转录组
生物
基因
计算机科学
基底细胞
计算生物学
基因表达
生物信息学
病理
医学
遗传学
作者
Yin Li,Jie Gu,Fan Xu,Qinqing Zhu,Di Ge
标识
DOI:10.1038/s41598-018-34160-w
摘要
Lung squamous cell carcinoma (LUSC) is associated with poor clinical prognosis and lacks available targeted therapy. Novel molecules are urgently required for the diagnosis and prognosis of LUSC. Here, we conducted our data mining analysis for LUSC by integrating the differentially expressed genes acquired from Gene Expression Omnibus (GEO) database by comparing tumor tissues versus normal tissues (GSE8569, GSE21933, GSE33479, GSE33532, GSE40275, GSE62113, GSE74706) into The Cancer Genome Atlas (TCGA) database which includes 502 tumors and 49 adjacent non-tumor lung tissues. We identified intersections of 129 genes (91 up-regulated and 38 down-regulated) between GEO data and TCGA data. Based on these genes, we conducted our downstream analysis including functional enrichment analysis, protein-protein interaction, competing endogenous RNA (ceRNA) network and survival analysis. This study may provide more insight into the transcriptomic and functional features of LUSC through integrative analysis of GEO and TCGA data and suggests therapeutic targets and biomarkers for LUSC.
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