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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 2/2018

Open Access 01-07-2018 | Research

A semantics-oriented computational approach to investigate microRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia

Authors: Huiqin Chen, Dihua Zhang, Guoping Zhang, Xiaofeng Li, Ying Liang, Mohan Vamsi Kasukurthi, Shengyu Li, Glen M. Borchert, Jingshan Huang

Published in: BMC Medical Informatics and Decision Making | Special Issue 2/2018

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Abstract

Background

Acute lymphoblastic leukemia is the most prevalent neoplasia among children. Despite the tremendous achievements of state-of-the-art treatment strategies, drug resistance is still a major cause of chemotherapy failure leading to relapse in pediatric acute lymphoblastic leukemia. The underlying mechanisms of such phenomenon are not yet clear and subject to further exploration. Prior research has shown that microRNAs can act as post-transcriptional regulators of many genes related to drug resistance. However, details of microRNA regulation mechanisms in pediatric acute lymphoblastic leukemia are far from completely understood.

Methods

We utilized a computational approach based upon emerging biomedical and biological ontologies and semantic technologies to investigate the important roles of microRNA: mRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia. In particular, various filtering mechanisms were designed based on the user-provided MeSH term to narrow down the most promising microRNAs in an effective manner.

Results

During our manual search on background literature, we found a total of 18 candidate microRNAs that possibly regulate glucocorticoid resistance in pediatric acute lymphoblastic leukemia. After the first-round filtering using the Broader-Match option where both the user-provided MeSH term and its direct parent term were utilized, the number of targets for 18 microRNAs was reduced from 232 to 74. During the second-round filtering with the Exact-Match option where only the MeSH term itself was utilized, the number of targets was further reduced to 19. Finally, we conducted semantic searches in the OmniSearch software tool on the five likely regulating microRNAs and identified two most likely microRNAs.

Conclusions

We successfully identified two microRNAs, hsa-miR-142-3p and hsa-miR-17-5p, which are computationally predicted to closely relate to glucocorticoid resistance, thus potentially serving as novel biomarkers and therapeutic targets in pediatric acute lymphoblastic leukemia.
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Metadata
Title
A semantics-oriented computational approach to investigate microRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia
Authors
Huiqin Chen
Dihua Zhang
Guoping Zhang
Xiaofeng Li
Ying Liang
Mohan Vamsi Kasukurthi
Shengyu Li
Glen M. Borchert
Jingshan Huang
Publication date
01-07-2018
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s12911-018-0637-3

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