Abstract
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.
Highlights
Every year, nearly 1.8 million people are diagnosed with lung cancer[1,2]
We conducted our data mining analysis for Lung squamous cell carcinoma (LUSC) by integrating the differentially expressed genes acquired from Gene Expression Omnibus (GEO) database into The Cancer Genome Atlas (TCGA) database
New classes of biomarkers with high efficiency, high specificity, and high sensitivity are required as novel molecules for diagnosis and prognosis of LUSC
Summary
Nearly 1.8 million people are diagnosed with lung cancer[1,2]. Lung cancer has become the leading cancer cause of death and kills more people annually than colorectal, breast, prostate and pancreatic cancers combined[3]. High throughput microarray platforms emerge as a promising and useful tool for detection of genetic alterations in carcinogenesis and discovering biomarkers for many diseases[6]. Identification of high-abundance molecules would become much more reliable via integrating the differentially expressed genes derived from multiple microarray datasets analysis with sequence-based data. We conducted our data mining analysis for LUSC by integrating the differentially expressed genes acquired from Gene Expression Omnibus (GEO) database into The Cancer Genome Atlas (TCGA) database. We discovered some co-differentially expressed genes in LUSC. Based on these genes, we performed a series of analyses including functional enrichment analysis, protein-protein interaction analysis, survival analysis, construction of competing endogenous RNA network. Gender Female Male Age Mean (SD) Median [Min, Max] Race Asian Black Or African American White Stage Stage IA Stage IB Stage II Stage IIA Stage IIB Stage IIIA Stage IIIB Stage IV Stage I Stage III
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