Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma
Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma
复制标题
跨组学生物标志物模型提高了早期肺腺癌的预后预测准确性。
DOI：
10.18632/aging.102189
复制
发表时间：
2019-08-31
期刊：
影响因子：
5.2
通讯作者：
Christiani, David C.
中科院分区：
文献类型：
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作者：
Dong, Xuesi;Zhang, Ruyang;Christiani, David C.
有限的研究集中在开发早期肺腺癌（LUAD）的跨组学生物标志物的预后模型。我们对来自5个队列的825例早期LUAD患者的临床信息、DNA甲基化和基因表达数据进行了综合分析。使用Ranger算法筛选肿瘤相关生物标志物，并通过验证阶段进行确认。使用iCluster plus算法融合临床和生物标志物信息，该算法将患者显著区分为高危低死亡率风险组（P-发现= 0.01和P-验证= 2.71x10（-3））。此外，潜在的功能DNA甲基化-基因表达-总生存途径进行了评估的因果中介分析。DNA甲基化水平对LUAD存活的影响是通过基因表达水平介导的。通过将DNA甲基化和基因表达生物标志物添加到仅包含临床数据的模型中，trans-omics模型的AUC在发现和验证阶段分别提高了18.3%（至87.2%）和16.4%（至85.3%）。此外，诺模图的一致性指数在发现和验证阶段分别为0.81和0.77。基于对已发表文献的系统回顾，我们的模型上级所有现有的早期LUAD模型。总之，我们的反式组学模型可以帮助医生准确地识别具有高死亡风险的患者。
Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into hig-hand low-mortality risk groups (P-discovery = 0.01 and P-validation = 2.71x10(-3)). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.