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Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network.

ABSTRACT: BACKGROUND: As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI) network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. RESULTS: We constructed a weighted human PPI network (WHPN) using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN) was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching PubMed manually. We found that 31 of the optimized genes were targeted as drug response markers in DrugBank. CONCLUSIONS: Here we have shown that network theory combined with epigenetic characteristics provides a favorable platform from which to identify cancer-related genes. We prioritized 154 potential cancer-related genes with aberrant methylation that might contribute to the further understanding of cancers.

SUBMITTER: Liu H

PROVIDER: S-EPMC3224234 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network.

Liu Hui H Su Jianzhong J Li Junhua J Liu Hongbo H Lv Jie J Li Boyan B Qiao Hong H Zhang Yan Y

BMC systems biology 20111011

<h4>Background</h4>As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI) network o ...[more]

PMID: 21985575

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Project description:BACKGROUND: The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs) have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN). METHODS: In this study, we proposed a method to identify CRGs based on Gene Ontology (GO) and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene) from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs' GO and network characteristics. Lastly, we evaluated the performance of the proposed method. RESULTS: We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC) for our method is 65.2%, whereas that for the traditional method is 55.2%. CONCLUSIONS: Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable database for pharmacogenomics research.

Project description:Background: Asthma is a complex pulmonary inflammatory disease which is common among older adults. Aging-related alterations have also been found in structural cells and immune cells of asthma patients. Nonetheless, the underlying mechanism by which differenced aging-related gene contributes to asthma pathology remains unclear. Of note, DNA methylation (DNAm) has been proven to play a critical mechanism for age-related gene expression changes. However, the methylation changes of aging-related genes in asthma patients are still obscure. Methods: First, changes in DNAm and gene expression were detected with multiple targeted bisulfite enrichment sequencing (MethTarget) and qPCR in peripheral blood of 51 healthy controls (HCs) and 55 asthmatic patients. Second, the correlation between the DNAm levels of specific altered CpG sites and the pulmonary function indicators of asthma patients was evaluated. Last, the receiver operator characteristic (ROC) curve and principal component analysis (PCA) were used to identify the feasibility of the candidate CpG sites as biomarkers for asthma. Results: Compared with HCs, there was a differential mRNA expression for nine aging-related genes in peripheral blood of asthma patients. Besides, the methylation levels of the nine aging-related genes were also altered in asthma patients, and a total of 68 CpG sites were associated with the severity of asthma. Notably, 9 of the 68 CpG sites were significantly associated with pulmonary function parameters. Moreover, ROC curve and PCA analysis showed that the candidate differential methylation sites (DMSs) can be used as potential biomarkers for asthma. Conclusions: In summary, this study confirmed the differentially expressed mRNA and aberrant DNAm level of aging-related genes in asthma patients. DMSs are associated with the clinical evaluation indicators of asthma, which indicate the involvement of aging-related genes in the pathogenesis of asthma and provide some new possible biomarkers for asthma.

Dataset Information

Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network.

Publications

Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network.

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