Most patients with lung adenocarcinoma (LUAD) present high recurrence rate and poor prognosis after therapy. Therefore, the purpose of this study was to identify prognostic factors involved in LUAD. Five microarray datasets (including GSE75037, GSE63459, GSE43458, GSE32863, and GSE10072) were downloaded. After data preprocessing and quality control, meta-analysis was performed to screen differentially expressed genes (DEGs) using the MetaDE.ES method in MetaDE package. Subsequently, network construction and module identification were conducted by the Weighted Gene Co-expression Network Analysis method. Moreover, survival-associated genes were identified using the univariate and multivariate Cox regression method in survival package. The risk score model was constructed by prognosis associated genes, followed by the Kaplan-Meier survival analysis. Oncomine expressions analysis of several prognosis associated genes was conducted. The expression levels of key genes were detected using quantitative real-time PCR experiments. A total of 1434 DEGs between LUAD and normal samples were identified. Nine disease-associated modules were identified, in which M8 module was most correlated with LAUD phenotype. A total of 89 indicators (including T stage, M stage, and ADIPOR2) were significantly associated with LAUD prognosis, while only T stage and 9 DEGs (e.g., ARHGEF3, GTSE1, RBM15 and CD52) were retained as the potential prognostic factors following multivariate COX regression analysis. The upregulated adiponectin receptor 2 (ADIPOR2), rho guanine nucleotide exchange factor 3 (ARHGEF3), and CD52 molecule (CD52), and downregulated GTSE1 were validated in LAUD samples of Oncomine database. Importantly, ADIPOR2 and ARHGEF3 were confirmed to be down-regulated in LUAD tissues. ADIPOR2, ARHGEF3, G2 and S-phase expressed 1 (GTSE1) and CD52 might be promising prognostic factors in LUAD.

中文翻译：

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微阵列数据集的生物信息学分析，以鉴定肺腺癌的预后因素。

大多数肺腺癌（LUAD）患者在治疗后复发率高，预后差。因此，本研究的目的是确定与LUAD有关的预后因素。下载了五个微阵列数据集（包括GSE75037，GSE63459，GSE43458，GSE32863和GSE10072）。经过数据预处理和质量控制后，使用MetaDE软件包中的MetaDE.ES方法进行荟萃分析，以筛选差异表达基因（DEG）。随后，通过加权基因共表达网络分析方法进行网络构建和模块识别。此外，在生存软件包中使用单变量和多变量Cox回归方法鉴定了与生存相关的基因。通过预后相关基因构建风险评分模型，然后进行Kaplan-Meier生存分析。进行了几种预后相关基因的Oncomine表达分析。使用定量实时PCR实验检测关键基因的表达水平。在LUAD和正常样品之间总共鉴定出1434个DEG。确定了九个疾病相关模块，其中M8模块与LAUD表型最相关。共有89个指标（包括T期，M期和ADIPOR2）与LAUD预后显着相关，而只有T期和9 DEG（例如，其中M8模块与LAUD表型最相关。共有89个指标（包括T期，M期和ADIPOR2）与LAUD预后显着相关，而只有T期和9 DEG（例如，其中M8模块与LAUD表型最相关。共有89个指标（包括T期，M期和ADIPOR2）与LAUD预后显着相关，而只有T期和9 DEG（例如，经过多变量COX回归分析后，保留了ARHGEF3，GTSE1，RBM15和CD52）作为潜在的预后因素。在Oncomine数据库的LAUD样本中验证了脂联素受体2（ADIPOR2），rho鸟嘌呤核苷酸交换因子3（ARHGEF3）和CD52分子（CD52）的上调以及GTSE1的下调。重要的是，确认ADIPOR2和ARHGEF3在LUAD组织中被下调。ADIPOR2，ARHGEF3，G2和S相表示为1（GTSE1）和CD52 在LUAD中可能是有希望的预后因素。