Epigenetic landscapes can shape physiologic and disease phenotypes. We used integrative, high resolution multi-omics methods to delineate the methylome landscape and characterize the oncogenic drivers of esophageal squamous cell carcinoma (ESCC). We found 98% of CpGs are hypomethylated across the ESCC genome. Hypo-methylated regions are enriched in areas with heterochromatin binding markers (H3K9me3, H3K27me3), while hyper-methylated regions are enriched in polycomb repressive complex (EZH2/SUZ12) recognizing regions. Altered methylation in promoters, enhancers, and gene bodies, as well as in polycomb repressive complex occupancy and CTCF binding sites are associated with cancer-specific gene dysregulation. Epigenetic-mediated activation of non-canonical WNT/β-catenin/MMP signaling and a YY1/lncRNA ESCCAL-1/ribosomal protein network are uncovered and validated as potential novel ESCC driver alterations. This study advances our understanding of how epigenetic landscapes shape cancer pathogenesis and provides a resource for biomarker and target discovery.
Epigenetic regulation is an important determinant of many biological phenotypes in both physiologic and pathophysiological contexts1. Nevertheless, epigenetic forces that shape the evolution of complex diseases, such as cancer, remain incompletely defined. Esophageal cancer is the sixth leading cause of cancer-related death and the eighth most common cancer worldwide2. In China and East Asia, esophageal squamous cell carcinoma (ESCC) is the most prevalent pathohistological type of esophageal cancer3. Comprehensive analysis by whole-genome and whole-exome sequencing uncovered the genetic landscape of ESCC4,5,6,7,8,9 and multi-region whole-exome sequencing revealed intra-tumor genetic heterogeneity in ESCC10. This intra-tumor genomic heterogeneity could serve as a prognostic predictor in esophageal cancer11 and as a foundation for improved treatments. Notable and frequently mutated epigenetic modulator genes in ESCC include KMT2D, KMT2C, KDM6A, EP300, and CREBBP, and epigenetic perturbations might interact with other somatic genomic alterations to promote the progression of ESCC. The interplay between epigenetic perturbations and other somatic genetic alterations may play a critical role during ESCC tumorigenesis4.
The Cancer Genome Atlas research group (TCGA) identified ESCC-related biomarkers at different multi-omics levels (genomic, epigenomic, transcriptomic, and proteomic) and highlighted 82 altered DNA methylation events, along with transcriptional and genomic alterations9. While genomic and transcriptomic-level studies of ESCC produced valuable biological discoveries and resources, the single-nucleotide resolution of the epigenetic landscape of ESCC, and of most other cancers, at the whole-genome level remains poorly studied. This knowledge gap is due to the comparatively high cost, computational complexity, and technical challenges of capturing genome-wide single-nucleotide resolution of the epigenetic landscape. Integrative and causal analyses using orthogonal multi-omics datasets are incomplete without a high-resolution methylome profile. We addressed this challenge by using an integrated multi-omics study that includes whole-genome bisulfite sequencing (WGBS), whole-genome sequencing (WGS), whole-transcriptome sequencing (RNA-seq), and proteomic experiments on a cohort of ESCC samples and their adjacent non-tumor esophageal tissues, along with orthogonal analysis and validation using the large TCGA-esophageal cancer (ESCA) dataset. Our goal was to understand the extent and complexity of DNA methylation alterations and their consequent dysregulation of both protein-coding and non-coding gene expression.
WGBS reveals the epigenetic landscape in ESCC
Different types of cancers exhibit unique epigenetic alterations, particularly in the DNA methylome12,13,14. We initially collected ten pairs of primary ESCC samples and their adjacent non-tumor tissues (Supplementary Fig. 1), performed WGBS with over 99% of a bisulfite conversion ratio, and generated a mean 15× sequencing depth per sample (Supplementary Data File 1, Supplementary Fig. 2). Over 99% of CpG dinucleotides were covered and ~95% of CpGs were reliably mapped by more than five reads. We orthogonally validated approximately 300K CpG methylation changes detected in our sample cohort with their methylation level changes detected in TCGA-ESCA sample cohort using Human Methylation 450K (HM450K) array (Supplementary Fig. 3a) and showed overall high correlation of CpG methylation changes in all normal and tumor samples between the two cohorts (Pearson’s r = 0.9644, p value < 0.01, Supplementary Fig. 3b), and within subtype of tumors (Pearson’s r = 0.7570 for our ESCC versus TCGA-ESCC, Pearson’s r = 0.5554 for our ESCC versus TCGA-EAC (esophageal adenocarcinoma), Supplementary Fig. 3c, d). DNA methylation at non-CpG contexts was present in less than 0.5% in our samples.
More than five million significantly differentially methylated cytosines (DMCs) were identified (one-way ANOVA test, multiple hypothesis tests were adjusted by the Benjamini–Hochberg method, q value < 0.05) (Fig. 1a). Among them, 57.5% of DMCs were located at known annotated regions (e.g., introns, exons, promoter and enhancer regions, and CpG islands) and 42.5% were located at unannotated regions of the genome (Supplementary Fig. 4a). Methylation loss in cytosines in ESCC accounted for 97.3% of the DMCs and was mostly confined to intergenic regions of the genome. Only 2.7% of the DMCs were gains of methylation in ESCC (proportional test for hyper- and hypomethylation, p value < 2.2e−16, Fig. 1b) and 83.67% of them mapped to gene bodies, promoters and enhancers, and CpG islands with RefSeq annotation (Supplementary Fig. 4b, c). Of the hypomethylated DMCs in ESCC, 63.08% were mapped to lncRNA regions with ENCODE annotation (v27lift37), which is significantly higher than that in random regions (permutation test, p = 0.001, Z-score = 15.021, 1000 permutation), whereas 58.01% of hypermethylated DMCs in ESCC were dispersed in antisense RNA regions of the genome (Supplementary Fig. 5a, b). We found 36.24% and 69.79% of DMCs overlap with enhancers and CpG islands, respectively (permutation test, p value = 0.00099, number of iterations = 1000), which are dispersed in regulatory regions in the genome.
Epigenetic features shape cellular identity
We found that the genome-wide CpG methylation levels can discriminate normal tissues from tumor tissues in an unbiased manner, as measured by unsupervised principal component analysis (PCA) (Fig. 1c), similar to unsupervised transcriptome-mediated clustering of normal and tumor samples (PCA in Supplementary Fig. 6a and Dendrogram in Supplementary Fig. 6b). In the larger sample set (n = 202) of TCGA-ESCA, CpG probes present in the lower resolution Illumina HM450K array were also able to discriminate normal and tumor and even subtypes of esophageal cancer using t-distributed stochastic neighbor embedding (t-SNE), a nonlinear dimensionality reduction algorithms (Fig. 1d). Twenty-seven of DMC probes were identified in ESCC versus EAC using the criteria of absolute log2(FC) ≥ 0.59 and FDR < 0.05 (Supplementary Fig. 6c). The data suggest that alterations in DNA methylation can characterize the distinct biological features of cellular states of physiologic and pathophysiologic phenotypes. DNA methylation heterogeneity has been observed in other cancer types14,15 and stochastically increasing variation in DNA methylation appears to be a property of the cancer epigenome16. We found significantly higher variance of methylation changes in ESCC (275.76 ± 204.01) compared with normal samples (95.67 ± 112.38, two-sample t-test, p value ≈ 0) in our cohort. This was also observed in our analysis of TCGA-ESCC cohort (n = 98, two-sample t-test, p < 2.2e−16) (Supplementary Fig. 6d). As a further measurement of the level of epigenetic variance, we calculated Shannon’s entropy of methylation levels at each CpG locus. We observed increased entropy in ESCC compared with normal samples (two-sample t-test p value ≈ 0) (Fig. 1e). This is consistent with the increase in stochastic noise (heterogeneity) in tumors. Our simulation using the Euler–Murayama method17 also reflected increased DNA methylation heterogeneity in ESCC (Supplementary Fig. 6e).
The clinical significance of such high variance of DNA methylation changes in cancer remains unclear. Using the independent TCGA-ESCC clinical cohort, we stratified patient samples into low or high variance groups by their median variance of methylation level along with other clinical variables (age, gender, alcohol usage) for multivariate Cox regression analysis. Although heavy alcohol intake is a known risk factor in ESCC development18, we observed a trend toward to inferior overall survival time in patients with alcohol consumption but no impact on methylation variance: only three DMC probes associated with alcohol users (log2(FC) ≥ 0.2 and FDR < 0.05) (Supplementary Fig. 7a–c). The group with a lower variance (n = 49) of methylation levels showed a favorable overall survival time (hazard ratio = 0.36, 95% confidence interval (0.16–0.80), Wald test p value = 0.002) after normalized to age, gender, and alcohol consumption (Fig. 1f). We examined additional squamous types of cancer including TCGA-head and neck squamous carcinoma cohort (n = 516) and found that again the high variance of DNA methylation changes associates with poor survival outcome (p value <2e−16) (Supplementary Fig. 7d). The above findings provide potential clinical relevance of the epigenetic heterogeneity within ESCC.
Differentially methylated regions (DMRs) associate with chromatin modifications
We further defined 295,295 DMRs (p value ≤ 0.05, FDR ≤ 0.05) between tumor and normal tissues, resulting from a CpG density peak of 4% and a DMR peak size of 200–400 base pairs (bp) (Fig. 2a, Supplementary Fig. 8a, b). Only 1.8% of these DMRs are hypermethylated, while 98.2% of DMRs are hypomethylated (proportional test, p value <2.2e−16) in tumors relative to normal tissues. DMRs in regions of −2990 to +6990 bp to transcription start site (TSS) appear hypermethylated while gene bodies, intergenic, and non-coding regions are in general hypomethylated in tumors (Fig. 2b). The DMRs distribution in Chromosome (Chr)8, Chr19, and Chr20 is higher than in other Chrs after normalization to chromosome size (Supplementary Fig. 9a) and DMRs are enriched in gene promoters at Chr19 ((Supplementary Fig. 9b (normalized by gene size), 9c (normalized by gene counts in each Chrs)).
We integrated the most significant DMRs with all CpGs, CpG island, chromatin state, and potential transcription factor (TF) binding data using the ENCODE dataset19. We observed Chr8 harbors three large genomic regions with unique DMR patterns and these regions contain the SOX17, RGS22, and ESCCAL-1 (CASC9) gene loci, respectively. For example, around the gene of SOX17 (Chr8: 55,360,000–55,400,000), CpG island regions were hypermethylated but CpG shore regions were hypomethylated (Fig. 2c). Two CpG islands with a significant hypermethylation upstream of the SOX17 gene were observed but that was not associated with low gene expression (p value = 0.668, Fig. 2d). In the region 100,650,000–101,190,000 of Chr8, hypo-DMRs covered all of the gene body of RGS22 (regulator of G protein signaling), which is a putative tumor suppressor20. This was associated with decreased RGS22 expression (p value = 0.2) (Supplementary Fig. 10). The region around the lncRNA ESCCAL-1 (Chr8:76,130,000–76,240,000), which was previously identified by us21, contained significantly hypomethylated DMRs in its promoters and we further investigated the uncharacterized biological function of this lncRNA later in this study.
TFs play important biological roles in gene regulations and their binding affinities can be affected by DNA methylation changes22. The occupancy of each TF binding consensus varies in the genome (161 TF-binding sites from ENCODE), with POLR2A (DNA-directed RNA polymerase II subunit A) (5.23%) and CTCF (CCCTC-binding factor) (3.55%) ranking at the top (Supplementary Fig. 11a, Supplementary Data File 2). We searched CpG content in these TF-binding sequences and the top 20 TFs affected by methylation alterations in consensus-binding sites were identified. Notably, the Polycomb Repressor Complex 2 (PRC2) subunits SUZ12 and EZH2 binding sites were substantially affected by hypermethylation in the CpGs (Supplementary Fig. 11b–d). EZH2 and SUZ12 and other TFs tend to preferentially bind to promoter regions compared to enhancer regions (p value <0.001) (Supplementary Fig. 12a–c). These observations indicated the possibility of a paradoxical activation mechanism for PRC2 target genes through loss of PRC2 occupancy in gene promoters in tumor cells.
A link between hypomethylated blocks, variable gene expression, and large heterochromatin domains such as Large Organized Chromatin lysine (K) modification (LOCK) or lamina-associated domains was previously reported in cancer23. We performed the genomic region set enrichment analysis with LOLA24 to test these DMRs for enrichment against the LOLA Core database, which contains DNaseI hypersensitive elements and chromatin immunoprecipitation sequencing (ChIP-seq) peaks from a variety TFs and chromatin modifiers. ESCC-derived hypo-DMRs were enriched in genomic regions with heterochromatin markers such as H3K9me3 and H3K27me3, whereas hyper-DMRs were enriched in genomic regions with EZH2 or SUZ12 protein-binding sites (Fig. 2e, Supplementary Data File 3). The DMRs enriched in regulatory elements in cancer indicates cancer-specific dysregulation of gene expression contributing to cancer progression, which we explore further later in this study.
Aberrant DNA methylation in promoter regions mediates transcriptional dysregulation in ESCC
From our WGBS analysis, we identified 4391 promoter regions (−4500 to +500 bp to TSS) of coding and non-coding genes whose CpGs were significantly differentially methylated (FDR < 0.01). Functional annotation of these target genes harboring promoter hypomethylation indicated an over-representation of WNT/β-catenin signaling, whereas gene promoters harboring hypermethylation were enriched for KIT signaling genes (Supplementary Fig. 13a). The RNA-seq dataset displayed 4074 significantly differentially expressed genes (DEGs) in ESCC relative to the adjacent normal tissues. The functional annotation of these DEGs indicated enrichment for genes regulating cell cycle pathways and metallopeptidase activity (Supplementary Fig. 13b).
DNA methylation at regulatory regions can influence transcript expression levels25. We merged DMRs and DEGs and identified 694 genes that showed significant differential methylation alteration in promoters and concomitant dysregulation of gene expression (Fig. 3a). The genes were systematically classified into four distinct clusters (denoted as C1, C2, C3, and C4) according to methylation and gene expression pattern (Fig. 3b). Genes in C1 and C2 followed the well-documented canonical model, showing anti-correlation in promotor methylation and gene expression13; in contrast, genes in C3 and C4 showed a non-canonical pattern in that promotor methylation and gene expression was positively correlated (Fig. 3c, Supplementary Data File 4). Among the 694 genes, only 1.5% of them harbor non-synonymous mutations from our selected cases of WGS and no copy number changes of these genes were detected from WGS and inferred from RNA-seq analysis (Supplementary Fig. 15). As TF expression can lead to changes in gene expression, we examined the RNA expression of 161 TFs in our ESCC samples and found no significant changes in expression of these TFs in tumors versus normal samples (Supplementary Fig. 16). Therefore, the majority (98.5%) of these dysregulated genes in cluster C1–C4 appear to occur via epigenetic dysregulation (epimutation)26. This phenomenon was recapitulated in the independent TCGA-ESCC (n = 96) sample cohort with available multi-OMICs datasets (Supplementary Fig. 17a–d).
The underlying mechanisms of the divergent regulation of gene expression are complex and involve DNA methylation, chromatin remodeling, and DNA accessibility27. We explored a potential explanation for the non-canonical patterns in C3 and C4 across epigenetic regulatory features. First, we cross-referenced 13,000 TCGA-ESCA chromatin-accessible regions as determined by ATAC-seq27 to the regulatory regions of the 694 genes. Accessibility in promoter regions was higher in C2 and C3 than in C4 and C1, respectively (Supplementary Fig. 18a, b). Second, methylation in defined promoter regions and in gene bodies showed a differential phenotype between C1 and C3 (but not between C2 and C4): methylation levels in gene bodies were higher in C3 (−5.4587 ± 26.3450) than in C1 (−26.8551 ± 16.4716, p < 0.05). There is no significant difference in related enhancer regions of genes in all clusters (Fig. 3d). Third, hypermethylation at cohesion and CCCTC-binding factor-binding sites could compromise binding of this methylation-sensitive insulator protein and result in gene activation22. Thus, we searched for CTCF-binding sites within promoter regions of the 694 genes and observed that the CTCF-binding sites were enriched in C3 (Fig. 3e), which could partially explain the phenotype of high promoter methylation and high gene expression. The data also indicated that the promoters of genes in C4, despite being hypomethylated in the tumor, have scarce accessible regions (Supplementary Fig. 18b). This highlights the importance of both accessibility and absence of methylation as linked features of the gene expression pattern in the C4. Enhancer–promoter interaction is a fundamental mechanism of gene regulation28. We found the pattern of enhancer-gene expression was highly correlated to promoter-gene expression pattern, with the exception of non-canonical C3 and C4 groups, in which the methylation change in enhancers was opposite to that in promoters (Supplementary Fig. 14b–d).
Functional annotation of the 694 genes was performed using multiple databases (KEGG29, WikiPathways30, ENCODE19, ChEA31) and showed that PRC2 subunit (EZH2 and SUZ12)-mediated polycomb repressive gene sets were enriched in the non-canonical clusters C3 and C4 (Fig. 4a). We searched for ENCODE-defined EZH2 and SUZ12-binding sites across gene promoters in C1–C4 and observed that EZH2 occupancy was enriched in C3 (1.5970 ± 1.2316) and C4 (0.6000 ± 0.7684) compared with C1 (0.9167 ± 0.8464) and C2 (0.2336 ± 0.5870), respectively (p value < 0.001) (Fig. 4b). SUZ12 occupancy was higher only in C3 gene promoter regions (1.5522 ± 1.7946) (Fig. 4c). To understand the functional mechanism that is responsible for differential methylation at target-gene promoters, we performed unsupervised hierarchical clustering of ENCODE-defined known TF-binding sites in 694 gene promoters.
The analysis showed that EZH2 and SUZ12 binding sites clustered together and were enriched in genes in C3 compared to other TFs in other clusters (Fig. 4d, Supplementary Figs. 19 and 20). Increased WNT2 gene expression was significantly associated with increased methylation in promoter regions in C3 (Fig. 4e). The comprehensive analyses show the non-canonical gene expression pattern (C3) appears to arise via de-repression of the EZH2-mediated suppressor effects on promoter regions of genes in C3 to increase gene expression, which we experimentally validate later in this study.
DNA methylation gain at the promoter region activates the WNT2/ β-catenin pathway in ESCC
WNT2 belongs to the structurally related WNT family of genes that function as secretory ligands for the WNT signaling pathway32. Canonical WNT signaling pathway results in stabilization of the transcriptional co-regulator β-catenin and subsequent upregulation of downstream target genes32.
Epigenetic dysregulation of the components of MAPK, AKT, and WNT pathway can promote aberrant activation of these pro-growth pathways in ESCC33. In our RNAseq dataset, we identified only WNT2 in the WNT pathway as significantly highly expressed in the tumor samples compared to normal samples (Supplementary Fig. 21a, b). WNT2 protein was highly expressed in the cytoplasm of cancer cells relative to normal cells (Supplementary Fig. 22a, b). Although high expression of WNT2 is not a prognostic marker, it was associated with tumor progression (Supplementary Fig. 22c). These results indicate selective and specific upregulation of WNT2 in ESCC tumors through a putative non-canonical epigenetic regulatory mechanism.
To gain mechanistic insight into the epigenetic regulation of the WNT2 promoter, we queried our TF target-gene hierarchical clustering analysis for genes in C3 and found that the EZH2-binding site along with SUZ12-binding sites were present within the WNT2 promoter region compared to other regulatory factors (Fig. 4d, Supplementary Fig. 19b). EZH2 and SUZ12 are subunits of PRC2, which has histone methyltransferase activity to primarily tri-methylate histone H3 on lysine 27 (H3K27me3)34 resulting in gene silencing. We also found that the EZH2-binding site within the WNT2 promoter overlaps with the hyper-CpG methylation sites in the WNT2 promoter region in cancer cells (Fig. 5a).
The WNT2 promoter region (Chr7: 116,960,000–116,965,000) was hypermethylated, but paradoxically associated with increased gene expression in tumors (Supplementary Fig. 23 from our WGBS dataset, Supplementary Fig. 24a–c from TCGA-dataset). The WNT2 gene shows rare mutation (0.55%, 1/183 cases) and copy number alteration (16%, 30/183 shallow loss; 4.4%, 8/183 shallow gain) in TCGA-ESCA cohort (Supplementary Fig. 24d). Thus, we reasoned that de-repression of EZH2 occupancy may cause non-canonical methylation-mediated activation of WNT2 gene expression in ESCC. We validated EZH2 occupancy on the DMRs of the WNT2 promoter by performing chromatin immunoprecipitation sequencing (ChIP-seq) in normal immortalized esophageal epithelial cells (Het-1A) and the patient-derived ESCC cell line, EC109. The ChIP-seq analysis showed EZH2-binding peaks at the WNT2 promoter region in normal cells compared to minimal binding peaks in the ESCC cells (Fig. 5a). Furthermore, we confirmed the promoter region of WNT2 was hypermethylated in three esophageal cancer cell lines (EC9706, EC109, and EC1) while no methylation was detected in normal esophagus epithelial cells (Het-1A) (Fig. 5b). Accordingly, WNT2 protein expression was higher in three cancer cell lines (Fig. 5c). DNA methyltransferase inhibitor (5-Azacytidine) treatment partially decreased the methylation level in the promoter region of WNT2 (Fig. 5d) and increased EZH2 binding (Fig. 5e), leading to the attenuation of WNT2 expression in cancer cells (Fig. 5f). In tissue samples, WNT2 mRNA and protein expression was also increased (Fig. 5g, h).
To identify downstream effector genes of WNT/β-catenin signaling that might promote ESCC, functional annotation of differential expression from proteomic data and Gene Set Enrichment Analysis (GSEA) from RNAseq data revealed extracellular matrix organization (Supplementary Fig. 25) and extracellular metalloproteins (MMP3 and MMP9, known β-catenin targets)35 gene sets (Supplementary Fig. 26) were enriched in tumor samples. We validated that both MMP3 and MMP9 transcripts and proteins were highly expressed in ESCC relative to normal tissues (Fig. 5h, Supplementary Fig. 26a). As WNT2 RNA expression in ESCC cell lines is significantly higher than in normal cell lines (Supplementary Fig. 27a), we tested whether WNT2-mediated signaling was required for tumor cell growth, we suppressed WNT2 expression using two independent short interfering (si)RNAs in two ESCC cell lines (EC9706 and EC109) (Supplementary Fig. 27b, c). WNT2 knockdown significantly inhibited ESCC cell growth (p value < 0.01) (Supplementary Fig. 27d, e). WNT2 knockdown decreased expression of MMP3 and MMP9, known targets of WNT/β-catenin signaling (Fig. 5i). Since MMPs can promote tumor invasion and metastasis36, we tested whether WNT2 knockdown abrogates the migratory and invasive potential of ESCC tumor cells. In two ESCC cell lines (EC9706 in Fig. 5j, and EC109 in Supplementary Fig. 27f, g), silencing of WNT2 significantly reduced cellular invasion and migration (p value < 0.01). The protein but not RNA levels of β-catenin, encoded by CTNNB1 gene, was significantly elevated in ESCC tumors, which indicates that β-catenin protein has higher stability in these tumors (Supplementary Fig. 27h). Knockdown of WNT2 reduced the elevated protein level of β-catenin in patient-derived cell lines, as expected by previous findings37,38 (Supplementary Fig. 27i). Moreover, WNT2 knockdown in EC9706 tumors significantly suppressed tumor growth and tumor burden (Student’s t-test, p value < 0.05, Fig. 5k and Supplementary Fig. 27j). The combined in vitro and in vivo evidence showed that a WNT2/β-catenin/MMP signaling axis was required for tumor cell growth, migration, and invasion in ESCC. Together, our results demonstrate a non-canonical mechanism for increased WNT2 expression in the absence of EZH2-PRC2 occupancy of the WNT2 promoter with hypermethylated CpGs. This non-canonical epigenetic activation of WNT2-mediated signaling and exerted functional consequences in ESCC progression (Fig. 5l).
Epigenetic activation of lncRNA ESCCAL-1 is an oncogenic driver in ESCC
Increasing evidence indicates dysregulation of lncRNAs during cancer progression and metastasis; however, the mechanisms of dysregulation and of action of lncRNAs in cancer are relatively poorly understood39. Previously, we showed that the lncRNA ESCCAL-1 was overexpressed in ESCC21, and overexpression of ESCCAL-1 has been reported in other cancer types40,41,42,43. The mechanism underlying ESCCAL-1 upregulation in cancer is unknown.
We found ESCCAL-1 is one of the most notable candidates for increased gene expression in C2 in association with decreased methylation (Fig. 6a). One DMR in the promoter of ESCCAL-1 showed decreased CpG methylation in cancer, leading to increased transcription of lncRNA ESCCAL-1 (Fig. 6b, c). There was no mutation or copy number variation of ESCCAL-1 reported or observed in TCGA-ESCA genomic dataset or in our WGS data of ESCC patients (Fig. 6b, top panel, Supplementary Fig. 24d). Independent verification of the methylation status of the ESCCAL-1 promoter region showed 62.5% (20/32) hypomethylation in ESCC tumors versus 71.8% (23/32) hypermethylation in adjacent normal tissues (chi square test p value < 0.01) (Fig. 6d, e). In agreement, ESCCAL-1 expression was significantly higher in ESCC compared to adjacent normal tissues (p value = 0.00113, FDR < 0.05) (Fig. 6c, from RNAseq). We corroborated these observations by analysis of an independent cohort of 73 ESCC tissues relative to their normal counterparts (Fig. 6f). We also noted a hypermethylated ESCCAL-1 promoter region in normal esophageal cells (Het-1A), whereas methylation was not detected in three ESCC cell lines (EC1, EC109, and EC9706) (Fig. 7a). ESCCAL-1 expression was substantially overexpressed in ESCC cell lines compared with normal cells Het-1A (Fig. 7b). Furthermore, increased expression of ESCCAL-1 was a biomarker of worse overall survival time and progression-free survival in ESCC patients (Fig. 7c, d). Knockdown of ESCCAL-1 reduced growth of patient-derived ESCC cells in vitro (Supplementary Fig. 28) and in vivo (Fig. 7e, f), suggesting a cancer-promoting function.
To identify a possible mechanism of ESCCAL-1 upregulation, we examined sequence motifs of known TFs in the ESCCAL-1 hypomethylated promoter region in silico and found a predicted binding site for YY1. YY1 is an TF belonging to the GLI-Kruppel class of zinc-finger proteins and contributes to tumorigenesis44. Using ChIP-PCR, we validated YY1 binding at the hypomethylated promoter region of ESCCAL-1 (Fig. 7g). RNA-seq profiling in cells with YY1 knockdown also revealed ESCCAL-1 as a YY1-regulated target-gene (Supplementary Fig. 29). RT-PCR assays also showed decreased expression of ESCCAL-1 in cells with YY1 deficiency (Fig. 7h), indicating YY1 transcriptionally regulates ESCCAL-1 in ESCC.
Since the downstream mechanism of ESCCAL-1’s contribution to ESCC pathogenesis was unclear, we performed a “guilty-by-association” co-expression analysis using our RNA-seq dataset. The ESCCAL-1-related gene expression modules are enriched in cell cycle pathways, RNA binding and the Myc pathway (Supplementary Fig. 30a–c). In order to explore the causative roles of ESCCAL-1 in ESCC progression, we conducted RNA-seq profiling in EC9706 cells with ESCCAL-1 knockdown by shRNA (or with shControl). We hypothesized that depletion of ESCCAL-1 could reverse the phenotype of cells to the relative normal cell state. Thus, we also compared RNA-seq data from a normal cell line, Het-1A with that of the ESCCAL-1 knockdown tumor cells, EC9706. Using an iterative clustering approach, we identified 210 significant genes whose expression was differential between shControl EC9706 and shESCCAL-1 EC9706 cohort and was similar between shESCCAL-1 EC9706 and Het-1A (normal) cohort (gene list in Supplementary Data File 5)45. Functional annotation on these identified DEGs exhibit an enrichment of RNA binding, ribosomal proteins, and Myc target-gene sets (Fig. 7i, Supplementary Fig. 31). Myc knockdown depleted ribosomal translational processes in ESCC, phenocopying the effect of ESCCAL-1 knockdown (Fig. 7j). These results indicate that ESCCAL-1 participates in the biological process of ribosome regulation and Myc-mediated regulation of genes, which extends current knowledge on the potential role of translational machinery and Myc signaling in ESCC46. Moreover, simultaneously abolishing ESCCAL-1 and WNT2 expression increased apoptosis compared to individual knockdown in vitro (Supplementary Fig. 32). Thus, beyond WNT2-mediated WNT pathway activation, other aberrant signaling pathways activated by ESCCAL-1 upregulation also contribute to ESCC tumorigenesis. Therefore, epigenetic dysregulation promotes ESCC through divergent and multi-factorial mechanisms.
The development of ESCC is a complex, dynamic biological process that involves multiple steps of genetic and epigenetic alterations. Numerous genetic studies of ESCC revealed genomic alterations in genes within the cell cycle, p53, AKT/mTOR, and Hippo signaling pathways4,6,8,47,48. It remained unclear in ESCC, as in most other cancer types, whether and how the epigenetic landscape contributes to cancer pathogenesis. We performed WGBS, RNA-seq, and proteomic analyses on matched normal and tumor samples along with analysis of TCGA-ESCA datasets and validated our findings in independent samples. We observed 98% of global CpG hypomethylation and 2% of local CpG hypermethylation across the ESCC genome. The trend of global loss of DNA methylation is consistent with studies in colon and other types of cancers, but the degree of hypomethylation in ESCC appears to be significantly larger than reported in other cancers12,49. The characteristics of CpG methylation alterations can discriminate cellular states between tumor and normal conditions, and histological subtypes of esophageal cancer. DNA methylation is a defining feature of cellular identity and is essential for cell development50. Cancer-specific DMRs have been identified in colon cancer and such stochastic methylation variations distinguish cancer from normal and may serve as diagnostic or therapeutic biomarkers13. We found that the heterogeneity of DNA methylation alteration is greater in ESCC relative to normal esophageal tissues. Higher variance of DNA methylation alteration in squamous carcinomas (ESCC, HNSC) is strongly associated with poor clinical outcome. Our findings provide insight into the potential clinical relevance of epigenetic dysregulation and heterogeneity as a molecular biomarker of clinical outcome in cancer.
We validated prominent epigenetically altered coding and non-coding genes from the non-canonical cluster (C3) and canonical cluster (C2), respectively. The WNT pathway is epigenetically dysregulated in ESCC by the inactivation of negative regulators (SFRP1/2/4/5, SOX17, and WIF1)33. Our multi-omics data identified high WNT2 expression along with a highly methylated promoter region in ESCC. We verified this observation in independent samples and ESCC cell lines. We experimentally demonstrated decreased EZH2 binding to the hypermethylated promoter region of WNT2 in cancer cells relative to normal cells, in association with higher expression of WNT2 in cancer. DNA methyltransferase inhibitor treatment in cancer cells reversed this phenotype. Our findings indicate that hypermethylation-mediated de-repression of WNT2 activates the WNT pathway in ESCC. TCGA-ESCC cohort suggested higher expression of WNT2 is a biomarker of tumor progression. Knockdown of WNT2 expression suppressed cancer cell growth, reduced cellular invasion and migration, reduced β-catenin target-gene expression (MMP3/9) in vitro, and inhibited xenograft tumor growth in vivo. Our data provide new insight into the mechanism of epigenetic dysregulation that results in non-canonical gene-expression regulation in cancer and the underlying molecular events promoting WNT pathway activation in ESCC.
LncRNA dysregulation is an emerging but poorly understood feature of oncogenesis21. We reported ESCCAL-1 overexpression in ESCC21, which is also overexpressed in other cancer types40,41,42. Overexpression of ESCCAL-1/CASC9 promotes cancer cell growth51, invasion52, and metastasis53. We discovered that loss of methylation in its promoter and an increase of YY1 TF binding is a principle molecular mechanism of ESCCAL-1 dysregulation in ESCC, resulting in cell cycle and ribosomal pathway dysfunction. We found that ESCCAL-1 plays an epigenetic-mediated causal role in tumor growth and is a biomarker of worse clinical outcome in ESCC. ESCCAL-1 is also overexpressed in other cancer types and associated with drug resistance in lung cancer40. Whether ESCCAL-1 is similarly dysregulated by epigenetic mechanisms in other cancer types beyond ESCC remains to be investigated. Nevertheless, suppressing ESCCAL-1 expression, potentially using antisense RNA54 or CRISPR-based strategies55, may be a promising therapeutic approach in ESCC and other cancers.
Overall, our study provides a rationale and a roadmap for delineating the landscape and functional roles of epigenetic dysregulation in cancer at a genome-wide, high resolution. Further analysis will provide a better understanding of the impact of epigenetic dysregulation and heterogeneity on various cancer-associated phenotypes and treatment responses14. Multi-regional WGBS or single-cell DNA bisulfite sequencing could facilitate this opportunity in the future. Our study provides a resource that includes comprehensive profiling of the epigenetic landscape to enable the discovery of additional biomarkers and therapeutic targets in ESCC and potentially other cancers.
Primary ESCC specimens
Matched clinical samples of esophageal squamous carcinoma and adjacent normal esophageal tissue are obtained from ten patients (Linzhou Cancer Hospital) as fresh frozen specimens at Translational Medical Center, Zhengzhou Central Hospital, affiliated to Zhengzhou University, China (Supplementary Fig. 1). Written informed consent was obtained from patients before surgery. The collection of human samples and the protocols for the investigations were under the approval of the Institutional Ethics Committee of Zhengzhou, Henan Province. These specimens were used for sequencing and different assays; WGBS (n = 20), WGS (n = 6), RNA-seq (n = 20), and iTRAQ proteomic assay (n = 20). For validation, independent 93 matched ESCC and adjacent normal samples with clinical follow-up (n = 73) were prepared for gene expression analysis.
DNA was extracted using the QIAamp DNA Mini Kit (Qiagen), fragmented using Bioruptor® Pico. Libraries were constructed using VAHTSTM Universal DNA Library Prep Kit for Illumina V3 ND607-02 (Vazyme, Nanjing). Libraries were sequenced with an Illumina HiSeq 4000 to obtain 150 bp paired-end reads. Base calling was performed with the Illumina Real Time Analysis version 2.7.7 and the output was demultiplexed and converted to FastQ format with the Illumina Bcl2fastq v126.96.36.1996. The FastQC package (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) was used to check the quality of the sequencing reads. Sequencing adapters were trimmed from raw reads with Trimmomatic (version 0.36)56. We performed mapping, duplicates marking, and mutation calling with bcbio-nextgen (https://github.com/bcbio/bcbio-nextgen), a community developed platform for variant calling. Specifically, Reads were mapped to the human reference genome assembly GRCh37 using BWA-MEM in the BWA package (version 0.7.17) with the default parameters57. Duplicates were marked using the tool Sambamba version 0.6.6 (ref. 58). Somatic mutations include single-nucleotide variants (SNVs), small insertions and/or deletions (indels), and structural variants (SVs). The detection of somatic mutations was performed using tumor and matched normal whole-genome BAM files generated in the steps described above. We used a series of software packages, including VarDict59, MuTect2 (ref. 60), and Strelka2 (ref. 61), to detect somatic SNVs and indels, and packages including LUMPY62, Manta63, CNVkit64, and MetaSV65 to detect SVs.
Whole-genome bisulfite sequencing
Genomic DNA was extracted with QIAmp DNA Mini kits (Qiagen) from fresh frozen tissue samples. One microgram DNA was fragmented by sonication with a base pair peak of 300 bp for the resulting fragment, and adaptors were then ligated to both ends of the fragments. Bisulfite conversion was performed to whole genomes of ten pairs of ESCC and matched normal tissues, where converts cytosine residues of the dinucleotide CpG to uracil but leaves methylated cytosine unaffected66. PCR amplification and purification were carried out. The uracil-binding pocket of KAPA HiFi DNA polymerase has been inactivated, enabling amplification of uracil-containing DNA. The high quality of the library was estimated by The Qubit® 3.0 Fluorometer. Bisulfite conversion success ratio is 99.18% and 99.49%, respectively, in normal and ESCC samples (Supplementary Data File 1). The WGBS library was sequenced on an Illumina HiSeq2500 sequencers and generated 400M of paired-end reads (2 × 125 bp).
Whole-transcriptome sequencing (RNA-seq)
The RNA was extracted with TRAzol from ten pairs of fresh frozen tissue samples. The input material for total RNA-seq library preparation was 2 μg per sample. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (#E7530L, NEB, USA) following the manufacturer’s recommendations. Index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext RNA First Strand Synthesis Reaction Buffer (5×). First-strand cDNA was synthesized using random hexamer primer and RNase H. Second-strand cDNA synthesis was subsequently performed using buffer, dNTPs, DNA polymerase I, and RNase H. The library fragments were purified with QiaQuick PCR kits and elution with EB buffer. Terminal repair, A-tailing, and adapter ligation were implemented. The aimed products were retrieved by agarose gel electrophoresis followed by PCR, and then the library was completed. RNA concentration of library was measured using Qubit® RNA Assay Kit in Qubit® 3.0 to preliminary quantify and then diluted to 1 ng/µl. Insert size was assessed using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA), and qualified insert size was accurately quantified using StepOnePlus™ Real-Time PCR System (Library valid concentration >10 nM). The clustering of the index-coded samples was performed on a cBot cluster generation system using HiSeq PE Cluster Kit v4-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the libraries were sequenced on an Illumina Hiseq 4000 platform to 2 × 150 bp paired-end reads.
Proteomic assay and data analysis
Reduced and tryptic digested peptides from samples were labeled with eight isobaric ItraQ reagent for an individual run and mixed at an equimolar ratio. Resuspended labeled peptides were pH optimized and separated through strong cation ion chromatography. Samples prepared as such were run through reverse phase liquid chromatography–mass spectrometry (LC-MS). The isobaric labeling and LC-MS quantifications were operated in Beijing Genomics Institute (BGI) using optimized quantitative MS-MS protocols67. For Protein identification and data analysis, IQuant was used68. For improved protein identification, a Mascot Percolator and Mascot Parser, a customized post-processing tool was used. The signal-to-noise ratio was decreased by variance stabilization normalization. Due to the low abundance or low ionization of peptides, missing the reporter ions is a common phenomenon in isobaric data, and may hinder downstream analysis. A missing reporter was imputed as the lowest observed values to avoid estimation bias. Nonunique peptides and outlier peptide ratios are removed before quantitative calculation73. The weight approach proposed is employed to evaluate the ratios of protein quantity based on reporter ion intensities69. The ratio between normal and tumor samples were generated for each match control pairs. This way three distinct datasets were generated for ten pairs of tumor and normal samples. Sample number 7 (for both tumor and normal, T7 versus N7) was run in each time to standardize among three datasets. For dataset integration, each dataset was normalized by the T7/N7 ratio for the abundance of the protein in all datasets. Dataset normalized as such was represented as a matrix so that protein abundance (row-wise) can be compared across ten different samples as tumor versus normal quantitative ratios (column-wise) generated from three separate runs. The raw data in Supplementary Dataset file 6.
Chromatin immunoprecipitation with DNA sequencing (ChIP-seq)
ChIP assays were performed according to the protocol supplied with the kit (catalog no. 9003) from Cell Signaling Technology. Briefly, EC109 and Het-1A cells were cross-linked with 37% formaldehyde at a final concentration of 1% at room temperature for 10 min. Fragmented chromatin was treated with nuclease and subjected to sonication. Chromatin immunoprecipitation was performed with anti-KMT6/EZH2 antibody (ab195409, Abcam), Anti-Histone H3 (acetyl K27) antibody ChIP Grade (ab4729, Abcam), Anti-YY1 antibody (ab38422, Abcam), rabbit anti-histone H3 (a technical positive control; 1:50) (catalog no. 4620; Cell Signaling Technologies), and normal rabbit IgG (a negative control; 5 μg) (catalog no. 2729, Cell Signaling Technologies). After reverse cross-linking and DNA purification, ChIP-Seq libraries were prepared and sequenced on a HiSeq 4000 sequencer (Illumina, San Diego, CA). To ensure the accuracy of subsequent bioinformatics analysis, the original sequencing data were filtered to obtain high-quality sequencing data (clean data). Quality control of the sequencing data was performed using Sickle (https://github.com/najoshi/sickle) and SeqPrep (https://github.com/jstjohn/SeqPrep). The sequencing output raw reads were trimmed by stripping the adaptor sequences and ambiguous nucleotides and reads with quality scores less than 20 and lengths below 20 bp were removed. The cleaned reads were aligned to human reference genome hg19 using BWA. MACS2 (model-based analysis of ChIP-seq) algorithm70 was used for peak calling. The reads of EZH2 binding on WNT2 promoter region were visualized using Integrative Genomics Viewer (IGV, Broad Institute).
WGBS data preprocessing
Bisulfite-treated DNAs are further sequenced using Illumina HiSeq 2500 system. Approximately 400 M of paired-end reads (125 bp x 2) and 100 Gbp per sample are generated except one sample (N15). The reads are sent out to the pipeline. Our pipeline consists of four steps (Supplementary Fig. 2a). Firstly, we trimmed using Trim Galore! (v0.4.1) to remove Illumina adaptors with the options of “--paired –length 50 --clip_R1 6 --clip_R2 6”. After trimming, around 90 Gbp of data remained per sample. Then trimmed reads were aligned to the HG19 reference genome using BSMAP (v2.89) with the option of “-p 8 -R”. Then SAMtools (v. 1.3.1) was used to sort by genomic coordination and make a bam file index. Picard Tools (v.1.92) is used to remove PCR duplicates. After deduplication, ~70 Gbp remained per sample. Lastly, we ran MOABS (v. 1.3.4) to compute the methylation ratio per CpG with the option of “--cytosineMinScore 20 --skipRandomChrom 1 -p 4 --keepTemp 0 --processPEOverlapSeq 1 --requiredFlag 2 --excludedFlag 256 --minFragSize 110 --reportCpX G --qualityScoreBase 0 --trimRRBSEndRepairSeq 0 --trimWGBSEndRepairPE1Seq 5 --trimWGBSEndRepairPE2Seq 5”. Around 95% of CpGs are covered by at least five reads.
WGBS data matrix
Given the methylation ratio and coordination computed, we built a data matrix, whose columns are samples and rows are CpGs. The normal tissue WGBS of sample 15 (N15) has generated a small amount of volume (~4.5 Gbp), which is just 6% compared to average (72 Gbp) and covers the half of CpGs with notably low coverage (1.58×) than other samples (~15×). Thus, we decided to eliminate N15 for the further downstream analysis. As a result, we have ten ESCC samples and nine normal esophageal tissue samples. For more robust analysis, we applied the minimum threshold 5× and also selected CpGs that all samples have its methylation ratio. This screening process gave 13 M of CpGs with confident methylation ratio (Supplementary Data File 1).
Cross-validation between WGBS and TCGA ESCA
We aligned our data with TCGA ESCA data (n = 202). The methylation data of TCGA ESCA exploited Illumina Infinium Human Methylation 450K Beadchip to measure methylation level for 202 samples: 186 esophageal cancer tissue samples and 16 esophageal normal tissue samples. To compare TCGA ESCA HM 450K methylation ratio and WGBS methylation ratio of our data, we computed the mean methylation ratio of the tumor and normal samples per CpG for both TCGA and our WGBS data. Around 300K of CpGs are in the intersection of TCGA ESCA HM 450K and WGBS. We computed the Pearson correlation coefficient (PCC) to measure the representative power in our dataset albeit a rather small sample size. First, we calculated the PCC of the tumor and normal from TCGA ESCA HM 450K and WGBS. The highest PCC (=0.9674) is between TCGA ESCA normal and WGBS normal since normal tissues are relatively homologous. The PCC (0.9639) between TCGA ESCA tumor and WGBS tumor was followed due to tumor heterogeneity but still showed a high correlation. The third and fourth PCCs are between TCGA ESCA tumor and WGBS normal (=0.9512), and TCGA normal and WGBS tumor (=0.9468) due to the difference between tumor and normal esophageal tissues.
We downloaded clinical annotation to match the histological type of each sample. All of the WGBS data is ESCC. The PCC between TCGA ESCC and WGBS ESCC is 0.757, the PCC between TCGA EAC and WGBS ESCC is 0.5554 (Supplementary Fig. 3).
Data processing of RNA-seq
RNA-Seq reads were mapped to the HG19 reference genome using STAR (Spliced Transcripts Align to a Reference, v2.4.2a). The expression level of transcript per million (TPM) reads were quantified using RNA-Seq by Expectation-Maximization algorithm (RSEM v1.2.29). The quantified gene expressions of 26,334 transcripts (including coding genes and non-coding genes) were processed in Rstudio console with R programme (v 3.4). Differentially expressed genes between tumor and normal samples were identified using the EdgeR algorithm.
Differentially methylated CpG (DMCs)
Among 18,421,444 of CpGs, we computed the F-statistics from one-way analysis of variance (ANOVA) to identify confident CpGs. Almost half of them has very low p values (<0.05). The p values are further adjusted by Benjamini–Hochberg procedure to compute FDR. In total, 5,092,845 of CpGs has q values less than 0.05. The 5,092,845 of DMCs is used for the downstream study in this paper. Among 5,092,845 of DMCs 97.29% are hypomethylated in ESCC samples while only 2.71% are hypermethylated in ESCC samples.
Entropy is computed per CpG in both ESCC and normal esophageal cohorts separately as a measure of variance. The “entropy” function was used in the “stats” package of SciPy (v 0.19.1) on top of python3 (v3.5.2). The bin size was 10%. The distribution of CpG entropy was plotted using MatLab (v. 9.2) “plot” and “histogram” function with the default option.
DMC enrichment analysis with genomic annotation
We annotated the regulatory elements of the confident DMCs. The genomic coordination of exons and introns were downloaded from UCSC Genome Browser (http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/refGene.txt.gz). The promoter is computed 4.5 kbp upstream and 500 bp downstream given TSS. The genomic coordinates of enhancers are downloaded from VISTA Enhancer Browser (https://enhancer.lbl.gov). We learned that hypomethylated DMCs outnumber hypermethylated DMCs in ESCC, 4.95M versus 138K, respectively. Among hypermethylated in DMCs in ESCC, 83.67% has overlapped with regulatory elements while only 56.77% of hypomethylated DMCs has overlaps. Such overlaps were further dissected into enhancers, promoters, exons, introns, and CpG islands (Supplementary Fig. 4).
DMC enrichment analysis with functional annotation
We performed functional annotation on the confident DMCs except for protein-coding RNAs. Functional annotation includes long noncoding RNA (lncRNA), antisense RNA, MicroRNA (miRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), small cytoplasmic RNA (scRNA), ribosomalRNA (rRNA), vaultRNA, and Mt_tRNA. The functional annotation was downloaded from the GENCODE project (https://www.gencodegenes.org/releases/27lift37.html). The composition of the functional annotation is illustrated (Supplementary Fig. 5). We deconvoluted functional mapping of hypermethylated DMCs and hypomethylated DMCs. The majority of the hypermethylated DMCs are mapped to antisense RNAs (58.01%) followed by lncRNA (39.28%) while that of the hypomethylated DMCs are mapped to lncRNAs (63.08%) followed by antisense RNA (29.89%). The permutation test was used to perform statistical analysis with regioneR package (Supplementary Fig. 5).
Transcription factor-binding site analysis
Transcription factors and their binding sites annotation were downloaded from the ENCODE project (http://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeRegTfbsClustered/). First of all, we computed the proportion of each TF. With regard to base-pair counting, POLR2A, the largest contributor to the composition, followed by CTCF (Supplementary Fig. 11a). We mapped DMCs to TF-binding sites and calculated the composition of TFs both in DMCs and HG19. Enrichment was computed as a ratio of the proportion of TFBS mapped to DMCs over TFs in HG19 (Supplementary Fig. 11b). Relatively DMC are most enriched in SUZ12 followed by EZH2, which are the component of the Polycomb Repressive Complex 2 (PRC2) (Supplementary Fig. 11c). Methylation type has been studies among top 20 TFs that DMCs are enriched. Hypomethylated DMCs dominated in the most TFs except for three TFs: SUZ12, EZH2, and CTBP2, which is related to endometrial cancer pathway and WNT pathway (Supplementary Fig. 11d).
TSS methylation level analysis
The methylation ratio was summarized in every 200-bp window relative to TSS. Then the methylation ratio per bin was normalized and averaged for both ESCC and normal cohorts. Methylation conversion is observed both upstream and downstream given TSS. The normalized methylation ratio of ESCC samples is higher between around −3000 to 7000 bp given TSS. The graph was made by R with spline interpolation with default options.
Differentially methylated regions
DMRs were computed from 5,092,845 of confident DMCs. The window size is flexible as long as any two CpGs locate in 150 bp and have consistent methylation pattern either keeping hypermethylated or hypomethylated. The criteria make sure the minimum CpG density is at least 0.01. DMRs peak size is of 150–350 bp and CpG density peak is of 0.04–0.05 (Supplementary Fig. 8a, b). The genomic region enrichment analysis for DMRs was conducted using LOLA package (http://code.databio.org/LOLA) in R.
Integrating and clustering methylome and transcriptome data
To integrate methelomic and transcriptomic data, we focused on methylation in the promoter regions which are defined 4.5 kbp upstream and 500 bp downstream given TSS. We aligned DMRs that are identified previously with q value <0.01 to the promoter regions of all genes. We also increased the promoter size up to 11 kbps to see if any level of extension of promoter size can affect the final gene set whose promoters hold any DMRs, but that added only four more genes. As a result, we learned that 4391 genes have significant DMRs in their promoter regions. High-throughput transcriptome sequencing (RNA-Seq) was conducted. Around 20,000 gene expression levels are estimated for both normal and ESCC pairs of ten patients. Significantly differentially expressed transcriptomes between two cohorts are selected given absolute log2(fold change) ≥1 and q value <0.05. In total, 4768 genes are significantly highly expressed in the ESCC cohort or the normal cohort. The number of genes in the intersection, i.e. the genes whose promoters are differentially methylated and expressions are significantly different between normal and ESCC cohorts, is 694 in total. We categorized those genes into four groups (1) C1: hypermethylated in their promoters with low gene expression in ESCC; (2) C2: hypomethylated in their promoters with high gene expression in ESCC; (3) C3: hypermethylated in their promoters with high gene expression in ESCC; (4) C4: hypomethylated in their promoters and low gene expression in ESCC. The genes in the former two groups (C1 and C2) are explained by canonical promoter methylation and gene expression model while the genes in the latter two groups (C3 and C4) are not. The gene list of the four groups is in Supplementary Data File 4. The heat map with dendrograms is made using “clustergram” command of MatLab v9.4 with parameters of “Standardize”, “Row”, “Colormap”, “redgreencmap”.
CTCF analysis in gene promoter and gene body
The genomic coordination of CTCF was retrieved from ENCODE Project (https://www.gencodegenes.org/releases/27lift37.html). We identified the number of CTCF-binding site overlapped with promoter regions by BEDTools (v2.26.0) with “intersect -wa -wb -a -b” option. The CTCF ratio in the gene promoter region is highest in C3.
Gene body methylation level analysis
DMCs (FDR ≤ 0.05) in the gene body and promoter region is selected for C1 and C3 to search hypothesis of noncanonical correlation in C3, where promoters are hypermethylated with high gene expression. Overall gene body is hypomethylated for both C1 and C3, but such reverse methylation between promoter and gene body is much prominent in C1 genes.
Copy number alteration inferred from RNAseq
CNVkit-RNA71 was used to infer copy number alterations from RNAseq reads. The segments and recurrent copy number gains or loss across samples were generated and plotted using GISTIC 2.0 algorithm (Supplementary Fig. 15a, b).
Simulation of CpG methylation heterogeneity
Simulations of Ornstein–Uhlenbeck processes have been performed for CpG islands at selected promoter regions in both normal and cancer samples17. The model is described as θ (μ−M)dt + σdW, with M being the methylation value, μ the equilibrium point, θ the restoring force, σ the noise level (4%), and dW a Wiener process increment. Shown are ten example traces of simulated methylation levels. Regulatory forces (θ) are set high in the normal tissue and low after a carcinogenic event. For the data in this study, the model works well for regions with single-peaked or broad methylation distributions. The histogram shows methylation variation by the stochastic simulation if more samples collected (Supplementary Fig. 6e).
Co-expression analysis of RNAseq
Co-expression analysis was conducted in R environment using RedeR package72. Co-expression analysis is to compute a null distribution via permutation and returning the significant correlation values. In all, 1000 permutations were performed to build the null distribution with Pearson correlation. We considered correlations with p value less than 0.01 with FDR adjustment as significant. The hierarchical clustering analysis used the complete method and considers the distances of each individual component to progressively computing the clusters until it finds a stable state. The final result is a dendrogram presenting hierarchical leaves, which has been used to plot the network. To clear the visualization, clusters ware nested using the fourth level of dendrogram to build the nests.
Methylation-specific PCR (MS-PCR)
DNA was extracted from cells using the AllPrep DNA mini kit (Qiagen) according to the manufacturer’s instructions and was quantified by NanoDrop analysis. Bisulfite modification was carried out on 200–500 ng of DNA using the EZ DNA Methylation-Gold Kit (Zymo Research) according to the manufacturer’s instructions. MS-PCR analysis was carried out using a BioRad T100TM Thermal Cycler with 20 μl reaction mixtures. Primers for modified methylated sequences and modified unmethylated sequences are listed below. The PCR reactions were carried out under the following conditions: 95 °C for 10 min, 95 °C for 30 s, 50 °C for 30 s, and 72 °C for 30 s for a total of 45 cycles, 72 °C for 10 min. Five microliters of PCR product were used for electrophoresis on 2% agarose gel, and the methylated strip and unmethylated strip were analyzed by gel imaging analyzer. Representative results showed the ECSSAL-1 promoter methylation status identified by MS-PCR in ESCCs. Lanes M and U indicate the amplified products with primer recognizing methylated and unmethylated sequences, respectively. NC, negative control; PC, positive control. Primers for MS-PCR are listed in Supplementary Table 1.
Functional annotation analysis
Curated gene sets were collected from multiple curated databases such as ENCODE, CHEA, EnrichR, KEGG, WikiPathways, Reactome, GO molecular function, Panther, and BIOGRID. Overlapping between differentially methylated and/or expressed genes were estimated for significance using hypergeometric statistics. P values were adjusted to Bonferroni correction. −Log10(p value) for significance was measured and compared.
Human ESCC cell lines (EC109, EC9706, EC1) and immortalized esophageal epithelial cell line Het-1A were purchased from the Shanghai Institutes for Biological Science (Shanghai, China). All cell lines were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (Hyclone, Logan, UT, USA) and maintained at 37 °C in a humidified 5% CO2 incubator.
ESCCAL-1 shRNA, WNT2 siRNA, YY1 siRNA, Myc siRNA, and their control shRNA or siRNAs were synthesized from GenePharma (Shanghai, China). EC109 and EC9706 cells were transfected using Lipofectamine™ 2000 (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions. Transfection efficacy was validated by RTq-PCR or western blot assay.
Transwell migration and invasion assay
Cell migration and invasion assays were carried out in transwell chambers (Costar, Lowell, MA, US) inserted into 24-well plates. For invasion assay, the upper chamber of the transwell plate was coated with matrigel and allowed to solidify at 37 °C and 5% CO2 incubator for 30 min. The transfected EC109 and EC9706 cells were trypsinized, resuspended in serum-free culture medium, and adjusted to 2 × 106 cells/ml, added 200 μl cell suspension and 500 μl DMEM containing 10% fetal bovine serum to the lower chamber. After incubation for 48 h, the transwell chamber was fixed with 10% methanol, followed by the staining with crystal violet. Cells were counted under an inverted microscope. The protocol used for migration assay was similar to the invasion assay without the need to coat the upper chamber of the transwell with matrigel. Each experiment was conducted in triplicates.
Western blot analysis
Total proteins were extracted from tissues or cells using RIPA lysis buffer (Solarbio, China) containing protease inhibitor and then denatured at 100 °C for 10 min. Equal amounts of proteins in each group were separated by 10% SDS-PAGE. After PVDF membrane transfer, blocking in 5% skim milk and incubation with primary antibodies (MMP3, CST, USA; MMP9, CST, USA; Wnt2, Abcam, USA; β-actin, Santa Cruz, USA) and secondary antibody, respectively, the immunoreactive bands on the membrane were incubated with ECL kit and detected by using the Chemidoc EQ system (BioRad, USA).
Mouse xenograft experiment
Six-week-old male BALB/c immunodeficient mice were purchased from the Shanghai Experimental Animal Center, Chinese Academy of Sciences (Shanghai, China). Animal experimental procedures were carried out according to the Ethical Committee of Zhengzhou University. Mice were housed under a 12 h light/dark cycle and automatically given food and water. The EC9706 cells expressing with ESCCAL-1-shRNA, WNT2-shRNA, or shControl were subcutaneously injected into back flank of mice as the knockdown group (n = 5 for WNT2, n = 6 for ESCCAL-1) or the control group (n = 5 or 6). The tumor volumes were calculated as length × width2 × 0.5 from day 13 to day 22 or 23 every two days, the mice were sacrificed at day 22 or 23 after injection.
Chromatin immunoprecipitations were performed using digested chromatin from EC109 cells or Het-1A cells and the indicated antibody YY1. The antibody Histone H3 (D2B12) as a positive control. Purified DNA was analyzed by standard PCR methods using SimpleChIP® Human RPL30 Exon 3 Primers and ESSCAL-1 primers. Equal amounts of total genomic DNA (Input) were used for immunoprecipitation in each condition. Primer sequence information are given in Supplementary Table 1.
Immunohistochemistry for WNT2 expression
Immunostaining was performed on the paraffin-embedded tumor and adjacent normal tissues from ESCC patients’ surgical removal samples. The avidin-biotin-peroxidase method was used to determine the location and relative expression level of the proteins. In briefly, xylene is used for paraffin section dewaxing, citric acid antigen repair buffer is used to repair the antigen, blocked endogenous peroxidase by 3% H2O2 and blocked with 3% BSA, and incubated overnight at 4 °C with primary antibodies WNT2 (1:100 dilution, Bioworld, USA). Then, secondary antibody was incubated at room temperature for 50 min, DAB stained and hematoxylin restained nucleus. Sections were visualized under a microscope at ×400 or ×200 (Olympus, Japan).
Student's t-test was used for two group mean comparison. Wilcoxon rank-sum test was used for non-parametric mean comparison. One-way ANOVA was used for multiple groups comparison. Hypergeometric test was used in GSEA. Permutation test was used for comparison between observed target versus random evaluation. Multiple hypothesis test was adjusted with the Benjamini–Hochberg method. The standard α = 0.05 was used as cutoff, and the null hypothesis is rejected when p value <0.05. Different significant levels were used: *p value < 0.05; **p value < 0.01; ***p value < 0.005; ****p value <0.001. The 95% confidence interval (CI) for the median duration of progression-free survival and overall survival were computed with the robust nonparametric Brookmeyer and Crowley method. Hazard ratio with 95% CI and p values were calculated with the Cox proportional-hazards regression model with survival package in R.
Computational resources and code sharing for reproducibility
Most of the analysis was done using SCG4 cluster of the Genome Sequencing Service Center by Stanford Center for Genomics and Personalized Medicine Sequencing Center. The SCG4 cluster has 20 compute nodes, each with 384 GB RAM, 56 CPUs each, and 40 compute nodes with 16 and 48 CPUs and 10 GbE connectivity. It shares 4+ PB of storage NIH dbGaP compliant and have 350+ software packages installed.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
The WGBS, RNAseq, and ChIP-seq sequence data that support the findings of this study have been deposited in NCBI GEO database with the accession codes, GSE149608, GSE149609, GSE151838. WGS has been deposited in NCBI sequencing read archive (SRA) database with the accession code PRJNA630082. The proteomic data have been deposited in ProteomXchange database with the accession code, PXD019834. All the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file. Source data are provided with this paper.
Code for WGBS analysis is available upon request.
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This study was supported by the National Natural Science Foundation of China (Grants 81171992, 31570899), the Natural Science Foundation of Henan (Grants 182102310328, 162300410279, 182300410374, 192102310096), the Education Department of Henan Province(18B310022,19A320037). This work used the Genome Sequencing Service Center by Stanford Center for Genomics and Personalized Medicine Sequencing Center, supported by the grant award NIH S10OD020141. E.C. acknowledges funding support from NCI Grants R01 [CA178015, CA222862, CA227807, CA239604, CA230263] and U24 [CA210974]. T.G.B. acknowledges funding support from NIH/NCI U01CA217882, NIH/NCI U54CA224081, NIH/NCI R01CA204302, NIH/NCI R01CA211052, NIH/NCI R01CA169338, and the Pew-Stewart Foundations.
E.C. is consultant at Takeda, Merck, Loxo, and Pear Diagnostics, reports receiving commercial research grants from AstraZeneca, Ferro Therapeutics, Senti Biosciences, Merck KgA and Bayerand stock ownership of Tatara Therapeutics, Clara Health, BloodQ Guardant Health, Illumina, Pacific Biosciences and Exact Biosciences. T.G.B. is an advisor to Revolution Medicine, Novartis, Astrazeneca, Takeda, Springworks, Jazz, and Array Biopharma, and receives research funding from Revolution Medicine and Novartis. M.S. is Cofounder and scientific advisory board member of Personalis, SensOmics, Mirvie, Qbio, January, Filtricine, and Genome Heart. He serves on the scientific advisory board of these companies and Genapsys and Jupiter. Other authors declare no competing interests.
Peer review information Nature Communications thanks Rebecca Fitzgerald and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Cao, W., Lee, H., Wu, W. et al. Multi-faceted epigenetic dysregulation of gene expression promotes esophageal squamous cell carcinoma. Nat Commun 11, 3675 (2020). https://doi.org/10.1038/s41467-020-17227-z