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Small cell lung cancer (SCLC) is the subtype of lung cancer with the highest degree of malignancy and the lowest degree of differentiation. The purpose of this study was to investigate the molecular mechanisms of SCLC using bioinformatics analysis, and to provide new ideas for the early diagnosis and targeted therapy of SCLC. Microarray data were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) in SCLC were compared with the normal lung samples and identified. Gene Ontology (GO) function and pathway analysis of DEGs was performed through the DAVID database. Furthermore, microarray data was analyzed by using the clustering analysis tool GoMiner. Protein-protein interaction (PPI) networks of DEGs were constructed using the STRING online database. Protein expression was determined from the Human Protein Atlas, and SCLC gene expression was determined using Oncomine. In total, 153 DEGs were obtained. Functional enrichment analysis suggested that the majority of DEGs were associated with the cell cycle. CCNB1, CCNB2, MAD2L1 and CDK1 were identified to contribute to the progression of SCLC through combined use of GO, Kyoto Encyclopedia of Genes and Genomes enrichment analysis and a PPI network. mRNA and protein expression were also validated in an integrative database. The present study indicated that the formation of SCLC may be associated with cell cycle regulation. In addition, the four crucial genes CCNB1, CCNB2, MAD2L1 and CDK1 , which are downstream of p53, may have important roles in the occurrence and progression of SCLC, and thus may be promising potential biomarkers and therapeutic targets.

experimental and therapeutic medicine

Comprehensive analysis of differential expression profiles reveals potential biomarkers associated with the cell cycle and regulated by p53 in human small cell lung cancer