Project description:Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms. Bisulphite modification of 46 tumor DNA samples using EZ-96 DNA METHYLATIONGOLD KIT (Zymo Research, Irvin, CA, USA), CpG methylation data was generated using the Infinum Human Methylation 27 BeadChip Kit that detected the CpG sites with two probes, one designed against the unmethylated site (signal U) and the other against the methylated site (signal M).

Project description:Genetic abnormalities of cholangiocarcinoma (CCA) have been widely studied; however, epigenomic changes related to cholangiocarcinogenesis have been less well characterized. We have profiled the DNA methylomes of 28 primary CCA and six matched adjacent normal tissues using Infinium’s HumanMethylation27 BeadChips with the aim of identifying gene sets aberrantly epigenetically regulated in CCA. Using a linear model for microarray data we identified 1610 differentially methylated autosomal CpG sites with 809 CpG sites (representing 603 genes) being hypermethylated and 801 CpG sites (representing 712 genes) being hypomethylated in CCA versus adjacent normal (false discovery rate ? 0.05). Gene Ontology and Gene Set Enrichment analyses identified gene sets significantly associated with hypermethylation at linked CpG sites in CCA including homeobox genes and target genes of PRC2, EED, SUZ12 and Histone H3 trimethylation at lysine 27. We confirmed frequent hypermethylation at the homeobox genes HOXA9 and HOXD9 by bisulfite pyrosequencing in a larger cohort of CCA (n = 103). Our findings indicate a key role for hypermethylation of multiple CpG sites at genes associated with a stem cell-like phenotype as a common molecular aberration in CCA. These data have implications for CCA carcinogenesis, as well as possible novel treatment options using histone methyltransferase inhibitors. Methods: Thirty-two primary CCA, 6 matched adjacent normal, 5 CCA cell lines and an immortal biliary cell line were analyzed for DNA methylation profiles using the Infinium HumanMethylation27 BeadChip. Differential methylation was statistically analyzed using LIMMA. Specific functional terms and Gene Set Enrichment Analysis were performed to identify specific function of aberrantly methylated genes. Results: We identified 711 and 590 autosomal CpG sites as being hypermethylated and hypomethylated representing 527 and 521 genes, respectively, in CCA compared to adjacent normal (false discovery rate 0.05). A number of gene sets were significantly associated with hypermethylation at linked CpG sites in CCA including homeobox genes and the target genes of PRC2, EED, SUZ12, and H3K27me3, whereas target genes of NOS and OCT4 were hypomethylated at linked CpG sites in tumors. Frequent aberrant DNA methylation at HOXA9 and HOXD9 was confirmed by bisulfite pyrosequencing in a larger cohort of CCA (n = 107). Conclusions: This is the first report of a DNA methylation profile of multiple CpG sites at genes associated with a stem cell-like phenotype as a common molecular aberration in CCA. These data have implications for CCA carcinogenesis, as well as possible novel treatment options using histone methyltransferase inhibitors. Array-based methylation profiling was performed using the Infinium HumanMethylation27 BeadChip in 28 primary cholangiocarcinomas and six matched adjacent normal tissues. The reproducibility of the Infinium HumanMethylation27 BeadChips was evaluated using five technical replicates of the ovarian cancer cell line PEO1. Differential methylation cutoff was estimated from five technical replicates by bootstrap resampling and set at ?? ? 0.15 corresponding to a FDR ? 0.05.

Project description:To better understand the biology of hormone receptor-positive and negative breast cancer and to identify methylated gene markers of disease progression, we performed a genome-wide methylation array analysis on 103 primary invasive breast cancers and 21 normal breast samples using the Illumina Infinium HumanMethylation27 array that queried 27,578 CpG loci. Estrogen and/or progesterone receptor-positive tumors displayed more hypermethylated loci than ER-negative tumors. However, the hypermethylated loci in ER-negative tumors were clustered closer to the transcriptional start site compared to ER-positive tumors. An ER-classifier set of CpG loci was identified, which independently partitioned primary tumors into ER-subtypes. Forty (32 novel, 8 previously known) CpG loci showed differential methylation specific to either ER-positive or ER-negative tumors. Each of the 40 ER-subtype-specific loci was validated in silico using an independent, publicly available methylome dataset from The Cancer Genome Atlas (TCGA). In addition, we identified 100 methylated CpG loci that were significantly associated with disease progression; the majority of these loci were informative particularly in ER-negative breast cancer. Overall, the set was highly enriched in homeobox containing genes. This pilot study demonstrates the robustness of the breast cancer methylome and illustrates its potential to stratify and reveal biological differences between ER-subtypes of breast cancer. Further, it defines candidate ER-specific markers and identifies potential markers predictive of outcome within ER subgroups. Frozen breast cancer tissues that were excised from patients with Stage 1-3 disease prior to treatment (n=103) were retrieved from Surgical Pathology at Johns Hopkins Hospital (Baltimore, Maryland) and confirmed to contain > 50% epithelial cells. Normal breast organoids were prepared by enzymatic digestion of reduction mammoplasty specimens (n=15; median patient age = 52 years, range 47 to 71). Normal ducts from breast tissue > 2 cm away from the tumor (n=6) were isolated from cryosections using laser-capture micro-dissection (PALM MicroBeam, Carl Zeiss Microimaging, North America). To determine the differences in breast cancer biology/behavior between ER subtypes, we characterized methylation patterns at 8376 selected CpG loci according to ER status. These loci met two criteria: 1) showed the most variation across primary tumors (SD >0.100) and 2) had probe detection p-values <0.0001. To develop an epigenomic signature that predicts outcome in patients with breast cancer, we conducted differential methylation analysis on primary tumors from recurrent versus non-recurrent breast cancers. We used a subgroup of 82 well-annotated, invasive breast tumors derived from the discovery set of 103 tumors that included 44 ER-positive (7 recurrences) and 38 ER-negative (11 recurrences) breast cancers and independently queried the ER-positive and ER-negative tumor groups

Dataset Information

Integration analysis of three omics data using penalized regression methods: An application to bladder cancer

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure