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A two-circRNA signature predicts tumour recurrence in clinical non-functioning pituitary adenoma

机译:两环RNA标记可预测临床无功能的垂体腺瘤中的肿瘤复发

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摘要

Clinical non-functioning pituitary adenoma (NFPA) accounts for >30% of all pituitary adenomas, and the recurrence rate is notably high. The ability to predict tumour recurrence during initial surgery will aid in determining if adjunctive therapy is required to reduce recurrence. With the aim of developing a circular RNA (circRNA) signature to improve prognosis prediction in NFPA, the present study examined the circRNA expression profiles in 73 patients with NFPA from Beijing Tiantan Hospital using high-throughput RNA chip technology. The dataset was randomly separated into a training group and a test group using an R program. In the training group (n=37), a Cox proportional hazards regression model was used to analyse the genes associated with the recurrence and progression-free survival (PFS) of patients with NFPA. Meanwhile, a random survival forest algorithm, Kaplan-Meier and receiver operating characteristic curve (ROC) analyses were used to determine the multi-circRNA signature with the largest area under the ROC curve (AUROC) and verify its efficacy in the test group (n=36). In the training and test groups, the signatures of two circRNAs (hsa_circ_0000066 and hsa_circ_0069707) were specifically associated with the PFS of patients with NFPA (log-rank P<0.05). Furthermore, the two-circRNA signature had a high prediction accuracy for tumour recurrence, with an AUROC of 0.87 and 0.67 in the training and test groups, respectively; and the discriminative power of the signature was greater compared with that of age. The present study is the first to suggest a circRNA signature with a clinical application value for predicting recurrence/progression in patients with NFPA.
机译:临床非功能性垂体腺瘤(NFPA)占所有垂体腺瘤的30%以上,复发率极高。在初次手术期间预测肿瘤复发的能力将有助于确定是否需要辅助治疗以减少复发。为了开发环状RNA(circRNA)标记以改善NFPA的预后预测,本研究使用高通量RNA芯片技术检查了北京天坛医院73例NFPA患者的circRNA表达谱。使用R程序将数据集随机分为训练组和测试组。在训练组(n = 37)中,使用Cox比例风险回归模型分析与NFPA患者的复发和无进展生存期(PFS)相关的基因。同时,采用随机生存森林算法,Kaplan-Meier和接收者操作特征曲线(ROC)分析来确定ROC曲线下面积最大的多circRNA特征(AUROC),并在测试组中验证其有效性(n = 36)。在训练和测试组中,两个circRNA(hsa_circ_0000066和hsa_circ_0069707)的签名与NFPA患者的PFS特异性相关(log-rank P <0.05)。此外,双环RNA标记对肿瘤复发具有较高的预测准确性,训练组和测试组的AUROC分别为0.87和0.67。与年龄相比,签名的辨别力更大。本研究首次提出了具有临床应用价值的circRNA签名,可预测NFPA患者的复发/进展。

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