DNA Methylation Biomarker Discovery for Colorectal Cancer Diagnosis Assistance Through Integrated Analysis.

Integrative Genome-Wide DNA Methylation and Gene Expression Analysis Reveals Biological and Clinical Insights In Adult Acute Lymphoblastic Leukemia

Background: Colorectal cancer (CRC) is a common human malignancy worldwide. The prognosis of patients is largely frustrated by delayed diagnosis or misdiagnosis. DNA methylation alterations have been previously proved to be involved in CRC carcinogenesis.Methods: In this study, we proposed to identify CRC-related diagnostic biomarkers by analyzing DNA methylation and gene expression profiles. TCGA-COAD datasets downloaded from the Cancer Genome Atlas (TCGA) were used as the training set to screen differential expression genes (DEGs) and methylation CpG sites (dmCpGs) in CRC samples. A logistic regression model was constructed based on hyper-methylated CpG sites which were located in downregulated genes for CRC diagnosis. Another two independent datasets from the Gene Expression Omnibus (GEO) were used as a testing set to evaluate the performance of the model in CRC diagnosis.Results: We found that CpG island methylator phenotype (CIMP) was a potential signature of poor prognosis by dividing CRC samples into CIMP and noCIMP groups based on a set of CpG sites with methylation standard deviation (sd) > 0.2 among CRC samples and low methylation levels (mean β < 0.05) in adjacent samples. Hyper-methylated CpGs tended to be more closed to CpG island (CGI) and transcription start site (TSS) relative to hypo-methylated CpGs (p-value < 0.05, Fisher exact test). A logistic regression model was finally constructed based on two hyper-methylated CpGs, which had an area under receiver operating characteristic curve of 0.98 in the training set, and 0.85 and 0.95 in the two independent testing sets.Conclusions: In conclusion, our study identified promising DNA methylation biomarkers for CRC diagnosis.

Gastric cancer is a heterogeneous disease where diverse genetic and epigenetic alternations can accumulate in different molecular and histological subtypes. We applied our recently developed causality test between genome-wide DNA methylation and gene expression profiles to three independent cohorts of gastric tumors (97 in Hong Kong University (HKU), 159 in University of Singapore (Singapore) and 365 samples in TCGA stomach adenocarcinoma (TCGA) ). We focused on methylation variations within CpG islands in promoter regions, where global hypermethylation was observed, and identified 37, 62, and 537 key regulators in HKU, Singapore, and TCGA dataset respectively. There were 5 common key regulators (ADHFE1, CDO1, COX7A1, FSTL1, and TCF21) whose methylation variations had high impact on mRNA level changes of large number of downstream genes in all three dataset where different cohorts were profiled using different platforms. When we compared two dataset, there were 5 common key regulators in HKU and Singapore dataset (Fisher's exact test (FET) p-value = 2.9×e-07), 27 common key regulators in HKU and TCGA dataset (FET p-value = 6.8×e-35), and 30 common key regulators in Singapore and TCGA dataset (FET p-value = 1.0×e-29). By combining these, 52 genes were identified as key regulators within at least two dataset. Several of the key regulators were known for the association between their epigenetic disruption and the disease (for example, BNIP3, CDO1, TCF21, ZSCAN18, and so on) while other genes have not implicated in the gastric cancer previously. More interestingly, the downstream genes of these key regulators were significantly overlapped and the directions of correlation with methylation levels were almost same within the three dataset. Further clustering key regulators based on their downstream genes overlaps revealed that there were two distinct groups of downstream genes commonly regulated by these key regulators and the expression of these two groups were anti-correlated. One group was enriched for cell cycle related genes and the other group was enriched for genes involved in immune responses. This result indicates that cell cycle and immune response functions were inversely regulated by methylation variations of the same set of genes. It is worth to note that methylation patterns of some key regulators were subtype dependent and the subtype specific methylation patterns were only observed in tumor samples, but not in adjacent normal tissues. Based on integrative analysis of genome-wide DNA methylation and gene expression profiles within three independent gastric cancer dataset, we identified a set of key regulators whose methylation changes might play a ‘causal' role in the transcriptional regulation associated with the gastric cancer. Further experiments are needed to validate and dissect these putative candidate genes' roles in tumorigenesis and progression of this complex and heterogeneous disease. Citation Format: Seungyeul Yoo, Suet Yi Leung, Jun Zhu. Integrative analysis of DNA methylation and gene expression data reveals complex regulation of gastric cancer. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Sep 24-27, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2016;76(2 Suppl):Abstract nr B22.

Aberrant DNA methylation of CpG islands, CpG island shores and first exons is known to play a key role in the altered gene expression patterns in all human cancers. To date, a systematic study on the effect of DNA methylation on gene expression using high resolution data has not been reported. In this study, we conducted an integrated analysis of MethylCap-sequencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines representing different breast tumor phenotypes. As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods. On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale. On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B. Our integrated analysis demonstrates that methylation status of different genomic regions may play a key role in establishing transcriptional patterns in molecular subtypes of human breast cancer.

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger persons. Established CRC screening methods include fecal immunochemical testing, which requires handling of stool, and colonoscopy, which is invasive. Many persons who remain unscreened might accept a blood-based screening test, which could have profound public health impact. DNA methylation is a stable, early, and tissue-specific event in cancer development and progression. Measuring the methylation status of tumor-derived cell-free DNA in plasma could enable identification of early-stage CRC. We first developed a plasma-based methylated DNA panel for all stages of CRC (Study 1). We then validated this panel in a subsequent study with independent samples (Study 2). Methods: Differentially methylated regions (DMRs) were initially selected by analyzing tissue data from The Cancer Genome Atlas (TCGA) database. In Study 1, candidate regions were evaluated in plasma samples of 215 patients (93 CRC, 122 controls including 31 with non-advanced adenoma) using methylation-sensitive restriction enzyme qPCR (MSRE-qPCR). This training set was used for marker evaluation and construction of a prediction model. The most promising methylation markers were selected by a support vector machine (SVM)-based machine learning classifier using a random forest algorithm. In Study 2, the selected markers were validated in an independent validation set of 724 samples collected in Spain, Ukraine, the UK, and the US (152 CRC, 622 controls including 148 with non-advanced adenoma and 52 with non-CRC cancer). Results: Tissue data analysis yielded 180 potential DMRs. In Study 1, a panel consisting of the top 12 methylation markers was selected. In Study 2, the prediction model based on this panel correctly classified 117/152 CRC (77%) patients. Sensitivity improved with CRC stage, ranging from 71% (25/35) for stage I, 76% (37/49) for stage II, 77% (37/48) for stage III, to 100% (14/14) for stage IV. Specificity of the model was 88% (544/622). Among non-CRC cancer cases, specificity was 83% overall (73% [19/26] for lung cancer and 92% [24/26] for breast cancer). Conclusions: We developed a prediction model based on a novel plasma 12-marker methylation panel that demonstrated highly promising test performance characteristics for early-stage CRC detection in a validation study using independent samples. This method could serve as the basis for a highly accurate and minimally invasive blood-based CRC screening test. Citation Format: Kristi Kruusmaa, Marko Bitenc, Walter Pulverer, Andreas Weinhaeusel, José Luis Rodrigo Agudo, Andrés Barrientos Delgado, Andrés Sánchez Yagüe, José Manuel Rodríguez Laiz, Marko Chersicola, Primož Knap, Silvia Schönthaler, Uri Ladabaum. Development and clinical performance of an accurate cell-free DNA (cfDNA) methylation assay for early detection of colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A05.

BackgroundColorectal cancer is a public health issue and was the third leading cause of cancer-related death worldwide in 2022. Early diagnosis can improve prognosis, making screening a central part of colorectal cancer management. Blood-based screening, diagnosis and follow-up of colorectal cancer patients are possible with the study of cell-free circulating tumor DNA. This study aimed to identify novel DNA methylation biomarkers of colorectal cancer that can be used for the follow-up of patients with colorectal cancer.MethodsA DNA methylation profile was established in the Gene Expression Omnibus (GEO) database (n = 507) using bioinformatics analysis and subsequently confirmed using The Cancer Genome Atlas (TCGA) database (n = 348). The in silico profile was then validated on local tissue and cell-free DNA samples using methylation-specific digital PCR in colorectal cancer patients (n = 35) and healthy donors (n = 35).ResultsThe DNA methylation of COL25A1 and METAP1D was predicted to be a colorectal cancer biomarker by bioinformatics analysis (ROC AUC = 1, 95% CI [0.999–1]). The two biomarkers were confirmed with tissue samples, and the combination of COL25A1 and METAP1D yielded 49% sensitivity and 100% specificity for cell-free DNA.ConclusionBioinformatics analysis of public databases revealed COL25A1 and METAP1D DNA methylation as clinically applicable liquid biopsies DNA methylation biomarkers. The specificity implies an excellent positive predictive value for follow-up, and the high sensitivity and relative noninvasiveness of a blood-based test make these biomarkers compatible with colorectal cancer screening. However, the clinical impact of these biomarkers in colorectal cancer screening and follow-up needs to be established in further prospective studies.

Advanced integrative analysis of DNA methylation and transcriptomics data may provide deeper insights into smoke-induced epigenetic alterations, their effects on gene expression and related biological processes, linking cigarette smoking and related diseases. We hypothesize that accumulation of DNA methylation changes in CpG sites across genomic locations of different genes might have biological significance. We tested the hypothesis by performing gene set based integrative analysis of blood DNA methylation and transcriptomics data to identify potential transcriptomic consequences of smoking via changes in DNA methylation in the Young Finns Study (YFS) participants (n = 1114, aged 34-49 years, women: 54%, men: 46%). First, we performed epigenome-wide association study (EWAS) of smoking. We then defined sets of genes based on DNA methylation status within their genomic regions, for example, sets of genes containing hyper- or hypomethylated CpG sites in their body or promoter regions. Gene set analysis was performed using transcriptomics data from the same participants. Two sets of genes, one containing 49 genes with hypomethylated CpG sites in their body region and the other containing 33 genes with hypomethylated CpG sites in their promoter region, were differentially expressed among the smokers. Genes in the two gene sets are involved in bone formation, metal ion transport, cell death, peptidyl-serine phosphorylation, and cerebral cortex development process, revealing epigenetic-transcriptomic pathways to smoking-related diseases such as osteoporosis, atherosclerosis, and cognitive impairment. These findings contribute to a deeper understanding of the pathophysiology of smoking-related diseases and may provide potential therapeutic targets.

Kashin-Beck disease (KBD) is an endemic chronic osteochondropathy. The etiology of KBD remains unknown. In this study, we conducted an integrative analysis of genome-wide DNA methylation and mRNA expression profiles between KBD and normal controls to identify novel candidate genes and pathways for KBD. Articular cartilage samples from 17 grade III KBD patients and 17 healthy controls were used in this study. DNA methylation profiling of knee cartilage and mRNA expression profile data were obtained from our previous studies. InCroMAP was performed to integrative analysis of genome-wide DNA methylation profiles and mRNA expression profiles. Gene ontology (GO) enrichment analysis was conducted by online DAVID 6.7. The quantitative real-time polymerase chain reaction (qPCR), Western blot, immunohistochemistry (IHC), and lentiviral vector transfection were used to validate one of the identified pathways. We identified 298 common genes (such as COL4A1, HOXA13, TNFAIP6 and TGFBI), 36 GO terms (including collagen function, skeletal system development, growth factor), and 32 KEGG pathways associated with KBD (including Selenocompound metabolism pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway). Our results suggest the dysfunction of many genes and pathways implicated in the pathogenesis of KBD, most importantly, both the integrative analysis and in vitro study in KBD cartilage highlighted the importance of selenocompound metabolism pathway in the pathogenesis of KBD for the first time.

Chronic Obstructive Pulmonary Disease (COPD) is a complex disease. Genetic, epigenetic, and environmental factors are known to contribute to COPD risk and disease progression. Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity. EPAS1 is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes. Genes predicted to be regulated by EPAS1 were enriched for biological processes including signaling, cell communications, and system development. We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that Epas1 gene expression is reduced in mice chronically exposed to cigarette smoke. As EPAS1 downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells, we tested EPAS1 function in human endothelial cells. EPAS1 knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with EPAS1 downstream genes in lung tissue including hypoxia responsive genes, and genes associated with emphysema severity. Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a ‘causal’ role in the molecular pathophysiology of COPD, but it can be leveraged to directly identify novel key mediators of this pathophysiology.

Background: Papillary thyroid carcinoma (PTC) is the histological subtype more frequently reported among the endocrine malignancies. Deregulation of DNA methylation has been extensively reported in cancer with hypermethylation usually found in promoter and hypomethylation in nonpromoter regions. The methylation profile of PTC revealed altered genome regions; however, its impact on the gene expression remains poorly understood. DNA methylation profile in PTC patients was performed, aiming to investigate the role of novel genic and intergenic regions on the expression of genes. Patients and Methods: The methylation profile of 41 patients diagnosed with PTC was evaluated using the Methylation 450 Human Infinium®BeadChip platform (Illumina). The data were normalized and analyzed using SVA, watermelon, and LIMMA packages. Delta beta of 0.15 and adjusted p-value &amp;lt;0.05 were used to identify differentially methylated probes between PTC and non-neoplastic thyroid tissue (NT). An integrative analysis was performed using the expression data obtained in 34 matched-cases. The results were submitted to a cross-validation with The Cancer Genome Atlas (TCGA) database. An in silico analysis was performed using genes/probes showing negative correlation and confirmed by TCGA using the Ingenuity Pathways Analysis. ERBB3, FGF1, FGFR2, GABRB2, HMGA2, and RDH5 were validated by pyrosequencing and RT-qPCR methods. Results: Methylation analysis revealed 4,995 differentially methylated probes. A global hypomethylation was found in PTC samples (88% hypomethylated probes) mostly in nonpromoter regions (especially in gene body), distant from CGI and enriched by enhancers. The integrative analysis between gene expression and DNA methylation revealed 185 and 38 genes with negative and positive correlation, respectively. Genes showing negative correlation highlighted FGF and retinoic acid signaling as crucial pathways disrupted by DNA methylation in PTC. The selected genes were confirmed as altered in both methylation and expression levels. Interestingly, four of them (ERBB3, FGF1, GABRB2, and HMGA2) were mapped in gene body, suggesting that methylation in this region regulates the gene expression. Conclusion: Loss of methylation was extensively observed in PTC, especially in nonpromoter, poor CGI, and enhancer-enriched regions. Gene body and promoter regions have the potential to influence the gene expression levels (both repressing and inducing). Genes potentially regulated by DNA methylation were identified from the integrative and cross-validation analyses. ERBB3, FGF1, FGFR2, GABRB2, HMGA2, and RDH5 biomarkers are regulated by methylation in PTC samples. Citation Format: Caroline Moraes Beltrami, Mateus Camargo Barros-Filho, Mariana Bisarro dos Reis, Fábio Albuquerque Marchi, Hellen Kuasne, Skirant Ambatipudi, Zdenko Herceg, Luiz Paulo Kowalski, Silvia Regina Rogatto. Gene body methylation could regulate the expression of genes associated to papillary thyroid carcinomas [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A13.

A Master microRNA miR-508-3p Modulates the Mesenchymal Subtype of Colorectal Cancer by Targeting ZEB1/BMI1/SALL4 Network

Genome-Wide Identification and Validation of a Novel Methylation Biomarker, SDC2, for Blood-Based Detection of Colorectal Cancer

Background: DNA methylation is a common event in the early development of various tumors, including breast cancer (BRCA), which has been studies as potential tumor biomarkers. Although previous studies have reported a cluster of aberrant promoter methylation changes in BRCA, none of these research groups have proved the specificity of these DNA methylation changes. Here we aimed to identify specific DNA methylation signatures in BRCA which can be used as diagnostic and prognostic markers.Methods: Differentially methylated sites were identified using the Cancer Genome Atlas (TCGA) BRCA data set. We screened for BRCA-differential methylation by comparing methylation profiles of BRCA patients, healthy breast biopsies and blood samples. These differential methylated sites were compared to nine main cancer samples to identify BRCA specific methylated sites. A BayesNet model was built to distinguish BRCA patients from healthy donors. The model was validated using three Gene Expression Omnibus (GEO) independent data sets. In addition, we also carried out the Cox regression analysis to identify DNA methylation markers which are significantly related to the overall survival (OS) rate of BRCA patients and verified them in the validation cohort.Results: We identified seven differentially methylated sites (DMSs) that were highly correlated with cell cycle as potential specific diagnostic biomarkers for BRCA patients. The combination of 7 DMSs achieved ~94% sensitivity in predicting BRCA, ~95% specificity comparing healthy vs. cancer samples, and ~88% specificity in excluding other cancers. The 7 DMSs were highly correlated with cell cycle. We also identified 6 methylation sites that are highly correlated with the OS of BRCA patients and can be used to accurately predict the survival of BRCA patients (training cohort: likelihood ratio = 70.25, p = 3.633 × 10−13, area under the curve (AUC) = 0.784; validation cohort: AUC = 0.734). Stratification analysis by age, clinical stage, Tumor types, and chemotherapy retained statistical significance.Conclusion: In summary, our study demonstrated the role of methylation profiles in the diagnosis and prognosis of BRCA. This signature is superior to currently published methylation markers for diagnosis and prognosis for BRCA patients. It can be used as promising biomarkers for early diagnosis and prognosis of BRCA.

Pathologic alterations in epigenetic regulation have long been considered a hallmark of many cancers, including hepatocellular carcinoma (HCC). In a healthy individual, the relationship between DNA methylation and microRNA (miRNA) expression maintains a fine balance; however, disruptions in this harmony can aid in the genesis of cancer or the propagation of existing cancers. The balance between DNA methylation and microRNA expression and its potential disturbance in HCC can vary by race. There is emerging evidence linking epigenetic events including DNA methylation and miRNA expression to cancer disparities. In this paper, we evaluate the epigenetic mechanisms of racial heterogenity in HCC through an integrated analysis of DNA methylation, miRNA, and combined regulation of gene expression. Specifically, we generated DNA methylation, mRNA-seq, and miRNA-seq data through the analysis of tumor and adjacent non-tumor liver tissues from African Americans (AA) and European Americans (EA) with HCC. Using mixed ANOVA, we identified cytosine-phosphate-guanine (CpG) sites, mRNAs, and miRNAs that are significantly altered in HCC vs. adjacent non-tumor tissue in a race-specific manner. We observed that the methylome was drastically changed in EA with a significantly larger number of differentially methylated and differentially expressed genes than in AA. On the other hand, the miRNA expression was altered to a larger extent in AA than in EA. Pathway analysis functionally linked epigenetic regulation in EA to processes involved in immune cell maturation, inflammation, and vascular remodeling. In contrast, cellular proliferation, metabolism, and growth pathways are found to predominate in AA as a result of this epigenetic analysis. Furthermore, through integrative analysis, we identified significantly differentially expressed genes in HCC with disparate epigenetic regulation, associated with changes in miRNA expression for AA and DNA methylation for EA.

DNA methylation is a crucial regulator of gene transcription in the etiology and pathogenesis of hepatocellular carcinoma (HCC). Thus, it is reasonable to identify DNA methylation-related prognostic markers. Currently, we aimed to make an integrative epigenetic analysis of HCC to identify the effectiveness of epigenetic drivers in predicting prognosis for HCC patients. By the software pipeline TCGA-Assembler 2, RNA-seq, and methylation data were downloaded and processed from The Cancer Genome Atlas. A bioconductor package MethylMix was utilized to incorporate gene expression and methylation data on all 363 samples and identify 589 epigenetic drivers with transcriptionally predictive. By univariate survival analysis, 72 epigenetic drivers correlated with overall survival (OS) were selected for further analysis in our training cohort. By the robust likelihood-based survival model, six epi-drivers (doublecortin domain containing 2, flavin containing monooxygenase 3, G protein-coupled receptor 171, Lck interacting transmembrane adaptor 1, S100 calcium binding protein P, small nucleolar RNA host gene 6) serving as prognostic markers was identified and then a DNA methylation signature for HCC (MSH) predicting OS was identified to stratify patients into low-risk and high-risk groups in the training cohort (p < 0.001). The capability of MSH was also assessed in the validation cohort (p = 0.002). Furthermore, a receiver operating characteristic curve confirmed MSH as an effective prognostic model for predicting OS in HCC patients in training area under curve (AUC = 0.802) and validation (AUC = 0.691) cohorts. Finally, a nomogram comprising MSH and pathologic stage was generated to predict OS in the training cohort, and it also operated effectively in the validation cohort (concordance index: 0.674). In conclusion, MSH, a six epi-drivers based signature, is a potential model to predict prognosis for HCC patients.