RESUMEN
OBJECTIVE: To identify key genes associated with squamous lung cancer (SLC) through analyzing gene expression data with bioinformatic tools, which could be potential biomarkers for diagnosis and treatment. MATERIALS AND METHODS: Gene expression data set GSE3268 was downloaded from Gene Expression Omnibus, including 5 SLC samples and 5 healthy controls. Data pre-treatment and differential analysis were performed with packages of R. Cluster analysis was done based on gene expression values to globally present the difference between the two states. Differentially expressed genes (DEGs) were divided into up-regulated and down-regulated genes, and then underwent functional enrichment analysis with DAVID tools. WebGestalt was used to retrieve microRNAs for the DEGs and then a regulatory network was constructed. GENECODIS was selected for functional annotation for all the genes in the network. RESULTS: A total of 537 DEGs were obtained. Functional enrichment analysis revealed that cell cycle was significantly enriched in up-regulated genes. Besides, two microRNAs (miRNAs), MIR-142-5p and miR-9, were retrieved, which were potential tools to regulate the expression of key genes. CONCLUSION: These DEGs may be involved in pathogenesis of SLC and some of them could be potential biomarkers. Besides, MIR-142-5p and miR-9 may be utilized to treat SLC as they could modulate cell cycle.