Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for...

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Vydáno v:PloS one Ročník 10; číslo 10; s. e0140072
Hlavní autoři: Sehgal, Vasudha, Seviour, Elena G., Moss, Tyler J., Mills, Gordon B., Azencott, Robert, Ram, Prahlad T.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 27.10.2015
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Apoptosis
Binding sites
Bioinformatics
Biology
Biomarkers
Biomarkers, Tumor - biosynthesis
Biomarkers, Tumor - genetics
Breast cancer
Cancer
Computational Biology
Data management
Data processing
Datasets
Development and progression
Gene expression
Gene Expression Regulation, Neoplastic
Genomes
Genomics
Homeostasis
Humans
Identification methods
Integration
Medical prognosis
MicroRNA
MicroRNAs
MicroRNAs - biosynthesis
MicroRNAs - genetics
miRNA
Neoplasms - genetics
Network analysis
Normality
Ovarian cancer
Patients
Resampling
Risk factors
RNA, Messenger - biosynthesis
Robustness
Suppressors
Survival
Survival analysis
United States
United States > US
Texas
ISSN:1932-6203, 1932-6203
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Shrnutí:MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: VS PTR RA. Performed the experiments: VS EGS TJM PTR RA. Analyzed the data: VS EGS TJM. Contributed reagents/materials/analysis tools: VS EGS TJM PTR RA GBM. Wrote the paper: VS EGS PTR RA.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0140072