Department of Respiration, The First Hospital of Jilin University, No. 1 Xinminda Street, Changchun, 130021, China.
PICU, The First Hospital of Jilin University, Changchun, 130021, China.
BMC Cancer. 2020 Apr 16;20(1):329. doi: 10.1186/s12885-020-06829-x.
The aim of this study was to gain further investigation of non-small cell lung cancer (NSCLC) tumorigenesis and identify biomarkers for clinical management of patients through comprehensive bioinformatics analysis.
miRNA and mRNA microarray datasets were downloaded from GEO (Gene Expression Omnibus) database under the accession number GSE102286 and GSE101929, respectively. Genes and miRNAs with differential expression were identified in NSCLC samples compared with controls, respectively. The interaction between differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) was predicted, followed by functional enrichment analysis, and construction of miRNA-gene regulatory network, protein-protein interaction (PPI) network, and competing endogenous RNA (ceRNA) network. Through comprehensive bioinformatics analysis, we anticipate to find novel therapeutic targets and biomarkers for NSCLC.
A total of 123 DEmiRs (5 up- and 118 down-regulated miRNAs) and 924 DEGs (309 up- and 615 down-regulated genes) were identified. These genes and miRNAs were significantly involved in different pathways including adherens junction, relaxin signaling pathway, and axon guidance. Furthermore, hsa-miR-9-5p, has-miR-196a-5p and hsa-miR-31-5p, as well as hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p were shown to have higher degree in the miRNA-gene regulatory network and ceRNA network, respectively. Furthermore, BIRC5 and FGF2, as well as RTKN2 and SLIT3 were hubs in the PPI network and ceRNA network, respectively.
Several pathways (adherens junction, relaxin signaling pathway, and axon guidance) miRNAs (hsa-miR-9-5p, has-miR-196a-5p, hsa-miR-31-5p, hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p) and genes (BIRC5, FGF2, RTKN2 and SLIT3) may play important roles in the pathogenesis of NSCLC.
本研究旨在通过全面的生物信息学分析，进一步探讨非小细胞肺癌（NSCLC）的肿瘤发生机制，并确定用于患者临床管理的生物标志物。
从 GEO（基因表达综合数据库）数据库中分别下载 miRNA 和 mRNA 微阵列数据集，其基因标识符分别为 GSE102286 和 GSE101929。分别鉴定 NSCLC 样本与对照相比差异表达的基因和 miRNA。预测差异表达基因（DEGs）和差异表达 miRNA（DEmiRs）之间的相互作用，然后进行功能富集分析，并构建 miRNA-基因调控网络、蛋白质-蛋白质相互作用（PPI）网络和竞争性内源 RNA（ceRNA）网络。通过综合的生物信息学分析，我们期望为 NSCLC 找到新的治疗靶点和生物标志物。
共鉴定出 123 个 DEmiRs（上调 5 个，下调 118 个）和 924 个 DEGs（上调 309 个，下调 615 个）。这些基因和 miRNA 显著参与了不同的途径，包括黏着连接、松弛素信号通路和轴突导向。此外，hsa-miR-9-5p、has-miR-196a-5p 和 hsa-miR-31-5p，以及 hsa-miR-1、hsa-miR-218-5p 和 hsa-miR-135a-5p 在 miRNA-基因调控网络和 ceRNA 网络中具有更高的度数。此外，BIRC5 和 FGF2 以及 RTKN2 和 SLIT3 分别是 PPI 网络和 ceRNA 网络中的枢纽。
几个途径（黏着连接、松弛素信号通路和轴突导向）、miRNAs（hsa-miR-9-5p、has-miR-196a-5p、hsa-miR-31-5p、hsa-miR-1、hsa-miR-218-5p 和 hsa-miR-135a-5p）和基因（BIRC5、FGF2、RTKN2 和 SLIT3）可能在 NSCLC 的发病机制中发挥重要作用。
