Siamakpour-Reihani Sharareh, Owzar Kouros, Jiang Chen, Turner Taylor, Deng Yiwen, Bean Sarah M, Horton Janet K, Berchuck Andrew, Marks Jeffrey R, Dewhirst Mark W, Alvarez Secord Angeles
Radiation Oncology Department, Duke University Medical Center, United States.
Duke Department of Biostatistics and Bioinformatics, Duke University Medical Center, United States; Bioinformatics Shared Resource, Duke Cancer Institute, United States.
Gynecol Oncol. 2015 Oct;139(1):23-9. doi: 10.1016/j.ygyno.2015.08.001. Epub 2015 Aug 7.
To identify angiogenic biomarkers associated with tumor angiogenesis and clinical outcome in high-grade serous ovarian cancer (HGSC).
51 HGSC samples were analyzed using Affymetrix HG-U133A microarray. Microvessel density (MVD) counts were determined using CD31 and CD105. Associations between mRNA expression levels and overall survival were assessed using rank score statistic. Effect size was estimated as a hazard ratio (HR) under a proportional hazard model. The Storey q-value method was used to account for multiple testing within the false-discovery rate (FDR) framework. Publicly available databases including TCGA and GSE were used for external confirmation.
Thirty-one angiogenic-related genes were significantly associated with survival (q≤0.05). Of these 31 genes, 4 were also associated with outcome in the TCGA data: AKT1 (q=0.02; TCGA p=0.01, HR=0.8), CD44 (q=0.003; TCGA p=0.05, HR=0.9), EPHB2 (q=0.01; TCGA p=0.05, HR=1.2), and ERBB2 (q=0.02; TCGA p=0.05, HR=1.2). While 5 were associated with outcome in the GSE database: FLT1 (q=0.03; GSE26712 p=0.01, HR=3.1); PF4 (q=0.02; GSE26712 p=0.01, HR=3.0); NRP1 (q=0.02; GSE26712 p<0.04, HR>1.4); COL4A3 (q=0.04; GSE26712 p=0.03, HR=1.3); and ANGPTL3 (q=0.02; GSE14764 p=0.02, HR=1.5). High AKT1 and CD44 were associated with longer survival. In contrast, high expression of EPHB2, ERBB2, FLT1; PF4, NRP1, COL4A3, and ANGPTL3 were associated with shorter survival. CD105-MVD and CD31-MVD were not significantly associated with angiogenic gene expression.
Thirty-one angiogenic-related genes were associated with survival in advanced HGSC and nine of these genes were confirmed in independent publicly available databases.
鉴定与高级别浆液性卵巢癌（HGSC）肿瘤血管生成及临床结局相关的血管生成生物标志物。
使用Affymetrix HG-U133A微阵列分析51例HGSC样本。采用CD31和CD105测定微血管密度（MVD）计数。使用秩和统计评估mRNA表达水平与总生存期之间的关联。在比例风险模型下将效应大小估计为风险比（HR）。采用Storey q值法在错误发现率（FDR）框架内进行多重检验校正。利用包括TCGA和GSE在内的公开可用数据库进行外部验证。
31个血管生成相关基因与生存显著相关（q≤0.05）。在这31个基因中，4个基因在TCGA数据中也与结局相关：AKT1（q = 0.02；TCGA p = 0.01，HR = 0.8）、CD44（q = 0.003；TCGA p = 0.05，HR = 0.9）、EPHB2（q = 0.01；TCGA p = 0.05，HR = 1.2）和ERBB2（q = 0.02；TCGA p = 0.05，HR = 1.2）。而5个基因在GSE数据库中与结局相关：FLT1（q = 0.03；GSE26712 p = 0.01，HR = 3.1）；PF4（q = 0.02；GSE26712 p = 0.01，HR = 3.0）；NRP1（q = 0.02；GSE26712 p< = 0.04，HR> = 1.4）；COL4A3（q = 0.04；GSE26712 p = 0.03，HR = 1.3）；和ANGPTL3（q = 0.02；GSE14764 p = 0.02，HR = 1.5）。高AKT1和CD44与更长生存期相关。相反，EPHB2、ERBB2、FLT1、PF4、NRP1、COL4A3和ANGPTL3的高表达与更短生存期相关。CD105-MVD和CD3-1MVD与血管生成基因表达无显著关联。
31个血管生成相关基因与晚期HGSC的生存相关，其中9个基因在独立的公开可用数据库中得到验证。
