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. 2016 Nov;101(11):e454-e456.
doi: 10.3324/haematol.2016.146555. Epub 2016 Jul 28.

MicroRNA expression-based outcome prediction in acute myeloid leukemia: novel insights through cross-platform integrative analyses

Velizar Shivarov  1 , Anna Dolnik  2 , Katharina M Lang  2 , Jan Krönke  2 , Florian Kuchenbauer  2 , Peter Paschka  2 , Verena I Gaidzik  2 , Hartmut Döhner  2 , Richard F Schlenk  2 , Konstanze Döhner  2 , Lars Bullinger  3
Affiliations

MicroRNA expression-based outcome prediction in acute myeloid leukemia: novel insights through cross-platform integrative analyses

Velizar Shivarov et al. Haematologica. 2016 Nov.
No abstract available

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Figures

Figure 1.
Figure 1.
Overall survival (OS) in the training and validation sets based on the discrete microRNA expression-based score. (A, C, E, G) training dataset; (B, D, F, H) validation dataset; (A, B, C, D) included all patients from both datasets; (E, F, G, H) included only cytogenetically normal acute myeloid leukemia (CN-AML) patients aged 60 years or under from both datasets. P-values are from Cox regression, unless stated otherwise in the plot. Horizontal axes represent the time in days and the vertical axes represent the probability for OS.
Figure 2.
Figure 2.
Gene expression and epigenetic profiles discriminating Low and High Score validation dataset cytogenetically normal acute myeloid leukemia (CN-AML) cases (≤60 years). (A) Hierarchical clustering of differentially expressed probe sets discriminating Low and High Score CN-AML patients. (B) Enrichment plot for the “RNA_SPLICING” signature identified in GSEA of CN-AML patients aged 60 years or under. (C) Unsupervised clustering of differentially methylated CpG sites in CN-AML patients. Samples’ annotation in (A) and (C): blue: Low Score patients; red: High Score patients. ES: enrichment score; FWER: familywise error rate.
See this image and copyright information in PMC

References

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