Li Xiuwei, Ma Chao, Luo Huan, Zhang Jian, Wang Jinan, Guo Hongtao
Department of Radiotherapy, Zhoukou Central Hospital, Zhoukou, China.
Department of Cardiology.
Medicine (Baltimore). 2020 Mar;99(11):e19086. doi: 10.1097/MD.0000000000019086.
Small cell lung cancer (SCLC) is one of the most lethal cancer, mainly attributing to its high tendency to metastasis. Mounting evidence has demonstrated that genes and microRNAs (miRNAs) are related to human cancer onset and progression including invasion and metastasis.An eligible gene dataset and an eligible miRNA dataset were downloaded from the Gene Expression Omnibus (GEO) database based our screening criteria. Differentially expressed genes (DE-genes) or DE-miRNAs for each dataset obtained by the R software package. The potential target genes of the top 10 DE-miRNAs were predicted by multiple databases. For annotation, visualization and integrated discovery, Metascape 3.0 was introduced to perform enrichment analysis for the DE-genes and the predicted target genes of the selected top 10 DE-miRNAs, including Pathway and Process Enrichment Analysis or protein-protein interaction enrichment analysis. The intersection of predicted target genes and DE-genes was taken as the final DE-genes. Then apply the predicted miRNAs-targets relationship of top 10 DE-miRNAs to the final DE-genes to gain more convinced DE-miRNAs, DE-genes and their one to one relationship.GSE19945 (miRNA microarray) and GSE40275 (gene microarray) datasets were selected and downloaded. 56 DE-miRNAs and 861 DE-genes were discovered. 297 miRNAs-targets relationships (284 unique genes) were predicted as the target of top 10 upregulating DE-miRNAs. 245 miRNAs-targets relationships (238 unique genes) were identified as the target of top 10 downregulating DE-miRNAs. The key results of enrichment analysis include protein kinase B signaling, transmembrane receptor protein tyrosine kinase signaling pathway, negative regulation of cell differentiation, response to growth factor, cellular response to lipid, muscle structure development, response to growth factor, signaling by Receptor Tyrosine Kinases, epithelial cell migration, cellular response to organic cyclic compound, Cell Cycle (Mitotic), DNA conformation change, cell division, DNA replication, cell cycle phase transition, blood vessel development, inflammatory response, Staphylococcus aureus infection, leukocyte migration, and myeloid leukocyte activation. Differential expression of genes-upstream miRNAs (RBMS3-hsa-miR-7-5p, NEDD9-hsa-miR-18a-5p, CRIM1-hsa-miR-18a-5p, TGFBR2-hsa-miR-9-5p, MYO1C-hsa-miR-9-5p, KLF4-hsa-miR-7-5p, EMP2-hsa-miR-1290, TMEM2-hsa-miR-18a-5p, CTGF-hsa-miR-18a-5p, TNFAIP3-hsa-miR-18a-5p, THBS1-hsa-miR-182-5p, KPNA2-hsa-miR-144-3p, GPR137C-hsa-miR-1-3p, GRIK3-hsa-miR-144-3p, and MTHFD2-hsa-miR-30a-3p) were identified in SCLC.RBMS3, NEDD9, CRIM1, KPNA2, GPR137C, GRIK3, hsa-miR-7-5p, hsa-miR-18a-5p, hsa-miR-144-3p, hsa-miR-1-3p along with the pathways included protein kinase B signaling, muscle structure development, Cell Cycle (Mitotic) and blood vessel development may gain a high chance to play a key role in the prognosis of SCLC, but more studies should be conducted to reveal it more clearly.
小细胞肺癌(SCLC)是最致命的癌症之一,主要归因于其高转移倾向。越来越多的证据表明,基因和微小RNA(miRNA)与人类癌症的发生和进展有关,包括侵袭和转移。根据我们的筛选标准,从基因表达综合数据库(GEO)下载了一个合格的基因数据集和一个合格的miRNA数据集。通过R软件包获得每个数据集的差异表达基因(DE-基因)或DE-miRNA。通过多个数据库预测前10个DE-miRNA的潜在靶基因。为了进行注释、可视化和综合发现,引入了Metascape 3.0对DE-基因和所选前10个DE-miRNA的预测靶基因进行富集分析,包括通路和过程富集分析或蛋白质-蛋白质相互作用富集分析。将预测靶基因与DE-基因的交集作为最终的DE-基因。然后将前10个DE-miRNA的预测miRNA-靶标关系应用于最终的DE-基因,以获得更可靠的DE-miRNA、DE-基因及其一对一关系。选择并下载了GSE19945(miRNA微阵列)和GSE40275(基因微阵列)数据集。发现了56个DE-miRNA和861个DE-基因。预测297个miRNA-靶标关系(284个独特基因)为前10个上调DE-miRNA的靶标。鉴定出245个miRNA-靶标关系(238个独特基因)为前10个下调DE-miRNA的靶标。富集分析的关键结果包括蛋白激酶B信号传导、跨膜受体蛋白酪氨酸激酶信号通路、细胞分化的负调控、对生长因子的反应、对脂质的细胞反应、肌肉结构发育、对生长因子的反应、受体酪氨酸激酶信号传导、上皮细胞迁移、对有机环状化合物的细胞反应、细胞周期(有丝分裂)、DNA构象变化、细胞分裂、DNA复制、细胞周期阶段转变、血管发育、炎症反应、金黄色葡萄球菌感染、白细胞迁移和髓系白细胞激活。在SCLC中鉴定出基因-上游miRNA(RBMS3-hsa-miR-7-5p、NEDD9-hsa-miR-18a-5p、CRIM1-hsa-miR-18a-5p、TGFBR2-hsa-miR-9-5p、MYO1C-hsa-miR-9-5p、KLF4-hsa-miR-7-5p、EMP2-hsa-miR-1290、TMEM2-hsa-miR-18a-5p、CTGF-hsa-miR-18a-5p、TNFAIP3-hsa-miR-18a-5p、THBS1-hsa-miR-182-5p、KPNA2-hsa-miR-144-3p、GPR137C-hsa-miR-1-3p、GRIK3-hsa-miR-144-3p和MTHFD2-hsa-miR-30a-3p)的差异表达。RBMS3、NEDD9、CRIM1、KPNA2、GPR137C、GRIK3、hsa-miR-7-5p、hsa-miR-18a-5p、hsa-miR-144-3p、hsa-miR-1-3p以及包括蛋白激酶B信号传导、肌肉结构发育、细胞周期(有丝分裂)和血管发育在内的通路可能在SCLC的预后中发挥关键作用,但需要进行更多研究以更清楚地揭示这一点。