antitumor ways of celecoxib in gastric cancer

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In silico analyses on the underlying antitumor mechanism of celecoxib in gastric cancer

Running title: antitumor ways of celecoxib in gastric cancer

Highlights:

Celecoxib suppresses Leukocyte transendothelial migration pathway in gastric cancer.

Celecoxib suppresses the FAK-PKB/Akt signalling in Focal adhesion pathway.

Celecoxib promotes rescue pathways of Lysosome and Other glycan degradation.

Abstract

Background: Celecoxib is a nonsteroidal anti-inflammatory drug which possesses anticancer effects as a new candidate therapy for gastric cancer. This study aimed to explore the antitumor mechanism of celecoxib in gastric cancer with bioinformatic methods.

Methods: The gene expression data GSE56807 (gastric cancer samples and normal controls) and GSE54657 (celecoxib-treated gastric cancer samples and non-treated gastric cancer samples) were downloaded from GEO. After screening the differentially expressed genes (DEGs) of the two data sets with Limma package in R language respectively, the common DEGs were then screened out by computing the intersection. Sequentially, KEGGpathways enrichment was performed using DAVID online tools. Besides, the Protein-protein Interactions (PPIs) of the common DEGs were obtained via mapping the common DEGs to the integration set of PPI data from IntAct, DIP, BIND and HPRD databases. Then, the PPI network was constructed using the Cytoscape software.

Results: After analyzed, 137 common DEGs were obtained between the two DEG sets of gastric cancer samples vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples. The common DEGs were mainly enriched in the pathways of Lysosome, Other glycan degradation, Focal adhesion and Leukocyte transendothelial migration. Besides, three PPI modules of the common DEGs were found.

Conclusions: Celecoxib might play its antitumor effects in gastric cancer potentially by suppressing Leukocyte transendothelial migration pathway and the FAK-PKB/Akt signalling in Focal adhesion pathway, as well as by promoting rescue pathways of Lysosome and Other glycan degradation. However, relevant experiments are needed to confirm our conclusion.

Keywords: celecoxib, gastric cancer, antitumor mechanism, pathway

Introduction

Gastric cancer is the fifth most common cancer and the third leading cause of cancer mortality. Currently, more than 70% of gastric cancers occur in developing countries, among which three East Asian countries: China, Japan and Korea account for 60% of total cases. (Fock, 2014). The prognosis of gastric cancer is generally poor as gastric cancer can easily spread from the stomach to other parts of human body, particularly the liver, lungs, bones, lining of the abdomen and lymph nodes (Ruddon, 2007), and the 5-year survival rate for gastric cancer is reported to be less than 10% (Orditura et al., 2014). Thus, researches on the treatments for gastric cancer are of great importance.

Treatments for gastric cancer include surgery (Chen et al., 2013), chemotherapy (Wagner et al., 2010), and radiation therapy(Milano et al., 2014). New treatment approaches and improved ways of current methods are being studied in clinical trials. Drugs used in gastric cancer treatment have included: fluorouracil or its analog capecitabine, carmustine, mitomycin C, semustine and doxorubicin, as well as cisplatin and taxotere, often in various combinations (Scartozzi et al., 2007; Wagner et al., 2006; Wagner et al., 2010). Unfortunately, these treatments for gastric cancer have displayed disappointing results.

Celecoxib is a classic COX-2 (Cyclooxygenase-2) selective nonsteroidal anti-inflammatory drug (NSAID) and is used to treat the signs and symptoms of osteoarthritis, rheumatoid arthritis and ankylosing spondylitis in patients. Previous epidemiological studies demonstrated that prolonged treatment with NSAIDs could reduce the risks of various kinds of cancers including gastric cancer (Entezari Heravi et al., 2011; Fischer et al., 2011). It has also been reported that celecoxib possessed anticancer effects as a new candidate therapy for gastric cancer in previous studies. Kim et al. suggested that the antitumor effects of celecoxib on gastric cancer cells might be partly mediated by down-regulation of Akt, GSK3β, FKHR, and up-regulation of caspase-9, in the mitochondrial apoptotic pathway (Kim et al., 2009). The mechanism involving cell cycle arrest, mitochondrial cytochrome C release and caspase activation was also proposed by Wang et al. (Wang et al., 2013). The impact on E-cadherin, VEGF and microvessel density of celecoxib to suppress the invasion of advanced gastric cancer was reported as well (Zhou et al., 2007). However, its antitumor mechanism remains debatable.

This study aimed to gain a better understanding about the anticancer mechanism of celecoxib on gastric cancer with bioinformatics methods. With the advantage of relevant microarrays, we conducted integrated gene expression profiling analyses on gastric cancer vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples and identified the common Differently Expressed Genes (DEGs). Further pathways enrichment analysis and Protein-protein Interaction (PPI) network construction were performed to predict the underlying mechanism during the treatment.

Materials and Methods

Gene Expression Profiles

The expression data GSE56807 (Wang et al., 2014) and GSE54657 were downloaded from GEO (Gene Expression Omnibus) database (http://www.ncbi.nlm.nih.gov/geo/). The data GSE56807 were based on the GPL5175 [HuEx-1_0-st] Affymetrix Human Exon 1.0 ST Array [transcript (gene) version] platform including 10 samples: 5 paired of gastric cancer vs. normal controls. The data GSE54657 were based on the GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version] platform including 6 samples: 3 celecoxib-treated AGS (the human gastric epithelial cell line AGS) samples and 3 control AGS samples, among which the celecoxib-treated AGS samples were harvested after incubated with celecoxib in triplicates for 24 hours.

Data Acquisition and DEGs Screening

Gene expression profiles (CEL format) were analyzed by R language (Team, 2012). These data were first normalized using the Robust Multichip Averaging (RMA) algorithm (Irizarry et al., 2003). Limma package (Smyth, 2005) in R language was applied to identify the DEGs. For DEGs between gastric cancer samples and normal controls, after t-test was applied, the adj.P-value which was adjusted by Benjamini-Hochberg method (Benjamini and Hochberg, 1995) less than 0.05 were set as threshold. Due to different sensitivities of different platforms, for DEGs between celecoxib-treated AGS samples and control AGS samples, P-value without adjustment less than 0.01 was set as the cut-off criterion. Then, the common DEGs shared by the two groups of DEGs were screened out.

Pathways Enrichment of DEGs

The pathways enrichment analysis of the common DEGs were carried out with DAVID (Database for Annotation, Visualization and Integrated Discovery) (Dennis Jr et al., 2003) software based on KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway databases (Kanehisa and Goto, 2000). P-value less than 0.1 and the gene count in each pathway no less than 2 were set as cut-off criteria.

Protein-protein Interaction (PPI) Network Construction

With the downloaded PPI data from IntAct (Kerrien et al., 2011), DIP (Xenarios et al., 2002), BIND (Bader et al., 2003) and HPRD (Peri et al., 2004) databases and the investigations of Rual et al.(Rual et al., 2005), Stelzl et al. (Stelzl et al., 2005) and Ramani et al. (Ramani et al., 2005), we integrated these PPI data as a PPI set and mapped the DEGs identified above with this set to achieve the PPI of the DEGs. Then, the PPI network was gotten by Cytoscape software.

Results

DEGs Screening

After analyzed, 5190 DEGs between gastric cancer and normal controls as well as 540 DEGs between celecoxib-treated gastric cancer samples and non-treated gastric cancer samples were screened out. Then, 137 common DEGs were obtained by computing the intersection of the two groups of DEGs. The clustering heatmap of the common DEGs was shown in Figure 1. The two data sets were based on different platforms, so the expression level of genes displayed a huge difference. However, we can still get the qualitative conclusions from the heatmap that these microarrays were reliable and celecoxib did make a difference during the treatment.

Pathways Enrichment Analysis

To explore the potential effects of celecoxib on gastric cancer cells, the common DEGs were subjected to KEGG pathways enrichment analysis. As shown in Table 1, the common DEGs were mainly enriched in the pathway of Lysosome, Other glycan degradation, Focal adhesion and Leukocyte transendothelial migration, among which Focal adhesionand Leukocyte transendothelial migration were enriched by DEGs that displayed opposite regulation direction in the two results sets of gastric cancer vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples. Clustering heatmaps of the common DEGs in the enriched pathways were shown in Figure 2.

Protein-protein Interaction (PPI) network

To further investigate the underlying function mechanism of celecoxib in gastric cancer, PPI interaction networks of the common DEGs were predicted. As shown in Figure 3, three interaction modules were found. One module contained three nods including EPS8, CFTR and MUC13; another contained CSRP1, VCL and ACTN1; the last contained GLB1 and NEU1. Among these modules, the one contained CSRP1, VCL and ACTN1 showed opposite regulation direction in the two results sets of gastric cancer vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples.

Discussion

To gain insight into the anticancer mechanism of celecoxib on gastric cancer, we systematically analyzed the gene expression profiles with bioinformatics methods. After analyzed, 5190 DEGs between gastric cancer and normal controls as well as 540 DEGs between celecoxib-treated gastric cancer samples and non-treated gastric cancer samples were screened out. Then, 137 common DEGs were obtained by computing the intersection of the two groups of DEGs. The biological functions of the common DEGs were further explored based on the pathways enrichment data. Moreover, the PPI network of the common DEGs was also identified. These results might reveal the anticancer mechanism of celecoxib on gastric cancer.

The common DEGs which displayed opposite regulation direction in the two results sets of gastric cancer vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples visually reflected the impact of celecoxib on gastric cancer. The result of pathways enrichment analysis showed that Focal adhesion and Leukocyte transendothelial migration were enriched by DEGs with the situation mentioned above. The common DEGs enriched in Focal adhesion pathway showed up-regulation in gastric cancer and down-regulation after treated with celecoxib. Focal adhesion pathway plays essential roles in important biological processes (Petit and Thiery, 2000), in which the FAK-PKB/Akt signalling is crucial for cell survival. The up-regulation of FAK-PKB/Akt pathway is frequently found in human cancers, while the down-regulation of this pathway by drugs can lead to apoptosis of the cancer cells (Vara et al., 2004). This outcome of our analysis consisted with the researches of Kim et al. (Kim et al., 2009) and Wang et al. (Wang et al., 2013). Also, the common DEGs enriched in Leukocyte transendothelial migration pathway showed up-regulation in gastric cancer and down-regulation after treated with celecoxib. Leukocyte transendothelial migration pathway is generally activated in cancer, thus hampering the anti-tumour responses of the host (Enarsson et al., 2007). This outcome of our analysis suggested that celecoxib might also suppressed Leukocyte transendothelial migration pathway to exert its antitumor effects.

However, pathways of Lysosome and Other glycan degradation were more significantly enriched in this study. The common DEGs enriched in the two pathways were up-regulated in gastric cancer and up-regulated to higher level after treated with celecoxib. Since pathways of Lysosome and Other glycan degradation are both common ways of catabolism, they might be activated as a rescue response of the host in cancer. It was also reported that lysosome could mediate a mode of cell death with apoptotic or apoptosis-like features which was called lysosomal cell death (Aits and Jäättelä, 2013). Celecoxib might also promote this process to play antitumor roles during the treatment.

Moreover, PPI network of the common DEGs were constructed to further step into the mechanism prediction. Nevertheless, only 3 modules were found during this analysis probably due to the low count of the common DEGs. The module which contained VCL and ACTN1 consisted with the results of the pathways enrichment analysis as they were both enriched in significant pathways. The significant nod in this network, CSRP1 (Cysteine and glycine-rich protein 1), is a member of the CSRP family of genes encoding a group of proteins with LIM domain which are generally transcription regulators related to gene regulation, cell growth, and somatic differentiation (Weiskirchen and Günther, 2003). The dysregulation of CSRP1 might promote apoptosis during celecoxib treatment in gastric cancer by abnormally regulating VCL and ACTN1. Therefore, the PPI network provided a corroborative evidence for the pathways enrichment results.

Taken together, we screened out the common DEGs in the two results sets of gastric cancer vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples. Further bioinformatic analyses including pathways enrichment and PPI network construction of the common DEGs were conducted. We propose that celecoxib might play its antitumor effects in gastric cancer potentially by suppressing Leukocyte transendothelial migration pathway and the FAK-PKB/Akt signalling in Focal adhesion pathway, as well as by promoting rescue pathways of Lysosome and Other glycan degradation. However, relevant experimental data are needed to confirm our conclusion.

Figure Legends

Figure 1. The clustering heatmap ofthe common Differently Expressed Genes (DEGs) of gastric cancer samples vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples. Blue terms: normal controls; yellow terms: gastric cancer samples; red terms: non-treated AGS samples; green terms: non-treated AGS samples. Colors associated with expression values are present above the heatmap. Due to different sensitivities of different platforms, adj.P-value < 0.05 in gastric cancer samples vs. normal controls and P-value < 0.01 in celecoxib-treated AGS samples vs. non-treated AGS samples were set as criteria.

Figure 2. Clustering heatmaps of the common Differently Expressed Genes (DEGs) of gastric cancer samples vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples in the significantly enriched pathways. Blue terms: normal controls; yellow terms: gastric cancer samples; red terms: non-treated AGS samples; green terms: non-treated AGS samples. Colors associated with expression values are present above the heatmap. P-value < 0.1 and the gene count < 2 were set as cut-off criteria.

Figure 3. Protein-protein Network of the common Differently Expressed Genes (DEGs) of gastric cancer samples vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples. Pink nods represent DEGs and the size of each nod represents its significance. Blue lines represent interactions between the nods.

Tables

Table 1. Significantly enriched pathways of the common differentially expressed genes (DEGs) of gastric cancer samples vs. normal controls and celecoxib-treated gastric cancer samples vs. non-treated gastric cancer samples.

Pathway ID

DEGs

RD

Pathway

Count

P-value

hsa04142

NEU1, GLB1, FUCA1, CLN5, ATP6AP1, CTSD

same

Lysosome

6

0.002834

hsa00511

NEU1, GLB1, FUCA1

same

Other glycan degradation

3

0.007776

hsa04510

THBS1, MYL9, FLNA, ACTN1, VCL, LAMC2,

opposite

Focal adhesion

6

0.026095

hsa04670

MYL9, VCL, ACTN1, CLDN1

opposite

Leukocyte transendothelial migration

4

0.076932

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