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Significant DEGs in Bladder Cancer

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Differences analysis of gene expression in bladder cancer with microarrays

Highlights

  1. Totally 619 common DEGs were screened by using 2 mocroarrays
  2. PGR, MAFG, CDC6 and MCMs may play key roles in BC
  3. The core histones were also considered to have major functions in BC.

Abstract

Purpose: We aim to identify significant differentially expressed genes (DEGs) and analyze the modification of gene expression in bladder cancer (BC) by using bioinformatics analysis.

Methods: GSE24152 and GSE42089 microarray datasets were downloaded from the Gene Expression Omnibus database for further analysis. GSE24152 gene expression data included 17 samples (10 tumor cells from bladder and 7 normal tissues from bladder) while GSE42089 included 18 samples (10 tissues from urothelial cell carcinoma and 8 tissues from normal bladder). Differentially expressed genes (DEGs) were screened, followed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis. Furthermore, protein-protein interaction (PPI) network and sub-networks were constructed for the identification of key genes and main pathways.

Results: Totally 1325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, such as nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, HNF4, PGR, MAFG and CDC6 were identified as key genes in BC. Besides, the histones were also considered to be significant factors in BC.

Conclusion: the DEGs, including HNF4, PGR, MAFG and CDC6, and core histones family were closely related to the development of bladder cancer via pathways associated with mitotic and chromosome assembly.

Key words: bladder cancer; differentially expressed genes; interaction network; clustering analysis.

Introduction

Bladder cancer (BC) is a heterogeneous disease with a variable disease history and at present is the ninth most common tumor world wide [1]. In 2009, 70980 new cancer cases of the urinary bladder were diagnosed in the United States and 14330 patients died from bladder cancer [2]. The most common type of BC recapitulates the normal histology of the urothelium cell carcinoma and the 5-year survival rate in America is approximately 77% [3]. Depending on the depth of of invasion, BC can be classified as 5 forms, including papillary (pTa), lamina propria invasion (pT1), muscle invasion (pT2), invasion to peri-vesical fat (pT3), and locally advanced (pT4) [4]. Surgery is the standard therapy and the use of radiotherapy is considered as an alternative, especially in less fit patients [5].

In recent years, numerous researches have identified risk factors or related genes for the development of BC [6] [7]. Shen et al have analyzed the differentially expressed genes and interacting pathways in BC by bioinformatics analysis and genes such as activator protein 1, nuclear factor of activated T-cells were identified to be significant in BC[8]. Zhou et al analyzed the gene expression in human BC samples by using microarray GSE42089 and a set of genes leading to mitotic spindle checkpoint dysfunction were identified to be key genes in BC [9].

In the present study, significant differentially expressed genes (DEGs) in bladder tumor cells were identified and two microarray profiles, GSE24152 and GSE42089, were used for the screening of significant differentially expressed genes (DEGs), followed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis. Furthermore, protein-protein interaction (PPI) network and sub-networks were constructed for the identification of key genes and main pathways. By using the bioinformatics methods above, we aim to identify significant differentially expressed genes (DEGs) and analyze the modification of gene expression in bladder cancer (BC) by using bioinformatics analysis.

Materials and methods

Microarray data

Two gene expression profiles, GSE24152 and GSE42089 were downloaded from Gene Expression Omnibus (GEO) database in National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/geo/) based on the platform of GPL6791 and GPL9828 in Affymetrix GeneChip Human Genome U133 Plus 2.0 Array, respectively. The microarray GSE24152 was based on 17 samples including10 tumor cells from bladder and 7 normal tissues from bladder while the microarray GSE42089 was based on 18 samples including 10 tissues from urothelial cell carcinoma and 8 tissues from normal bladder.

Data preprocessing and DEGs analysis

Robust multiple average (RMA) algorithm in affy package [10] was used for the normalization of microarray data and boxplots were generated. The microarray data were divided into two groups, a bladder carcinoma set and a normal set. By using Limma package [11], the probe-level data of two sets were converted into expression measures and two groups of DEGs were obtained from two microarrays. Venn diagram was generated using VennDiagram package [12] to screen common DEGs for the further analysis. A combination of FDR < 0.01 and |log2FC (fold change)| > 0.5 was used as the threshold. Heat maps were generated by heatmap.2 function in ggplot 2 [13] to display the relative expression differences of DEGs. What`s more, cor.test function was used for evaluating the changing trends of two DEGs groups.

Gene ontology (GO) and pathway enrichment analysis

GO analysis has become a widely used approach for the studies of large-scale genomic or transcriptomic data in function [14]. Kyoto encyclopedia of genes and genomes (KEGG) is a widely used collection of online database which deals with genomes, enzymatic pathways, and biological chemicals. [15] In this study, the functions and pathways of the screened DEGs were analyzed using the DAVID [16] from the GO and KEGG pathway database with the p-value < 0.01, respectively.

Interaction network and sub-network construction

Cytoscape [17] is an open source bioinformatics software platform, which is used for the visualization of molecular interaction networks and integrating with gene expression profiles and other state data. In this study, Biosgenet in Cytoscape [18] was used to predict and visualize the interactions of selected DEGs and proteins assistant with BIND database [19] with p-value < 0.05. Besides, sub-networks were constructed and clustering analysis was performed on DEGs using ClusterOne in cytoscape [20] with p-value < 0.01.

Results

DEGs selection

As shown in Figure 1, the obscuring variations in raw expression data were normalized after preprocessed. A total of 1325 genes were differentially expressed in GSE24152 and 637 genes were differentially expressed in GSE42089. The volcano plots of both two microarrays were shown in Figure 2. Totally 619 common DEGs were identified using Venn diagram which was shown in Figure 3, including 313 up-regulated genes and 306 down-regulated genes. The heat maps of the 619 common DEGs expression in GSE24152 and GSE42089 were shown in Figure 3, in which significant expression levels were observed. Person r value was 0.998.

GO and KEGG enrichment analysis of DEGs

GO and KEGG enrichment were performed with p-value < 0.01 for the functional and pathway analysis of DEGs and totally 74 GO terms and 4 KEGG pathways were obtained. The main GO terms and KEGG pathways of 619 common DEGs were listed in Table 1. We can see from the results that the DEGs were mainly enriched in the pathways related to chromosome and cell cycle.

Interaction network and sub-network construction

The network on all DEGs was constructed (Figure 3) and thereby clustering analysis was performed on the network. Totally 6 sub-networks were obtained, which were shown in Figure 4 and Table 2. Progesterone receptor (PGR) was a key gene in cluster 1 and the proteins enriched in cluster 1 were all histone proteins. In cluster 2, v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog G (MAFG) was identified as a key gene, which was also detected in cluster 5. Cell division cycle 6 (CDC6) was a key gene in both cluster 3 and cluster 4. minichromosome maintenance complex component (MCM) family members, including MCM2, MCM4, MCM7 and MCM10, were also key genes in cluster 3 and cluster 4. Nucleosome assembly and sequence-specific DNA binding were significant GO terms of sub-networks cluster 1 and cluster 2, respectively. No GO terms were obtained in clusters 3-6.

Discussion

Bladder cancer is a common malignancy, which requires a high degree of surveillance because of the frequency recurrences and poor clinical outcome of invasive disease [21]. Bioinformatics analysis on the gene level of bladder cancer cells provides a new insight for the research of this disease. In the present study, by using the expression profile microarray GSE24152 and GSE42089, the significant DEGs in BC were identified. In the interaction network, PGR, MAFG, CDC6 and MCMs were identified as key genes in BC. Besides, the histones were also considered to have major functions in BC. According to the GO term analysis and pathways enrichment, the main GO terms or pathways were associated with cell cycle and chromosome assembly, such as nucleosome assembly, spindle checkpoint and DNA replication.

PGR encodes a member of the steroid receptor superfamily, which mediates the physiological effects of progesterone [22]. In the present study, PGR was an important gene in cluster, which was regulated by a set of transcriptions, revealing the significance of PGR in BC. There is a fact that men are more frequently affected than women, which indicates the hormone as a regulation factor [8] and Miyamoto et al clarified the sex hormone androgen receptor (AR), were involved in BC [7]. PGR may also has functions in BC, as bothAR and PGR are determining sex hormone in gonadal [23]. A significant pathway detected was progesterone-mediated oocyte maturation, which may be the pathway PGR functions in BC.

Histones are the main structural proteins that are associated with DNA in eukaryotic cells and can be divided into two groups including core histones and the nucleosomal histone [24]. Core histones are some of the most conserved proteins in eukaryotes play key roles in organizing DNA folding [25]. The altered patterns of modifications on histone in various human cancers have been studied in recent years [26]. Schneider et al show that global histone modification levels are lower in BC than normal urinary tissue [27]. Besides, the conserved histone H2A variant has been reported to be over-expressed in BC cells and contributes to cancer-related transcription pathways [28]. In the present study, a set of core histones were clustered in cluster 1 and enriched in the process of nucleosome assembly. Considering the function of histones in the mitotic, we concluded that the nucleosome and chromatin assembly were modified in BC.

MAF encodes for the nuclear transcriptional regulating proteins, which are characterized by a basic region and leucine zipper structure have crucial roles in a variety of cellular processes [29]. MAFG is a small MAF protein member of the family and encode slightly more than the DNA binding and dimerization motif [30]. MAFG is able to partially co-localize with FBJ murine osteosarcoma viral oncogene homolog (FOS) in the nucleus and form heterodimers with FOS [31]. FOS is a member that the transcription factor activator protein 1 (AP-1) is composed of. AP-1 family members are immediate early genes induced by a variety of stress signals and control the stress response including cell proliferation, apoptosis and tumoregenesis [32]. According to our data, the expression of MAFG in BC was up-regulated, which may increase the tumorigenesis via the process mentioned above.

CDC6 is an essential regulator of DNA replication in eukaryotic cells with the function of assembly of prereplicative complexes at origins of replication during the G1 phase of the cell division cycle [33]. MCMs encode highly conserved proteins that presumed to act as an enzymatically active helicase [34]. MCMs drive the formation of prereplicative complexes (PRCs), which is the first key event during the G1 phase during cell cycle [35]. Both MCM and CDC6 are key proteins in the mechanism of DNA replication licensing and have related functions during the cell cycle [36]. CDC6 is responsible for the loading of MCM proteins onto origins of replication and in the absence of CDC6, MCM could not associated with the chromatin. The increased expression of CDC6 and MCM has been seen in dysplastic cells and as a consequence, CDC6 and MCM are considered as specific biomarkers of proliferating cells [36]. Recent studies have unveiled the proto-oncogenic activity of CDC6, with the overexpression interfering with the expression of some tumor suppressor genes and may promote the DNA hyperreplication and induce a senescence response similar to that caused by oncogene activation. Besides, some members of MCM family, such as MCM7, were also suggested to be overexpressed and amplified in a variety of human malignancies [37]. In the present study, the expression of CDC6 was detected to be up-regulated, revealing that in BC cells, the process of DNA replication was aberrant,

In conclusion, our data reveals that the genes, PGR, MAFG, CDC6 and MCMs, and a set of histones are important factors in BC and play key roles in the processes related to mitotic, such as nucleosome assembly, spindle checkpoint and DNA replication. However, the small size of the microarray sample and no experimental variation was the limitation of this study. Thus further study on larger size sample and experiment should be performed to confirm our conclusion.


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