Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China.
Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China.
Cancer Lett. 2018 Jul 1;425:43-53. doi: 10.1016/j.canlet.2018.03.043. Epub 2018 Mar 31.
Lung adenocarcinoma (LAC) is the most lethal cancer and the leading cause of cancer-related death worldwide. The identification of meaningful clusters of co-expressed genes or representative biomarkers may help improve the accuracy of LAC diagnoses. Public databases, such as the Gene Expression Omnibus (GEO), provide rich resources of valuable information for clinics, however, the integration of multiple microarray datasets from various platforms and institutes remained a challenge. To determine potential indicators of LAC, we performed genome-wide relative significance (GWRS), genome-wide global significance (GWGS) and support vector machine (SVM) analyses progressively to identify robust gene biomarker signatures from 5 different microarray datasets that included 330 samples. The top 200 genes with robust signatures were selected for integrative analysis according to "guilt-by-association" methods, including protein-protein interaction (PPI) analysis and gene co-expression analysis. Of these 200 genes, only 10 genes showed both intensive PPI network and high gene co-expression correlation (r > 0.8). IPA analysis of this regulatory networks suggested that the cell cycle process is a crucial determinant of LAC. CENPA, as well as two linked hub genes CDK1 and CDC20, are determined to be potential indicators of LAC. Immunohistochemical staining showed that CENPA, CDK1 and CDC20 were highly expressed in LAC cancer tissue with co-expression patterns. A Cox regression model indicated that LAC patients with CENPA/CDK1 and CENPA/CDC20 were high-risk groups in terms of overall survival. In conclusion, our integrated microarray analysis demonstrated that CENPA, CDK1 and CDC20 might serve as novel cluster of prognostic biomarkers for LAC, and the cooperative unit of three genes provides a technically simple approach for identification of LAC patients.
肺腺癌（LAC）是最致命的癌症，也是全球癌症相关死亡的主要原因。鉴定有意义的共表达基因簇或代表性生物标志物，可能有助于提高 LAC 诊断的准确性。公共数据库，如基因表达综合数据库（GEO），为临床提供了丰富的有价值信息资源，但整合来自不同平台和机构的多个微阵列数据集仍然是一个挑战。为了确定 LAC 的潜在标志物，我们逐步进行了全基因组相对显著性（GWRS）、全基因组全局显著性（GWGS）和支持向量机（SVM）分析，从包含 330 个样本的 5 个不同微阵列数据集中鉴定出稳健的基因生物标志物特征。根据“关联有罪”方法，选择前 200 个具有稳健特征的基因进行综合分析，包括蛋白质-蛋白质相互作用（PPI）分析和基因共表达分析。在这 200 个基因中，只有 10 个基因表现出密集的 PPI 网络和高基因共表达相关性（r>0.8）。该调控网络的 IPA 分析表明，细胞周期过程是 LAC 的关键决定因素。CENPA 以及两个相关的枢纽基因 CDK1 和 CDC20，被确定为 LAC 的潜在标志物。免疫组织化学染色显示，CENPA、CDK1 和 CDC20 在 LAC 癌组织中高度表达，具有共表达模式。Cox 回归模型表明，CENPA/CDK1 和 CENPA/CDC20 高表达的 LAC 患者在总生存方面属于高危组。总之，我们的综合微阵列分析表明，CENPA、CDK1 和 CDC20 可能作为 LAC 的新型预后生物标志物簇，三个基因的协作单位为识别 LAC 患者提供了一种技术简单的方法。
