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

Abstract

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.

Keywords: DAVID; cell cycle; function enrichment; microarray analysis; small cell lung cancer.

Figure 1.

Identification of expression differences between normal and SCLC. data normalization boxplot. (A) The original data of the boxplot. (B) the normalized boxplot. The original data were normalized by R package limma. (C) Volcano plot of the differential mRNA expression analysis. X-axis: -log10 (FDR P-value); Y-axis: log2 fold-change for each probes; Vertical dotted lines: Fold-change ≥2 or ≤2; Horizontal dotted line: the significance cutoff (FDR P-value=0.01). the Valcano figure compiled from data of GSE43346, which demonstrated 21,653 probe sets representing 153 DEGs between the two groups of SCLC vs. normal cell in tumor patients, the red indicate the DEGs. SCLC, small cell lung cancer; FDR, false discovery rate; DEG, differentially expressed genes.

Figure 2.

KEGG pathway and enriched GO terms of functional enrichment analysis. (A) four pathways enriched KEGG pathway. gene count of the cell cycle, oocyte meiosis, p53 signaling pathway, progesterone-mediated oocyt. (B) Top five enriched GO for differentially expressed genes. Gene count of M phase, cell cycle, nuclear division, mitosis, M phase of mitotic cell cycle. (C) CIM cluster with functional categories related to cell cycle, FDR <0.05. The cluster is enriched for cell division and cell cycle regulation. Red, genes are mapped to GO categories; yellow, no association; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; FDR, false discovery rate

Figure 3.

PPI networks of DEGs. (A) Nodes stand for proteins and edges represent interactions between two proteins. Yellow, downregulated genes; blue, upregulated genes; red, key genes. (B) Histogram statistics of node distribution in PPI. (C) distribution of shortest path. PPI, protein-protein interaction; DEGs, differentially expressed genes.

Figure 4.

Gene expression in human small cell lung cancer specimens. (A) CCNB1, (B) CCNB2, (C) CDK1, (D) MAD2L1. Genes expression of four genes in normal lung tissue and small cell lung cancer specimens were indicated. Images were taken from the Human Protein Atlas (http://www.proteinatlas.org) online database (left). Oncomine data showing gene expression in normal vs. tumor of lung (n=22) (right). CCNB1, cyclin B1; CDK1, cyclin-dependent kinase 1; MAD2L1, mitotic spindle assembly checkpoint protein.

Figure 5.

A schematic representation of CDK1/CCNB1 in regulation of small cell lung cancer cell cycle. In normal cells, p53 protein activates and inhibiting the binding of CDK1 and CCNB1, resulting in Rb non-phosphorylation possibly, then, cell cycle arrest (left). In the mutant, p53 expression is abnormal, cdk inhibitory activity is not activated, After binding to CCNB1, the CDK1 protein acted on the E2F-Rb complex, which could lead to the phosphorylation of Rb and promote the transition of the cell cycle from the G2 phase to the M phase (right). CDK1 cyclin-dependent kinase 1; CCNB1, cyclin B1.

Figure 6.

A schematic representation of MAD2L1 in regulation of small cell lung cancer mitotic arrest. Cell cycle abnormalities, mitotic monitoring points are activited, MAD2L1 binding to the APC/C-CDC20 complex, inhibit the ubiquitination function of complex, making mitotic arrest (left). In tumor cells, MAD2L1 is highly expressed in small cell lung cancer, which may affect the function of APC/C-CDC20 complex, then separation of chromosomal abnormalities, resulting in polyploid generation (right). MAD2L1, mitotic spindle assembly checkpoint protein.