Nordström Malin, Wingren Christer, Rose Carsten, Bjartell Anders, Becker Charlotte, Lilja Hans, Borrebaeck Carl A K
Department of Immunotechnology, Lund University, Medicon Village, Lund, Sweden; CREATE Health, Lund University, Medicon Village, Lund, Sweden.
Proteomics Clin Appl. 2014 Dec;8(11-12):951-62. doi: 10.1002/prca.201300059. Epub 2014 Oct 22.
Early detection of prostate cancer (PC) using prostate-specific antigen (PSA) in blood reduces PC-death among unscreened men. However, due to modest specificity of PSA at commonly used cut-offs, there are urgent needs for additional biomarkers contributing enhanced risk classification among men with modestly elevated PSA.
Recombinant antibody microarrays were applied for protein expression profiling of 80 plasma samples from routine PSA-measurements, a priori divided into four risk groups, based on levels of total and %free PSA.
The results demonstrated that plasma protein profiles could be identified that pin-pointed PC (a malignant biomarker signature) and most importantly that showed moderate to high correlation with biochemically defined PC risk groups. Notably, the data also implied that the risk group with midrange PSA and low %free PSA, a priori known to be heterogeneous, could be further stratified into two subgroups, more resembling the lowest and highest risk groups, respectively.
In this pilot study, we have shown that plasma protein biomarker signatures, associated with risk groups of PC, could be identified from crude plasma samples using affinity proteomics. This approach could in the longer perspective provide novel opportunities for improved risk classification of PC patients.
通过检测血液中的前列腺特异性抗原(PSA)来早期发现前列腺癌(PC),可降低未筛查男性的PC死亡率。然而,由于常用临界值下PSA的特异性一般,迫切需要其他生物标志物来增强PSA轻度升高男性的风险分类。
应用重组抗体微阵列对80份来自常规PSA检测的血浆样本进行蛋白质表达谱分析,这些样本根据总PSA和游离PSA百分比水平预先分为四个风险组。
结果表明,可以识别出能精准定位PC的血浆蛋白质谱(一种恶性生物标志物特征),最重要的是,这些蛋白质谱与生化定义的PC风险组显示出中度至高相关性。值得注意的是,数据还表明,PSA处于中等范围且游离PSA百分比低的风险组(已知该组具有异质性)可进一步分为两个亚组,分别更类似于最低风险组和最高风险组。
在这项初步研究中,我们表明使用亲和蛋白质组学方法可从粗血浆样本中识别出与PC风险组相关的血浆蛋白质生物标志物特征。从长远来看,这种方法可为改善PC患者的风险分类提供新的机会。