靶向测序的体细胞突变可识别晚期卵巢透明细胞癌中的风险分层
Somatic mutation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma
影响因子:4.10000
分区:医学2区 Top / 妇产科学1区 肿瘤学2区
发表日期:2024 Dec
作者:
Shimeng Wan, Yang Gao, Sisi Wu, Hua Wang, Jiyu Tong, Wei Wei, Hang Ren, Danni Yang, Hao He, Hong Ye, Hongbing Cai
摘要
卵巢透明细胞癌（OCCC）是上皮卵巢癌的独特亚型。晚期OCCC的预后不佳。因此，我们旨在对精确医学进行风险分层。我们对FIGO II-IV中的44例OCCC患者进行了大型的下一代测序（NGS）基因面板。然后，通过机器学习算法，包括极端梯度提升（XGBOOST），随机生存森林（RSF）和Cox回归，我们筛选了与预后相关的特征基因，并构建了一个5基因面板进行风险分层。通过接收器操作特征曲线和决策曲线分析将5基因面板与FIGO阶段和残留疾病进行了比较。与预后相关的特征突变基因，包括机器学习算法选择，包括MUC16，ATM，NOTCH3，NOTCH3，KMT2A和CTNNA1。 5基因面板可以有效地区分内部和外部队列中晚期OCCC的预后以及铂反应，预测能力优于FIGO阶段和残留疾病。基因的含量为MUC16，ATM，NOTCH3，NOTCH3，KMT2A，KMT2A和CTNNA1，与较差的ADVACT OPCC相关。根据这些基因的风险分层表现出可接受的预测能力和铂反应的能力，这表明有可能成为精确医学的新颖靶标。
Abstract
Ovarian clear cell carcinoma (OCCC) is a unique subtype of epithelial ovarian cancer. Advanced OCCC display a poor prognosis. Therefore, we aimed to make risk stratification for precise medicine.We performed a large next generation sequencing (NGS) gene panel on 44 patients with OCCC in FIGO stage II-IV. Then, by machine learning algorithms, including extreme gradient boosting (XGBoost), random survival forest (RSF), and Cox regression, we screened for feature genes associated with prognosis and constructed a 5-gene panel for risk stratification. The prediction efficacy of the 5-gene panel was compared with FIGO stage and residual disease by receiver operating characteristic curve and decision curve analysis.The feature mutated genes related to prognosis, selected by machine learning algorithms, include MUC16, ATM, NOTCH3, KMT2A, and CTNNA1. The 5-gene panel can effectively distinguish the prognosis, as well as platinum response, of advanced OCCC in both internal and external cohorts, with the predictive capability superior to FIGO stage and residual disease.Mutations in genes, including MUC16, ATM, NOTCH3, KMT2A, and CTNNA1, were associated with the poor prognosis of advanced OCCC. The risk stratification according to these genes demonstrated acceptable prediction power of prognosis and platinum response, suggesting the potential to be a novel target for precision medicine.