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BMC Bioinformatics. 2018 Dec 21;19(Suppl 20):504. doi: 10.1186/s12859-018-2536-0.

MicroRNA dysregulational synergistic network: discovering microRNA dysregulatory modules across subtypes in non-small cell lung cancers.

Author information

1
Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.
2
UTARI Research Institute, The University of Texas at Arlington, 7300 Jack Newell Blvd S, Fort Worth, TX, 76118, USA.
3
Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.
4
Department of Biology, The University of Texas at Arlington, Arlington, TX, 76019, USA.
5
Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA. gao@uta.edu.

Abstract

BACKGROUND:

The majority of cancer-related deaths are due to lung cancer, and there is a need for reliable diagnostic biomarkers to predict stages in non-small cell lung cancer cases. Recently, microRNAs were found to have potential as both biomarkers and therapeutic targets for lung cancer. However, some of the microRNA's functions are unknown, and their roles in cancer stage progression have been mostly undiscovered in this clinically and genetically heterogeneous disease. As evidence suggests that microRNA dysregulations are implicated in many diseases, it is essential to consider the changes in microRNA-target regulation across different lung cancer subtypes.

RESULTS:

We proposed a pipeline to identify microRNA synergistic modules with similar dysregulation patterns across multiple subtypes by constructing the MicroRNA Dysregulational Synergistic Network. From the network, we extracted microRNA modules and incorporated them as prior knowledge to the Sparse Group Lasso classifier. This leads to a more relevant selection of microRNA biomarkers, thereby improving the cancer stage classification accuracy. We applied our method to the TCGA Lung Adenocarcinoma and the Lung Squamous Cell Carcinoma datasets. In cross-validation tests, the area under ROC curve rate for the cancer stages prediction has increased considerably when incorporating the learned microRNA dysregulation modules. The extracted modules from multiple independent subtypes differential analyses were found to have high agreement with microRNA family annotations, and they can also be used to identify mutual biomarkers between different subtypes. Among the top-ranked candidate microRNAs selected by the model, 87% were reported to be related to Lung Adenocarcinoma. The overall result demonstrates that clustering microRNAs from the dysregulation pattern between microRNAs and their targets leads to biomarkers with high precision and recall rate to known differentially expressed disease-associated microRNAs.

CONCLUSIONS:

The results indicated that our method improves microRNA biomarker selection by detecting similar microRNA dysregulational synergistic patterns across the multiple subtypes. Since microRNA-target dysregulations are implicated in many cancers, we believe this tool can have broad applications for discovery of novel microRNA biomarkers in heterogeneous cancer diseases.

KEYWORDS:

Biomarker discovery; Differential analysis; Scale-free network; Synergistic module; microRNA dysregulation

PMID:
30577741
PMCID:
PMC6302368
DOI:
10.1186/s12859-018-2536-0
[Indexed for MEDLINE]
Free PMC Article

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