Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

MiRNAs expression profiling of rat ovaries displaying PCOS with insulin resistance.

PURPOSE: The present study established microRNA (miRNA) expression profiles for rat ovaries displaying polycystic ovary syndrome (PCOS) with insulin resistance and explored the underlying biological functions of differentially expressed miRNAs. METHODS: A PCOS with insulin resistance rat model was created by administering letrozole and a high-fat diet. Total RNA was extracted from the ovaries of PCOS with insulin resistance rats and normal rats. Three ovaries from each group were used to identify differentially expressed miRNAs by deep sequencing. A hierarchical clustering heatmap and volcano plot were used to display the pattern of differentially expressed miRNAs. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the potential target genes of the differentially expressed miRNAs and identify their putative biological function. Nine of the differentially expressed miRNAs were selected for validation by Real-time Quantitative PCR (qRT-PCR). RESULTS: A total of 58 differentially expressed miRNAs were identified in the rat ovaries exhibiting PCOS with insulin resistance compared with control ovaries, including 23 miRNAs that were upregulated and 35 miRNAs that were downregulated. GO and KEGG pathway analyses revealed that the predicted target genes were related to metabolic processes, cellular processes, and metabolic pathways. Furthermore, qRT-PCR confirmed that miR-3585-5p and miR-30-5p were significantly upregulated and miR-146-5p was downregulated in the ovaries of PCOS with insulin resistance rats compared with the controls. CONCLUSION: These results indicate that differentially expressed miRNAs in rat ovaries may be involved in the pathophysiology of insulin resistance in PCOS. Our study may be beneficial in establishing miRNAs as novel diagnostic and therapeutic biomarkers for insulin resistance in PCOS.

Characterization and Evolutionary Analysis of a Novel H3N2 Influenza A Virus Glycosylation Motif in Southern China.

An influenza A (H3N2) virus epidemic occurred in China in 2017 and the causative strain failed to bind red blood cells (RBCs), which may affect receptor binding and antibody recognition. The objective of this study was to analyze the genetic characteristics and glycosylation changes of this novel H3N2 strain. We directly sequenced the hemagglutinin (HA) genes of H3N2 clinical specimens collected from patients with acute respiratory tract infection during 2017 in Guangdong, China. We aligned these sequences with those of A/Hong Kong/1/1968 (H3N2) and A/Brisbane/10/2007 (H3N2). Glycosylation changes were analyzed by C18 Chip-Q-TOF-MS. A/China/LZP/2017 (H3N2) was negative by HA assay, but was positive by quantitative real-time Polymerase Chain Reaction (qPCR) and direct immunofluorescence assay (DFA). We found that the HA1 residue 160T of A/China/LZP/2017 (H3N2) could block virus binding to receptors on RBCs. Furthermore, the ASN (N)-X-Thr (T) motif at HA1 residues 158-160, encoding a glycosylation site as shown by C18 Chip-Q-TOF-MS, predominated worldwide and played a critical role in RBC receptor binding. Ten glycoforms at HA1 residue 158 were identified [4_3_1_0, 5_6_0_1, 3_3_0_1, 4_4_3_0, 6_7_0_0 (SO3), 3_6_2_0, 4_3_1_2 (SO3), 7_5_2_0 (SO3), 3_6_2_1 (SO3), and 3_7_0_2]. Glycosylation changes at HA1 residues 158-160 of a circulating influenza A (H3N2) virus in Guangdong, China, in 2017 blocked binding to RBC receptors. Changes to these HA1 residues may have reduced protective antibody responses as well. Understanding these critical epitopes is important for selecting vaccine strains.