BackgroundMacrophages secrete many cytokines and chemokines, which can provoke either an anti-tumor or pro-tumor immune response. P-selectin glycoprotein ligand-1 (PSGL-1) is expressed in macrophages and plays a vital role in synergizing for a more robust anti-tumor response. However, there are few studies about PSGL-1 expression status and clinical value of biological function in cervical cancer.MethodsIn this study, 565 participants were enrolled. PSGL-1 mRNA was detected by real-time quantitative PCR (qPCR) with cervical cytology specimens. The relationship between PSGL-1 and cervical intraepithelial neoplasia in two grades and more (CIN2+) was analyzed, and the optimal cut-off values of PSGL-1 to predict CIN2+ were calculated. In addition, the clinical significance of PSGL-1 in cervical cancer was determined by Kaplan-Meier Cox regression based on the database.ResultsThe mean PSGL-1 increased significantly with cervical lesion development, especially compared with CIN2+ (p<0.05). Moreover, the expression of PSGL-1 increased significantly in HPV-16/18 positive and HPV-18 positive, but not in HPV-16 positive and other HR-HPV positive. And then, it demonstrated that the area under the receiver operating characteristic curve (AUC) of PSGL-1 was 0.820, and an optimal cut-off 0.245. Furthermore, the PSGL-1 had the highest odds ratio and highest OR (OR= 8.707; 95% CI (.371-19.321)) for the detection of CIN 2+. In addition, our result also indicated that higher PSGL-1 expression was significantly related to a better prognosis in cervical cancer due to immune cell infiltration.ConclusionsPSGL-1≥0.245 in cervical cytology specimens is a new auxiliary biomarker of CIN2+, and it may be a promising prognosis predictor and potential immunotherapy target linked with immune infiltration of cervical cancer.
Background: Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women's physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools. Methods: EMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility. Results: Our analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. Conclusions: The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools. However, all the results were based on online bioinformatics databases, and as such require validation experiments. And some of the drugs highlighted as possibly relevant may be toxic and as such safely data is required before any experiments are undertaken in humans. Keywords: differentially expressed genes; endometriosis; infertility; drugs; text miningTable 1. The Gene Ontology of the genes involved in EM-induced infertility. Category Term Count p value GOTERM_BP_FAT GO:0010033 response to organic substance 328 3.90E-127 GOTERM_BP_FAT GO:0070887 cellular response to chemical stimulus 304 1.29E-112 GOTERM_BP_FAT GO:0042127 regulation of cell proliferation 227 1.43E-98 GOTERM_BP_FAT GO:0071310 cellular response to organic substance 261 1.01E-95 GOTERM_BP_FAT GO:0008283 cell proliferation 241 3.58E-94 GOTERM_BP_FAT GO:1901700 response to oxygen-containing compound 215 1.48E-92 GOTERM_CC_FAT GO:0005615 extracellular space 190 1.87E-63 GOTERM_CC_FAT GO:0005576 extracellular region 305 2.15E-39 GOTERM_CC_FAT GO:0009986 cell surface 110 5.22E-38 GOTERM_CC_FAT GO:0044421 extracellular region part 265 1.
Aim. To investigate the adverse pregnancy outcomes associated with endometriosis and its influencing factors. Methods. A total of 188 endometriosis patients who gave birth at our hospital between June 2018 and January 2021 were screened for eligibility and included in the research group, while a control group of 188 nonendometriosis women who delivered at our hospital during the same period were also included as healthy controls. Pregnancy outcomes were the key outcome measure, and the relationship between endometriosis and unfavorable pregnancy outcomes, as well as the influencing factors, were explored. Results. There was no significant difference in the risk of adverse pregnancy events such as miscarriage, ectopic pregnancy, termination of pregnancy, and fetal death between the two groups ( P > 0.05 ). The differences in hypertensive disorder in pregnancy, gestational diabetes, placental abruption, fetal growth restriction, and luteal support between the two groups also failed to reach the statistical standard ( P > 0.05 ). The two groups significantly differed in terms of cesarean delivery, preterm delivery, and placenta previa (1.92 (95% CI 1.33–2.85), 2.43 (95% CI 1.05–5.58), and 4.51 (95% CI 1.23–16.50)) ( P < 0.05 ). Conclusion. Endometriosis is an influential factor in adverse pregnancy outcomes and results in a high risk of preterm delivery, placenta previa, and cesarean delivery in patients. Mutual interactions exist among adverse pregnancy outcomes and thus require appropriate management.
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