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Integrative Ranking of Enhancer Networks Facilitates the Discovery of Epigenetic Markers in Cancer.

ABSTRACT: Regulation of gene expression through multiple epigenetic components is a highly combinatorial process. Alterations in any of these layers, as is commonly found in cancer diseases, can lead to a cascade of downstream effects on tumor suppressor or oncogenes. Hence, deciphering the effects of epigenetic alterations on regulatory elements requires innovative computational approaches that can benefit from the huge amounts of epigenomic datasets that are available from multiple consortia, such as Roadmap or BluePrint. We developed a software tool named IRENE (Integrative Ranking of Epigenetic Network of Enhancers), which performs quantitative analyses on differential epigenetic modifications through an integrated, network-based approach. The method takes into account the additive effect of alterations on multiple regulatory elements of a gene. Applying this tool to well-characterized test cases, it successfully found many known cancer genes from publicly available cancer epigenome datasets.

SUBMITTER: Wang Q

PROVIDER: S-EPMC8201988 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Integrative Ranking of Enhancer Networks Facilitates the Discovery of Epigenetic Markers in Cancer.

Wang Qi Q Wu Yonghe Y Vorberg Tim T Eils Roland R Herrmann Carl C

Frontiers in genetics 20210531

Regulation of gene expression through multiple epigenetic components is a highly combinatorial process. Alterations in any of these layers, as is commonly found in cancer diseases, can lead to a cascade of downstream effects on tumor suppressor or oncogenes. Hence, deciphering the effects of epigenetic alterations on regulatory elements requires innovative computational approaches that can benefit from the huge amounts of epigenomic datasets that are available from multiple consortia, such as Ro ...[more]

PMID: 34135941

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Project description:BackgroundTo discover cancer specific DNA methylation markers, large-scale screening methods are widely used. The pharmacological unmasking expression microarray approach is an elegant method to enrich for genes that are silenced and re-expressed during functional reversal of DNA methylation upon treatment with demethylation agents. However, such experiments are performed in in vitro (cancer) cell lines, mostly with poor relevance when extrapolating to primary cancers. To overcome this problem, we incorporated data from primary cancer samples in the experimental design. A strategy to combine and rank data from these different data sources is essential to minimize the experimental work in the validation steps.AimTo apply a new relaxation ranking algorithm to enrich DNA methylation markers in cervical cancer.ResultsThe application of a new sorting methodology allowed us to sort high-throughput microarray data from both cervical cancer cell lines and primary cervical cancer samples. The performance of the sorting was analyzed in silico. Pathway and gene ontology analysis was performed on the top-selection and gives a strong indication that the ranking methodology is able to enrich towards genes that might be methylated. Terms like regulation of progression through cell cycle, positive regulation of programmed cell death as well as organ development and embryonic development are overrepresented. Combined with the highly enriched number of imprinted and X-chromosome located genes, and increased prevalence of known methylation markers selected from cervical (the highest-ranking known gene is CCNA1) as well as from other cancer types, the use of the ranking algorithm seems to be powerful in enriching towards methylated genes.Verification of the DNA methylation state of the 10 highest-ranking genes revealed that 7/9 (78%) gene promoters showed DNA methylation in cervical carcinomas. Of these 7 genes, 3 (SST, HTRA3 and NPTX1) are not methylated in normal cervix tissue.ConclusionThe application of this new relaxation ranking methodology allowed us to significantly enrich towards methylation genes in cancer. This enrichment is both shown in silico and by experimental validation, and revealed novel methylation markers as proof-of-concept that might be useful in early cancer detection in cervical scrapings.

Project description:Although technological advances now allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach towards understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Because enhancer activity is thought to be critical in regulating cell fate decisions, we have focused our studies on distal regulatory elements and transcription factors that bind to these elements. Using DNA methylation data, we identified more than 25,000 enhancers that are differentially activated in breast, prostate, and kidney tumor tissues, as compared to normal tissues. We then developed an analytical approach called TENET (Tracing Enhancer Networks using Epigenetic Traits) that correlates DNA methylation levels at enhancers with gene expression to identify more than 800,000 genome-wide links from enhancers to genes and from genes to enhancers. We found more than 1,200 transcription factors to be involved in these tumor-specific enhancer networks. We further characterized several transcription factors linked to a large number of enhancers in each tumor type, including GATA3 in non-basal breast tumors, HOXC6 and DLX1 in prostate tumors, and ZNF395 in kidney tumors. We showed that HOXC6 and DLX1 are associated with different clusters of prostate tumor-specific enhancers and confer distinct transcriptomic changes upon knockdown in C42B prostate cancer cells. We also discovered de novo motifs enriched in enhancers linked to ZNF395 in kidney tumors. Our studies characterized tumor-specific enhancers and revealed key transcription factors involved in enhancer networks for specific tumor types and subgroups. Our findings, which include a large set of identified enhancers and transcription factors linked to those enhancers in breast, prostate, and kidney cancers, will facilitate understanding of enhancer networks and mechanisms leading to the development of these cancers. Examination of FAIRE and ChIP assays in prostate and breast epithelial cells. RNA-seq assays in C42B, prostate cancer cell line after knocking down of TFs (HOXC6, DLX1).

Dataset Information

Integrative Ranking of Enhancer Networks Facilitates the Discovery of Epigenetic Markers in Cancer.

Publications

Integrative Ranking of Enhancer Networks Facilitates the Discovery of Epigenetic Markers in Cancer.

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