Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

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基因组和转录组变异分析作为肺腺癌的预后标志

肺癌是全球死亡人数最多的主要原因，而肺腺癌是最常见的肺癌形式。为了了解肺腺癌的分子基础，已经通过使用基因组学，转录组学，表观基因组学，蛋白质组学和临床数据进行了综合分析。此外，通过利用肿瘤样品中的基因表达水平，已经产生了针对肺腺癌的分子预后标记。但是，我们需要包括不同类型的分子数据的签名，甚至包括基于队列或基于患者的生物标志物，它们都是分子靶向的候选物。我们建立了一条R管线来对包括单核苷酸变异和拷贝数变异在内的基因组变异进行整合的荟萃分析，癌症基因组图谱项目中通过RNA序列和肺腺癌患者临床数据的转录组学变异。我们整合了重要的基因，包括单核苷酸变异或拷贝数变异，差异表达的基因以及活跃子网中的基因，以构建预后标志。使用具有套索罚分和LOOCV的Cox比例风险模型来识别不同基因类别之间的最佳基因签名。我们根据肺腺癌患者的总生存时间确定了12个基因的签名（BCHE，CCNA1，CYP24A1，DEPTOR，MASP2，MGLL，MYO1A，PODXL2，RAPGEF3，SGK2，TNNI2，ZBTB16）用于预后风险预测。通过基于选择的基因特征计算出的患者风险评分，将训练数据和测试数据中的患者分为高风险和低风险组。对于培训和测试数据集，这些风险组的总体生存概率差异很大。这12个基因的特征可以预测TCGA中肺腺癌患者的预后风险，并且它们是肺腺癌患者基于生存的风险聚类的潜在预测因子。这些基因可用于根据分子性质对患者进行聚类，并且可以提出针对患者聚类的最佳药物候选物。这些基因还具有用于肺腺癌患者的靶向癌症治疗的高潜力。对于培训和测试数据集，这些风险组的总体生存概率差异很大。这12个基因的特征可以预测TCGA中肺腺癌患者的预后风险，并且它们是肺腺癌患者基于生存的风险聚类的潜在预测因子。这些基因可用于根据分子性质对患者进行聚类，并且可以提出针对患者聚类的最佳药物候选物。这些基因还具有用于肺腺癌患者的靶向癌症治疗的高潜力。对于培训和测试数据集，这些风险组的总体生存概率差异很大。这12个基因的特征可以预测TCGA中肺腺癌患者的预后风险，并且它们是肺腺癌患者基于生存的风险聚类的潜在预测因子。这些基因可用于根据分子性质对患者进行聚类，并且可以提出针对患者聚类的最佳药物候选物。这些基因还具有用于肺腺癌患者的靶向癌症治疗的高潜力。这12个基因的特征可以预测TCGA中肺腺癌患者的预后风险，并且它们是肺腺癌患者基于生存的风险聚类的潜在预测因子。这些基因可用于根据分子性质对患者进行聚类，并且可以提出针对患者聚类的最佳药物候选物。这些基因还具有用于肺腺癌患者的靶向癌症治疗的高潜力。这12个基因的特征可以预测TCGA中肺腺癌患者的预后风险，并且它们是肺腺癌患者基于生存的风险聚类的潜在预测因子。这些基因可用于根据分子性质对患者进行聚类，并且可以提出针对患者聚类的最佳药物候选物。这些基因还具有用于肺腺癌患者的靶向癌症治疗的高潜力。