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Immune mediator expression signatures are associated with improved outcome in ovarian carcinoma

机译:免疫介质表达签名与卵巢癌的改善结果相关

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Immune and inflammatory cascades may play multiple roles in ovarian cancer. We aimed to identify relationships between expression of immune and inflammatory mediators and patient outcomes. We interrogated differential gene expression of 44 markers and marker combinations (n = 1,978) in 1,656 ovarian carcinoma patient tumors, alongside matched 5-year overall survival (OS) data in silico. Using machine learning methods, we investigated whether genomic expression of these 44 mediators can discriminate between malignant and non-malignant tissues in 839 ovarian cancer and 115 non-malignant ovary samples. We furthermore assessed inflammation markers in 289 ovarian cancer patients' sera in the Swedish Apolipoprotein MOrtality-related RISk (AMORIS) cohort. Expression of the 44 mediators could discriminate between malignant and non-malignant tissues with at least 96% accuracy. Higher expression of classical Th1, Th2, Th17, anti-parasitic/infection and Ml macrophage mediator signatures were associated with better OS. Contrastingly, inflammatory and angiogenic mediators, CXCL-12, C-reactive protein (CRP) and platelet-derived growth factor subunit A (PDGFA) were negatively associated with OS. Of the serum inflammatory markers in the AMORIS cohort, women with ovarian cancer who had elevated levels of haptoglobin (≥1.4 g/L) had a higher risk of dying from ovarian cancer compared to those with haptoglobin levels <1.4 g/L (HR = 2.09, 95% Cl:1.38-3.16). Our findings indicate that elevated "classical" immune mediators, associated with response to pathogen antigen challenge, may confer immunological advantage in ovarian cancer, while inflammatory markers appear to have negative prognostic value. These highlight associations between immune protection, inflammation and clinical outcomes, and offer opportunities for patient stratification based on secretome markers.
机译:免疫和炎症级联可能在卵巢癌中发挥多种作用。我们旨在识别免疫和炎症介质和患者结果表达之间的关系。我们在1,656个卵巢癌患者肿瘤中询问了44个标记和标记组合(n = 1,978)的差异基因表达,以及硅中的5年整体存活(OS)数据。使用机器学习方法,我们研究了这44个介质的基因组表达是否可以区分839个卵巢癌和115个非恶性卵巢样品的恶性和非恶性组织之间。我们还评估了289例卵巢癌患者血清中的炎症标志物在瑞典载脂蛋白死亡率相关风险(Amoris)队列中。 44例介质的表达可以区分恶性和非恶性组织,至少96%的准确性。经典TH1,TH2,TH17,抗寄生/感染和ML巨噬细胞介质签名的较高表达与更好的OS相关。比较的,炎症和血管生成介质,CXCL-12,C反应蛋白(CRP)和血小板衍生的生长因子亚基A(PDGFA)与OS负相关。在Amoris Cohort中的血清炎症标志物中,卵巢癌水平升高的患者(≥1.4克/升)的妇女与卵巢癌的风险较高,与患有哈达洛蛋白水平<1.4克/ L(HR = 2.09,95%CL:1.38-3.16)。我们的研究结果表明,与对病原体抗原攻击的反应相关的“古典”免疫介质升高,可能在卵巢癌中赋予免疫优势,而炎症标记似乎具有负预后值。这些突出了免疫保护,炎症和临床结果之间的关联,并为基于乳糜标志物的患者分层提供了机会。

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