Cen Shu Yi, Fu Kai You, Shi Yue, Jiang Han Liang, Shou Jia Wei, You Liang Kun, Han Wei Dong, Pan Hong Ming, Liu Zhen
Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.
School of Medicine, Zhejiang University, Hangzhou 310016, China.
Math Biosci Eng. 2020 Feb 27;17(3):2557-2568. doi: 10.3934/mbe.2020140.
Lymph node metastasis (LNM) of lung cancer is an important factor associated with prognosis. Dysregulated microRNAs (miRNAs) are becoming a new powerful tool to characterize tumorigenesis and metastasis. We have developed and validated a miRNA disease signature to predict LNM in lung adenocarcinoma (LUAD). LUAD miRNAs and clinical data from The Cancer Genome Atlas (TCGA) were obtained and divided randomly into training (n = 259) and validation (n = 83) cohorts. A miRNA signature was built using least absolute shrinkage and selection operator (LASSO) (λ =-1.268) and logistic regression model. The performance of the miRNA signature was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). We performed decision curve analysis (DCA) to assess the clinical usefulness of the signature. We also conducted a miRNA-regulatory network analysis to look for potential genes engaged in LNM in LUAD. Thirteen miRNAs were selected to build our miRNA disease signature. The model showed good calibration in the training cohort, with an AUC of 0.782 (95% CI: 0.725-0.839). In the validation cohort, AUC was 0.691 (95% CI: 0.575-0.806). DCA demonstrated that the miRNA signature was clinically useful. The miRNA disease signature can be used as a noninvasive method to predict LNM in patients with lung adenocarcinoma objectively and the signature achieved high accuracy for prediction.
肺癌的淋巴结转移（LNM）是一个与预后相关的重要因素。失调的微小RNA（miRNA）正成为表征肿瘤发生和转移的一种新的有力工具。我们已经开发并验证了一种miRNA疾病特征，以预测肺腺癌（LUAD）中的LNM。从癌症基因组图谱（TCGA）获取LUAD的miRNA和临床数据，并将其随机分为训练队列（n = 259）和验证队列（n = 83）。使用最小绝对收缩和选择算子（LASSO）（λ = -1.268）和逻辑回归模型构建miRNA特征。使用受试者操作特征曲线（ROC）的曲线下面积（AUC）评估miRNA特征的性能。我们进行了决策曲线分析（DCA）以评估该特征的临床实用性。我们还进行了miRNA调控网络分析，以寻找参与LUAD中LNM的潜在基因。选择了13个miRNA来构建我们的miRNA疾病特征。该模型在训练队列中显示出良好的校准，AUC为0.782（95%CI：0.725 - 0.839）。在验证队列中，AUC为0.691（95%CI：0.575 - 0.806）。DCA表明该miRNA特征具有临床实用性。该miRNA疾病特征可作为一种非侵入性方法，客观地预测肺腺癌患者的LNM，并且该特征在预测方面具有较高的准确性。
