Wilms' tumors

The Wilms’ tumor or nephroblastoma is the most common malignant renal cancer in children, affecting one in 10,000 children [1]. Approximately 75% of the cases occur in children younger than five years old, with a peak incidence in two- to three- year-olds [2]. Until recently, this tumor was always considered lethal, but serves today as a prime example of a curable malignant disease with survival rates of 90% [3]. Current therapeutic approaches rely on the classification of the tumor stage and histological subtype [4]. Identifying and understanding the different subtypes is crucial for finding a proper therapy and to assess the degree of malignancy of the tumor and thereby its aggressive potential. In order to describe the histology of the analyzed tumors the SIOP-classification scheme [5] was used. This classification scheme is the standard for renal tumors of the childhood that underwent a preoperative chemotherapy.


In this use case we compare miRNA expression profiles of regressive Wilms’ tumors, which show a positive response to the chemotherapy, and the blastemal subtype, which seems to have a stronger chemoresistance. We use GeneTrail2 to detect biological processes and molecular functions that show significant differences between the two groups.

Technical Background

As both groups only contain a few samples we decided to use the Shrinkage-t-test to compute the differences in gene expression between them (compare Workflow, Implemented methods). To perform the enrichment analysis we decided to use the unweighted version of the gene set enrichment analysis (GSEA) [6], which is equivalent to the standard Kolmogorov-Smirnov statistic [7]. It is a non-parametric hypothesis test, which is based solely on the order of an input list L. Focusing on ranks rather than on the absolute value has the advantage that the method is more robust and can penalize outliers, which might otherwise have a big influence on the results. (compare Workflow, Implemented methods). Another huge advantage of the unweighted GSEA is that an exact p-value for the test statistic can be computed via a dynamic programming algorithm [8].


  • Test set: Ordered list of expressed miRNAs
  • Identifier-level statistics: Shrinkage t-test
  • Set-level statistics: Gene set enrichment analysis (GSEA)
  • P-value adjustment method: Benjamini-Hochberg

Step-by-step slideshow

The following slideshow depicts the different analysis steps of the GeneTrail2 workflow.


Since most miRNAs are assumed to trigger translational repression [9], we expect to see the same processes that are up-regulated on mRNA level to be down-regulated on miRNA level.

miRNAs with oncogenic activity

In fact, this is the case for our analysis. We see that immune system and apoptosis are significantly down-regulated in the regressive tissue. Additionally, we see that the expression of onco-miRNAs is also down-regulated. The down-regulation of these microRNAs has been associated with oncogenic activity [10].

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Important miRNA families

We can also detect miRNA families or clusters that are associated with cancer. The miR-17 family is well known for its oncogenic role in cancer and stem cell development [11].

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  1. Maschietto, Mariana and Piccoli, Fabio S and Costa, Cecilia ML and Camargo, Luiz P and Neves, Jose I and Grundy, Paul E and Brentani, Helena and Soares, Fernando A and de Camargo, Beatriz and Carraro, Dirce M Gene expression analysis of blastemal component reveals genes associated with relapse mechanism in Wilms tumour European Journal of Cancer Elsevier
  2. Dangio, G. J. Wilms tumor status report, 1990 Journal of Clinical Oncology WB Saunders Co Independence Square West Curtis Center, STE 300, Philadelphia, PA 19106-3399
  3. Graf, Norbert and Tournade, Marie-France and de Kraker, Jan The Role of Preoperative Chemotherapy in the Management of Wilms Tumor: The SIOP Studies Urologic Clinics of North America Elsevier
  4. Green, Daniel M The treatment of stages I--IV favorable histology Wilms' tumor Journal of clinical oncology American Society of Clinical Oncology
  5. Vujanic, Gordan M and Sandstedt, Bengt and Harms, Dieter and Kelsey, Anna and Leuschner, Ivo and de Kraker, Jan Revised International Society of Paediatric Oncology (SIOP) working classification of renal tumors of childhood Medical and pediatric oncology Wiley Online Library
  6. Backes, Christina and Keller, Andreas and Kuentzer, Jan and Kneissl, Benny and Comtesse, Nicole and Elnakady, Yasser A and Müller, Rolf and Meese, Eckart and Lenhof, Hans-Peter GeneTrail—advanced gene set enrichment analysis Nucleic acids research Oxford Univ Press (View online)
  7. Hollander, Myles and Wolfe, Douglas A and Chicken, Eric Nonparametric statistical methods John Wiley and Sons
  8. Keller, A. and Backes, C. and Lenhof, H. P. Computation of significance scores of unweighted Gene Set Enrichment Analyses BMC Bioinformatics (View online)
  9. Wilczynska, A and Bushell, M The complexity of miRNA-mediated repression Cell Death and Differentiation Nature Publishing Group
  10. Hammond, Scott M RNAi, microRNAs, and human disease Cancer chemotherapy and pharmacology Springer
  11. Dauvilliers, Yves Insomnia in patients with neurodegenerative conditions Sleep medicine Elsevier