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Identification of Disease-miRNA Networks Across Different Cancer Types Using SWIM
Giulia Fiscon1,2, Federica Conte1,2, Lorenzo Farina3
1Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Rome, Italy.
Abstract:
MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).
Insights
This study introduces SWIM software to identify key genes in biological networks. It builds microRNA-disease networks using gene expression data for cancer research.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- MicroRNAs (miRNAs) are small noncoding RNAs regulating gene expression post-transcriptionally.
- Predicting miRNA-binding sites is complex, often relying solely on sequence complementarity.
- Integrating miRNA and gene expression data enhances biological network construction.
Purpose of the Study:
- To present an application of the SWIM software for identifying key genes within biological networks.
- To demonstrate the utility of SWIM in constructing microRNA-disease networks.
- To apply this approach to tumor data from The Cancer Genome Atlas (TCGA).
Main Methods:
- Utilizing the SWIM software to define gene "roles" based on network positioning.
- Integrating miRNA and protein-coding gene expression data.
- Applying the methodology to analyze tumor data from TCGA.
Main Results:
- SWIM successfully identifies key genes by analyzing their network roles.
- The approach enables the construction of specific microRNA-disease networks.
- Analysis of TCGA tumor data provides insights into miRNA-disease associations.
Conclusions:
- SWIM is a valuable tool for network analysis and identifying crucial genes.
- Integrating expression data and network analysis improves understanding of miRNA functions in disease.
- This method offers a novel approach for exploring miRNA-disease relationships in cancer.
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