346 research outputs found

    Pathway relevance ranking for tumor samples through network-based data integration

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    The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival

    Identification of Novel Cancer-Related Genes with a Prognostic Role Using Gene Expression and Protein-Protein Interaction Network Data

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    Early cancer diagnosis and prognosis prediction are necessary for cancer patients. Effective identification of cancer-related genes and biomarkers and survival prediction for cancer patients would facilitate personalized treatment of cancer patients. This study aimed to investigate a method for integrating data regarding gene expression and protein-protein interaction networks to identify cancer-related prognostic genes via random walk with restart algorithm and survival analysis. Known cancer-related genes in protein-protein interaction networks were considered seed genes, and the random walk algorithm was used to identify candidate cancer-related genes. Thereafter, using the univariant Cox regression model, gene expression data were screened to identify survival-related genes. Furthermore, candidate genes and survival-related genes were screened to identify cancer-related prognostic genes. Finally, the effectiveness of the method was verified through gene function analysis and survival prediction. The results indicate that the cancer-related genes can be considered prognostic cancer biomarkers and provide a basis for cancer diagnosis

    An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways

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    Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotypes. Importantly, we discover tissue independent age-associated hotspots targeting stem-cell differentiation pathways, which we validate in independent DNA methylation data sets, encompassing over 1000 samples from different tissue types. We further show that these pathways would not have been discovered had we used a non-network based approach and that the use of the protein interaction network improves the overall robustness of the inference procedure. The proposed algorithm will be useful to any study seeking to identify interactome hotspots associated with common phenotypes

    Genome-wide analysis of DNA methylation topology to understand cell fate

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    DNA methylation is an epignetic modification associated with gene regulation. It has extensively been studied in the context of small regulatory regions. Yet, not so much is known about large domains characterized by fuzzy methylation patterns, termed Partially Methylated Domains (PMDs). The present thesis comprises PMD analyses in various contexts and provides several new aspects to study DNA methylation. First, a comprehensive analysis of PMDs across a large cohort of WGBS samples was performed, to identify structural and functional features associated with PMDs. A newly developed approach, ChromH3M, was proposed for the analysis and integration of a large spectrum of WGBS data sets. Second, PMDs were found to be indicators of the cellular proliferation history and segmented loss of DNA methylation in PMDs supports the sequential linear differentiation model of memory T-cells. Third, assessment of genome-wide methylation changes in PMDs of Multiple Sclerosis-discordant monozygotic co-twins did not show significant differences, but local changes (DMRs) were identified. Taken together, the outcomes of the presented studies shed light on a so far neglected aspect of DNA methylation, that is PMDs, in different contexts; lineage specialization, differentiation, replication, disease, chromatin organization and gene expression.Die DNA-Methylierung ist eine epigenetische Modi1kation, die funktionell mit der Genregulation verbunden ist. Sie wurde bereits ausfĂŒhrlich im Kontext kleiner regulatorischer Regionen untersucht. Es ist jedoch noch nicht sehr viel bekannt ĂŒber große DomĂ€nen, welche erstmals in WGBS-Daten beschrieben wurden. Sie werden als partiell methylierte Regionen (PMDs) bezeichnet und sind durch das Vorhandensein variabler Methylierungsmuster charakterisiert. Die vorliegende Arbeit umfasst PMD-Analysen in unterschiedlichen Kontexten und liefert verschiedene neue Aspekte zur Untersuchung der DNA-Methylierung. Zuerst wurde eine umfassende Analyse von PMDs in einer großen Kohorte von WGBS-Proben durchgefĂŒhrt, um strukturelle und funktionelle Merkmale zu identi 1zieren, die mit PMDs assoziert sind. Ein neu entwickelter Ansatz, ChromH3M, wurde fĂŒr die Analyse und Integration einer großen Kohorte vonWGBS DatensĂ€tzen angewandt. Zweitens wurde festgestellt, dass PMDs Indikatoren fĂŒr die Zellproliferationshistorie sind, und der zu beobachtende graduelle Verlust der globalen DNAMethylierung bei der Differenzierung von T-GedĂ€chtniszellen unterstĂŒtzt die Hypothese der sequenziellen linearen Differenzierung. Drittens zeigte die Bewertung der genomweiten MethylierungsĂ€nderungen in PMDs von Multiple Sklerose-diskordanten monozygoten Zwillingen keine signi1kanten Unterschiede, jedoch wurden lokale Änderungen (DMRs) identi1ziert. Insgesamt geben die Ergebnisse der vorgestellten Studien Aufschluss ĂŒber einen bislang eher vernachlĂ€ssigten Aspekt der DNA-Methylierung, d.h. PMDs, in verschiedenen ZusammenhĂ€ngen: der Festlegung der Zell-entwicklungsbahnen, der Zelldifferenzierung, der Replikation, die Krankheit, der Organisation des Chromatins, sowie der Regulation der Genexpression

    Network-guided data integration and gene prioritization

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    How Long Non-Coding RNAs and MicroRNAs Mediate the Endogenous RNA Network of Head and Neck Squamous Cell Carcinoma: a Comprehensive Analysis

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    Background/Aims: Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to compete for microRNAs (miRNAs) in cancer metastasis. Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers and rare biomarkers could predict the clinical prognosis of this disease and its therapeutic effect. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify differentially expressed mRNAs (DEmRNAs) that might be key genes. GO enrichment and protein–protein interaction (PPI) analyses were performed to identify the principal functions of the DEmRNAs. An lncRNA-miRNA-mRNA network was constructed to understand the regulatory mechanisms in HNSCC. The prognostic signatures of mRNAs, miRNAs, and lncRNAs were determined by Gene Expression Profiling Interactive Analysis (GEPIA) and using Kaplan–Meier survival curves for patients with lung squamous cell carcinoma. Results: We identified 2,023 DEmRNAs, 1,048 differentially expressed lncRNAs (DElncRNAs), and 82 differentially expressed miRNAs (DEmiRNAs). We found that eight DEmRNAs, 53 DElncRNAs, and 16 DEmiRNAs interacted in the ceRNA network. Three ceRNAs (HCG22, LINC00460 and STC2) were significantly correlated with survival. STC2 transcript levels were significantly higher in tumour tissues than in normal tissues, and the STC2 expression was slightly upregulated at different stages of HNSCC. Conclusion: LINC00460, HCG22 and STC2 exhibited aberrant levels of expression and may participate in the pathogenesis of HNSCC
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