Introduction

Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk.

Objectives

To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles.

Methods

Four gene expression datasets from public sources were used as training (n = 1) or validation (n = 3) sets. The performance of our proposed algorithm was compared with fine-tuning and other existing methods. Moreover, the biological function of selected genetic features was further interpreted, and the response to a panel of approved drugs was predicted for different risk levels.

Results

The proposed algorithm showed good sensitivity (74–100%) in the validation sets, compared with two simple models whose sensitivity only reached 47% and 60%. The prognostic gene signature used in this study was highly connected to AKT, a key component of the PI3K/AKT/mTOR signaling pathway, which influences the tumorigenesis, proliferation, and progression of OC.

Conclusion

These findings demonstrated an improvement in the sensitivity of risk classification of OC patients with our risk prediction models compared with other methods. Ongoing effort is needed to validate the outcomes of this approach for precise clinical treatment.

中文翻译：

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通过 GA-XGBoost 模型装袋的卵巢癌基因特征风险预测模型

 介绍

卵巢癌（OC）是女性最常见的妇科癌症之一，高精度的风险预测技术对于有效地为不同风险水平的OC患者选择最佳的干预策略和临床管理至关重要。目前用于 OC 的风险预测模型灵敏度较低，很少有模型能够识别高死亡风险的 OC 患者，这既可以优化高风险患者的治疗，又可以防止对低风险患者进行不必要的医疗干预。

 目标

为此，我们开发了一种基于 bagging 的算法，采用 GA-XGBoost 模型，利用基因表达谱预测 OC 死亡风险。

 方法

来自公共来源的四个基因表达数据集被用作训练集（n = 1）或验证集（n = 3）。我们提出的算法的性能与微调和其他现有方法进行了比较。此外，还进一步解释了选定遗传特征的生物学功能，并预测了不同风险水平对一组已批准药物的反应。

 结果

与灵敏度仅达到 47% 和 60% 的两个简单模型相比，所提出的算法在验证集中表现出良好的灵敏度（74-100%）。本研究中使用的预后基因特征与AKT高度相关，AKT 是 PI3K/AKT/mTOR 信号通路的关键组成部分，影响 OC 的肿瘤发生、增殖和进展。

 结论

这些结果表明，与其他方法相比，我们的风险预测模型对 OC 患者风险分类的敏感性有所提高。需要持续努力来验证这种方法的结果，以实现精确的临床治疗。