Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, Jiangsu Province, China.
Department of Thoracic Surgery, The Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China.
BMC Cancer. 2018 Oct 11;18(1):966. doi: 10.1186/s12885-018-4881-9.
The current TNM staging system is far from perfect in predicting the survival of individual non-small cell lung cancer (NSCLC) patients. In this study, we aim to combine clinical variables and molecular biomarkers to develop a prognostic model for patients with NSCLC.
Candidate molecular biomarkers were extracted from the Gene Expression Omnibus (GEO), and Cox regression analysis was performed to determine significant prognostic factors. The survival prediction model was constructed based on multivariable Cox regression analysis in a cohort of 152 NSCLC patients. The predictive performance of the model was assessed by the Area under the Receiver Operating Characteristic Curve (AUC) and Kaplan-Meier survival analysis.
The survival prediction model consisting of two genes (TPX2 and MMP12) and two clinicopathological factors (tumor stage and grade) was developed. The patients could be divided into either high-risk group or low-risk group. Both disease-free survival and overall survival were significantly different among the diverse groups (P < 0.05). The AUC of the prognostic model was higher than that of the TNM staging system for predicting survival.
We developed a novel prognostic model which can accurately predict outcomes for patients with NSCLC after surgery.
目前的 TNM 分期系统在预测个体非小细胞肺癌（NSCLC）患者的生存方面远非完美。在这项研究中，我们旨在结合临床变量和分子生物标志物来为 NSCLC 患者开发一种预后模型。
从基因表达综合数据库（GEO）中提取候选分子生物标志物，并进行 Cox 回归分析以确定显著的预后因素。基于 152 名 NSCLC 患者队列中的多变量 Cox 回归分析构建了生存预测模型。通过接受者操作特征曲线下的面积（AUC）和 Kaplan-Meier 生存分析评估模型的预测性能。
建立了由两个基因（TPX2 和 MMP12）和两个临床病理因素（肿瘤分期和分级）组成的生存预测模型。患者可以分为高风险组或低风险组。不同组之间的无病生存率和总生存率均有显著差异（P<0.05）。预测生存的预后模型的 AUC 高于 TNM 分期系统。
我们开发了一种新的预后模型，可准确预测 NSCLC 患者手术后的结局。
