医学                        
                
                                
                        
                            肿瘤科                        
                
                                
                        
                            内科学                        
                
                                
                        
                            子宫内膜癌                        
                
                                
                        
                            癌                        
                
                                
                        
                            荟萃分析                        
                
                                
                        
                            阶段(地层学)                        
                
                                
                        
                            比例危险模型                        
                
                                
                        
                            妇科                        
                
                                
                        
                            癌症                        
                
                                
                        
                            生物                        
                
                                
                        
                            古生物学                        
                
                        
                    
            作者
            
                Nicoletta D’Alessandris,Antonio Travaglino,Angela Santoro,Damiano Arciuolo,Giulia Scaglione,Antonio Raffone,Frediano Inzani,Gian Franco Zannoni            
         
                    
        
    
            
            标识
            
                                    DOI:10.1016/j.ygyno.2021.08.011
                                    
                                
                                 
         
        
                
            摘要
            
            Ovarian endometrioid carcinoma (OEC) shares morphological and molecular features with endometrial endometrioid carcinoma (EEC). Several studies assessed the four TCGA groups of EEC, i.e. POLE-mutated (POLEmut), mismatch repair-deficient (MMRd), no specific molecular profile (NSMP) and p53-abnormal (p53abn), in OEC; however, it is unclear whether the TCGA groups have the same distribution and clinicopathological features between OEC and EEC.To assess the distribution and clinicopathological features of the TCGA groups in OEC.A systematic review and meta-analysis was carried out by searching 7 electronic databases from January 2013 to April 2021 for studies assessing the TCGA classification in OEC. Prevalence of each TCGA group in OEC and of FIGO grade 3 and stage>I was pooled using a random-effect model. Prevalence of TCGA groups was compared between OEC and EEC, extracting EEC data from a previous meta-analysis. Kaplan-Meier and Cox regression survival analyses were performed for progression-free survival (PFS). A significant p-value<0.05 was adopted.Four studies with 785 patients were included. The frequency of the TCGA groups in OEC vs EEC was: POLEmut = 5% vs 7.6% (p = 0.594); MMRd = 14.6% vs 29.2% (p < 0.001); p53abn = 14% vs 7.8% (p = 0.097); NSMP = 66.4% vs 55.4% (p = 0.002). The pooled prevalence of FIGO grade 3 was: POLEmut = 19.2%; MMRd = 18.3%; p53abn = 38.1%; NSMP = 14.5%. The pooled prevalence of FIGO stage >I was: POLEmut = 31.6%; MMRd = 42.8%; p53abn = 48.5%; NSMP = 24.6%. Two-, 5- and 10-year PFS was: POLEmut = 100%, 100%, and 100%; MMRd = 89.1%, 82.2% and 73.3%; p53abn = 61.7%, 50.2% and 39.6%; NSMP = 87.7%, 79.6% and 65.5%. The hazard ratio for disease progression (reference = NSMP) was: POLEmut = not estimable (no events); MMRd = 0.825 (p = 0.626); p53abn = 2.786 (p = 0.001).The prognostic value of the TCGA groups was similar between OEC and EEC, despite the differences in the frequency and pathological features of each group.
         
            
 
                 
                
                    
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