Wang Jing, Wang Ning, Li Zheng-Jing, Yang Li-Jie, Jing Yong-Guang, Cheng Jia-Mao, Li Jun
School of Clinical Medicine, Dali University, Xue-ren Road, Xia guang District, Dali, Yunnan 671000, China.
School of Clinical Medicine, Dali University, Xue-ren Road, Xia guang District, Dali, Yunnan 671000, China.
Cancer Genet. 2018 Dec;228-229:47-54. doi: 10.1016/j.cancergen.2018.08.003. Epub 2018 Aug 27.
Non-small cell lung cancer (NSCLC) is the most common type of lung tumor. Deregulation of microRNA may be involved in the occurrence of NSCLC and we aimed to find the potential prognostic biomarkers for NSCLC. The microRNA microarray expression profiles were downloaded from GEO dataset and then generated by applying robust multi-array average (RMA). The normalized data was analyzed with a Bioconductor package linear model for microarray data and an independent dataset was used to inspect the results. Then, the differentially expressed genes were identified using the limma package. Besides, in order to investigate the function of the differentially expressed microRNA in NSCLC, the GO and KEGG functional enrichment analysis were applied, and the GSEA analysis was performed for mining the therapeutic candidates. A total of 160 differentially expressed microRNAs were identified, among which 37 microRNAs showed significant expression changes (up-regulated and down-regulated) with the same method in the validation dataset GSE74190. Multiple cancer-related pathways, such as AMPK signaling pathway, AMPK signaling pathway, non-small cell lung cancer signaling pathway, were determined by performing the functional enrichment analysis. Besides, the results of GSEA analysis showed that the CCND1 was mostly enriched in lung cancer group. In conclusion, a set of differentially expressed microRNAs in NSCLC was identified and the CCND1 gene was determined as the potential prognostic biomarkers for NSCLC, providing useful information for discovery of future therapeutic targets and candidates in the clinical management of NSCLC.
非小细胞肺癌（NSCLC）是最常见的肺部肿瘤类型。微小RNA的失调可能与NSCLC的发生有关，我们旨在寻找NSCLC潜在的预后生物标志物。从GEO数据集中下载微小RNA微阵列表达谱，然后通过应用稳健多阵列平均法（RMA）生成。使用生物导体软件包微阵列数据线性模型对标准化数据进行分析，并使用独立数据集检查结果。然后，使用limma软件包鉴定差异表达基因。此外，为了研究差异表达的微小RNA在NSCLC中的功能，进行了GO和KEGG功能富集分析，并进行GSEA分析以挖掘治疗候选物。共鉴定出160个差异表达的微小RNA，其中37个微小RNA在验证数据集GEO74190中使用相同方法显示出显著的表达变化（上调和下调）。通过进行功能富集分析确定了多个癌症相关途径，如AMPK信号通路、非小细胞肺癌信号通路等。此外，GSEA分析结果表明CCND1在肺癌组中富集程度最高。总之，鉴定出一组NSCLC中差异表达的微小RNA，并确定CCND1基因为NSCLC潜在的预后生物标志物，为NSCLC临床管理中未来治疗靶点和候选物的发现提供了有用信息。
