Open Access

Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data

  • Authors:
    • Zhonglu Ren
    • Wenhui Wang
    • Jinming Li
  • View Affiliations

  • Published online on: November 24, 2015     https://doi.org/10.3892/ijo.2015.3263
  • Pages: 690-702
  • Copyright: © Ren et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Identifying colon cancer subtypes based on molecular signatures may allow for a more rational, patient-specific approach to therapy in the future. Classifications using gene expression data have been attempted before with little concordance between the different studies carried out. In this study we aimed to uncover subtypes of colon cancer that have distinct biological characteristics and identify a set of novel biomarkers which could best reflect the clinical and/or biological characteristics of each subtype. Clustering analysis and discriminant analysis were utilized to discover the subtypes in two different molecular levels on 153 colon cancer samples from The Cancer Genome Atlas (TCGA) Data Portal. At gene expression level, we identified two major subtypes, ECL1 (expression cluster 1) and ECL2 (expression cluster 2) and a list of signature genes. Due to the heterogeneity of colon cancer, the subtype ECL1 can be further subdivided into three nested subclasses, and HOTAIR were found upregulated in subclass 2. At DNA methylation level, we uncovered three major subtypes, MCL1 (methylation cluster 1), MCL2 (methylation cluster 2) and MCL3 (methylation cluster 3). We found only three subtypes of CpG island methylator phenotype (CIMP) in colon cancer instead of the four subtypes in the previous reports, and we found no sufficient evidence to subdivide MCL3 into two distinct subgroups.

References

1 

Minsky BD: Unique considerations in the patient with rectal cancer. Semin Oncol. 38:542–551. 2011. View Article : Google Scholar : PubMed/NCBI

2 

Yagi K, Akagi K, Hayashi H, Nagae G, Tsuji S, Isagawa T, Midorikawa Y, Nishimura Y, Sakamoto H, Seto Y, et al: Three DNA methylation epigenotypes in human colorectal cancer. Clin Cancer Res. 16:21–33. 2010. View Article : Google Scholar

3 

Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 487:330–337. 2012. View Article : Google Scholar : PubMed/NCBI

4 

Walther A, Johnstone E, Swanton C, Midgley R, Tomlinson I and Kerr D: Genetic prognostic and predictive markers in colorectal cancer. Nat Rev Cancer. 9:489–499. 2009. View Article : Google Scholar : PubMed/NCBI

5 

Wang Y, Jatkoe T, Zhang Y, Mutch MG, Talantov D, Jiang J, McLeod HL and Atkins D: Gene expression profiles and molecular markers to predict recurrence of Dukes' B colon cancer. J Clin Oncol. 22:1564–1571. 2004. View Article : Google Scholar : PubMed/NCBI

6 

Barrier A, Boelle PY, Roser F, Gregg J, Tse C, Brault D, Lacaine F, Houry S, Huguier M, Franc B, et al: Stage II colon cancer prognosis prediction by tumor gene expression profiling. J Clin Oncol. 24:4685–4691. 2006. View Article : Google Scholar : PubMed/NCBI

7 

Oh SC, Park YY, Park ES, Lim JY, Kim SM, Kim SB, Kim J, Kim SC, Chu IS, Smith JJ, et al: Prognostic gene expression signature associated with two molecularly distinct subtypes of colorectal cancer. Gut. 61:1291–1298. 2012. View Article : Google Scholar :

8 

Slattery ML, Wolff E, Hoffman MD, Pellatt DF, Milash B and Wolff RK: MicroRNAs and colon and rectal cancer: differential expression by tumor location and subtype. Genes Chromosomes Cancer. 50:196–206. 2011. View Article : Google Scholar : PubMed/NCBI

9 

Toyota M, Ahuja N, Ohe-Toyota M, Herman JG, Baylin SB and Issa JP: CpG island methylator phenotype in colorectal cancer. Proc Natl Acad Sci USA. 96:8681–8686. 1999. View Article : Google Scholar : PubMed/NCBI

10 

Weisenberger DJ, Siegmund KD, Campan M, Young J, Long TI, Faasse MA, Kang GH, Widschwendter M, Weener D, Buchanan D, et al: CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat Genet. 38:787–793. 2006. View Article : Google Scholar : PubMed/NCBI

11 

Hinoue T, Weisenberger DJ, Lange CP, Shen H, Byun HM, Van De Berg D, Malik S, Pan F, Noushmehr H, van Dijk CM, et al: Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res. 22:271–282. 2012. View Article : Google Scholar :

12 

Shen L, Toyota M, Kondo Y, Lin E, Zhang L, Guo Y, Hernandez NS, Chen X, Ahmed S, Konishi K, et al: Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci USA. 104:18654–18659. 2007. View Article : Google Scholar : PubMed/NCBI

13 

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D and Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics. 17:520–525. 2001. View Article : Google Scholar : PubMed/NCBI

14 

Monti S, Tamayo P, Mesirov J and Golub T: Consensus Clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 52:91–118. 2003. View Article : Google Scholar

15 

Rousseeuw P: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 20:53–65. 1987. View Article : Google Scholar

16 

Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, Miller CR, Ding L, Golub T, Mesirov JP, et al: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 17:98–110. 2010. View Article : Google Scholar : PubMed/NCBI

17 

Lovmar L, Ahlford A, Jonsson M and Syvänen AC: Silhouette scores for assessment of SNP genotype clusters. BMC Genomics. 6:352005. View Article : Google Scholar : PubMed/NCBI

18 

Tusher VG, Tibshirani R and Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 98:5116–5121. 2001. View Article : Google Scholar : PubMed/NCBI

19 

Tibshirani R, Hastie T, Narasimhan B and Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA. 99:6567–6572. 2002. View Article : Google Scholar : PubMed/NCBI

20 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4:32003. View Article : Google Scholar

21 

Huang da W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar :

22 

Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al: The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38(Web Server issue): W214–W220. 2010. View Article : Google Scholar : PubMed/NCBI

23 

Siegmund KD: Statistical approaches for the analysis of DNA methylation microarray data. Hum Genet. 129:585–595. 2011. View Article : Google Scholar : PubMed/NCBI

24 

Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L and Lin SM: Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 11:5872010. View Article : Google Scholar : PubMed/NCBI

25 

Houseman EA, Christensen BC, Yeh RF, Marsit CJ, Karagas MR, Wrensch M, Nelson HH, Wiemels J, Zheng S, Wiencke JK, et al: Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics. 9:3652008. View Article : Google Scholar : PubMed/NCBI

26 

Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5:R802004. View Article : Google Scholar : PubMed/NCBI

27 

R Development Core Team. 2011, R: A Language and Environment for Statistical Computing. Vienna, Austria: the R Foundation for Statistical Computing; ISBN: 3-900051-07-0Available online at http://www.R-project.org/urisimplehttp://www.R-project.org/.

28 

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI

29 

Hajjari M and Salavaty A: HOTAIR: an oncogenic long non-coding RNA in different cancers. Cancer Biol Med. 12:1–9. 2015.PubMed/NCBI

30 

Wu ZH, Wang XL, Tang HM, Jiang T, Chen J, Lu S, Qiu GQ, Peng ZH and Yan DW: Long non-coding RNA HOTAIR is a powerful predictor of metastasis and poor prognosis and is associated with epithelial-mesenchymal transition in colon cancer. Oncol Rep. 32:395–402. 2014.PubMed/NCBI

31 

Kogo R, Shimamura T, Mimori K, Kawahara K, Imoto S, Sudo T, Tanaka F, Shibata K, Suzuki A, Komune S, et al: Long noncoding RNA HOTAIR regulates polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers. Cancer Res. 71:6320–6326. 2011. View Article : Google Scholar : PubMed/NCBI

32 

Kim MS, Lee J and Sidransky D: DNA methylation markers in colorectal cancer. Cancer Metastasis Rev. 29:181–206. 2010. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

February 2016
Volume 48 Issue 2

Print ISSN: 1019-6439
Online ISSN:1791-2423

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Ren, Z., Wang, W., & Li, J. (2016). Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data. International Journal of Oncology, 48, 690-702. https://doi.org/10.3892/ijo.2015.3263
MLA
Ren, Z., Wang, W., Li, J."Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data". International Journal of Oncology 48.2 (2016): 690-702.
Chicago
Ren, Z., Wang, W., Li, J."Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data". International Journal of Oncology 48, no. 2 (2016): 690-702. https://doi.org/10.3892/ijo.2015.3263