Table 1.
AJCC cancer staging.
Fig 1.
Linear modelling with M-value matrix, all probes.
(A) Venn distribution of significant DM genes in each stage relative to control. (B) Distribution of samples based on the top two principal components of the top 100 genes shows a clear separation of cancer cases (labelled by stage) from controls. (C) Stagewise methylation portraits of the top four significant stage-specific DMGs. The contrast with the control is especially evident. Also shown are the stagewise methylation levels of (D) TMEM179, and (E) MEOX2.
Fig 2.
Top DMGs identified from linear modelling.
(A) GPR75-ASB3, (B) TM4SF19, (C) CNRIP1, (D) KRTAP11-1, (E) ADHFE1 and (F) PDE4A. For each gene, notice that the trend in methylation could be either hyper-or hypo-methylation relative to the control. TM4SF19 and KRTAP11-1 are hypomethylated whereas CNRIP1, GPR75-ASB3, ADHFE1, PDE4A are hypermethylated.
Table 2.
Top ten genes of the linear model at the probe level.
Fig 3.
Linear modelling with M-value matrix, avereps transformation.
(A) Venn distribution of significant DM genes in each stage relative to control. (B) Distribution of samples based on the top two principal components of the top 100 genes shows a clear separation of cancer cases (labelled by stage) and controls. (C) Stagewise methylation portraits of the top four significant stage-specific DMGs. The stark contrast with the control is especially evident. Also shown are the stagewise methylation levels of (D) NALCN, and (E) GLRX.
Table 3.
Top ten genes of linear modelling with averaging of multiple probes.
Fig 4.
Distribution of probes based on (A) genomic position: opensea, shore, island, shelf; (B) gene context: transcription start site (TSS), exons, untranscribed regions (UTRs), and intergenic regions (IGR).
Fig 5.
DMP and DMR plots using ChAMP.
(A) DMP plot of FCN2 for stage-I vs control illustrating significant hypomethylation (B) DMR plot of transcriptional activator EYA4 for stage-I vs control illustrating significant hypermethylation. Solid lines represent mean values while dashed lines represent the loess.
Table 4.
Contrast-wise counts of DM probes and DM regions.
Fig 6.
Mixture models and Correlation plots for (A) FAM123A, (B) LAMA1 and (C) NELL1. The x-axis indicates the level of methylation (in terms of β values); y-axis, the frequency. Mixture component curves represent density fits of the histogram. A negative correlation between methylation and expression is evident, indicating that methylation acts to repress gene transcription, though the strength of the inverse correlation varies from gene to gene. olour indicates the mixture model fit.
Fig 7.
UpSet plot of BioMethyl-based stagewise gene expression modelling.
The intersection of all stages yielded 3268 genes, which represent consistently differentially regulated genes.
Fig 8.
Violin plots of stage-salient genes.
(A) Stage-I Gene FBN1, (B) Stage-II Gene–FOXG1, (C) Stage-III Gene–HCN1 and Stage-IV genes (D) LAMA1, (E) NELL1, (F) FAM123A, (G) ZNF135.
Table 5.
Stage-salient biomarkers.
Table 6.
GO analysis of stage-salient genes in the order of decreasing significance (i.e, increasing p–value).
Fig 9.
K-M plots for the prognostically significant stage-salient genes.
(A) FBN1, (B) FOXG1, (C) HCN1, and (D) LAMA1.
Fig 10.
Survival analysis of combination biomarker panels shows significance.
(A) Early-stage panel, CRS12; and (B) Late-stage panel, CRS34.
Table 7.
Summary of selected multivariate prognostic models.
Fig 11.
Mixture models and correlation plots of stage-salient genes.
Shown are FBN1, FOXG1, HCN1, and ZNF135. Two mixture components are seen for FBN1, HCN1, and ZNF135, and three for FOXG1. A strong inverse correlation exists for all genes, except HCN1. Other stage-salient genes are shown in Fig 6.
Table 8.
Location of the major DM CpG site in stage-salient genes.