Prediction of peritoneal metastasis in advanced gastric cancer by gene expression profiling of the primary site
Prediction of peritoneal metastasis in advanced gastric cancer by gene expression profiling of the primary site
复制标题
DOI：
10.1016/j.ejca.2006.04.007
复制
发表时间：
2006-08-01
影响因子：
8.4
通讯作者：
Kato, Kikuya
中科院分区：
文献类型：
--
作者：
Motoori, Masaaki;Takemasa, Ichiro;Kato, Kikuya
腹膜转移是进展期胃癌进展的最常见原因。临床病理结果包括腹腔灌洗细胞学检查已被应用于评估腹膜转移的风险，但有时不足以预测腹膜转移的个人。因此，我们尝试构建一个新的腹膜转移预测系统，使用基于PCR的高通量阵列与2304个基因。该预测系统由30名患者组成的学习集构建，其中18个基因信息量最大，将每个病例分类为“良好特征组”或“不良特征组”。然后，我们在由24名患者组成的额外验证集中证实了预测性能，腹膜转移的预测准确率为75%。Kaplan-Meier分析显示两组间有显著性差异（P = 0.0225）。结合我们的系统与传统的临床病理因素，我们可以更准确地识别腹膜转移的高危病例。
Peritoneal metastasis is the most common cause of tumour progression in advanced gastric cancer. Clinicopathological findings including cytologic examination of peritoneal lavage have been applied to assess the risk of peritoneal metastasis, but are sometimes inadequate for predicting peritoneal metastasis in individuals. Hence, we tried to construct a new prediction system for peritoneal metastasis by using a PCR-based high throughput array with 2304 genes. The prediction system, constructed from the learning set comprised of 30 patients with the most informative 18 genes, classified each case into a 'good signature group' or 'poor signature group'. Then, we confirmed the predictive performance in an additional validation set comprised of 24 patients, and the prediction accuracy for peritoneal metastasis was 75%. Kaplan-Meier analysis with peritoneal metastasis revealed significant difference between these two groups (P = 0.0225). By combining our system with conventional clinicopathological factors, we can identify high risk cases for peritoneal metastasis more accurately.