Zalfa Francesca, Perrone Maria Grazia, Ferorelli Savina, Laera Luna, Pierri Ciro Leonardo, Tolomeo Anna, Dimiccoli Vincenzo, Perrone Giuseppe, De Grassi Anna, Scilimati Antonio
Predictive Molecular Diagnostic Unit, Pathology Department, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.
Microscopic and Ultrastructural Anatomy Unit, CIR, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.
Cancers (Basel). 2022 Aug 2;14(15):3764. doi: 10.3390/cancers14153764.
Ovarian cancer is the second most prevalent gynecologic malignancy, and ovarian serous cystadenocarcinoma (OSCA) is the most common and lethal subtype of ovarian cancer. Current screening methods have strong limits on early detection, and the majority of OSCA patients relapse. In this work, we developed and cross-validated a method for detecting gene expression biomarkers able to discriminate OSCA tissues from healthy ovarian tissues and other cancer types with high accuracy. A preliminary ranking-based approach was applied, resulting in a panel of 41 over-expressed genes in OSCA. The RNA quantity gene expression of the 41 selected genes was then cross-validated by using NanoString nCounter technology. Moreover, we showed that the RNA quantity of eight genes (, , , , , , and ) discriminates each OSCA sample from each healthy sample in our data set with sensitivity of 100% and specificity of 100%. For the other three genes (, and ) in combination, their RNA quantity may distinguish OSCA from other 29 tumor types.
卵巢癌是第二常见的妇科恶性肿瘤，卵巢浆液性囊腺癌（OSCA）是卵巢癌最常见且致死率最高的亚型。目前的筛查方法在早期检测方面存在很大局限性，大多数OSCA患者会复发。在这项研究中，我们开发并交叉验证了一种检测基因表达生物标志物的方法，该方法能够高精度地区分OSCA组织与健康卵巢组织以及其他癌症类型。我们应用了一种基于初步排名的方法，得到了一组在OSCA中过表达的41个基因。然后使用NanoString nCounter技术对所选41个基因的RNA定量基因表达进行交叉验证。此外，我们发现，在我们的数据集中，八个基因（此处原文缺失基因具体名称）的RNA量能够以100%的敏感性和100%的特异性区分每个OSCA样本与每个健康样本。对于另外三个基因（此处原文缺失基因具体名称）组合而言，它们的RNA量可能将OSCA与其他29种肿瘤类型区分开来。
