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Development and validation of a novel mRNA signature for predicting early relapse in non-small cell lung cancer
Japanese Journal of Clinical Oncology ( IF 2.4 ) Pub Date : 2021-05-25 , DOI: 10.1093/jjco/hyab075
Jingping Lin 1 , Jinsen Weng 1 , Shaofeng Lin 2 , Cuibo Lin 3 , Jieping Huang 4 , Chunxia Zhang 1 , Shen Zhang 1 , Chuanpeng Dong 5 , Haizhou Ji 3 , Xi Ke 6
Affiliation  

Abstract
Background
Recurrence after initial primary resection is still a major and ultimate cause of death for non-small cell lung cancer patients. We attempted to build an early recurrence associated gene signature to improve prognostic prediction of non-small cell lung cancer.
Methods
Propensity score matching was conducted between patients in early relapse group and long-term survival group from The Cancer Genome Atlas training series (N = 579) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumour-specific mRNAs. Finally, using LASSO Cox regression model, we built a multi-gene early relapse classifier incorporating 40 mRNAs. The prognostic and predictive accuracy of the signature was internally validated in The Cancer Genome Atlas patients.
Results
A total of 40 mRNAs were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in both discovery (HR: 3.244, 95% CI: 2.338-4.500, P < 0.001) and internal validation series (HR 1.970, 95% CI 1.181-3.289, P = 0.009). Further analysis revealed that the prognostic value of this signature was independent of tumour stage, histotype and epidermal growth factor receptor mutation (P < 0.05). Time-dependent receiver operating characteristic analysis showed that the area under receiver operating characteristic curve of this signature was higher than TNM stage alone (0.771 vs 0.686, P < 0.05). Further, decision curve analysis curves analysis at 1 year revealed the considerable clinical utility of this signature in predicting early relapse.
Conclusions
We successfully established a reliable signature for predicting early relapse in stage I–III non-small cell lung cancer.


中文翻译:

用于预测非小细胞肺癌早期复发的新型 mRNA 特征的开发和验证

摘要
背景
初次初次切除后的复发仍然是非小细胞肺癌患者死亡的主要和最终原因。我们试图建立与早期复发相关的基因特征,以改善非小细胞肺癌的预后预测。
方法
对癌症基因组图谱训练系列(N = 579)中早期复发组和长期生存组的患者进行倾向评分匹配,患者按 1:1 匹配。然后在配对组之间进行全局转录组分析以鉴定肿瘤特异性 mRNA。最后,使用 LASSO Cox 回归模型,我们构建了一个包含 40 个 mRNA 的多基因早期复发分类器。签名的预后和预测准确性在癌症基因组图谱患者中得到了内部验证。
结果
最终确定了总共 40 个 mRNA 以构建早期复发分类器。根据特定风险评分公式,将患者分为高危组和低危组。在发现 (HR: 3.244, 95% CI: 2.338-4.500, P  < 0.001) 和内部验证系列 (HR 1.970, 95% CI 1.181-3.289, P  = 0.009) 中,两组的无复发生存率显着不同. 进一步分析表明,该特征的预后价值与肿瘤分期、组织型和表皮生长因子受体突变无关(P  < 0.05)。时间依赖性接收器操作特征分析表明,该特征的接收器操作特征曲线下面积高于单独的 TNM 阶段(0.771 对 0.686,P  < 0.05)。此外,1 年时的决策曲线分析曲线分析揭示了该特征在预测早期复发方面的相当大的临床效用。
结论
我们成功建立了一个可靠的特征来预测 I-III 期非小细胞肺癌的早期复发。
更新日期:2021-08-03
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