Madden Stephen F, Clarke Colin, Stordal Britta, Carey Mark S, Broaddus Russell, Gallagher William M, Crown John, Mills Gordon B, Hennessy Bryan T
Molecular Therapeutics for Cancer Ireland, National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
Mol Cancer. 2014 Oct 24;13:241. doi: 10.1186/1476-4598-13-241.
Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade.
We have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples.
To demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10-6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12.
CONCLUSIONS/IMPACT: OvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://glados.ucd.ie/OvMark/index.html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities.
卵巢癌在所有妇科癌症中生存率最低，其特点是缺乏早期症状且晚期诊断频繁。目前缺乏独立于疾病分期和肿瘤分级等临床参数并与之互补的可靠分子标志物。
我们开发了一个用户友好的基于网络的系统，用于评估基因/微小RNA（miRNA）与卵巢癌预后的关联。OvMark算法整合了来自多个微阵列平台的数据（包括靶向miRNA的探针集），并将它们与临床参数（如肿瘤分级、分期）和预后（无病生存期（DFS）、总生存期）相关联。OvMark总共整合了来自7个不同阵列平台的14个数据集，这些数据集测量了2129个卵巢癌样本中约17000个基因和341个miRNA的表达。
为了证明该系统的实用性，我们证实了14个已知在卵巢癌中起作用的基因和2个miRNA的预后能力。在这些基因中，CXCL12是DFS最显著的预测因子（风险比=1.42，p值=2.42×10⁻⁶）。令人惊讶的是，那些被发现与预后相关性最大的基因在卵巢癌中尚未得到深入研究，在某些情况下在任何癌症中都未得到深入研究。例如，与生存相关性最大的三个基因是SNAI3、VWA3A和DNAH12。
结论/影响：OvMark是一种用于检测卵巢癌中假定的基因/miRNA预后生物标志物的强大工具（可在http://glados.ucd.ie/OvMark/index.html获取）。该工具的影响将体现在对卵巢癌中假定生物标志物的初步评估中，特别是对于生物信息学设施有限的研究小组。
