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非小细胞肺癌相关潜在机制的研究。

Investigation of Potential Mechanisms Associated with Non-small Cell Lung Cancer.

机构信息

Department of Radiotherapy, Affiliated Hospital of Nantong University, Nantong, China.

Department of Anesthesiology, Nantong Tumor Hospital, Nantong, China.

出版信息

J Comput Biol. 2020 Sep;27(9):1433-1442. doi: 10.1089/cmb.2019.0081. Epub 2020 Feb 12.

Abstract

This study aimed at investigating the crucial mechanisms underlying non-small cell lung cancer (NSCLC). NSCLC-related microarray data GSE27262 were downloaded from Gene Expression Omnibus, including 7 NSCLC 1a samples, 18 NSCLC 1b samples, and their matched normal samples. The common differentially expressed genes (DEGs) between NSCLC 1a and NSCLC 1b samples were identified, followed by protein-protein interaction (PPI) network construction, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA). Further, the key DEGs were confirmed based on the lung adenocarcinoma (LUAD) data from the Cancer Genome Atlas (TCGA) database, followed by clinical prognostic analysis. There were 802 (NSCLC 1a) and 734 (NSCLC 1b) DEGs identified. By intersection analysis, we obtained 255 upregulated and 97 downregulated common DEGs. Upregulated DEGs were significantly enriched in the plasma membrane and extracellular region, whereas the downregulated DEGs were significantly enriched in the cytoskeleton and cell cycle process. Topoisomerase (DNA) II alpha (TOP2A) and cyclin B1 (CCNB1) were hub nodes in the PPI network. Based on WGCNA, 5 modules were obtained. In the module MEgreen, DEGs were significantly enriched in cytokine-cytokine receptor interaction and focal adhesion. Notably, 1797 DEGs were identified based on the LUAD data from the TCGA database; among them, 285 DEGs were common DEGs identified from GSE27262 data. Upregulation of TOP2A and CCNB1 was correlated with poor survival of patients. The hub genes and key pathways identified in this study are helpful for a comprehensive knowledge of the molecular mechanisms of NSCLC.

摘要

本研究旨在探讨非小细胞肺癌(NSCLC)的关键机制。从基因表达综合数据库中下载了 NSCLC 相关的微阵列数据集 GSE27262,其中包括 7 个 NSCLC 1a 样本、18 个 NSCLC 1b 样本及其配对的正常样本。鉴定 NSCLC 1a 和 NSCLC 1b 样本之间的常见差异表达基因(DEGs),然后构建蛋白质-蛋白质相互作用(PPI)网络,进行功能富集分析和加权基因共表达网络分析(WGCNA)。进一步基于癌症基因组图谱(TCGA)数据库中的肺腺癌(LUAD)数据,确认关键 DEGs,并进行临床预后分析。鉴定出 802 个(NSCLC 1a)和 734 个(NSCLC 1b)DEGs。通过交集分析,获得了 255 个上调和 97 个下调的共同 DEGs。上调的 DEGs 在质膜和细胞外区域显著富集,而下调的 DEGs 在细胞骨架和细胞周期过程中显著富集。拓扑异构酶(DNA)II 阿尔法(TOP2A)和细胞周期蛋白 B1(CCNB1)是 PPI 网络中的枢纽节点。基于 WGCNA,获得了 5 个模块。在模块 MEgreen 中,DEGs 在细胞因子-细胞因子受体相互作用和焦点黏附中显著富集。值得注意的是,从 TCGA 数据库中的 LUAD 数据中鉴定出 1797 个 DEGs;其中,285 个 DEGs 是从 GSE27262 数据中鉴定出的共同 DEGs。TOP2A 和 CCNB1 的上调与患者的不良预后相关。本研究中鉴定的枢纽基因和关键途径有助于全面了解 NSCLC 的分子机制。

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