Institute of Cancer and Basic medicine (ICBM), Chinese Academy of Sciences, Zhejiang, China.
Ultrasonic Department, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang, China.
Sci Rep. 2020 May 22;10(1):8575. doi: 10.1038/s41598-020-65479-y.
Non-small lung cancer (NSCLC) is a common malignant disease with very poor outcome. Accurate prediction of prognosis can better guide patient risk stratification and treatment decision making, and could optimize the outcome. Utilizing clinical and methylation/expression data in The Cancer Genome Atlas (TCGA), we conducted comprehensive evaluation of early-stage NSCLC to identify a methylation signature for survival prediction. 349 qualified cases of NSCLC with curative surgery were included and further grouped into the training and validation cohorts. We identified 4000 methylation loci with prognostic influence on univariate and multivariate regression analysis in the training cohort. KEGG pathway analysis was conducted to identify the key pathway. Hierarchical clustering and WGCNA co-expression analysis was performed to classify the sample phenotype and molecular subtypes. Hub 5'-C-phosphate-G-3' (CpG) loci were identified by network analysis and then further applied for the construction of the prognostic signature. The predictive power of the prognostic model was further validated in the validation cohort. Based on clustering analysis, we identified 6 clinical molecular subtypes, which were associated with different clinical characteristics and overall survival; clusters 4 and 6 demonstrated the best and worst outcomes. We identified 17 hub CpG loci, and their weighted combination was used for the establishment of a prognostic model (RiskScore). The RiskScore significantly correlated with post-surgical outcome; patients with a higher RiskScore have worse overall survival in both the training and validation cohorts (P < 0.01). We developed a novel methylation signature that can reliably predict prognosis for patients with NSCLC.
非小细胞肺癌（NSCLC）是一种常见的恶性疾病，预后非常差。准确预测预后可以更好地指导患者的风险分层和治疗决策，并优化治疗结果。我们利用癌症基因组图谱（TCGA）中的临床和甲基化/表达数据，对早期 NSCLC 进行了全面评估，以确定用于生存预测的甲基化特征。纳入了 349 例有根治性手术的合格 NSCLC 病例，并进一步分为训练和验证队列。我们在训练队列中发现了 4000 个对单变量和多变量回归分析有预后影响的甲基化位点。进行了 KEGG 通路分析以确定关键途径。进行了层次聚类和 WGCNA 共表达分析以对样本表型和分子亚型进行分类。通过网络分析确定了枢纽 5'-C-磷酸-G-3'（CpG）位点，然后进一步将其应用于预后特征的构建。该预后模型的预测能力在验证队列中得到了进一步验证。基于聚类分析，我们确定了 6 种临床分子亚型，它们与不同的临床特征和总生存相关；簇 4 和簇 6 表现出最好和最差的结局。我们确定了 17 个枢纽 CpG 位点，它们的加权组合用于建立预后模型（RiskScore）。RiskScore 与术后结果显著相关；在训练和验证队列中，RiskScore 较高的患者总生存较差（P<0.01）。我们开发了一种新的甲基化特征，可以可靠地预测 NSCLC 患者的预后。
