Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks

Zheng, Fang; Le, Wei; Zhao, Liang; Ni, Fuchuan

doi:10.1155/2018/5670210

Cited by 16 publications

(9 citation statements)

References 49 publications

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“…Our results underpin the complex character of diseases pathophysiology, which involves deregulations in multiple biological pathways and cellular networks (Zheng et al, 2018) often in a population-specific fashion (Ran et al, 2011). In summary, our results demonstrated considerable specificity of the distribution of genes and biological processes associated with the same diseases at the geographic levels.…”

Section: Resultsmentioning

confidence: 67%

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

et al. 2019

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Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

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Section: Resultsmentioning

confidence: 67%

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

et al. 2019

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“…Centrality measures provide essential information about the organization of complex systems in network analysis [31,32]. Classical centrality measures such as those first formalized by Freeman et al [33], i.e., degree, closeness, and betweenness, can extract important information in biological networks by identifying hub and bottleneck nodes [34–36].…”

Section: Methodsmentioning

confidence: 99%

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

Boniolo

Hoffmann

Roggendorf

et al. 2022

Preprint

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Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA-gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. We show how spongEffects can be used for downstream machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects can perform classification tasks and prioritize ceRNA modules as biomarkers, hence offering insights into the miRNA regulatory landscape from gene expression data alone.

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“…Centrality measures provide important information about the organization of complex systems in network analysis ( 42 , 43 ). Del Rio et al.…”

Section: Methodsmentioning

confidence: 99%

SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions

et al. 2021

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microRNAs (miRNAs) are post-transcriptional regulators involved in many biological processes and human diseases, including cancer. The majority of transcripts compete over a limited pool of miRNAs, giving rise to a complex network of competing endogenous RNA (ceRNA) interactions. Currently, gene-regulatory networks focus mostly on transcription factor-mediated regulation, and dedicated efforts for charting ceRNA regulatory networks are scarce. Recently, it became possible to infer ceRNA interactions genome-wide from matched gene and miRNA expression data. Here, we inferred ceRNA regulatory networks for 22 cancer types and a pan-cancer ceRNA network based on data from The Cancer Genome Atlas. To make these networks accessible to the biomedical community, we present SPONGEdb, a database offering a user-friendly web interface to browse and visualize ceRNA interactions and an application programming interface accessible by accompanying R and Python packages. SPONGEdb allows researchers to identify potent ceRNA regulators via network centrality measures and to assess their potential as cancer biomarkers through survival, cancer hallmark and gene set enrichment analysis. In summary, SPONGEdb is a feature-rich web resource supporting the community in studying ceRNA regulation within and across cancer types.

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Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks

Cited by 16 publications

References 49 publications

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions

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