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首页> 外文期刊>The journal of clinical investigation >Predicting time to ovarian carcinoma recurrence using protein markers
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Predicting time to ovarian carcinoma recurrence using protein markers

机译：使用蛋白质标记物预测卵巢癌复发的时间

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Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression–based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.
机译：卵巢癌患者有很高的肿瘤复发风险。对治疗结果的预测可以提供改善患者预后的治疗途径。使用反相蛋白阵列，我们从TCGA生成了412例卵巢癌蛋白表达谱，并构建了PRotein驱动的OVARian癌指数（PROVAR）。 PROVAR将226个高级别浆液性卵巢癌的独立队列显着区分为肿瘤复发的高风险和低风险组以及短期和长期幸存者。与基于基因表达的结果分类模型的比较表明，基于蛋白质的PROVAR预测肿瘤进展的能力显着提高。与疾病复发相关的蛋白质标记物的鉴定可以产生对肿瘤生物学的见解。当与已知与结果相关的特征（例如BRCA突变）结合使用时，PROVAR可以提供肿瘤复发时间的临床有用预测。

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