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Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia

Cellular Oncology volume 40, pages 33–45 (2017)Cite this article

Abstract

Purpose

Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations. Here, we aimed at studying GC resistance in MLL-rearranged infant ALL patients by inferring an associated module of genes using co-expression network analysis. The implications of newly identified candidate genes with associations to other well-known relevant genes from the same module, or with associations to known transcription factor or microRNA interactions, were substantiated using literature data.

Methods

A weighted gene co-expression network was constructed to identify gene modules associated with GC resistance in MLL-rearranged infant ALL patients. Significant gene ontology (GO) terms and signaling pathways enriched in relevant modules were used to provide guidance towards which module(s) consisted of promising candidates suitable for further analysis.

Results

Through gene co-expression network analysis a novel set of genes (module) related to GC-resistance was identified. The presence in this module of the S100 and ANXA genes, both well-known biomarkers for GC resistance in MLL-rearranged infant ALL, supports its validity. Subsequent gene set net correlation analyses of the novel module provided further support for its validity by showing that the S100 and ANXA genes act as ‘hub’ genes with potentially major regulatory roles in GC sensitivity, but having lost this role in the GC resistant phenotype. The detected module implicates new genes as being candidates for further analysis through associations with known GC resistance-related genes.

Conclusions

From our data we conclude that available systems biology approaches can be employed to detect new candidate genes that may provide further insights into drug resistance of MLL-rearranged infant ALL cases. Such approaches complement conventional gene-wise approaches by taking putative functional interactions between genes into account.

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Acknowledgments

The authors would like to thank the Center of High Performance Computing (CHPC), School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran for providing their cluster to meet our computational needs. We would like to thank the anonymous reviewer/editor for scientific and English editing the manuscript.

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Affiliations

  1. Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Zaynab Mousavian & Ali Masoudi-Nejad

  2. School of Mathematics and Computer Science, University of Tehran, Tehran, Iran

    Abbas Nowzari-Dalini

  3. Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands

    Ronald W. Stam

  4. Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA

    Yasir Rahmatallah

Authors
  1. Zaynab Mousavian
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  2. Abbas Nowzari-Dalini
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  3. Ronald W. Stam
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  4. Yasir Rahmatallah
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  5. Ali Masoudi-Nejad
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Correspondence to Ali Masoudi-Nejad.

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Mousavian, Z., Nowzari-Dalini, A., Stam, R.W. et al. Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia. Cell Oncol. 40, 33–45 (2017). https://doi.org/10.1007/s13402-016-0303-7

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Keywords

  • Acute lymphoblastic leukemia
  • Glucocorticoid treatment
  • Drug resistance
  • Systems biology
  • Gene co-expression network