1. Volume 26, Issue 12, Pages 3461-3474.e5 (March 2019)
Dependency of the Cancer-Specific Transcriptional Regulation Circuitry on the Promoter DNA Methylome 
Yu Liu, Yang Liu, Rongyao Huang, Wanlu Song, Jiawei Wang, Zhengtao Xiao, Shengcheng Dong, Yang Yang, Xuerui Yang 
Cell Reports 
Volume 26, Issue 12, Pages e5 (March 2019)
DOI: /j.celrep
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2. Cell Reports 2019 26, 3461-3474.e5DOI: (10.1016/j.celrep.2019.02.084)
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3. Figure 1
Genome-wide Identifications of the TF-Target Gene Regulatory Circuits Modulated by Promoter CpG Methylation Levels
(A) Schematic description of the methodology for search of the TF-target gene circuits that depend on the level of methylation at specific promoter CpG sites. Taking a TF i (TFi), a potential target gene j (Genej) and its DNA copy number (CNVj), as well as a CpG site m within the promoter region of the target gene j (CpGjm) as examples, the diagram shows how the multi-omics data were used to test whether the TF-target gene association depends on promoter CpG methylation. Simulated data entries of true negatives were added into the original datasets and used to estimate false discovery rates at each step of the pipeline. Details of the pipeline are provided in the Method Details section.
(B and C) Examples showing positive (B) and negative (C) correlation patterns between the methylation levels at promoter CpG sites and the gene expression profiles in tumors of BLCA.
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4. Figure 2
Target Genes of 3 TFs, JUNB, STAT1, and JUND, which Are Present in the MeTRNs
(A–C) Target genes of 3 TFs, JUNB (A), STAT1 (B), and JUND (C), which are present in the MeTRNs of at least 3 cancer types. The biological functions and processes enriched in the target gene lists were provided, and the genes annotated to an enrichment term were shown by a line connecting the term and the gene. Target genes supported by TF-specific ChIP-seq data were highlighted with blue outline.
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5. Figure 3 Prediction Powers of the MeTRNs for Gene Expression Profiles
(A) Linear regression models with different predictor variables (Target∼TF+CpG sites, Target∼CNV, and Target∼TF+CpG sites + CNV) were used to fit the expression profile of each target gene in the MeTRN for each cancer type. The coefficient of determination (R2) for each gene was calculated from these models with data from a particular cancer type. Boxplots were prepared to show the distributions of the R2 values of all the genes with these 3 different models for each cancer type. Whiskers extend to 1.5 times of interquartile range.
(B) A scatterplot example (BLCA) showing the R2 values of each gene from the two regression models (Target∼TF+CpG sites and Target∼CNV). The dots are colored by the R2 values of each gene from the combinatory linear regression model of Target∼TF+CpG+CNV. Several diagonals were marked by gray lines, on which the x axis value plus the y axis value of the dots are the same. The same scatterplots for the other cancer types are provided in Figure S3.
(C and D) The biological and physiological processes enriched in the top genes that are highly dependent on the TF-CpG circuits in the MeTRNs (R2 of Target∼TF+CpG > 0.4; C) and the processes enriched in the top genes that are highly dependent on the CNV (R2 of Target∼CNV > 0.4; D). Saturation of the color indicates the statistical significance (–log10(Pv)) of each term.
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6. Figure 4
Prediction Powers of the MeTRNs and CNV for Expression Profiles of the Cancer-Related Genes
(A) Linear regression models with different predictor variables were used to fit the expression profile of each cancer-related gene in the MeTRN in the tumors for each cancer type. The coefficient of determination (R2) for each gene was calculated from these models (Target∼TF+CpG sites, Target∼CNV, and Target∼TF+CpG sites + CNV) with data from a particular cancer type. Finally, boxplots were prepared to show the distributions of the R2 values of all the genes with these 3 different models for each cancer type. Whiskers extend to 1.5 times of interquartile range.
(B) Top 10 genes were shown as examples of the cancer genes that are highly dependent on the MeTRN regulators in multiple types of cancer, as shown by their R2 values from the linear regression model of Target∼TF+CpG sites. The specific R2 values from the model of Target∼TF+CpG, Target∼CpG sites, or Target∼TFs in particular cancer types were marked with vertical bars, of which the color indicates the type of cancer. Numbers of cancer types in which a gene was found as a target in the MeTRN networks are shown in parentheses following the gene names.
(C) 5 TFs predicted to target FLI1 in at least 3 types of cancers. 42 out of 46 promoter CpG sites were involved in these transcriptional circuits.
(D) 18 TFs predicted to target FLI1 in STAD. 12 of these 18 TFs were predicted to target FLI1 only in STAD. 36 of the FLI1 promoter CpG sites were involved.
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7. Figure 5
Similarities between Cancers Based on Prediction Powers of the MeTRNs and CNV for the Cancer Gene Expressions
(A) Cancer similarity matrices indicating the Pearson correlation between the R2 values of the cancer genes in each pair of the cancers. The R2 values were calculated from linear regression models of Target∼TF+CpG (upper triangle) or from the model of Target∼CNV (lower triangle).
(B) Scatterplots showing the dependencies of the cancer genes on the MeTRN regulators (left) or on the CNV (right), in two cancers as examples, STAD and LUAD.
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8. Figure 6
TFs and CpG Sites in MeTRNs Serve as Classifiers of Prognostically Different Patient Subgroups
(A) Take KIRP as an example. Unsupervised hierarchical clustering analysis was performed with the CpG site methylation and TF expression profiles of the DNA methylation-dependent transcriptional regulation circuits in the MeTRN that exhibited strong prediction power for the target gene expression profiles, as shown from the linear combinatory model Target∼TF+CpG. The patient subgroups (k1–k4) from the clustering analysis are marked by different colors. Similar results for the other 20 cancer types are provided in Figure S4.
(B–D) Kaplan-Meier survival curves showing comparisons of the overall survival between different subgroups of KIRP patients identified based on their MeTRN regulators (B), the top variable CpG sites (C), or the TFs (D) predicted by ARACNe. The p value for the statistical significance of the largest prognosis difference among the cancer subtypes was inferred with a log-rank test.
The survival curves of the other 20 cancer types are shown in Figures S5–S7.
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9. Figure 7
Comparison between Two Prognostically Different KIRP Subtypes Classified by MeTRN Regulators
(A and B) Differential methylation (A) and differential expression (B) analyses of the MeTRN regulators (CpG sites and TFs, respectively) that were used for the clustering analysis in Figure  CpG sites and 17 TFs were deemed strongly up- and downregulated, respectively, in k3 versus k1.
(C) 598 target genes of the CpG sites and TFs identified in (A) and (B) were found in the KIRP MeTRN. GSEA analysis was performed to show enrichment of the 598 target genes in the downregulated genes by comparing k3 versus k1.
(D) Gene functional enrichment analysis of the 598 target genes. The enrichment p value cut-off was set at 0.01.
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