Background: Ovarian cancer is one of the lethal gynecological diseases in women. However, using tumor microenvironment related genes to identify prognostic signature of ovarian cancer has not been discussed in detail. Methods: The mRNA profiles of 386 ovarian cancer patients were retrieved from The Cancer Genome Atlas. Univariate Cox regression and LASSO Cox regression analyses were performed and 14 optimized prognostic genes related to tumor microenvironment were identified. Results: The multivariate Cox hazards regression showed risk score was an independent prognostic signature for ovarian cancer. Nomogram model could reliably predict the patients’ survival. Furthermore, M1 macrophages, M2 macrophages, and follicular helper T cells, differentially expressed between the high- and low-risk groups, were found to be associated with the risk score. Conclusion: CTL-associated antigen 4 (CTLA4) and indoleamine 2,3-Dioxygenase 1 (IDO1), which were previously shown to be important immune checkpoints, probably contribute to the immunosuppressive microenvironment aberration. This study may shed light on the prognosis of ovarian cancer.
Objective To assess the diagnostic value of the Copenhagen index for ovarian malignancy. Methods PubMed, Web of Science, the Cochrane Library, Embase, CBM, CNKI, and WanFang databases were searched throughout June 2021. Statistical analyses were performed using Stata 12, Meta-DiSc, and RevMan 5.3. The pooled sensitivity, specificity, and diagnostic odds ratio were calculated, the summary receiver operating characteristic curve was drawn, and the area under the curve was calculated. Results Ten articles, including 11 studies with a total of 5266 patients, were included. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.82 [95% CI (0.80–0.83)], 0.88 [95% CI (0.87–0.89)], and 57.31 [95% CI (32.84–100.02)], respectively. The area under the summary receiver operating characteristics curve and the Q index were 0.9545 and 0.8966, respectively. Conclusion Our systematic review shows that the sensitivity and specificity of the Copenhagen index are high enough for it to be used in a clinical setting to provide accurate ovarian cancer diagnosis without considering menopausal status.
Background Ovarian cancer is one of the lethal gynecological in women. Tumor microenvironment (TME) is emerging as a pivotal biomarker for patients’ therapeutic sensitivity and prognosis. In this study, we proposed to explore the prognostic role of TME-related genes in ovarian cancer. Methods The data of whole genome expression profiles and detailed clinicopathological information of three cohorts of ovarian cancer patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Univariate Cox regression analysis was used to screen TME-related genes with significantly prognostic value based on TCGA cohort. LASSO Cox regression analysis was adapted to the construction of prognostic model. Ovarian cancer cohorts from GEO were used as validation set for verifying the reliability of the prognostic model. Relative infiltrating proportion of 22 immune cells were estimated through CIBERSORT software. Results This study identified a total of 14 TME-related genes that finally incorporated into the prognostic model. The risk score that calculated through the prognostic model was proved as an independent prognostic signature in ovarian cancer. Nomogram that contains TNM stage and risk score could reliably predict the long-term overall survival probability. Additionally, risk score was significantly associated with the relative infiltrating proportion of several immune cells in ovarian cancer and mRNA levels of some immune checkpoint genes. Conclusions This study constructed a prognostic model for ovarian cancer, which was closely associated with the prognosis and immune status. This should provide novel clue for prognosis study in ovarian cancer.
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