Could METTL3 and METTL14-mediated N⁶-methyladenosine (m⁶A) modification play possible cooperative roles in pathogenesis and progression of endometriosis? An investigation into m⁶A methylation profiles and the roles of METTL3 and METTL14 in the m⁶A regulation and pathogenesis of endometriosis. The m⁶A methylation and mRNA levels in paired ectopic endometrium and eutopic endometrium were measured using m⁶A-mRNA epitranscriptomic microarrays. The functions of m⁶A methylation in mRNAs were predicted using bioinformatics analysis. The levels of m⁶A methyltransferases were detected using quantitative polymerase chain reaction. The role of METTL3 and METTL14 in endometriosis was explored using eutopic endometrium stromal cells. The m⁶A methylation levels were decreased in 1312 mRNAs and increased in 518 mRNAs; 1797 mRNAs were increased and 2580 mRNAs were reduced in the ectopic endometrium compared with the eutopic endometrium. Pathway analysis found that the genes with hypo-methylated m⁶A were significantly associated with important pathways in endometriosis, including oestrogen, Hippo, and PI3K-Akt signalling and cell-cell adhesion. Furthermore, METTL3 and METTL14 were downregulated in the ectopic endometrium compared with the eutopic endometrium (P < 0.001). Simultaneous METTL3 and METTL14 knockdown increased cell proliferation and invasion. Taken together, these data reveal a differential m⁶A epitranscriptomic pattern in endometriosis. The N⁶-methyladenosine modification mediated by METTL3 and METTL14 play a cooperative role in promoting cell proliferation and invasion in a model of endometriosis. Therefore, METTL3 and METTL14 may be a novel treatment target of the disease.

Background: Endoplasmic reticulum (ER) stress is closely related to the occurrence, development and treatment of tumors. Recent studies suggest ER stress as a therapeutic strategy of choice for cancer. However, ER stress-related long non-coding RNA (lncRNA) predictive value in endometrial carcinoma (UCEC) remains to be further evaluated. The purpose of this study was to establish relies on the signature of ER stress-related lncRNA forecast to predict the prognosis of patients with UCEC.Methods: We downloaded the RNA expression profile dataset and matched clinical data from the Cancer Genome Atlas (TCGA) database, and applied univariate and multivariate Cox regression analysis to build predictive signature. Kaplan-meier method was used to evaluate overall survival (OS) and disease-free survival (DFS). Gene set enrichment analysis (GSEA) was used to study the functional characteristics. Single sample Gene set enrichment analysis (ssGSEA) was used to analyze the relationship between immune status and predicted signature. Correlations between the potential usefulness of treatment for UCEC patients and predictive signature were also analyzed.Results: We established a signature composed of eight ER stress-related lncRNAs (MIR34AHG, AC073842.2, PINK1AS, AC024909.2, MIR31HG, AC007422.2, AC061992.1, AC003102.1). The signature of ER stress-related lncRNA provided better diagnostic value compared with age and tumor grade, and the area under the receiver operating curve was 0.788. The overall and disease-free survival probability of patients in the high-risk group is lower than that in the low-risk group. GSEA indicated that the pathways were mainly enriched for cancer, immunity and reproduction related pathways. ss-GSEA shows that prediction signature and activation of dendritic cells, immature dendritic cells, T helper cells and immune status of the Treg are significantly related. High-risk groups may against PD - 1/L1 immunotherapy and JNK inhibitors VIII, Z.LLNle.CHO, DMOG and JNK. 9 l more sensitive.Conclusion: The ER stress signature can independently predict the prognosis of UCEC patients, and provide guidance for conventional chemotherapy and immunotherapy of UCEC patients.

Background: Hepatitis B surface antigen (HBsAg) loss is durable in some patients with chronic hepatitis B (CHB) after pegylated interferon α (Peg-IFNα)–based treatment. However, factors associated with durable HBsAg loss are not clear. This study aimed to investigate predictors of durable HBsAg loss. Methods: Patients with CHB and HBsAg loss were enrolled from 2018 to 2022 in the Everest Project and followed up for 48 weeks after Peg-IFNα therapy discontinuation. Sustained response (SR) was defined as durable HBsAg loss with or without hepatitis B surface antibody (HBsAb) appearance at the end of follow-up, while recurrence was characterized by HBsAg reversal and/or detectable HBV DNA during follow-up. Data on the risk factors of durable HBsAg loss were analyzed between SR and recurrence. Results: A total of 1046 patients were included in this study: 822 with SR and 224 with recurrence. At end of follow-up, 1.41% (224/1046) had relapsed. HBsAb at the end of Peg-IFNα treatment and Peg-IFNα consolidation therapy time were associated with SR (area under the receiver operating curve [AUROC], 0.709 [P < .001]; AUROC, 0.620 [P < .001], respectively), with the optimal cutoff values of 25.67 IU/L and 12.786 weeks. The prediction model was based on HBsAb at end of treatment and consolidation therapy time, and AUROCs of the model for predicting efficacy in the training and validation sets were 0.7071 and 0.736. Conclusions: HBsAb at end of treatment and Peg-IFNα consolidation therapy time are potential predictors of durable HBsAg loss.

Natural sepiolite has great potential for application in wound healing, haemostasis and medicines. This paper introduces a versatile solid-state sintering technique for preparing sepiolite-based nanocomposites with enhanced antibacterial properties, and the physical, structural, rheological and antibacterial properties of which were determined to be enhanced. The incorporation of nanosized Ag and metal oxides into sepiolite composites results in a notable improvement in their antibacterial efficacy against Escherichia coli and Staphylococcus aureus in comparison to the unmodified sepiolite. With a low silver content of just 5%, the sepiolite–Ag composite achieves an antibacterial rate of ~100%. Furthermore, the rheological properties exhibited by the sepiolite composites are noteworthy, suggesting their suitability for use in wound-dressing applications due to their exceptional workability. The methodology employed in this research has the potential to offer a viable substitute for the production of economical and effective natural antibacterial nanocomposites.

Endometriosis is characterized by the ectopic proliferation of endometrial cells, posing considerable diagnostic and therapeutic challenges. Our study investigates AGPAT4’s involvement in endometriosis pathogenesis, aiming to unveil new therapeutic targets. Our investigation by analyzing eQTL data from GWAS for preliminary screening. Subsequently, within the GEO dataset, we utilized four machine learning algorithms to precisely identify risk-associated genes. Gene validity was confirmed through five Mendelian Randomization methods. AGPAT4 expression was measured by Single-Cell Analysis, ELISA and immunohistochemistry. We investigated AGPAT4’s effect on endometrial stromal cells using RNA interference, assessing cell proliferation, invasion, and migration with CCK8, wound-healing, and transwell assays. Protein expression was analyzed by western blot, and AGPAT4 interactions were explored using AutoDock. Our investigation identified 11 genes associated with endometriosis risk, with AGPAT4 and COMT emerging as pivotal biomarkers through machine learning analysis. AGPAT4 exhibited significant upregulation in both ectopic tissues and serum samples from patients with endometriosis. Reduced expression of AGPAT4 was observed to detrimentally impact the proliferation, invasion, and migration capabilities of endometrial stromal cells, concomitant with diminished expression of key signaling molecules such as Wnt3a, β-Catenin, MMP-9, and SNAI2. Molecular docking analyses further underscored a substantive interaction between AGPAT4 and Wnt3a.Our study highlights AGPAT4’s key role in endometriosis, influencing endometrial stromal cell behavior, and identifies AGPAT4 pathways as promising therapeutic targets for this condition.

Sialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment. We obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples. We developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups. This lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.