|Year : 2013 | Volume
| Issue : 3 | Page : 467-470
Expression profiling based on graph-clustering approach to determine colon cancer pathway
Xiao-qu Zhu1, Mei-lan Hu2, Feng Zhang3, Yu Tao4, Chun-ming Wu1, Shang-zhu Lin1, Fu-le He2
1 Department of Gastroenterology and Hepatology, Wenzhou Hospital of Traditional Chinese Medicine, 27 Dashimen Xinhe Road, Wenzhou, Zhejiang 325000, China
2 Department of Traditional Chinese Medicine, Hangzhou First People's Hospital, 261 Huansha Road, Hangzhou, Zhejiang 310006, China
3 Postgraduate student of 2011 grade, The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, Zhejiang 310053, China
4 Department of Oncology, Wenzhou Hospital of Traditional Chinese Medicine, 27 Dashimen Xinhe Road, Wenzhou, Zhejiang 325000, China
|Date of Web Publication||8-Oct-2013|
Department of Traditional Chinese Medicine, Hangzhou First People's Hospital, 261 Huansha Road, Hangzhou, Zhejiang 310006
Source of Support: None, Conflict of Interest: None
Context: Colorectal cancer is the second leading cause of cancer deaths worldwide. DNA microarray-based technologies allow simultaneous analysis of expression of thousands of genes.
Aim: To search for important molecular markers and pathways that hold great promise for further treatment of patients with colorectal cancer.
Materials and Methods: Here, we performed a comprehensive gene-level assessment of colorectal cancer using 35 colorectal cancer and 24 normal samples.
Results: It was shown that AURKA, MT1G, and AKAP12 had a high degree of response in colorectal cancer. Besides, we further explored the underlying molecular mechanism within these different genes.
Conclusions: The results indicated calcium signaling pathway and vascular smooth muscle contraction pathway were the two significant pathways, giving hope to provide insights into the development of novel therapeutic targets and pathways.
Keywords: Colon cancer, expression profiles, graph cluster, significant pathways
|How to cite this article:|
Zhu Xq, Hu Ml, Zhang F, Tao Y, Wu Cm, Lin Sz, He Fl. Expression profiling based on graph-clustering approach to determine colon cancer pathway. J Can Res Ther 2013;9:467-70
|How to cite this URL:|
Zhu Xq, Hu Ml, Zhang F, Tao Y, Wu Cm, Lin Sz, He Fl. Expression profiling based on graph-clustering approach to determine colon cancer pathway. J Can Res Ther [serial online] 2013 [cited 2020 Jul 15];9:467-70. Available from: http://www.cancerjournal.net/text.asp?2013/9/3/467/119351
| > Introduction|| |
Colorectal cancer (CRC) is one of the most frequent malignancies in western countries and the third most common cause of cancer-related deaths worldwide, despite remarkable progress being made in surgical techniques and therapeutic options. 
CRC arises as a consequence of the accumulation of genetic and epigenetic alterations.  Till date, inactivation of the tumor suppressor genes, Adenomatous polyposis coli (APC) and p53, and activation of the oncogene, Kirsten-ras (K-ras), are thought to be particularly important determinants of colorectal tumor initiation and progression. , Besides, CRC development is also an epigenetic gene inactivation mechanism by DNA methylation of its promoter region. CpG island methylation affects a number of genes in colon cancer, and the significance of the epigenetic alterations in the pathogenesis of colon cancer has been reported widely, such as the netrin-1 receptors are aberrantly methylated in primary CRC and are significantly correlated with Dukes' stage C. 
Numerous new therapies hold great promise for the treatment of patients with brain cancer, but the main challenge is to determine which treatment is most likely to benefit an individual patient.
DNA microarray technology offers the ability to compare gene expression at a genome-wide level and to explore the transcriptional programs that are turned on or off in tumors during progression from normal through premalignant stages to cancer.  However, extracting such gene sets information from large data sets derived from heterogeneous biological samples has proven to be difficult. Pathway analysis programs such as graph clustering can be of help.  Therefore, in our study, we aimed to yield sets of significant, differentially expressed genes (DEGs) by DNA microarray analysis. From these confirmed gene sets, relevant pathways' networks could be reconstructed, ultimately leading to a more reliable understanding of the underlying biology mechanism in CRC. 
| > Materials and Methods|| |
Microarray analysis was performed between 35 CRC and 24 normal samples to identify differential genes. The microarray expression data can be accessed through the Gene Expression Omnibus under accession number GSE23878 ( http://www.ncbi.nlm.nih.gov/geo/ ) which is based on the Affymetrix Human Genome U133 Plus 2.0 Array.
For the GSE23878 dataset, the limma method  was used to identify DEGs. The original expression data sets from all conditions were processed into expression estimates using the Robust Multichip Average (RMA) method with the default settings implemented in bioconductor, and then the linear model was constructed. The DEGs only with the fold change >2 and P value <0.05 were selected.
For demonstrating the potential connection, the Spearman rank correlation (r) was used for comparative target genes' correlations. The significance level was set at r > 0.9 and local false discovery rate (fdr)  <0.05. All statistical tests were performed with the R program ( http://www.r-project.org/ ).
To identify co-expressed groups, we used DPClus,  a graph-clustering algorithm that can extract densely connected nodes as a cluster. It is based on density and periphery tracking of clusters. DPClus is freely available from http://kanaya.naist.jp/DPClus/ . In this study, we used the overlapping mode with the DPClus settings. We set the parameter settings of cluster property (cp); density values were set to 0.5  and minimum cluster size was set to 5.
The pathway  database records networks of molecular interactions in the cells and variants of them specific to particular organisms ( http://www.genome.jp/kegg/ ).
The Gene Ontology (GO)  project is a major bioinformatics initiative with the aim of standardizing the representation of gene and gene product attributes across species and databases.
InterPro  is an integrated database of predictive protein "signatures" used for the classification and automatic annotation of proteins and genomes.
The DAVID  was used to identify over-represented pathways, InterPro domains, and GO terms in biological process based on hypergeometric distribution test. P value <0.05 was the threshold for the analysis.
| > Results|| |
We obtained publicly available microarray data sets GSE23878 from Gene Expression Omnibus (GEO). After microarray analysis, 1383 DEGs with the fold change >2 and P value <0.05 were selected.
To get the relationships among DEGs, the co-expressed value (r > 0.9 and fdr < 0.05) was chosen as the threshold. Finally, 324 relationships among 59 DEGs were constructed a correlation network. The expression profiles of the 59 DEGs are seen in [Figure 1].
At r ≥ 0.9, DPClus  identified nine clusters in the correlation network for CRC; they ranged in size from 5 to 12 genes [Figure 2]. To assess the significance of the clusters, we used the over-represented KEGG pathways (so-called Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis) in the clusters. The results of graph clustering with KEGG enrichment analysis are presented in [Table 1].
|Table 1: List of enriched KEGG pathways in cluster2 and 9 detected by DPClus|
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|Figure 2: Graph clustering of correlated modules in CRC (threshold r ≥ 0.9). Using the DPClus algorithm we extracted 9 clusters in CRC. The internal nodes of the clusters are connected by green edges; neighboring clusters are connected by red edges|
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The enrichment analysis method yields two significant pathways, vascular smooth muscle contraction pathway and calcium signaling pathway [Table 1].
Several GO categories were enriched among these genes in the correlation network, including immune response, actomyosin structure organization, response to metal ion, etc. [Table 2].
Domains including Immunoglobulin (Ig), metallothionein (MT), vertebrate, and metal binding site [Table 3] were enriched among the genes in the graph-clusters.
|Table 3: List of enriched InterPro domains in clusters 1-9 detected by DPClus|
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| > Discussion|| |
According to our analysis results, we could find that many transcription factors (TFs) and pathways closely related with CRC had been linked by our method. Among them were AURKA, MT1G, and AKAP12 in the correlation network, suggesting that these genes might play important roles in CRC. We would discuss the relationship between CRC and identified genes as follows based on previous reports.
AURKA protein is a cell cycle-regulated kinase that appears to be involved in microtubule formation and/or stabilization at the spindle pole during chromosome segregation. The encoded protein is found at the centrosome in interphase cells and at the spindle poles in mitosis. AURKA overexpression was detected in colorectal carcinogenesis, especially in chromosomal instable carcinomas. 
MT1G gene belongs to the MT family encoding a class of metal-binding proteins involved in several cellular processes, including potent antioxidant function against various types of oxidative damage as well as regulation of zinc and copper homeostasis, and their expression is often dysregulated in human tumors. In a human colon cancer cell line, the level of MT achieves its maximum near the G1/S stage of the cell cycle, suggesting that MT plays a physiological role in cell proliferation. MT expression was stronger in CRC at early stage than in advanced carcinomas.  Furthermore, MT1G genes were found to be up-regulated in CRC cell lines HCT116 and SW620 cells after f-adiponectin treatment and were suggested to have an anticarcinogenic role in colorectal carcinogenesis based on previous reports. 
AKAP12 gene encodes a member of the A-kinase anchor protein (AKAP) family. It associates with protein kinases A and C and phosphatase, and serves as a scaffold protein in signal transduction. Promoter hypermethylation is one molecular pathogenic mechanism underlying CRC. The down-regulation or loss of AKAP12 mRNA expression was detected in colorectal carcinoma tissues and methylation of the AKAP12 promoter region was also detected in these tissues. These results indicate that AKAP12 is a novel promoter methylation target in CRC. ,
Aberrant expression of Ig by cancer cells has been documented in a number of malignant tumors. For instance, CD155 gene, a member of the Ig superfamily coding for a transmembrane protein, was identified overexpressed in colorectal carcinoma and this overexpression begins at an early stage in tumorigenesis and continues to late stages.  Furthermore, the expression of Igλ and Igλ was found in human CRC cell cytoplasm to mediate Bcl-xL expression. Using RNAi to knock out the genes of Igκ and/or Igλ, Bcl-xL expression in HT29 cells was significantly suppressed and the cells became apoptotic.  Therefore, the results suggested that the expression of Igβ and Igλ is required to stabilize Bcl-xL expression in cancer cells and could be potential therapeutic target.
To identify the relevant pathways changed, we used hypergeometric distribution test on pathway level. Finally, only calcium signaling pathway and vascular smooth muscle contraction pathway were the significant ones.
There is strong evidence that calcium signaling pathway is involved in CRC progression. For example, Ca 2+ has chemopreventive activity against colon cancer through the promotion of E-cadherin and suppression of β-catenin/T-cell factor. In addition, human colon carcinoma cell lines express the human parathyroid calcium sensing receptor (CaSR). The extracellular Ca 2+ and CaSR may function to regulate the differentiation of colonic epithelial cells and disruption of this ligand receptor system may contribute to abnormal differentiation and malignant progression. 
It is suggested that CRC might be prevented by vegetable intake. Myosin is a major component of the contractile elements of smooth muscle and is composed of two identical heavy chains (200 kDa) and two sets of light chains of 20 and 17 kDa. The 20 kDa chain, called regulatory light chain (MLRN), plays a central role in the regulation of smooth muscle contraction. One 2-DE experiment demonstrated that the expression of MLRN protein was increasing with more vegetable intake. Increased MLRN protein expression might contribute to enhanced bowel movement, and as a consequence reduces the passage time of mutagenic agents, lowering the risk for genetic damage and colon cancer. 
In conclusion, we have used network analysis as a conceptual framework to explore the pathobiology of colon cancer based on the assumption that colon cancer is a contextual attribute of distinct patterns of interactions between multiple genes. The salient results of our study include AURKA, MT1G, AKAP12, calcium signaling pathway, and vascular smooth muscle contraction pathway, which all are related with colon cancer in a direct or indirect manner. However, further experiments are indispensable to confirm our conclusion.
| > References|| |
|1.||Pancione M, Forte N, Fucci A, Sabatino L, Febbraro A, Di Blasi A, et al. Prognostic role of beta-catenin and p53 expression in the metastatic progression of sporadic colorectal cancer. Hum Pathol 2010;41:867-76. |
|2.||Yagi K, Akagi K, Hayashi H, Nagae G, Tsuji S, Isagawa T, et al. Three DNA methylation epigenotypes in human colorectal cancer. Clin Cancer Res 2010;16:21-33. |
|3.||Segditsas S, Tomlinson I. Colorectal cancer and genetic alterations in the Wnt pathway. Oncogene 2006;25:7531-7. |
|4.||Zlobec I, Bihl MP, Schwarb H, Terracciano L, Lugli A. Clinicopathological and protein characterization of BRAF- and K-RAS-mutated colorectal cancer and implications for prognosis. Int J Cancer 2010;127:367-80. |
|5.||Kim MS, Louwagie J, Carvalho B, Terhaar Sive Droste JS, Park HL, Chae YK, et al. Promoter DNA methylation of oncostatin m receptor-beta as a novel diagnostic and therapeutic marker in colon cancer. PLoS One 2009;4:e6555. |
|6.||Lassmann S, Danciu M, Müller M, Weis R, Makowiec F, Schulte-Mönting J, et al. Aurora A is differentially expressed and regulated in chromosomal and microsatellite instable sporadic colorectal cancers. Mod Pathol 2009;22:1385-97. |
|7.||Altaf-Ul-Amin M, Shinbo Y, Mihara K, Kurokawa K, Kanaya S. Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 2006;7:207. |
|8.||Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM. Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 2002;62:4427-33. |
|9.||Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:3. |
|10.||Strimmer K. fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 2008;24:1461-2. |
|11.||Fukushima A, Kusano M, Redestig H, Arita M, Saito K. Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach. BMC Syst Biol 2011;5:1. |
|12.||Kanehisa M. The KEGG database. Novartis Found Symp 2002;247:91-101. |
|13.||Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25-9. |
|14.||Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, et al. InterPro: The integrative protein signature database. Nucleic Acids Res 2009;37:D211-5. |
|15.||Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. |
|16.||Hishikawa Y, Kohno H, Ueda S, Kimoto T, Dhar DK, Kubota H, et al. Expression of metallothionein in colorectal cancers and synchronous liver metastases. Oncology 2001;61:162-7. |
|17.||Byeon JS, Jeong JY, Kim MJ, Lee SM, Nam WH, Myung SJ, et al. Adiponectin and adiponectin receptor in relation to colorectal cancer progression. Int J Cancer 2010;127:2758-67. |
|18.||Mori Y, Cai K, Cheng Y, Wang S, Paun B, Hamilton JP, et al. A genome-wide search identifies epigenetic silencing of somatostatin, tachykinin-1, and 5 other genes in colon cancer. Gastroenterology 2006;131:797-808. |
|19.||Liu W, Guan M, Su B, Li J, Ma W, Liu C, et al. Rapid determination of AKAP12 promoter methylation levels in peripheral blood using methylation-sensitive high resolution melting (MS-HRM) analysis: Application in colorectal cancer. Clin Chim Acta 2010;411:940-6. |
|20.||Masson D, Jarry A, Baury B, Blanchardie P, Laboisse C, Lustenberger P, et al. Overexpression of the CD155 gene in human colorectal carcinoma. Gut 2001;49:236-40. |
|21.||Yang SB, Chen X, Wu BY, Wang MW, Cai CH, Cho DB, et al. Immunoglobulin kappa and immunoglobulin lambda are required for expression of the anti-apoptotic molecule Bcl-xL in human colorectal cancer tissue. Scand J Gastroenterol 2009;44:1443-51. |
|22.||Chakrabarty S, Radjendirane V, Appelman H, Varani J. Extracellular calcium and calcium sensing receptor function in human colon carcinomas: Promotion of E-cadherin expression and suppression of beta-catenin/TCF activation. Cancer Res 2003;63:67-71. |
|23.||Breikers G, van Breda SG, Bouwman FG, van Herwijnen MH, Renes J, Mariman EC, et al. Potential protein markers for nutritional health effects on colorectal cancer in the mouse as revealed by proteomics analysis. Proteomics 2006;6:2844-52. |
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]