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Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

机译:发现和验证新型生物标志物检测上皮性卵巢癌

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摘要

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.
机译:上皮性卵巢癌(EOC)的检测提出了严峻的医学挑战。然而,用于诊断的新型生物标志物仍有待发现。因此,创新的方法对于患者的预后至关重要。在这里,我们提出了一种基于血液的生物标记物发现的概念,研究了上皮隔间和特别是基质隔间,这在寻找新的候选药物时被忽略了。我们查询了EOC的基因表达谱,其中包括来自良性和恶性肿瘤的微切上皮和邻近基质。检索在上皮或基质区室中显着差异表达的基因。通过NanoString分析,在盆腔包块患者的组织和血液中验证了健康供体血液中其产物已被分泌却不存在的基因的表达。综合基因表达数据库CSIOVDB(新加坡癌症科学研究所的卵巢癌数据库)证实了结果。探索了候选基因的前25%的生物标志物潜力,其中十二个能够在转录水平上区分良性和恶性肿瘤(p <0.05)。其中T细胞分化蛋白髓磷脂和淋巴细胞(MAL),极光激酶A(AURKA),基质衍生候选蛋白versican(VCAN)和syndecan-3(SDC)的性能明显优于最近报道的生物标志物成纤维细胞生长因子18(FGF18)区分良性疾病的恶性肿瘤。此外,升高的MAL和AURKA表达水平与不良预后显着相关。我们确定了有前途的新型候选物,并发现EOC基质是发现生物标志物的合适区室。

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