RESUMO
Background: Breast cancer (BC) is increasingly becoming the primary reason for death in women, which sounded the alarm. Thus, finding a novel management target for BC is imminent. Materials and Methods: The data on gene expression and clinicopathological characteristics were downloaded from The Cancer Genome Atlas (TCGA). The expression of GNPNAT1 in 40 paired breast cancer and adjacent tissues was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Univariate and Multivariate logistic regression methodology was applied to analyze the prognostic factors for lymph node metastasis (LNM). Based on the status of breast cancer-relative receptors, patients were distributed into six groups, and then the Kaplan-Meier survival analysis with a Log rank test was applied to investigate the involvement among the expression of GNPNAT1 and overall survival (OS). Results: We found higher expression of GNPNAT1 was connected with poor survival in breast cancer by COX regulation analysis. GO, KEGG, and GSEA analysis prompted that GNPNAT1 was connected with the defense mechanism of cells, cell proliferation, and division. Immunization infiltration analysis showed that high GNPNAT1 was negatively connected with 16 immunization infiltration cell types and positively connected with four immunization infiltration cell types. Conclusion: As a whole, our results indicated that GNPNAT1 might be a probable biomarker for diagnosis and prognosis in breast cancer.
RESUMO
INTRODUCTION: Endometriosis is an illness caused by the presence of foci of endometrial implants outside the uterine cavity. Laparoscopy (minimally invasive surgical method) is considered as the definitive treatment for Endometriosis. METHOD: Clinical data from January 2014 till December 2018, between the ages of 20 and 40 years were collected. A total of 175 women with pelvic Endometriosis complicated with infertility, underwent laparoscopy in our hospital, were followed up to assess fertility outcome. We analyzed using univariate logistic regression analysis as well as multivariate logistic analysis. RESULTS: We analyzed the relationship between them by logistic regression analysis. Univariate logistic regression analysis indicated that the significant factors for influencing pregnancy were the following factors: age, infertility types: primary or secondary infertility, treatment with Gonadotrophin Releasing Hormone-agonist, r-AFS grade, operative method: excision or ablation. And multivariate logistic regression using all the factors also revealed that age, infertility types: primary or secondary, treatment with GnRH-a, revised- American Fertility Society grading and operative method: excision or ablation were positively correlated and were the significant factors to influence pregnancy outcome. While the other factors such as Body Mass Index, and endometriosis along with other gynecological pathology were not statistically significant. CONCLUSIONS: In this study, we found out that age, infertility type, treatment with Laparoscopy surgery, use of GnRH-a after the operation, grading of the disease, and different types of operative methods were found to be significant and were found to be the factors which influenced the pregnancy outcome.
