Jhun Min A, Geybels Milan S, Wright Jonathan L, Kolb Suzanne, April Craig, Bibikova Marina, Ostrander Elaine A, Fan Jian-Bing, Feng Ziding, Stanford Janet L
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
Oncotarget. 2017 Jun 27;8(26):43035-43047. doi: 10.18632/oncotarget.17428.
Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide. Gleason score (GS) is one of the best predictors of PCa aggressiveness, but additional tumor biomarkers may improve its prognostic accuracy. We developed a gene expression signature of GS to enhance the prediction of PCa outcomes. Elastic net was used to construct a gene expression signature by contrasting GS 8-10 vs. ≤6 tumors in The Cancer Genome Atlas (TCGA) dataset. The constructed signature was then evaluated for its ability to predict recurrence and metastatic-lethal (ML) progression in a Fred Hutchinson (FH) patient cohort (N=408; NRecurrence=109; NMLprogression=27). The expression signature included transcripts representing 49 genes. In the FH cohort, a 25% increase in the signature was associated with a hazard ratio (HR) of 1.51 (P=2.7×10-5) for recurrence. The signature's area under the curve (AUC) for predicting recurrence and ML progression was 0.68 and 0.76, respectively. Compared to a model with age at diagnosis, pathological stage and GS, the gene expression signature improved the AUC for recurrence (3%) and ML progression (6%). Higher levels of the signature were associated with increased expression of genes in cell cycle-related pathways and decreased expression of genes in androgen response, estrogen response, oxidative phosphorylation, and apoptosis. This gene expression signature based on GS may improve the prediction of recurrence as well as ML progression in PCa patients after radical prostatectomy.
前列腺癌（PCa）是全球癌症相关死亡的主要原因之一。 Gleason评分（GS）是PCa侵袭性的最佳预测指标之一，但其他肿瘤生物标志物可能会提高其预后准确性。我们开发了一种基于GS的基因表达特征，以增强对PCa预后的预测。通过在癌症基因组图谱（TCGA）数据集中对比GS 8-10与≤6的肿瘤，使用弹性网络构建基因表达特征。然后在弗雷德·哈钦森（FH）患者队列（N = 408；N复发= 109；N ML进展= 27）中评估构建的特征预测复发和转移致死（ML）进展的能力。该表达特征包括代表49个基因的转录本。在FH队列中，特征增加25%与复发的风险比（HR）为1.51（P = 2.7×10-5）相关。该特征预测复发和ML进展的曲线下面积（AUC）分别为0.68和0.76。与包含诊断年龄、病理分期和GS的模型相比，基因表达特征提高了复发（3%）和ML进展（6%）的AUC。较高水平的特征与细胞周期相关途径中基因表达增加以及雄激素反应、雌激素反应、氧化磷酸化和凋亡相关基因表达降低有关。这种基于GS的基因表达特征可能会改善前列腺癌根治术后患者复发以及ML进展的预测。
