KCNN4 and S100A14 act as predictors of recurrence in optimally debulked patients with serous ovarian cancer.
KCNN4 and S100A14 act as predictors of recurrence in optimally debulked patients with serous ovarian cancer.
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KCNN4 和 S100A14 作为最佳减瘤浆液性卵巢癌患者复发的预测因子
DOI：
10.18632/oncotarget.9721
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发表时间：
2016-07-12
期刊：
影响因子：
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通讯作者：
Zhu T
中科院分区：
文献类型：
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作者：
Zhao H;Guo E;Hu T;Sun Q;Wu J;Lin X;Luo D;Sun C;Wang C;Zhou B;Li N;Xia M;Lu H;Meng L;Xu X;Hu J;Ma D;Chen G;Zhu T
大约50-75%的浆液性卵巢癌（SOC）患者在一线治疗后18个月内复发。目前的临床指标不足以预测复发的风险。在这项研究中，我们使用了7个公开的微阵列数据集来识别与最佳减容SOC患者复发相关的基因特征，并使用免疫组织化学（IHC）在127名患者的独立临床队列中验证了它们的表达。我们确定了一个包括KCNN 4和S100 A14的双基因签名，这与最佳减容SOC患者的复发有关。它们的mRNA表达水平与DNA拷贝数改变（CNA）（KCNN 4：p=1.918e-05）和DNA启动子甲基化（KCNN 4：p=0.0179; S100 A14：p=2.787e-13）呈正相关并受其调节。基于KCNN 4和S100 A14单独和组合在TCGA数据集中构建的复发预测模型在其他6个数据集中显示出良好的预测性能（AUC：0.5442-0.9524）。独立队列支持SOC复发之间的表达差异。此外，从STRING数据库中构建了以KCNN 4和S100 A14为中心的蛋白质相互作用子网络，并确定了它们之间的最短调控路径，称为KCNN 4-UBA 52-KLF 4-S100 A14轴。这一发现可能有助于SOC的个体化治疗。
Approximately 50-75% of patients with serous ovarian carcinoma (SOC) experience recurrence within 18 months after first-line treatment. Current clinical indicators are inadequate for predicting the risk of recurrence. In this study, we used 7 publicly available microarray datasets to identify gene signatures related to recurrence in optimally debulked SOC patients, and validated their expressions in an independent clinic cohort of 127 patients using immunohistochemistry (IHC). We identified a two-gene signature including KCNN4 and S100A14 which was related to recurrence in optimally debulked SOC patients. Their mRNA expression levels were positively correlated and regulated by DNA copy number alterations (CNA) (KCNN4: p=1.918e-05) and DNA promotermethylation (KCNN4: p=0.0179; S100A14: p=2.787e-13). Recurrence prediction models built in the TCGA dataset based on KCNN4 and S100A14 individually and in combination showed good prediction performance in the other 6 datasets (AUC:0.5442-0.9524). The independent cohort supported the expression difference between SOC recurrences. Also, a KCNN4 and S100A14-centered protein interaction subnetwork was built from the STRING database, and the shortest regulation path between them, called the KCNN4-UBA52-KLF4-S100A14 axis, was identified. This discovery might facilitate individualized treatment of SOC.