Systematically Prioritizing Functional Differentially Methylated Regions (fDMRs) by Integrating Multi-omics Data in Colorectal Cancer

Chronic intestinal inflammation and microbial dysbiosis are hallmarks of colorectal cancer (CRC) and inflammatory bowel diseases (IBD), such as Crohn’s disease and ulcerative colitis. However, the mechanistic relationship between gut dysbiosis and disease has not yet been fully characterized. Although the “trigger” of intestinal inflammation remains unknown, a wealth of evidence supports the role of the gut microbiome as a mutualistic pseudo-organ that significantly influences intestinal homeostasis and is capable of regulating host immunity. In recent years, culture-independent methods for assessing microbial communities as a whole (termed meta-omics) have grown beyond taxonomic identification and genome characterization (metagenomics) into new fields of research that collectively expand our knowledge of microbiomes. Metatranscriptomics, metaproteomics, and metabolomics are meta-omics techniques that aim to describe and quantify the functional activity of the gut microbiome. Uncovering microbial metabolic contributions in the context of IBD and CRC using these approaches provides insight into how the metabolic microenvironment of the GI tract shapes microbial community structure and how the microbiome, in turn, influences the surrounding ecosystem. Immunological studies in germ-free and wild-type mice have described several host-microbiome interactions that may play a role in autoinflammation. Chronic colitis is a precursor to CRC, and changes in the gut microbiome may be an important link triggering the neoplastic process in chronic colitis. In this review, we describe several microbiome-mediated mechanisms of host immune signaling, such as short-chain fatty acid (SCFA) and bile acid metabolism, inflammasome activation, and cytokine regulation in the context of IBD and CRC, and discuss the supporting role for these mechanisms by meta-omics data.

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Decitabine treatment demethylates vast majority of high-confidence differentially methylated regions in HCT-116 colorectal cancer cells

F1000Research ◽

10.12688/f1000research.20442.1 ◽

2020 ◽

Vol 9 ◽

pp. 886

Author(s):

Anna Sandler ◽

Jonathan Anderson ◽

Ariane Balaram ◽

Yoan Ganev ◽

Samuel Gascoigne ◽

...

Keyword(s):

Colorectal Cancer ◽

Cpg Island ◽

Promoter Hypermethylation ◽

Clonogenic Survival ◽

Causal Link ◽

Cancer Drug ◽

Transcriptional Enhancer ◽

Differentially Methylated Regions ◽

High Confidence ◽

Hct 116

Background: Gene silencing by CpG island hypermethylation often plays a role in colorectal cancer (CRC) progression. Certain regions of the genome, called high confidence differentially-methylated regions (DMRs), are consistently hypermethylated across numerous patient samples. Methods: In this study, we used bioinformatics and bisulfite PCR sequencing of HCT-116 cells to investigate methylation levels at DMRs in the promoters of six genes: DKK3, EN1, MiR34b, SDC2, SPG20, and TLX1. We then investigated whether the anti-cancer drug decitabine, had a demethylating effect at these promoter regions. Results: We found that hypermethylation correlated with lack of transcriptional enhancer binding in these six regions. Importantly, we observed that for all DMRs, decitabine significantly reduced CpG methylation. Decitabine also reduced clonogenic survival, suggesting that there is a correlation between lower CpG island methylation levels and reduced cancerous properties. Conclusions: Our study provided single-nucleotide resolution and revealed hypermethylated CpG sites not shown by previous genome-wide methylation studies. In the future, we plan to perform experiments that demonstrate a causal link between promoter hypermethylation and carcinogenesis and that more accurately model treatments in CRC patients.

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Constructing Tumor Immune Microenvironment and Identifying the Immune-Related Prognostic Signatures in Colorectal Cancer Using Multi-omics Data

10.1101/2021.08.30.21262762 ◽

2021 ◽

Author(s):

Shuai Zhang ◽

Jiali Lv ◽

Bingbing Fan ◽

Zhe Fan ◽

Chunxia Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Cross Validation ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Omics Data ◽

Epigenetic Alterations ◽

Lasso Regression ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

ABSTRACTBackgroundThe tumor immune microenvironment (TIME) plays a key role in occurrence, progression and prognosis of colorectal cancer (CRC). However, the genetic and epigenetic alterations and potential mechanisms in the TIME of CRC are still unclear.MethodsWe investigated the immune-related differences in three types of genetic or epigenetic alterations (gene expression, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and the immune-related biological processes by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step method based on LASSO regression and Cox regression was used to construct the prognostic prediction model. Cross validation was performed to validate the model.ResultsA total of 1,745 differentially expressed genes, 178 differentially mutated genes and 1,961 differentially methylation probes were identified between the high-immunity group and the low-immunity group. We retained 15 genetic and epigenetic variables after using LASSO regression and Cox regression. For the prognostic predictions on the TCGA profiles, the performance of the model with 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.861, 0.797, and 0.875. Finally, the overall risk score model was constructed based on genetic, epigenetic, demographic and clinical characteristics, which comprised 18 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.865), 3-year (AUC = 0.839), and 5-year (AUC = 0.914) survival predictions. Kaplan-Meier survival analysis demonstrated that the overall survival of the high-risk group was significantly poorer compared with the low-risk group. Prognostic nomogram, calibration plot and cross validation showed excellent predictive performance.ConclusionsOur study provides a new clue to explore the TIME of CRC patients in genetic and epigenetic aspects. Meanwhile, the prognostic model also has clinical prognostic value and may provide new indicators for the treatment of CRC patients.

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Comprehensive methylation analysis of imprinting-associated differentially methylated regions in colorectal cancer

Clinical Epigenetics ◽

10.1186/s13148-018-0578-9 ◽

2018 ◽

Vol 10 (1) ◽

Cited By ~ 9

Author(s):

Hidenori Hidaka ◽

Ken Higashimoto ◽

Saori Aoki ◽

Hiroyuki Mishima ◽

Chisa Hayashida ◽

...

Keyword(s):

Colorectal Cancer ◽

Methylation Analysis ◽

Differentially Methylated Regions

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Integrated multi‐omics data analyses for exploring the co‐occurring and mutually exclusive gene alteration events in colorectal cancer

Human Mutation ◽

10.1002/humu.24059 ◽

2020 ◽

Author(s):

Yuan Zhou ◽

Xiaoqing Cheng ◽

Fenglan Zhang ◽

Qingqing Chen ◽

Xinyu Chen ◽

...

Keyword(s):

Colorectal Cancer ◽

Omics Data ◽

Gene Alteration ◽

Data Analyses

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crcTRP: A Translational Research Platform for Colorectal Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/930362 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Ning Deng ◽

Ling Zheng ◽

Fang Liu ◽

Li Wang ◽

Huilong Duan

Keyword(s):

Colorectal Cancer ◽

Translational Research ◽

Clinical Data ◽

Genomic Data ◽

Basic Research ◽

Information Collection ◽

Omics Data ◽

Research Platform ◽

Epidemiology Data ◽

Er Model

Colorectal cancer is a leading cause of cancer mortality in both developed and developing countries. Transforming basic research results into clinical practice is one of the key tasks of translational research, which will greatly improve the diagnosis and treatments of colorectal cancer. In this paper, a translational research platform for colorectal cancer, named crcTRP, is introduced. crcTRP serves the colorectal cancer translational research by providing various types of biomedical information related with colorectal cancer to the community. The information, including clinical data, epidemiology data, individual omics data, and public omics data, was collected through a multisource biomedical information collection solution and then integrated in a clinic-omics database, which was constructed with EAV-ER model for flexibility and efficiency. A preliminary exploration of conducting translational research on crcTRP was implemented and worked out a set of clinic-genomic relations, linking clinical data with genomic data. These relations have also been applied to crcTRP to make it more conductive for cancer translational research.

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