目的 本研究通过分析衰老相关基因（senescence related genes,SRGs）的表达,并结合转录组和单细胞测序数据,开发一个高效的预后模型,探索与衰老相关的宫颈癌关键预后基因。方法 首先从Genecards数据库获取与关键词“senescence”相关的基因,利用基因表达综合数据库（gene expression omnibus,GEO）中的单细胞测序数据集GSE253690对每个细胞进行衰老相关评分,根据评分筛选出高评分组和低评分组之间的差异基因。然后通过单样本基因集富集分析（single-sample gene set enrichment analysis,ssGSEA）和加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）方法,识别宫颈癌患者中与衰老相关的模块基因。将差异表达基因和模块基因取交集,通过单因素Cox分析、最小绝对收缩和选择算子（least absolute shrinkage and selection operator,LASSO）回归分析和多因素Cox分析,构建基于SRGs的预后模型,同时选用GEO数据库中的GSE44001和GSE52903数据集作为外部验证集验证其准确性。通过构建临床列线图与受试者工作特征（receiver operating characteristic,ROC）曲线分析评估了模型在宫颈癌患者预后预测中的精准度。最后通过人类蛋白质图谱（human protein atlas,HPA）分析和实时荧光定量聚合酶链式反应（real-time quantitative polymerase chain reaction,RT-qPCR）检测关键基因在正常宫颈细胞与宫颈癌细胞上的表达差异。结果 成功构建了一个基于SRGs的预后模型,并具有良好的准确性与预测精度。结论 我们构建的预后模型可以有效地评估宫颈癌的预后,为临床医生选择对于宫颈癌的治疗方法提供新的参考。
Abstract
Objective This study aimed to develop an efficient prognostic model by analyzing the expression of senescence-associated genes (SRGs), combined with transcriptome and single-cell RNA sequencing data, to identify key senescence-related genes with prognostic significance in cervical cancer. Method Genes related to the keyword “senescence” were first obtained from the Genecards database. Using the single-cell RNA sequencing dataset GSE253690 from the Gene Expression Omnibus (GEO) database, senescence-associated scores were calculated for each cell. Based on these scores, differentially expressed genes (DEGs) between the high-score and low-score groups were identified. Subsequently, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were employed to identify senescence-related module genes in cervical cancer patients. The intersection of DEGs and module genes was subjected to univariate Cox analysis, followed by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to construct a prognostic model based on senescence-associated genes. The GSE44001 and GSE52903 datasets from GEO were used as external validation sets to verify the model's accuracy. The precision of the model in predicting the prognosis of cervical cancer patients was assessed by constructing a clinical nomogram and performing receiver operating characteristic (ROC) curve analysis. Finally, the expression differences of key genes between normal cervical cells and cervical cancer cells were detected via human protein atlas (HPA) analysis and real-time quantitative polymerase chain reaction (RT-qPCR). Results A prognostic model based on senescence-associated genes was successfully constructed and demonstrated good accuracy and predictive precision. Conclusion The senescence-associated gene-based model we developed can effectively assess the prognosis of cervical cancer, providing a new reference for clinicians in selecting treatment strategies.
关键词
宫颈癌 /
细胞衰老 /
衰老相关分泌表型 /
预后模型
Key words
cervical cancer /
cellular senescence /
senescence-associated secretory phenotype /
prognostic model
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基金
长沙市自然科学基金“TXNDC12调控APC/CDH1复合体泛素化途径促进宫颈癌血管新生和转移的机制研究”（kq2502172）; 长沙市自然科学基金“基于群体智能优化算法识别脑胶质瘤预后关键基因及其作用机制的研究”（KQ2208172）; 湖南省自然科学基金联合基金“衰老血管内皮细胞 MDK/SDC1信号轴在官颈癌免疫耐药中的作用机制及干预潜力研究”（2026JJ81871）
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