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Tumor Biology

, Volume 37, Issue 6, pp 7861–7872 | Cite as

Integrative computational in-depth analysis of dysregulated miRNA-mRNA interactions in drug-resistant pediatric acute lymphoblastic leukemia cells: an attempt to obtain new potential gene-miRNA pathways involved in response to treatment

  • Hamzeh Mesrian Tanha
  • Marjan Mojtabavi Naeini
  • Soheila RahgozarEmail author
  • Alireza Moafi
  • Mohammad Amin Honardoost
Original Article

Abstract

Acute lymphoblastic leukemia (ALL) is the major neoplasia type among children. Despite the tremendous success of current treatment strategies, drug resistance still remains a major cause of chemotherapy failure and relapse in pediatric patients. Overwhelming evidence illustrates that microRNAs (miRNAs) act as post-transcriptional regulators of drug-resistance-related genes. The current study was aimed at how dysregulated miRNA-mRNA-signaling pathway interaction networks mediate resistance to four commonly used chemotherapy agents in pediatric ALL, including asparaginase, daunorubicin, prednisolone, and vincristine. Using public expression microarray datasets, a holistic in silico approach was utilized to investigate candidate drug resistance miRNA-mRNA-signaling pathway interaction networks in pediatric ALL. Our systems biology approach nominated significant drug resistance and cross-resistance miRNAs, mRNAs, and cell signaling pathways based on anti-correlative relationship between miRNA and mRNA expression pattern. To sum up, our systemic analysis disclosed either a new potential role of miRNAs, or a possible mechanism of cellular drug resistance, in chemotherapy resistance of pediatric ALL. The current study may shed light on predicting drug response and overcoming drug resistance in childhood ALL for subsequent generations of chemotherapies.

Keywords

Acute lymphoblastic leukemia Drug resistance miRNA Signaling pathway Therapeutic targets 

Abbreviations

ALL

Acute lymphoblastic leukemia

ASNS

Asparagine synthetase

DAVID

Database for Annotation, Visualization and Integrated Discovery

ECM

Extracellular matrix

GEO

Gene Expression Omnibus

GR

Glucocorticoid receptor

miRNAs

MicroRNAs

eIF2α-P

Phosphorylated eIF2α

SEM

Standard error of the mean

TCR

T cell receptor

Top2

Topoisomerase II

11βHSD2

11-β-Hydroxysteroid dehydrogenase 2

Notes

Acknowledgments

We are grateful to Drs. Mohammad Dabaghi (Jena University Hospital, Jena, Germany), Marjan Abedi (University of Isfahan, Isfahan, Iran), and Mansureh Entezar-e-ghaem (University of Isfahan, Isfahan, Iran) for their kind consultations.

Compliance with ethical standards

Funding source

None.

Financial disclosure

The authors have no financial relationships relevant to this article to disclose.

Conflicts of interest

None

Authorship and disclosures

Hamzeh Mesrian Tanha: The study conception and design, data collection and analysis, interpretation, and manuscript writing.

Marjan Mojtabavi Naeini: Data collection and analysis, interpretation, and manuscript writing.

Soheila Rahgozar: Conception, manuscript writing, and final approval of manuscript.

Alireza Moafi: Conception and final approval of manuscript.

Mohammad Amin Honardoost: Data analysis.

Supplementary material

13277_2015_4553_MOESM1_ESM.pdf (237 kb)
Supplementary Figure S1 Schematic representation of miRNA scoring procedure. Each miRNA was scored to identify the most possible targeted pathways by miRNAs. The miRNA was scored upon multiplying the mean of SUM score for predicted and validated interaction in the pathway, an interaction ratio, and a fold enrichment of the pathway. Overall line illustrated whole dots of pathways with the same status simultaneously in a line. In our example, score 1–9 and 10–17 was enhanced for overall distribution of down- and up-regulated status, respectively. The SEM (standard error of the mean) of overall distribution scores was defined as confidential threshold. Accordingly, pathways containing mean scores approximate equal or above the threshold are the most relevant ones regarding miRNA-mediated targeting. For instance, among down-regulated pathways, pathway 2 and 3 were considered as confident down-regulated pathways. (PDF 237 kb)
13277_2015_4553_MOESM2_ESM.pdf (1.8 mb)
Supplementary Figure S2 MicroRNA-mRNA target-signaling pathway interaction network underlying asparaginase-resistance. Nodes contain miRNAs (rounded rectangle), mRNA targets (ellipse), and signaling pathways (green octagon). Node size represents proportionally the score average of miRNA interaction. Continuous mapping of blue and red nodes indicate down and up-regulation, respectively. Edge width reflects strength of mRNA-miRNA interaction. An annotation of mRNAs within pathways is shown in contiguous arrow edge with delta end. In addition, edge line style for validated (solid line with T end) and predicted (equal dash line with T end) interaction is represented in different way. (PDF 1865 kb)
13277_2015_4553_MOESM3_ESM.pdf (3.3 mb)
Supplementary Figure S3 MicroRNA-mRNA target-signaling pathway interaction network underlying daunorubicin-resistance. Nodes contain miRNAs (rounded rectangle), mRNA targets (ellipse), and signaling pathways (green octagon). Node size represents proportionally the score average of miRNA interaction. Continuous mapping of blue and red nodes indicate down and up-regulation, respectively. Edge width reflects strength of mRNA-miRNA interaction. An annotation of mRNAs within pathways is shown in contiguous arrow edge with delta end. In addition, edge line style for validated (solid line with T end) and predicted (equal dash line with T end) interaction is represented in different way. (PDF 3330 kb)
13277_2015_4553_MOESM4_ESM.pdf (1.9 mb)
Supplementary Figure S4 MicroRNA-mRNA target-signaling pathway interaction network underlying prednisolone-resistance. Nodes contain miRNAs (rounded rectangle), mRNA targets (ellipse), and signaling pathways (green octagon). Node size represents proportionally the score average of miRNA interaction. Continuous mapping of blue and red nodes indicate down and up-regulation, respectively. Edge width reflects strength of mRNA-miRNA interaction. An annotation of mRNAs within pathways is shown in contiguous arrow edge with delta end. In addition, edge line style for validated (solid line with T end) and predicted (equal dash line with T end) interaction is represented in different way. (PDF 1934 kb)
13277_2015_4553_MOESM5_ESM.pdf (2.1 mb)
Supplementary Figure S5 MicroRNA-mRNA target-signaling pathway interaction network underlying vincristine-resistance. Nodes contain miRNAs (rounded rectangle), mRNA targets (ellipse), and signaling pathways (green octagon). Node size represents proportionally the score average of miRNA interaction. Continuous mapping of blue and red nodes indicate down and up-regulation, respectively. Edge width reflects strength of mRNA-miRNA interaction. An annotation of mRNAs within pathways is shown in contiguous arrow edge with delta end. In addition, edge line style for validated (solid line with T end) and predicted (equal dash line with T end) interaction is represented in different way. (PDF 2180 kb)
13277_2015_4553_MOESM6_ESM.pdf (22 kb)
Supplementary Figure S6 MicroRNA-drug resistance interaction network underlying cross-resistance to more than one drug. Nodes contain miRNAs (rounded rectangle) and drugs (violet V shape). Node size represents proportionally the score average of miRNA interaction with drug resistance based on all potential interaction for miRNAs and levels of potential targeting by miRNAs for drugs. Continuous mapping of blue and red nodes indicate down and up-regulation, respectively. Edge width reflects strength of miRNA-drug resistance interaction. The edge number represents contributed primary pathway, including [I] cell growth and death; [II] cell motility; [III] cellular community; [IV] endocrine system; [V] folding, sorting and degradation; [VI] gene expression; [VII] glycan biosynthesis and metabolism; [VIII] immune system; [IX] lipid metabolism; [X] signal transduction; [XI] signaling molecules and interaction; [XII] transport and catabolism; and [XIII] xenobiotics biodegradation and metabolism. (PDF 21 kb)
13277_2015_4553_MOESM7_ESM.xls (1.5 mb)
Supplementary Sheet S1 (XLS 1548 kb)
13277_2015_4553_MOESM8_ESM.doc (113 kb)
Tables S1 (DOC 113 kb)

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Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2015

Authors and Affiliations

  • Hamzeh Mesrian Tanha
    • 1
  • Marjan Mojtabavi Naeini
    • 2
  • Soheila Rahgozar
    • 1
    Email author
  • Alireza Moafi
    • 3
  • Mohammad Amin Honardoost
    • 1
  1. 1.Division of Cell and Molecular Biology, Department of Biology, Faculty of ScienceUniversity of IsfahanIsfahanIran
  2. 2.Division of Genetics, Department of Biology, Faculty of ScienceUniversity of IsfahanIsfahanIran
  3. 3.Department of Pediatric Hematology Oncology, Sayed Al-Shohada HospitalIsfahan University of Medical SciencesIsfahanIran

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