Background

This study was aimed to identify an accurate gene expression signature to predict overall survival (OS) in patients with ovarian cancer (OC).

Methods

Expression data and corresponding clinical information were obtained from two independent databases: the Cancer Genome Atlas (TCGA) dataset and International Cancer Genome Consortium (ICGC) dataset. Multiple analysis methods including univariate and multivariate COX regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were utilized to build the signature. Receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analyses were used to assess the predictive accuracy of this gene signature.

Results

A novel 10-gene signature with high predictive accuracy for OS in OC patients was constructed and validated in the training and validation set. Based on the results of univariate and multivariate analyses, the presence of risk Score was identified as an independent prognostic factor for survival of OC patients. Moreover, we developed a nomogram model based on these 10 genes in the signature, which also displayed a favorable predictive efficacy for prognosis in OC.

Conclusions

Our results identified a robust 10-gene signature for OC prognosis prediction, which might be applied to assist clinical decision-making and individualized treatment.

中文翻译：

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鉴定和验证预测卵巢癌总体存活率的十个基因标记。

背景

这项研究旨在确定准确的基因表达特征，以预测卵巢癌（OC）患者的总生存期（OS）。

方法

表达数据和相应的临床信息是从两个独立的数据库中获得的：癌症基因组图谱（TCGA）数据集和国际癌症基因组协会（ICGC）数据集。利用包括单变量和多变量COX回归分析以及最小绝对收缩和选择算子（LASSO）回归分析在内的多种分析方法来构建签名。接收者操作特征（ROC）和Kaplan-Meier（KM）生存分析用于评估该基因签名的预测准确性。

结果

在训练和验证集中构建并验证了一种新的具有10个基因的，对OC患者的OS具有高预测准确性的特征。根据单因素和多因素分析的结果，风险评分的存在被确定为OC患者生存的独立预后因素。此外，我们基于签名中的这10个基因开发了列线图模型，这也显示了对OC预后的有利预测功效。

结论

我们的研究结果确定了用于OC预后预测的可靠10基因签名，可用于辅助临床决策和个性化治疗。