Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes.

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Novel insights into genetic susceptibility for colorectal cancer from transcriptome-wide association and functional investigation.

BACKGROUND: Transcriptome-wide association studies have been successful in identifying candidate susceptibility genes for colorectal cancer (CRC). To strengthen susceptibility gene discovery, we conducted a large transcriptome-wide association study and an alternative splicing transcriptome-wide association study in CRC using improved genetic prediction models and performed in-depth functional investigations. METHODS: We analyzed RNA-sequencing data from normal colon tissues and genotype data from 423 European descendants to build genetic prediction models of gene expression and alternative splicing and evaluated model performance using independent RNA-sequencing data from normal colon tissues of the Genotype-Tissue Expression Project. We applied the verified models to genome-wide association studies (GWAS) summary statistics among 58 131 CRC cases and 67 347 controls of European ancestry to evaluate associations of genetically predicted gene expression and alternative splicing with CRC risk. We performed in vitro functional assays for 3 selected genes in multiple CRC cell lines. RESULTS: We identified 57 putative CRC susceptibility genes, which included the 48 genes from transcriptome-wide association studies and 15 genes from splicing transcriptome-wide association studies, at a Bonferroni-corrected P value less than .05. Of these, 16 genes were not previously implicated in CRC susceptibility, including a gene PDE7B (6q23.3) at locus previously not reported by CRC GWAS. Gene knockdown experiments confirmed the oncogenic roles for 2 unreported genes, TRPS1 and METRNL, and a recently reported gene, C14orf166. CONCLUSION: This study discovered new putative susceptibility genes of CRC and provided novel insights into the biological mechanisms underlying CRC development.

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

BACKGROUND: High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. METHODS: A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. RESULTS: Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. IMPACT: The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Identification of intergenerational epigenetic inheritance by whole genome DNA methylation analysis in trios.

Genome-wide association studies have identified thousands of loci associated with common diseases and traits. However, a large fraction of heritability remains unexplained. Epigenetic modifications, such as the observed in DNA methylation have been proposed as a mechanism of intergenerational inheritance. To investigate the potential contribution of DNA methylation to the missing heritability, we analysed the methylomes of four healthy trios (two parents and one offspring) using whole genome bisulphite sequencing. Of the 1.5 million CpGs (19%) with over 20% variability between parents in at least one family and compatible with a Mendelian inheritance pattern, only 3488 CpGs (0.2%) lacked correlation with any SNP in the genome, marking them as potential sites for intergenerational epigenetic inheritance. These markers were distributed genome-wide, with some preference to be located in promoters. They displayed a bimodal distribution, being either fully methylated or unmethylated, and were often found at the boundaries of genomic regions with high/low GC content. This analysis provides a starting point for future investigations into the missing heritability of simple and complex traits.

Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.

Objective: Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC). Methods: We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (A, B, and C) and HLA class -II loci (DQB1, DRB1, and DPB1) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357). Results: Individuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97, p= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95, p= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87, p= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant. Conclusion: Our findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC.

Improving Species Level-taxonomic Assignment from 16S rRNA Sequencing Technologies.

Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3-V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing. Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology-based methods, and we propose a new re-annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3). This new re-annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm. Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment. Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database. Basic Protocol 4: Definitive selection of lineages among the three methods.

TALKIEN: crossTALK IntEraction Network. A web-based tool for deciphering molecular communication through ligand-receptor interactions.

Molecular crosstalk, the dialogue between different cell types, is attracting more attention in cancer research. On the one hand, the communication between tumor and non-tumor cells in the microenvironment or between different tumor clones has influential consequences for the progression and spread of tumors and response to treatment. On the other hand, novel techniques such as single-cell sequencing or spatial transcriptomics provide detailed information that needs to be interpreted. TALKIEN: crossTALK IntEraction Network is a simple and intuitive online R/shiny application to visualize molecular crosstalk information through the construction and analysis of a protein-protein interaction network. Taking two or more lists of genes or proteins as input, which are representative of cell lineages, TALKIEN extracts information about ligand-receptor interactions, builds a network and analyzes it using systems biology techniques such as centrality measures and component analysis, among others. Moreover, it expands the network displaying pathways downstream receptors. The application allows users to select different graphical layouts, performs functional analysis and gives information about drugs targeting receptors. In conclusion, TALKIEN allows users to detect ligand-receptor interactions generating new in silico predictions of cell-cell communication thus providing a translational rationale for future experiments. It is freely available at https://www.odap-ico.org/talkien.

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer.

We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries.

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients.

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.

Multimorbidity and chronic co-prescription networks and potential interactions in adult patients with epilepsy: MorbiNet study.

OBJECTIVE: We constructed epilepsy multimorbidity networks to study associations with chronic conditions, and co-prescriptions and drug-disease networks to assess potential interactions. We conducted a population-based study in Catalonia, Spain, with electronic files of 3,135,948 adult patients with multimorbidity, 32,625 of them with epilepsy (active diagnosis any time during 2006-2017). We constructed epilepsy comorbidity networks using logistic regression models from odds ratio estimates adjusted by age, sex, and comorbidities with R software and generated trajectories to study the progression of epilepsy. We constructed drug-disease and co-prescription networks using mixed models with repeated measures adjusting by age, sex, and period with chronic prescription invoiced data. Comorbidity more frequently preceding epilepsy included cerebrovascular accident (OR: 3.59), congenital anomalies (2.18), and multiple sclerosis (1.33); and following epilepsy: dementia (1.91), personality disorder (1.59), alcohol abuse (1.22), and Parkinson (1.21). Mental retardation (13.08), neurological cancer (8.49), benign neoplasm (4.69), infections (3.14), and psychosis (1.58) might precede or not epilepsy. A common progression was to schizophrenia, dementia, and other neurological diseases (mainly cerebral palsy and other degenerative diseases of nervous system). Co-prescription associations with major-moderate potential interactions were 54% for carbamazepine, 61% phenytoin, 53% phenobarbital, and 32% valproate. Major potential interactions were with antipsychotic, anxiolytic, opioid, cardiovascular, and other anti-seizure medications (ASMs). The most frequent comorbidities of epilepsy were congenital, cerebrovascular, and neurological and psychiatric conditions. High comorbidity and co-prescription with potential interactions can increase the complexity of care of patients with epilepsy.

Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes.

The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78−0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66−0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41−0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.

Evaluating the Potential of Polygenic Risk Score to Improve Colorectal Cancer Screening.

BACKGROUND: Colorectal cancer has high incidence and associated mortality worldwide. Screening programs are recommended for men and women over 50. Intermediate screens such as fecal immunochemical testing (FIT) select patients for colonoscopy with suboptimal sensitivity. Additional biomarkers could improve the current scenario. METHODS: We included 2,893 individuals with a positive FIT test. They were classified as cases when a high-risk lesion for colorectal cancer was detected after colonoscopy, whereas the control group comprised individuals with low-risk or no lesions. 65 colorectal cancer risk genetic variants were genotyped. Polygenic risk score (PRS) and additive models for risk prediction incorporating sex, age, FIT value, and PRS were generated. RESULTS: Risk score was higher in cases compared with controls [per allele OR = 1.04; 95% confidence interval (CI), 1.02-1.06; P < 0.0001]. A 2-fold increase in colorectal cancer risk was observed for subjects in the highest decile of risk alleles (≥65), compared with those in the first decile (≤54; OR = 2.22; 95% CI, 1.59-3.12; P < 0.0001). The model combining sex, age, FIT value, and PRS reached the highest accuracy for identifying patients with a high-risk lesion [cross-validated area under the ROC curve (AUROC): 0.64; 95% CI, 0.62-0.66]. CONCLUSIONS: This is the first investigation analyzing PRS in a two-step colorectal cancer screening program. PRS could improve current colorectal cancer screening, most likely for higher at-risk subgroups. However, its capacity is limited to predict colorectal cancer risk status and should be complemented by additional biomarkers. IMPACT: PRS has capacity for risk stratification of colorectal cancer suggesting its potential for optimizing screening strategies alongside with other biomarkers.

Transcriptome-Wide Association Study for Inflammatory Bowel Disease Reveals Novel Candidate Susceptibility Genes in Specific Colon Subsites and Tissue Categories.

BACKGROUND AND AIMS: Genome-wide association studies [GWAS] for inflammatory bowel disease [IBD] have identified 240 risk variants. However, the benefit of understanding the genetic architecture of IBD remains to be exploited. Transcriptome-wide association studies [TWAS] associate gene expression with genetic susceptibility to disease, providing functional insight into risk loci. In this study, we integrate relevant datasets for IBD and perform a TWAS to nominate novel genes implicated in IBD genetic susceptibility. METHODS: We applied elastic net regression to generate gene expression prediction models for the University of Barcelona and University of Virginia RNA sequencing project [BarcUVa-Seq] and correlated expression and disease association research [CEDAR] datasets. Together with Genotype-Tissue Expression project [GTEx] data, and GWAS results from about 60 000 individuals, we employed Summary-PrediXcan and Summary-MultiXcan for single and joint analyses of TWAS results, respectively. RESULTS: BarcUVa-Seq TWAS revealed 39 novel genes whose expression in the colon is associated with IBD genetic susceptibility. They included expression markers for specific colon cell types. TWAS meta-analysis including all tissues/cell types provided 186 novel candidate susceptibility genes. Additionally, we identified 78 novel susceptibility genes whose expression is associated with IBD exclusively in immune (N = 19), epithelial (N = 25), mesenchymal (N = 22) and neural (N = 12) tissue categories. Associated genes were involved in relevant molecular pathways, including pathways related to known IBD therapeutics, such as tumour necrosis factor signalling. CONCLUSION: These findings provide insight into tissue-specific molecular processes underlying IBD genetic susceptibility. Associated genes could be candidate targets for new therapeutics and should be prioritized in functional studies.

Novel insights into the molecular mechanisms underlying risk of colorectal cancer from smoking and red/processed meat carcinogens by modeling exposure in normal colon organoids.

Tobacco smoke and red/processed meats are well-known risk factors for colorectal cancer (CRC). Most research has focused on studies of normal colon biopsies in epidemiologic studies or treatment of CRC cell lines in vitro. These studies are often constrained by challenges with accuracy of self-report data or, in the case of CRC cell lines, small sample sizes and lack of relationship to normal tissue at risk. In an attempt to address some of these limitations, we performed a 24-hour treatment of a representative carcinogens cocktail in 37 independent organoid lines derived from normal colon biopsies. Machine learning algorithms were applied to bulk RNA-sequencing and revealed cellular composition changes in colon organoids. We identified 738 differentially expressed genes in response to carcinogens exposure. Network analysis identified significantly different modules of co-expression, that included genes related to MSI-H tumor biology, and genes previously implicated in CRC through genome-wide association studies. Our study helps to better define the molecular effects of representative carcinogens from smoking and red/processed meat in normal colon epithelial cells and in the etiology of the MSI-H subtype of CRC, and suggests an overlap between molecular mechanisms involved in inherited and environmental CRC risk.

Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci.

BACKGROUND & AIMS: The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource. METHODS: We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses. RESULTS: We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application. CONCLUSIONS: We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.

MorbiNet Study: Hypothyroidism Comorbidity Networks in the Adult General Population.

PURPOSE: Multimorbidity impacts quality of life. We constructed hypothyroidism comorbidity networks to identify positive and negative associations with other prevalent diseases. METHODS: We analyzed data of 285 342 patients with hypothyroidism from 3 135 948 adults with multimorbidity in a population-based study in Catalonia, Spain, (period: 2006-2017). We constructed hypothyroidism comorbidity networks using logistic regression models, adjusted by age and sex, and for men and women separately. We considered relevant associations those with odds ratios (OR) >1.2 or <0.8 and P value < 1e-5 to identify coexistence greater (or smaller) than the expected by the prevalence of diseases. Multivariate models considering comorbidities were used to further adjust OR values. RESULTS: The conditions associated included larynx cancer (adjusted OR: 2.48), congenital anomalies (2.26), thyroid cancer (2.13), hyperthyroidism (1.66), vitamin B12/folate deficiency anemia (1.57), and goiter (1.56). The network restricted to men had more connections (mental, cardiovascular, and neurological) and stronger associations with thyroid cancer (7.26 vs 2.55), congenital anomalies (5.11 vs 2.13), hyperthyroidism (4.46 vs 1.69), larynx cancer (3.55 vs 1.67), and goiter (3.94 vs 1.64). After adjustment for comorbidities, OR values were more similar in men and women. The strongest negative associations after adjusting for comorbidities were with HIV/AIDS (OR: 0.71) and tobacco abuse (0.77). CONCLUSIONS: Networks show direct and indirect hypothyroidism multimorbidity associations. The strongest connections were thyroid and larynx cancer, congenital anomalies, hyperthyroidism, anemia, and goiter. Negative associations included HIV/AIDS and tobacco abuse. The network restricted to men had more and stronger associations, but not after adjusting for comorbidities, suggesting important indirect interactions.

DNA methylation events in transcription factors and gene expression changes in colon cancer.

Aim: Gain insight about the role of DNA methylation in the malignant growth of colon cancer. Patients & methods: Methylation and gene expression from 90 adjacent-tumor paired tissues and 48 healthy tissues were analyzed. Tumor genes whose change in expression was explained by changes in methylation were identified using linear models adjusted for tumor stromal content. Results: No differences in methylation were found between adjacent and healthy tissues, but clear differences were found between adjacent and tumor samples. We identified hypermethylated CpG islands located in promoter regions that drive differential gene expression of transcription factors and their target genes. Conclusion: Changes in methylation of a few genes provoke important changes in gene expression, by expanding the signal through transcription activation/repression.