Open Access

Exploration of the mechanism of colorectal cancer metastasis using microarray analysis

  • Authors:
    • Shuo Chen
    • Yan Wang
    • Lin Zhang
    • Yinan Su
    • Mingqing Zhang
    • Juan Wang
    • Xipeng Zhang
  • View Affiliations

  • Published online on: September 25, 2017     https://doi.org/10.3892/ol.2017.7044
  • Pages:6671-6677
  • Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the present study was to investigate the mechanism of metastasis in colorectal cancer (CRC) using microRNA (miRNA) and mRNA expression profiles. The mRNA and miRNA expression profiles of the GSE2509 and GSE56350 datasets were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified using the limma software package. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs. The predicted target genes associated with the DEMs were identified using the miRWalk database and the enrichment analysis was conducted using the clusterProfiler package. The miRNA‑gene molecular interaction network was visualized using the Cytoscape software platform. A total of 544 DEGs and 42 DEMs were identified. DEGs were annotated in 320 GO terms and 11 KEGG pathways. Overall, 366 miRNA‑gene pairs were identified and the miRNA‑gene network was visualized. Furthermore, the predicted target genes were mainly classified in 12 pathways. The results of the present study suggest that fibronectin type III domain‑containing 3B, cysteine rich transmembrane BMP regulator 1 and forkhead box J2 may be potential therapeutic and prognostic targets of metastatic CRC. In addition, pathways in cancer, the Wnt signaling pathway and extracellular matrix‑receptor interaction may play a critical role in CRC metastasis.

Introduction

Colorectal cancer (CRC) is the third most common malignancy worldwide and the third leading cause of cancer-associated mortality in the United States (1,2). It is estimated that ~1.3 million new cases are diagnosed and ~0.7 million people succumb to the disease each year worldwide (3). According to recent statistics, it was estimated that 134,490 new cases and 49,190 fatalities would occur in America in 2016 (1). The incidence and mortality rates for men and women have improved in the last decades as a result of advances in screening and clinical treatment (4,5). Metastasis, the most common cause of cancer-associated mortality, is a multi-step process through which tumor cells spread from their primary site and form secondary growths at a distance (6). Metastasis is among the six initially described hallmarks of cancer and is a major cause of CRC-associated mortality (7,8). The 5-year survival rate of early-stage CRC ranges between 60 and 95%; however, for patients with metastatic tumors, the survival rate ranges from 10 to 35% (911). Despite improvements in diagnosis and treatment, ~90% of CRC-associated mortalities are due to metastases and it has been estimated that ~50% of all patients diagnosed with CRC eventually succumb to metastatic disease (12,13). Therefore, it is critical to investigate the mechanism of metastasis in CRC and identify novel molecular therapeutic and prognostic targets. To date, some progress has been made. Epithelial-mesenchymal transition was demonstrated to serve a pivotal and intricate role in promoting CRC metastasis (6,14). In addition, certain genes and proteins were found to be associated with CRC metastasis, including semaphoring 3F, stromal interaction molecule 1, forkhead box C2 and hes family BHLH transcription factor 1 (1417), as well as Cyclin b1, Angiopoietin-like 4 and p21-activated kinase 1 and 4 (1820). However, one study demonstrated that metastasis occurs through a multistep cascade of events, but that it was inefficient as a whole process (8). Even though mutations associated with metastasis have been investigated in the past, only a limited number of such genetic alterations have been identified and the underlying molecular mechanism remains unclear (21). The present study analyzed the microRNA (miR/miRNA) and mRNA expression profiles of CRC samples in order to investigate the mechanism of metastasis in CRC and identify novel molecular biomarkers.

Materials and methods

mRNA and miRNA expression data

The mRNA and miRNA expression profiles of the GSE2509 (22) and GSE56350 (23) datasets were obtained from the Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo/). GSE2509 contains the mRNA profile of 3 primary CRC samples and 3 samples with lymph node metastasis. These data were analyzed with the GPL96 [HG-U133A] Affymetrix Human Genome U133A Array platform version 2.0 (Affymetrix; Fisher Scientific, Inc., Waltham, MA, USA). Additionally, 46 primary CRC samples and 43 CRC samples with lymph node metastasis were obtained from the miRNA profile of GSE56350. Detection of the miRNA profile was performed using the PL16744 OSU-CCC Human and Mouse MicroRNA Microarray platform version 4.0 (Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA).

Data processing and differential expression analysis

The mRNA profile data were converted into recognizable format in R and then were normalized using the Robust Multi-Array Average algorithm from Affy version 1.40.0 package (http://www.bioconductor.org/packages/2.13/bioc/html/affy.html) (24). For the miRNA profile, the original expression value matrix was obtained and normalization was conducted using the preprocessCore function package version 3.5 (http://www.bioconductor.org/packages/release/bioc/html/preprocessCore.html) (25). Subsequently, the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified in the CRC samples with lymph node metastasis compared with the primary CRC samples using the limma version 3.18.13 software package (http://www.bioconductor.org/packages/2.13/bioc/html/limma.html) (26). P<0.05 and |log2 (fold-change)| >0.2 were used as threshold criteria.

Functional and pathway enrichment analysis of DEGs

The Database for Annotation, Visualization and Integrated Discovery version 6.8 (https://david.ncifcrf.gov/) (27) was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. GO terms and KEGG pathways with P<0.05 were selected.

Construction of the miRNA-gene network

The miRWalk version 2.0 database (mirwalk.uni-hd.de/) is a publicly available comprehensive resource containing the predicted and the experimentally validated miRNA-target interaction pairs (28). The DEM-associated predicted target genes were selected when they were included in at least four out of five databases (miRanda-rel2010, miRDB version 4.0, miRWalk version 2.0, RNA22 version 2.0 and TargetScan version 6.2). Subsequently, the overlapping target genes were identified and the miRNA-gene pairs and DEMs were selected. The miRNA-gene network was formed and visualized using the Cytoscape version 3.5.1 (29) software (http://www.cytoscape.org/download.php).

Functional enrichment analysis of predicted target genes regulated by DEMs

The clusterProfiler version 3.5 (http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html), an R package, was used to perform biological-term classification and enrichment analysis of gene clusters (30). Following the identification of the predicted target genes regulated by DEMs, enrichment analysis was performed and the enriched pathways with a P-value of <0.05 were selected.

Results

Differentially expressed mRNAs and differentially expressed miRNAs

A total of 544 DEGs (227 upregulated and 317 downregulated) were identified, and the heat map of hierarchical clustering is presented in Fig. 1. Additionally, 42 DEMs (25 upregulated and 17 downregulated) were identified. The 20 most significant DEGs and the 20 most significant DEMs are presented in Tables I and II, respectively.

Table I.

20 most significant differentially expressed genes in colorectal cancer samples with lymph node metastasis.

Table I.

20 most significant differentially expressed genes in colorectal cancer samples with lymph node metastasis.

GeneP-valuelog2 FC
RNF128 4.07×10−154.435925
CNN3 6.05×10−154.696778
WNT5A 8.42×10−15−5.04177
TOX3 9.65×10−155.640899
FZD10 1.13×10−143.438707
IGFBP3 3.26×10−14−4.23336
AKR1C3 4.50×10−144.643379
TRIP6 5.04×10−14−3.79486
NPC2 5.37×10−14−3.86253
KRT13 8.49×10−14−5.41489
KRT23 8.91×10−14−4.84643
NGFR 9.23×10−14−3.8684
KRT81 9.38×10−14−3.54854
CXCR4 1.25×10−13−3.41813
ANXA6 1.29×10−13−3.29244
MSX1 1.64×10−13−3.64506
EFNB2 1.76×10−133.675191
SLC2A3 1.77×10−13−4.2354
IGFBP7 2.18×10−13−3.27699
GNG11 2.77×10−13−5.77814

[i] FC, fold-change.

Table II.

20 most significant differentially expressed miRNAs in colorectal cancer samples with lymph node metastasis.

Table II.

20 most significant differentially expressed miRNAs in colorectal cancer samples with lymph node metastasis.

miRNAP-valuelog2 FC
hsa-miR-342-3p 5.2×10−101.3803
hsa-miR-150 1.31×10−91.5415
hsa-miR-155 2.73×10−71.5434
hsa-miR-92b 6.19×10−7−1.6535
hsa-miR-375 4.04×10−6−1.8421
hsa-miR-142-5p 3.72×10−61.752
hsa-miR-453 8.88×10−6−1.1169
hsa-miR-622 1.47×10−5−1.4782
hsa-miR-595 6.48×10−5−1.0289
hsa-miR-629 8.46×10−5−1.1146
hsa-mir-621 1.04×10−4−1.2234
hsa-miR-26a 1.19×10−42.4369
hsa-miR-146a 1.51×10−42.1241
hsa-miR-26b 1.56×10−42.7188
hsa-miR-200b 2.05×10−4−1.1071
hsa-miR-146b-5p 2.18×10−41.9222
hsa-miR-107 2.91×10−42.1441
hsa-miR-560 3.29×10−4−1.4887
hsa-mir-766 3.33×10−4−1.1362
hsa-miR-103 4.97×10−42.1098

[i] miRNA/miR, microRNA; FC, fold-change; hsa, Homo sapiens.

Enriched GO terms and KEGG pathways of differentially expressed mRNAs

DEGs were enriched in 320 GO terms and 11 KEGG pathways. The 10 most significant enriched GO terms and the enriched KEGG pathways are presented in Tables III and IV, respectively.

Table III.

10 most significant enriched GO terms of differentially expressed microRNAs.

Table III.

10 most significant enriched GO terms of differentially expressed microRNAs.

CategoryGO IDGO nameCountP-value
BPGO:0001944Vasculature development29 1.30×10−8
BPGO:0001568Blood vessel development28 3.08×10−8
BPGO:0016477Cell migration27 1.22×10−6
BPGO:0048514Blood vessel morphogenesis22 5.54×10−6
BPGO:0051674Localization of cell27 8.67×10−6
BPGO:0048870Cell motility27 8.67×10−6
BPGO:0042127Regulation of cell proliferation50 9.42×10−6
BPGO:0051094Positive regulation of developmental process25 1.40×10−5
BPGO:0006928Cell motion35 1.46×10−5
BPGO:0045597Positive regulation of cell differentiation22 1.95×10−5

[i] GO, Gene Ontology; BP, biological process.

Table IV.

Enriched KEGG pathways of differentially expressed microRNAs.

Table IV.

Enriched KEGG pathways of differentially expressed microRNAs.

CategoryPathway nameCountP-value
KEGG_PATHWAYhsa05200: Pathways in cancer240.00217
KEGG_PATHWAYhsa04360: Axon guidance120.008095
KEGG_PATHWAYhsa04310: Wnt signaling pathway130.009973
KEGG_PATHWAYhsa05222: Small cell lung cancer  90.012226
KEGG_PATHWAYhsa04115: p53 signaling pathway  80.012507
KEGG_PATHWAYhsa05217: Basal cell carcinoma  70.015538
KEGG_PATHWAYhsa04916: Melanogenesis  90.030049
KEGG_PATHWAYhsa04060: Cytokine-cytokine receptor interaction170.033293
KEGG_PATHWAYhsa05210: Colorectal cancer  80.035683
KEGG_PATHWAYhsa04512: ECM-receptor interaction  80.035683
KEGG_PATHWAYhsa04540: Gap junction  80.046579

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; hsa, Homo sapiens; ECM, extracellular matrix.

miRNA-gene pairs and miRNA-gene network

A total of 366 miRNA-gene pairs among the overlapped genes with the DEMs were selected, and the miRNA-gene network was generated and analyzed. The network is presented in Fig. 2 and the 20 highest degree nodes are presented in Table V. The term ‘degree’ represented connections of one node with other nodes.

Table V.

20 highest degree nodes in the miRNA-gene network.

Table V.

20 highest degree nodes in the miRNA-gene network.

NodeDegree
hsa-miR-20b38
hsa-miR-20a36
hsa-miR-106a33
hsa-miR-23a32
hsa-miR-200b30
hsa-miR-1726
hsa-miR-142-5p24
hsa-miR-15a23
hsa-miR-27a22
hsa-miR-10717
hsa-miR-10315
hsa-miR-1615
hsa-miR-29b14
hsa-let-7g11
hsa-miR-150  9
FNDC3B  8
CRIM1  7
FOXJ2  7
hsa-miR-146a  7
hsa-miR-155  7

[i] hsa, Homo sapiens; miRNA/miR, microRNA; FNDC3B, fibronectin type III domain-containing 3B; CRIM1, cysteine rich transmembrane BMP regulator 1; FOXJ2, forkhead box J2.

Enriched pathways of predicted target genes

In total 271, 237, 192, 275 and 120 genes were respectively regulated by each of the 5 miRNAs hsa-miR-106a, hsa-miR-15a, hsa-miR-16, hsa-miR-20a and hsa-miR-29b, respectively. Overall, 12 pathways were enriched and the results are presented in Fig. 3.

Discussion

In the present study, DEGs and DEMs were initially identified in colorectal cancer samples with lymph node metastasis compared with primary colorectal cancer samples. Subsequently, the functional and pathway enrichment analysis of DEGs and the predicted target genes regulated by DEMs was performed. The over-represented pathways were associated with pathways in cancer, the Wnt signaling pathway and extracellular matrix (ECM)-receptor interaction. The major enriched pathway of DEMs was pathways in cancer characterized by the lowest P-values (Table IV). Overall, ~12% of predicted target genes regulated by hsa-miR-15a, hsa-miR-16 and hsa-miR-20a were associated with this pathway; suggesting that it serves a critical role in CRC metastasis.

Wnt signaling is involved in embryonic development (31) and a number of studies demonstrated that aberrant Wnt signaling serves an important role in CRC, regulating several cellular processes, including cell migration and metastasis (32,33). Hu et al (34) reported that CXCR4 promotes CRC progression and epithelial-mesenchymal transition by activating the Wnt/β-catenin signaling pathway. A study by Ting et al (35) indicated that the genetic interaction profile of Wnt pathway genetic variants may increase the prognostic value of outcome prediction for CRC patients. Therefore, it was indicated that the Wnt signaling pathway may serve an important role in the processes of cell migration and metastasis, and that certain genes in this pathway may serve as potential metastatic biomarkers for CRC.

The ECM regulates tissue architecture and adipogenesis, which involves a complex mixture of structural and functional macromolecules, including glycosaminoglycans and fibrous proteins (36). One recent study revealed that twist-related protein 2 (Twist2) regulates the expression of integrin α-4 and CD44 antigen, two major proteins in the ECM-receptor interaction pathway (37). Furthermore, it was also demonstrated that the overexpression of Twist2 may be involved in cell growth regulation, apoptosis and motility, and that Twist2 may serve as a potential therapeutic target for the treatment of kidney cancer (37). Additionally, twist family BHLH transcription factor 2 was significantly overexpressed in several solid tumors and contributed to tumor progression (38). The results of the present study suggest that ECM-receptor interaction may be associated with CRC metastasis, however, further research is required to validate this association.

miRNA-gene network analysis revealed that fibronectin type III domain-containing 3B (FNDC3B), cysteine rich transmembrane BMP regulator 1 (CRIM1) and forkhead box J2 (FOXJ2) were the genes with the highest degree (Table V). It has been demonstrated that FNDC3B is a positive regulator of adipocyte differentiation, and that it suppresses the invasion and metastasis of melanoma cells (39). FNDC3B mutations were associated with rapid postnatal death and the inhibition of cellular proliferation, adhesion and migration (40). FNDC3B has been associated with the activation of several cancer pathways and tumor progression (41). CRIM1 encodes the cysteine-rich motor neuron 1 protein (CRIM1), which has been characterized as a potential cancer biomarker (42). It has been reported that increased CRIM1 inhibits the proliferation and migration of vascular endothelial cells (43). Additionally, increased CRIM1 expression has been reported in drug-resistant myeloid leukemia cells compared with drug-sensitive cells (44). FOXJ2 serves an important role in the migration of glioma cells (45) and FOXJ2 overexpression decreases the migration of breast cancer cells (46). Furthermore, abnormal expression of FOXJ2 suppressed migration and invasion in extrahepatic cholangiocarcinoma, which was associated with an improved prognosis (47). FNDC3B, CRIM1 and FOXJ2 have been associated with tumor migration and prognosis. The findings of the present study suggest that they may also be associated with CRC metastasis.

In conclusion, the present study demonstrated that DEGs and predicted target genes of the DEMs are enriched in pathways in cancer, the Wnt signaling pathway and ECM-receptor interaction, which may serve a critical role in the metastatic mechanism of CRC. Furthermore, FNDC3B, CRIM1 and FOXJ2 are proposed as potential biomarkers for metastatic CRC.

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APA
Chen, S., Wang, Y., Zhang, L., Su, Y., Zhang, M., Wang, J., & Zhang, X. (2017). Exploration of the mechanism of colorectal cancer metastasis using microarray analysis. Oncology Letters, 14, 6671-6677. https://doi.org/10.3892/ol.2017.7044
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Chen, S., Wang, Y., Zhang, L., Su, Y., Zhang, M., Wang, J., Zhang, X."Exploration of the mechanism of colorectal cancer metastasis using microarray analysis". Oncology Letters 14.6 (2017): 6671-6677.
Chicago
Chen, S., Wang, Y., Zhang, L., Su, Y., Zhang, M., Wang, J., Zhang, X."Exploration of the mechanism of colorectal cancer metastasis using microarray analysis". Oncology Letters 14, no. 6 (2017): 6671-6677. https://doi.org/10.3892/ol.2017.7044