Table_6_A Five-Gene-Pair-Based Prognostic Signature for Predicting the Relapse Risk of Early Stage ER+ Breast Cancer.XLSX

About 20–30% of early-stage breast cancer patients suffer relapses after surgery. To identify such high-risk patients, many signatures have been reported, but they lack robustness in data measured on different platforms. Here, we developed a signature which is robust across multiple profiling platforms, and identified reproducible omics features characterizing metastasis of estrogen receptor (ER)-positive breast cancer from the Gene Expression Omnibus database with the aid of the signature. Based on the stable within-sample relative expression orderings (REOs), we constructed a signature consisting of five gene pairs, named 5-GPS, whose REOs were significantly correlated with relapse-free survival using the univariate Cox regression model. Using 5-GPS, patients were classified into the low-risk and high-risk groups. Patients in the high-risk group have worse survival compared to those in the low-risk group using Kaplan-Meier curve analysis with the log-rank test. Applying 5-GPS to the RNA-sequencing data of stage I-IV breast cancer samples archived in The Cancer Genome Atlas (TCGA), we found that the proportion of the high-risk patients increases with the stage. The proposed REO-based signature shows potential in identifying early-stage ER+ breast cancer patients with high risk of relapse after surgery.

约20%至30%的早期乳腺癌患者会在术后出现复发。为甄别此类高风险患者，学界已报道多种预后特征签名，但这些特征在不同平台生成的检测数据中稳定性不足。为此，我们开发了一种可在多种表达谱检测平台间保持稳健性的基因特征签名，并借助该签名从基因表达综合数据库（Gene Expression Omnibus，GEO）中筛选出可重复的、用于表征雌激素受体（ER）阳性乳腺癌转移的组学特征。基于样本内稳定的相对表达排序（Relative Expression Orderings，REOs），我们构建了由5个基因对组成的特征签名，命名为5-GPS；通过单变量Cox回归模型分析证实，该签名的相对表达排序与患者无复发生存期显著相关。借助5-GPS可将患者划分为低风险组与高风险组；经Kaplan-Meier曲线分析及log-rank检验，高风险组患者的生存期显著劣于低风险组。我们将5-GPS应用于癌症基因组图谱（The Cancer Genome Atlas，TCGA）中存档的I-IV期乳腺癌样本的RNA测序数据，发现高风险患者的比例随临床分期升高而逐步增加。本研究提出的基于相对表达排序的特征签名，在甄别术后存在高复发风险的早期ER阳性乳腺癌患者方面具有潜在应用价值。