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Genes and Mechanisms in Colorectal Cancer (CRC)

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Identification ofpotential therapeutic target genes and mechanisms in colorectal cancer based on bioinformatics analysis

Highlights:

  1. Totally, 150 DEGs including 88 down- and 62 up-regulated genes were identified.
  2. Cytokine-cytokine receptor interaction may be associated with CRC development.
  3. CXCL3 and IL8 may play roles in CRC progress by regulating TNF signaling pathway.
  4. PTGS2, CXCL3 and IL8 may be potential therapeutic target genes for CRC.
  5. Bile secretion-related genes ABCG2, ATP1A2 and AQP8 may be target genes for CRC.

Abstract

Purpose: The study was aimed to explore the underlying mechanisms and identify the potential target genes for colorectal cancer (CRC) treatment by bioinformatics analysis.

Methods: The RNA-seq data of GSE29580 was downloaded from Gene Expression Omnibus (GEO) database. Paired samples (from the same patient) of tumor and normal tissues from 2 CRC patients were used to identify differentially expressed genes (DEGs). The functional enrichment analysis was performed. Furthermore, the protein-protein interaction (PPI) network of the DEGs was constructed by Cytoscape software.

Results: Totally, 150 DEGs were identified, including 88 down- and 62 up-regulated genes. The down-regulated genes were mainly enriched in the functions of cytokine-cytokine receptor interaction and TNF signaling pathway, while up-regulated genes were related to bile secretion function. DEGs including prostaglandin-endoperoxide synthase 2 (PTGS2), chemokine (C-X-C motif) ligand 3 (CXCL3), interleukin 8 (IL8), ATP-binding cassette, sub-family G, member 2 (ABCG2), ATPase, Na+/K+ transporting, alpha 2 polypeptide (ATP1A2) and aquaporin 8 (AQP8) were identified in these functions. In addition, PTGS2, CXCL3 and IL8 were hub nodes in PPI network.

Conclusions: The cytokine-cytokine receptor interaction, TNF signaling pathway and bile secretion functions may be associated with CRC development. Genes such as PTGS2, CXCL3, IL8, ABCG2, ATP1A2 and AQP8 may bepotential therapeutic target genes for CRC.

Key words: colorectal cancer; differentially expressed genes; bioinformatics analysis

Introduction

Colorectal cancer (CRC), the malignant neoplasm of colon or rectum, is the third most common cancer and the second most common cause of cancer death worldwide [1]. It is characterized by blood in the stool, a change in bowel movements and weight loss [2]. More than 1 million new cases of CRC are diagnosed annually . The 5-year survival rate for CRC is only 60% [3]. Therefore, an improved understanding mechanism on the pathogenesis of CRC would supply new insights for the diagnosis and treatment of CRC.

The development of CRC is a multistep progressive process and the pathogenesis of CRC is indicated to be caused by the stepwise accumulation of genetic alteration [4]. For instance, vascular endothelial growth factor (VEGF) is associated with the progression, invasion and metastasis of CRC, and overexpression of VEGF mRNA in the primary tumor is closely correlated with poor prognosis in CRC patients [5]. The work of Liu et al. found that miR-137 had a tumor suppressor function by directly targeting cell division cycle 42 to inhibit the proliferation and invasion activities of CRC cells [6]. Besides, modulation the Wnt signaling pathway in favor of inhibition of tumor proliferation in CRC patients [7]. Abnormalities in the JAK2/STAT3 pathway are involved in CRC cell growth and survival through regulating expression of genes, such as B-cell CLL/lymphoma 2 [8]. Inhibition of JAK2/STAT3 pathway induced apoptosis by the mitochondrial apoptotic pathway [9]. Although tremendous efforts have been made, the exact mechanism about CRC has not been fully elucidated. There is also lack of effective target genes for CRC treatment.

In this study, we downloaded the RNA-seq data of GSE29580 and analyzed the differentially expressed genes (DEGs) between CRC and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed. Besides, the protein-protein interaction (PPI) network was constructed. The purpose of this study was to explore the underlying mechanisms and identify the novel potential target genes for CRC therapy.

Data and methods

RNA-seq data

The gene expression profile data of GSE29580 were obtained 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 GPL9052 (Illuming Genome Analyzer (Homo sapients)), which was deposited by Luo et al. on May 28, 2011. The paired samples (from the same patient) of colorectal tumor and normal tissues from 2 patients were harvested.

Data preprocessing

Theraw sequencing data in Fastq files were downloaded. Various quality controls, including removal low-quality reads and reads containing asaptor/primer sequences, were performed by FastX-tool kit software (http://hannonlab.cshl.edu/fastx_toolkit/). The filtered high-quality reads were compared with Tophat software [10] based on hg19 reference sequences. The result of reads < 2 base mismatch and < 2 gap length were required during the comparison.

ANNOVAR (http://www.openbioinformatics.org/annovar/) [11] is a tool to annotate functional consequences of genetic variation from high-throughput sequencing data. ANNOVAR was used to annotate reads to one of the following classes: exonic, splicing, intronic or intergenic.

Differential expression analysis and clustering analysis

The transcriptome of each sample was assembled. For the estimation of expression values of genes fragments per kilobase of transcript per million mapped (FPKM) values were calculated with cuffdiff tool (ver. 2.0.2) in the Cufflinks software [12]. Cufflinks was used to estimate variance in gene expression levels. P-value < 0.05 and |log2FC| > 2 were used as the cutoff criteria for identification the differentially expressed genes (DEGs). Hierarchical clustering analysis of DEGs in colorectal cancer was performed.

Functional enrichment analysis

GO database (http://geneontology.org/) [13] is a collection of a large number of gene annotation terms. KEGG knowledge database (http://www.kegg.jp/) [14] is applied to identify the functional and metabolic pathway. The Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/) [15] is a tool that provides a comprehensive set of functional annotation for large list of genes. GO and KEGG pathway enrichment analyses were conducted for DEGs using DAVID. P-value < 0.05 was the cutoff criterion based on Fisher’s exact test.

Protein-protein interaction (PPI) network construction

Some widely-used available PPI databases, such as DIP (http://dip.doe-mbi.ucla.edu/) [16], BIND (http://www.bind.ca/) [17], MIPS (http://mips.helmholtz-muenchen.de/proj/ppi/) [18], STRING (http://string.embl.de/) [19] and HPRD (http://www.hprd.org/) [20], were used to analyze the interactions of protein pairs. PPI network of DEGs was constructed by Cytoscape software [21]. Connectivity degree was analyzed and used to obtain the hub protein in PPI network.

Results

Identification of DEGs

In total, 181 differentially expressed transcripts were identified in colorectal cancer samples. There were 92 down-regulated transcripts and 89 up-regulated transcripts, which corresponded to 88 down-regulated genes and 62 up-regulated genes, respectively. The clustering analysis result was shown in Figure 1. The top 10 down- and up-regulated genes were listed in Table 1.

Functional enrichment analysis

The top 10 GO terms of down- and up-regulated genes were shown in Table 2, respectively. The down-regulated genes were significantly enriched in cellular component (CC) term of extracellular region, molecular function (MF) term of receptor binding and biological process (BP) term of response to hormone stimulus. On the other hand, up-regulated gene were mainly enriched in muscle cell apoptosis, such as negative regulation of cardiac muscle cell apoptosis, negative regulation of striated muscle cell apoptosis and regulation of cardiac muscle cell apoptosis.

Total 23 pathways were obtained in KEGG enrichment analysis. The top 5 pathways of down- and up-regulated genes were shown in Table 3, respectively. The down-expressed genes were mainly involved in cytokine-cytokine receptor interaction, pertussis and tumor necrosis factor (TNF) signaling pathway. DEGs, such as chemokine (C-X-C motif) ligand 3 (CXCL3), interleukin 11 (IL11), interleukin 8 (IL8), INHBB, TNFRSF11B, BMP7, PPBP and CXCL5 were identified in cytokine-cytokine receptor interaction. CXCL3, CXCL5, prostaglandin-endoperoxide synthase 2 (PTGS2) and matrix metallopeptidase 3 (MMP3) were identified in TNF signaling pathway. The up-regulated genes were related to bile secretion and ABC transporters. DEGs including ATP-binding cassette, sub-family G, member 2 (ABCG2), ATPase, Na+/K+ transporting, alpha 2 polypeptide (ATP1A2) and aquaporin 8 (AQP8) were identified in bile secretion. ABCG2 and ATP-binding cassette, sub-family A (ABC1), member 8 (ABCA8) were identified in ABC transporters.

PPI network construction

The PPI network was constructed with 73 nodes and 105 edges (Figure 2). In this network, the proteins PTGS2 (degree = 14), IL8 (degree = 10), matrix metallopeptidase 7 (MMP7, degree = 9), plasminogen activator, urokinase (PLAU, degree = 9) and CXCL3 (degree = 8) were selected as hub nodes with the high connectivity degree.

Discussion

CRC is one of the most life-threatening cancers worldwide [1]. Understanding the molecular mechanism of CRC is of critical importance for management policy. In this study, the RNA-seq data of GSE29580 was downloaded from GEO database to identify DEGs between CRC and normal samples using bioinformatics analysis. Total 150 DEGs including 88 down- and 62 up-regulated genes were selected. The functional enrichment analysis results showed that down-regulated genes were significantly enriched in cytokine-cytokine receptor interaction and TNF signaling pathways, while up-regulated genes were related to bile secretion function. PTGS2, CXCL3, IL8, ABCG2, ATP1A2 and AQP8 were identified in these functions. In addition, down-regulated genes, such as PTGS2, CXCL3 and IL8, were hub nodes in PPI network. These DEGs and their related functions may be involved in CRC development.

The down-regulated genes were significantly enriched in cytokine-cytokine receptor interaction pathway in this study. IL8 and CXCL3 were identified in this pathway. IL8 is one of the major mediators of the inflammatory response. Deregulation of IL8 is involved in proliferation and invasiveness of various malignant tumor cells [22, 23]. Variants of the IL8 and IL8 receptor (IL8R) are associated with increased risks for gastric cardia adenocarcinoma [24]. Rubie et al. reported that over-expression of IL8 was correlated with induction, progression and metastatic of CRC and might be a useful indicator of poor prognosis [25]. In this study, IL8 was down-expressed and was hub node in PPI network. It suggested that down-expression of IL8 may inhibit CRC oncogenesis via regulating cytokine-cytokine receptor interaction pathway. In addition, CXCL3 is also a small cytokine belonging to the CXC chemokine family. Chemokines and their receptors regulate tumor-related angiogenesis, growth and tumor cell proliferation [26, 27]. Bandapalli et al. reported that down-regulation of CXCL1 (another member of the CXC chemokine family) inhibited tumor growth in colorectal liver metastasis [28]. However, the evidence concerning the impact of CXCL3 in CRC is rare. In this study, down-expressed CXCL3 was enriched in cytokine-cytokine receptor interaction pathway and was hub node in PPI network, suggesting that CXCL3 may play an important role in CRC progression by regulating cytokine-cytokine receptor interaction pathway. Therefore, IL8 and CXCL3 may be involved in CRC development. Their related pathway may be potential pathogenic mechanism of CRC.

CXCL3 was not only enriched in cytokine-cytokine receptor interaction pathway, but also enriched in TNF signaling pathway. Besides, PTGS2 was identified in TNF signaling pathway. PTGS2, also known as cyclooxygenase-2 (COX-2), is an enzyme involved in tumor promotion during CRC progression [29]. In this study, PTGS2 was down-regulated gene, which was consistent with a previous study that Karnes et al. reported that PTGS2 was reduced in CRC [30]. Inhibited COX-2 is an effective approach to CRC prevention and treatment [29]. Therefore, down-regulation of PTGS2 and CXCL3 may be effective for inhibiting CRC carcinogenesis via regulating TNF signaling pathway.

Apart from down-regulated genes and their functions, up-regulated genes were mainly enriched in the function of bile secretion in this study. It has been reported that the elevated bile secretion may increase production of the mutation and carcinogenic of the distal colon [31]. ABCG2, ATP1A2 and AQP8 were identified in this function. ABCG2 is a member of ATP binding cassette transporter family. In our study, it was up-regulated gene, which was corresponding to previous studies. Liu et al. reported that ABCG2 was highly expressed in CRC and ABCG2 might be important in the progression and metastasis of CRC [32]. Besides, ATP1A2 is a member of P-type cation transport ATPase family and belongs to Na, K-ATPase subfamily. Na, K-ATPase is a target of transforming growth factor β-mediated epithelial-to-mesenchymal transition (EMT) [33]. EMT is an important process, participates in pathological processes of CRC cell invasiveness, metastasis [34, 35]. The work of Sakai et al. found that Na+, K+-ATPase α1-isoform was down-regulated and α3-isoform was up-regulated in human CRC [36]. In this study, bile secretion-related gene ATP1A2 was over-expressed in CRC samples. Therefore, ATP1A2 may be involved in progression of CRC. Moreover, AQP8 is a water channel protein and belongs to aquaporin family. Water molecules play a key role in the modulation of the tumor microenvironment, tumorigenesis and tumor metabolism [37]. During colorectal carcinogenesis, the expression of AQP1 and AQP5 (another members of aquaporin family) were induced in early-stage disease (early dysplasia) and maintained through the late stages of CRC development [38]. Wang et al. reported that AQP8 was mainly expressed in paraneoplastic normal tissues and barely expressed in CRC cells [39]. In our study, AQP8 was over-expressed in CRC samples, suggesting that it may inhibit the development of CRC. These results showed that ABCG2, ATP1A2 and AQP8 may be potential therapeutic target genes for CRC. Their related pathway bile secretion may be potential pathogenic mechanisms of CRC.

In conclusion, our study shows that cytokine-cytokine receptor interaction, TNF signaling pathway and bile secretion functions may be closely associated with CRC development. Genes such as PTGS2, CXCL3, IL8, ABCG2, ATP1A2 and AQP8 may be potential therapeutic target genes for CRC. However, further studies are still needed to confirm our results.


 


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