转录组                        
                
                                
                        
                            基因                        
                
                                
                        
                            细胞                        
                
                                
                        
                            生物                        
                
                                
                        
                            宫颈癌                        
                
                                
                        
                            价值(数学)                        
                
                                
                        
                            计算生物学                        
                
                                
                        
                            癌症研究                        
                
                                
                        
                            遗传学                        
                
                                
                        
                            癌症                        
                
                                
                        
                            基因表达                        
                
                                
                        
                            计算机科学                        
                
                                
                        
                            机器学习                        
                
                        
                    
            作者
            
                Yao Fu,Xiubing Zhang,Lili Yu,Guiping Zhang,Xinyu Liu,Wei Ren            
         
                    
        
    
            
            标识
            
                                    DOI:10.1080/10255842.2025.2475483
                                    
                                
                                 
         
        
                
            摘要
            
            The relationship between cervical cancer (CESC) and T cells is mainly seen in the anti-tumor functions of T cells. This study aims to identify prognostic genes associated with CESC and T cells, providing a foundation for developing immunotherapy strategies. This study used data from public databases to identify T cell-related prognostic genes for CESC patients through differential expression analysis and single-cell clustering. A prognostic risk model and nomogram were constructed and validated based on these genes. Pseudotime analysis clarified T cell differentiation processes in CESC. Ultimately, Mendelian randomization (MR) was applied to determine the causal relationship between the prognostic genes and CESC. In this study, CXCL2, ANKRD22, SPP1, and C1QB were identified as prognostic genes for CESC. Survival analysis indicated that the survival rate of the high-risk cohort (HRC) was significantly lower compared to that of the low-risk cohort (LRC). A nomogram also demonstrated strong predictive capability. Notably, higher expression levels of prognostic genes were observed during the early stages of T cell differentiation. MR analyses revealed that SPP1 was a risk factor for CESC (OR = 1.165; 95% CI: 1.028-1.320; p = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685-0.983; p = .032). CXCL2, ANKRD22, SPP1, and C1QB showed strong prognostic characteristics in CESC and significant predictive capabilities for patient outcomes. The study also emphasized the critical role of T cells in CESC progression.
         
            
 
                 
                
                    
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