferation and differentiation, has been shown to alleviate free radical release from activated microglial cells. We previously found that activated microglia and proinflammatory factors could disrupt BBB after AIS. In this study we investigated the effects of GGF2 on AIS-induced BBB damage as well as the underlying mechanisms. Mouse middle cerebral artery occlusion model was established: mice received a 90-min ischemia and 22.5 h reperfusion (I/R), and were treated with GGF2 (2.5, 12.5, 50 ng/kg, i.v.) before the reperfusion. We showed that GGF2 treatment dose-dependently decreased I/R-induced BBB damage detected by Evans blue (EB) and immunoglobulin G (IgG) leakage, and tight junction protein occludin degradation. In addition, we found that GGF2 dose-dependently reversed AIS-induced upregulation of vesicular transcytosis increase, caveolin-1 (Cav-1) as well as downregulation of major facilitator superfamily domain containing 2a (Mfsd2a). Moreover, GGF2 decreased I/R-induced upregulation of PDZ and LIM domain protein 5 (Pdlim5), an adaptor protein that played an important role in BBB damage after AIS. In addition, GGF2 significantly alleviated I/R-induced reduction of YAP and TAZ, microglial cell activation and upregulation of inflammatory factors. Together, these results demonstrate that GGF2 treatment alleviates the I/R-compromised integrity of BBB by inhibiting Mfsd2a/Cav-1-mediated transcellular permeability and Pdlim5/YAP/TAZ-mediated paracellular permeability.
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OBJECTIVE: The major challenge in routine endocervical curettage (ECC) among Human Papillomavirus (HPV) 16/18-positive patients is that only a small fraction benefit. Nevertheless, current reported models often overestimate the validity and necessity of ECC, making it difficult to improve benefits for patients. This research hypothesized that assessing paired boxed gene 1 methylation levels (PAX1m) and clinical characteristics could enhance the predictive accuracy of detecting additional high-grade squamous intraepithelial lesions or worse (HSIL +) through ECC that were not identified by colposcopy-directed biopsy (CDB). METHODS: Data from 134 women with HPV16/18 positivity undergoing CDB and ECC between April 2018 and April 2022 were collected and analyzed. Quantitative methylation-specific polymerase chain reaction (qMSP) was utilized to measure PAX1m, expressed as ΔCp. Univariate and multivariate regression analyses were conducted to screen variables and select predictive factors. A nomogram was constructed using multivariate logistic regression to predict additional HSIL + detected by ECC. The discrimination, calibration, and clinical utility of the nomogram were evaluated using receiver operating characteristic curves (ROC) and the calibration plot. RESULTS: Age (odds ratio [OR], 5.654; 95% confidence interval [CI], 1.131-37.700), cytology (OR, 24.978; 95% CI, 3.085-540.236), and PAX1 methylation levels by grade (PAX1m grade) (OR, 7.801; 95% CI, 1.548-44.828) were independent predictive factors for additional detection of HSIL + by ECC. In HPV16/18-positive women, the likelihood of additional detection of HSIL + through ECC increased with the severity of cytological abnormalities, peaking at 43.8% for high-grade cytological lesions. Moreover, when cytological findings indicated low-grade lesions, PAX1 methylation levels were positively correlated with the additional detection of HSIL + by ECC (P value < 0.001). A nomogram prediction model was developed (area under curve (AUC) = 0.946; 95% CI, 0.901-0.991), demonstrating high sensitivity (90.9%) and specificity (90.5%) at the optimal cutoff point of 107. Calibration analysis confirmed the model's strong agreement between predicted and observed probabilities. CONCLUSION: The clinical nomogram presented promising predictive performance for the additional detection of HSIL + through ECC among women with HPV16/18 infection. PAX1 methylation level could serve as a valuable tool in guiding individualized clinical decisions regarding ECC for patients with HPV 16/18 infection, particularly in cases of low-grade cytological findings.
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Colposcopia , Metilação de DNA , Papillomavirus Humano 16 , Papillomavirus Humano 18 , Nomogramas , Fatores de Transcrição Box Pareados , Infecções por Papillomavirus , Neoplasias do Colo do Útero , Humanos , Feminino , Fatores de Transcrição Box Pareados/genética , Papillomavirus Humano 16/genética , Papillomavirus Humano 16/isolamento & purificação , Adulto , Metilação de DNA/genética , Pessoa de Meia-Idade , Papillomavirus Humano 18/genética , Papillomavirus Humano 18/isolamento & purificação , Infecções por Papillomavirus/diagnóstico , Infecções por Papillomavirus/genética , Infecções por Papillomavirus/virologia , Neoplasias do Colo do Útero/genética , Neoplasias do Colo do Útero/virologia , Neoplasias do Colo do Útero/diagnóstico , Neoplasias do Colo do Útero/patologia , Curetagem/métodos , Curva ROC , Displasia do Colo do Útero/virologia , Displasia do Colo do Útero/genética , Displasia do Colo do Útero/diagnóstico , Displasia do Colo do Útero/patologia , Colo do Útero/patologia , Colo do Útero/virologiaRESUMO
BACKGROUND: Endometrial cancer (EC) as one of the most common gynecologic malignancies is increasing in incidence during the past 10 years. Genome-Wide Association Studies (GWAS) extended to metabolic and protein phenotypes inspired us to employ multi-omics methods to analyze the causal relationships of plasma metabolites and proteins with EC to advance our understanding of EC biology and pave the way for more targeted approaches to its diagnosis and treatment by comparing the molecular profiles of different EC subtypes. METHODS: Two-sample Mendelian randomization (MR) was performed to investigate the effects of plasma metabolites and proteins on risks of different subtypes of EC (endometrioid and non-endometrioid). Pathway analysis, transcriptomic analysis, and network analysis were further employed to illustrate gene-protein-metabolites interactions underlying the pathogenesis of distinct EC histological types. RESULTS: We identified 66 causal relationships between plasma metabolites and endometrioid EC, and 132 causal relationships between plasma proteins and endometrioid EC. Additionally, 40 causal relationships between plasma metabolites and non-endometrioid EC, and 125 causal relationships between plasma proteins and non-endometrioid EC were observed. Substantial differences were observed between endometrioid and non-endometrioid histological types of EC at both the metabolite and protein levels. We identified 7 overlapping proteins (RGMA, NRXN2, EVA1C, SLC14A1, SLC6A14, SCUBE1, FGF8) in endometrioid subtype and 6 overlapping proteins (IL32, GRB7, L1CAM, CCL25, GGT2, PSG5) in non-endometrioid subtype and network analysis of above proteins and metabolites to identify coregulated nodes. CONCLUSIONS: Our findings observed substantial differences between endometrioid and non-endometrioid EC at the metabolite and protein levels, providing novel insights into gene-protein-metabolites interactions that could influence future EC treatments.