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A cellular senescence-related genes model allows for prognosis and treatment stratification of cervical cancer: a bioinformatics analysis and external verification

列线图 接收机工作特性 Lasso(编程语言) 肿瘤科 单变量 医学 内科学 一致性 曲线下面积 生存分析 基因签名 多元统计 多元分析 衰老 生物信息学 基因 生物 基因表达 统计 遗传学 计算机科学 万维网 数学
作者
Weiwei Yang,Lijuan An,Yanfei Li,Sumin Qian
出处
期刊:Aging [Impact Journals LLC]
卷期号:15 (18): 9408-9425 被引量:3
标识
DOI:10.18632/aging.204981
摘要

Background: Cervical cancer (CC) is highly lethal and aggressive with an increasing trend of mortality for females. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. Methods: The mRNAs expression data of CC patients and cellular senescence-related genes were obtained from the Cancer Genome Atlas (TCGA) and CellAge databases, respectively. Differentially expressed genes (DEGs) of senescence related genes between tumor and normal tissues were used for Least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. Univariate and LASSO regression analyses were applied to establish a predictive nomogram. The performance of the nomogram were evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index), and calibration curve. GSE44001 and GSE52903 were used for external validation. Results: We established a cellular senescence-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of CC patients in the TCGA database. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS, P =2.021e-05). The area under the ROC curve (AUC) of this model was 0.743 for OS. Multivariate analysis found that the 6-gene risk signature (HR=3.166, 95%CI: 1.660-6.041, P<0.001) was an independent risk factor for CC patients. We then designed an OS-associated nomogram that included the risk signature and clinicopathological factors. The AUC reached 0.860 for predicting 5-year OS. The nomogram showed excellent consistency between the predictions and actual survival observations. Two external GEO validations were corresponding to the gene expression pattern in TCGA. Conclusions: Our results suggested a six-senescence related signature and established a prognostic nomogram that reliably predicted the overall survival for CC. These findings may be beneficial to personalized treatment and medical decision-making.
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