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Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

肺癌患者的预测模型通过基于多种临床信息的人工神经网络识别EGFR突变状态。

  • 影响因子:3.23
  • DOI:10.1007/s00432-019-03103-x
  • 作者列表:"Qin X","Wang H","Hu X","Gu X","Zhou W
  • 发表时间:2020-03-01
Abstract

PURPOSE:Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making. METHODS:We collected data from 320 patients with lung carcinoma, including sex, age, smoking history, serum tumour marker levels, maximum standardized uptake value, pathological results, computed tomography images, and EGFR mutation status. Artificial neural network (ANN) models based on multiple clinical characteristics were proposed to predict EGFR mutation status. RESULTS:A training set (n = 200) was used to develop predictive models of the EGFR mutation status (Model 1: area under the receiver operating characteristic curve [AUROC] = 0.910, 95% CI 0.861-0.945; Model 2: AUROC = 0.859, 95% CI 0.803-0.904; Model 3: AUROC = 0.711, 95% CI 0.643-0.773). A testing set (n = 50) and temporal validation data set (n = 70) were used to evaluate the generalisation performance of the established models (testing set: Model 1, AUROC = 0.845, 95% CI 0.715-0.932; Model 2, AUROC = 0.882, 95% CI 0.759-0.956; Model 3, AUROC = 0.817, 95% CI 0.682-0.912; temporal validation dataset: Model 1, AUROC = 0.909, 95% CI 0.816-0.964; Model 2, AUROC = 0.855, 95% CI 0.751-0.928; Model 3, AUROC = 0.831, 95% CI 0.723-0.910). The predictive abilities of the three ANN models were superior to that of a previous logistic regression model (P < 0.001, 0.027, and 0.050, respectively). CONCLUSIONS:ANN models provide a non-invasive and readily available method for EGFR mutation status prediction.

摘要

目的: 表皮生长因子受体 (EGFR) 突变检测有几个局限性。因此,我们建立了预测模型来确定患者的EGFR突变状态并指导治疗决策。 方法: 我们收集了 320 例肺癌患者的资料,包括性别、年龄、吸烟史、血清肿瘤标志物水平、最大标准化摄取值、病理结果、计算机断层扫描图像、和EGFR突变状态。提出了基于多种临床特征的人工神经网络 (ANN) 模型来预测EGFR突变状态。 结果: 使用训练集 (n   =   200) 开发EGFR突变状态的预测模型 (模型 1: 受试者工作特征曲线下面积 [AUROC]  =   0.910,95% CI 0.861-0.945; 模型 2: AUROC   =   0.859,95% CI 0.803-0.904; 模型 3: auroc   = 0.711,95% CI 0.643-0.773)。使用测试集 (n   =   50) 和时间验证数据集 (n   = 70 70) 来评估已建立模型的泛化性能 (测试集: 模型 1,auroc   = 0.845,95% CI 0.715-0.932; 模型 2,auroc   = 0.882,95% CI 0.759-0.956; 模型 3,auroc   = 0.817,95% CI 0.682-0.912; 时态验证数据集:模型 1,auroc   = 0.909,95% CI 0.816-0.964; 模型 2,auroc   = 0.855,95% CI 0.751-0.928; 模型 3,auroc   = 0.831,95% CI 0.723-0.910)。3 种神经网络模型的预测能力均优于以往的logistic回归模型 (p <0.001 、 0.027 和 0.050)。 结论: 神经网络模型为EGFR突变状态预测提供了一种非侵入性和容易获得的方法。

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METHODS::Pulmonary artery sling is a rare congenital anomaly of the origin and course of the left pulmonary artery. Patients with this condition typically present with respiratory failure in young infancy, and asymptomatic cases are uncommon. We describe the case of an adult patient with a lung adenocarcinoma of the right upper lobe, extending into the hilum and superior mediastinum, and with a previously unknown pulmonary artery sling anomaly. The local invasiveness of the tumor and the peculiar vascular anatomy contributed to a unique surgical scenario, wherein multiple reconstructive procedures were required.

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影响因子:6.93
发表时间:2020-01-15
DOI:10.1002/ijc.32532
作者列表:["Hata A","Nakajima T","Matsusaka K","Fukuyo M","Morimoto J","Yamamoto T","Sakairi Y","Rahmutulla B","Ota S","Wada H","Suzuki H","Matsubara H","Yoshino I","Kaneda A"]

METHODS::Patients with idiopathic pulmonary fibrosis (IPF) have higher risk of developing lung cancer, for example, squamous cell carcinoma (SCC), and show poor prognosis, while the molecular basis has not been fully investigated. Here we conducted DNA methylome analysis of lung SCC using 20 SCC samples with/without IPF, and noncancerous lung tissue samples from smokers/nonsmokers, using Infinium HumanMethylation 450K array. SCC was clustered into low- and high-methylation epigenotypes by hierarchical clustering analysis. Genes hypermethylated in SCC significantly included genes targeted by polycomb repressive complex in embryonic stem cells, and genes associated with Gene Ontology terms, for example, "transcription" and "cell adhesion," while genes hypermethylated specifically in high-methylation subgroup significantly included genes associated with "negative regulation of growth." Low-methylation subgroup significantly correlated with IPF (78%, vs. 17% in high-methylation subgroup, p = 0.04), and the correlation was validated by additional Infinium analysis of SCC samples (n = 44 in total), and data from The Cancer Genome Atlas (n = 390). The correlation between low-methylation subgroup and IPF was further validated by quantitative methylation analysis of marker genes commonly hypermethylated in SCC (HOXA2, HOXA9 and PCDHGB6), and markers specifically hypermethylated in high-methylation subgroup (DLEC1, CFTR, MT1M, CRIP3 and ALDH7A1) in 77 SCC cases using pyrosequencing (p = 0.003). Furthermore, low-methylation epigenotype significantly correlated with poorer prognosis among all SCC patients, or among patients without IPF. Multivariate analysis showed that low-methylation epigenotype is an independent predictor of poor prognosis. These may suggest that lung SCC could be stratified into molecular subtypes with distinct prognosis, and low-methylation lung SCC that significantly correlates with IPF shows unfavorable outcome.

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影响因子:6.93
发表时间:2020-01-01
DOI:10.1002/ijc.32530
作者列表:["Zhang L","Yang Y","Chai L","Bu H","Yang Y","Huang H","Ran J","Zhu Y","Li L","Chen F","Li W"]

METHODS::The role of Fyn-related kinase (FRK) in malignant tumors remains controversial. Our study investigated the function of FRK in lung cancer. Immunohistochemistry staining and generating a knockout of FRK by CRISPR/Cas9 in H1299 (FRK-KO-H1299) cells were strategies used to explore the role of FRK. Immunohistochemistry staining indicated that FRK expression was elevated in 223 lung cancer tissues compared to 26 distant normal lung tissues. FRK contributed to poor survival status in lung cancer patients and acted as a predictor for poor prognosis of lung cancer. Knockout of FRK by CRISPR/Cas9 markedly inhibited proliferation, invasion, colony formation and epithelial-mesenchymal transition (EMT) process in the lung cancer cell line H1299. Further exploration indicated that FRK-KO damaged the stemness phenotype of H1299 by inhibiting CD44 and CD133 expression. Seahorse detection and a U-13 C flux assay revealed that FRK-KO induced metabolism reprogramming by inhibiting the Warburg effect and changing the energy type in H1299 cells. Epidermal growth factor stimulation recovered the expression of FRK and biological functions, metabolic reprogramming and stemness phenotype of H1299 cells. FRK plays an oncogenic role in lung cancer cells via a novel regulation mechanism of enhancing the stemness of H1299 cells by inducing metabolism reprogramming, which finally promotes EMT and metastasis. Our study also indicates that FRK could be used as a potential therapeutic target for drug development.

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肺肿瘤方向

肺肿瘤,又叫支气管肺癌,是常见的恶性肿瘤之一。肺肿瘤的治疗为包括手术、中药、放疗、化疗及免疫等多学科的综合治疗。

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