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构建免疫相关列线图预测Ⅰ期宫颈癌复发生存风险。

Construction of Immune-Associated Nomogram for Predicting the Recurrence Survival Risk of Stage I Cervical Cancer.

机构信息

Sanquan College of Xinxiang Medical University, West of Changjiang Avenue, Pingyuan New Area, Xinxiang City, Henan Province, China.

出版信息

Biomed Res Int. 2021 Jul 9;2021:6699131. doi: 10.1155/2021/6699131. eCollection 2021.

Abstract

BACKGROUND

Various studies reported that the prognosis of patients with cervical cancer (CC) was significantly associated with immunity, whereas limited studies have explored whether immune-associated genes could be classifiers for recurrence-free survival (RFS) of stage I CC. Thus, an improved immune-related gene signature for stage I CC patients' prognosis is urgently required.

MATERIALS AND METHODS

We retrospectively analyzed the gene expression profiles of stage I CC patients in the GSE44001 set from the Gene Expression Omnibus (GEO) database. The stage I CC patients were randomly divided into the training group and the internal validation group. The training patients were adopted to develop a prognostic immune gene-based signature; meanwhile, the internal validation patients were used to validate the power of the selected immune gene-related signature using univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The accuracy and reliability of the immune gene-related signature were evaluated based on Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves.

RESULTS

High power of the 8-immune gene signature was found on the basis of ROC analysis (AUC at 1, 3, and 5 years were exhibited in the internal validation group (0.702, 0.715, and 0.728, respectively), external validation group (0.702, 0.825, and 0.842, respectively), and entire GEO dataset (0.840, 0.894, and 0.852, respectively)). Besides, -index, ROC, calibration plots, and decision curve analysis (DCA) also acted well in our nomogram, suggestive of a high ability of the nomogram to elevate the prognostic prediction of stage I CC patients.

CONCLUSIONS

In this study, we successfully constructed an integrated 8-immune gene-based signature which could accurately identify patients with low prognostic risk from those with high prognostic risk. In addition, we developed an immune-related nomogram which can elevate the prognostic prediction of stage I CC patients.

摘要

背景

多项研究表明,宫颈癌(CC)患者的预后与免疫密切相关,然而,有限的研究探索了免疫相关基因是否可以作为 I 期 CC 无复发生存(RFS)的分类器。因此,迫切需要一种改进的免疫相关基因特征来预测 I 期 CC 患者的预后。

材料和方法

我们回顾性分析了 GEO 数据库中 GSE44001 集中 I 期 CC 患者的基因表达谱。将 I 期 CC 患者随机分为训练组和内部验证组。采用训练患者建立基于预后免疫基因的特征;同时,采用内部验证患者进行单因素 Cox 比例风险分析、最小绝对收缩和选择算子(LASSO)和多因素 Cox 回归分析验证所选免疫基因相关特征的能力。基于 Kaplan-Meier 分析和时间依赖性接收者操作特征(ROC)曲线评估免疫基因相关特征的准确性和可靠性。

结果

ROC 分析显示,该 8-免疫基因特征具有较高的效能(内部验证组、外部验证组和整个 GEO 数据集的 1、3 和 5 年 AUC 分别为 0.702、0.715 和 0.728、0.702、0.825 和 0.842、0.840、0.894 和 0.852)。此外,-指数、ROC、校准图和决策曲线分析(DCA)也在我们的列线图中表现良好,提示该列线图具有较高的能力,可以提高 I 期 CC 患者的预后预测能力。

结论

本研究成功构建了一个综合的 8 个免疫基因的特征,可以准确识别低预后风险和高预后风险的患者。此外,我们还开发了一个免疫相关的列线图,可以提高 I 期 CC 患者的预后预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c83/8289578/13a7a8834141/BMRI2021-6699131.001.jpg

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