Novel application of structural equation modeling to correlation structure analysis of CpG island methylation in colorectal cancer

Abstract

The CpG island methylator phenotype (CIMP-high, CIMP1) is a distinct phenotype associated with microsatellite instability (MSI) and BRAF mutation in colon cancer. Recent evidence suggests the presence of KRAS mutation-associated CIMP subtype (CIMP-low, CIMP2). We used cluster analysis, principal component analysis (PCA), and structural equation modeling (SEM), a novel strategy, to decipher the correlation structure of CpG island hypermethylation. Using a database of 861 colon and rectal cancers, DNA methylation at 16 CpG islands [CACNA1G, CDKN2A (p16/ink4a), CHFR, CRABP1, HIC1, IGF2, IGFBP3, MGMT, MINT-1, MINT-31, MLH1, NEUROG1, p14 (CDKN2A/arf), RUNX3, SOCS1, and WRN] was quantified by real-time PCR. Tumors were categorized into three groups: Group 1 with wild-type KRAS/BRAF (N = 440); Group 2 with mutant KRAS and wild-type BRAF (N = 308); and Group 3 with wild-type KRAS and mutant BRAF (N = 107). Tumors with mutant KRAS/BRAF (N = 6) were excluded. In unsupervised hierarchical clustering analysis, all but six markers (CACNA1G, IGF2, RUNX3, MGMT, MINT-1, and SOCS1) were differentially clustered with CIMP-high and CIMP-low according to KRAS and BRAF status. In SEM, the correlation structures between CIMP, locus-specific CpG island methylation, and MSI differed according to KRAS and BRAF status, which was consistent with PCA results. In conclusion, KRAS and BRAF mutations appear to differentially influence correlation structure of CpG island methylation. Our novel data suggest two distinct perturbations, resulting in differential locus-specific propensity of CpG methylation.

Figure 1

Unsupervised hierarchical clustering analysis in each tumor group. The correlation structure of the 15 CpG island methylation markers varies between the three tumor groups (A–C). A CIMP-high or CIMP-low cluster is indicated by an orange or blue circle, respectively. Group 1 (A) shows two latent clusters, CIMP-high and CIMP-low. Group 2 (B) shows two latent clusters, CIMP-high and CIMP-low. Note a difference in clustering compared to A. In Group 3 (C), CIMP-high and CIMP-low essentially colocalize. Red values are AU (approximately unbiased) P values estimated with the bootstrap samples from the samples with which the original cluster was generated, and purple and green values are AU P values estimated with the bootstrap samples from the other two groups. Blue values are bootstrap probability values with the 1000 bootstrap samples. P value of a cluster is a value between 0 and 1, which indicates how strong the cluster is supported by data.

Figure 2

Path diagrams among CIMP status, MSI, MLH1, and the other 15 CpG island markers in structural equation modeling (SEM) analysis. Path coefficients with t and P values are shown in parentheses. A, B, and C demonstrate differences in correlation structures of CIMP status, locus-specific CpG island methylation, and microsatellite instability (MSI) across the tumor groups. In Group 1 (A), the direct effect of CIMP-high on MLH1 (0.74) is much larger than that of CIMP-high on MSI (0.41). In Group 2 (B), CIMP-high is not significantly associated with MLH1, but directly associated with MSI (0.29; P = 0.017). In Group 3 (C), the direct effect of CIMP-high on MLH1 is substantial (0.67), whereas that of CIMP-high on MSI is not significant. MSI, microsatellite instability.