PDF(Pubmed)
Abstract：
Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30-50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I-IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.
摘要：
卵巢癌是女性中第八大最常见的癌症，5年生存率仅为30-50％。虽然I期肿瘤的存活率接近90%,但IV期的存活率仅为20%。目前的生物标志物不够敏感，也不够特异，迫切需要新的生物标志物。我们使用ExplorePEA技术对2943种血浆蛋白进行大规模分析，以使用两个独立的临床队列寻找新的生物标志物。使用第一个队列的发现分析确定了296种在恶性肿瘤中与良性肿瘤相比具有显着不同水平的蛋白质，其中269种（91％）。该关联在第二队列中得以复制.多变量建模，包括在单变量分析中独立于其关联的所有蛋白质，确定了一种用于从恶性肿瘤（I-IV期）中分离良性疾病的模型，该模型由三种蛋白质组成；WFDC2，KRT19和RBFOX3。该模型在复制组群中实现了0.92的AUC,在发现组群中开发的截止点实现了0.93和0.77的灵敏度和特异性。与发现群组相比,复制群组中的表现没有统计学差异。WFDC2和KRT19先前与卵巢癌相关，但RBFOX3先前尚未被鉴定为潜在的生物标志物。我们的结果证明了使用高通量精确蛋白质组学鉴定用于卵巢癌检测的新型血浆蛋白生物标志物的能力。
