Paper Info
Title
MicroRNA dysregulational synergistic network: Learning context-specific MicroRNA dysregulations in lung cancer subtypes
Abstract
Recently, microRNAs were found to have potential as both diagnostic biomarkers and therapeutic targets for lung cancer, especially in identifying early-stage cancer. However, a miRNA biomarker derived from the standard normal v.s tumor differential expression analysis is not robust, as its functional interactions with messenger-RNA targets may change between different lung cancer subtypes. Furthermore, as evidence suggests miRNA dysregulations and synergistic regulations are important to understanding many cancer diseases, it is important to consider the changes in miRNA-target associations among different lung cancer subtypes. We proposed a pipeline to identify miRNA synergistic modules with high context-specific functional similarity by constructing the MicroRNA Dysregulational Synergistic Network (MDSN). Then, we incorporated the extracted miRNA modules as prior knowledge to a Sparse Group Lasso classifier for more relevant selection of microRNA biomarkers, thereby improving classification results. We applied the method to the TCGA Lung Adenocarcinoma dataset and found extracted miRNA modules from independent subtypes differential analyses to have high agreement with known miRNA family assignments. Cross-validation test also demonstrates clustering miRNAs by considering the dysregulation between miRNAs and its targets results in more accurate prediction of cancer stage.
Year
DOI
Venue
2017
10.1109/BIBM.2017.8217640
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
DocType
ISSN
miRNA dysregulation,scale-free network,group lasso,biomarker selection
Conference
2156-1125
ISBN
Citations 
PageRank 
978-1-5090-3051-4
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Nhat Tran100.34
Vinay Abhyankar200.34
KyTai Nguyen300.68
Ishfaq Ahmad42884192.17
Jon Weidanz501.01
Jean X. Gao626741.79