Bengtsson Sofia, Krogh Morten, Szigyarto Cristina Al-Khalili, Uhlen Mathias, Schedvins Kjell, Silfverswärd Claes, Linder Stig, Auer Gert, Alaiya Ayodele, James Peter
Department of Protein Technology, Lund University, 221 84 Lund, Sweden.
J Proteome Res. 2007 Apr;6(4):1440-50. doi: 10.1021/pr060593y. Epub 2007 Feb 22.
Ovarian cancer is usually found at a late stage when the prognosis is often bad. Relative survival rates decrease with tumor stage or grade, and the 5-year survival rate for women with carcinoma is only 38%. Thus, there is a great need to find biomarkers that can be used to carry out routine screening, especially in high-risk patient groups. Here, we present a large-scale study of 64 tissue samples taken from patients at all stages and show that we can identify statistically valid markers using nonsupervised methods that distinguish between normal, benign, borderline, and malignant tissue. We have identified 217 of the significantly changing protein spots. We are expressing and raising antibodies to 35 of these. Currently, we have validated 5 of these antibodies for use in immunohistochemical analysis using tissue microarrays of healthy and diseased ovarian, as well as other, human tissues.
卵巢癌通常在晚期才被发现，而晚期的预后往往很差。相对生存率会随着肿瘤分期或分级的增加而降低，患有卵巢癌的女性的5年生存率仅为38%。因此，迫切需要找到可用于进行常规筛查的生物标志物，尤其是在高危患者群体中。在此，我们对从各阶段患者身上采集的64份组织样本进行了大规模研究，结果表明我们可以使用无监督方法识别出具有统计学意义的有效标志物，这些标志物能够区分正常组织、良性组织、交界性组织和恶性组织。我们已经鉴定出217个有显著变化的蛋白质斑点。我们正在对其中35个进行表达并制备抗体。目前，我们已经验证了其中5种抗体可用于使用健康和患病卵巢以及其他人体组织的组织微阵列进行免疫组织化学分析。
