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lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia

  • Authors:
    • Jia-Qi Pan
    • Yan-Qing Zhang
    • Jing-Hua Wang
    • Ping Xu
    • Wei Wang
  • View Affiliations / Copyright

    Affiliations: Department of Hematology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
    Copyright: © Pan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 663-671
    |
    Published online on: February 13, 2017
       https://doi.org/10.3892/ijmm.2017.2888
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Abstract

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA‑lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis‑related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs.
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Copy and paste a formatted citation
Spandidos Publications style
Pan J, Zhang Y, Wang J, Xu P and Wang W: lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia. Int J Mol Med 39: 663-671, 2017.
APA
Pan, J., Zhang, Y., Wang, J., Xu, P., & Wang, W. (2017). lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia. International Journal of Molecular Medicine, 39, 663-671. https://doi.org/10.3892/ijmm.2017.2888
MLA
Pan, J., Zhang, Y., Wang, J., Xu, P., Wang, W."lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia". International Journal of Molecular Medicine 39.3 (2017): 663-671.
Chicago
Pan, J., Zhang, Y., Wang, J., Xu, P., Wang, W."lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia". International Journal of Molecular Medicine 39, no. 3 (2017): 663-671. https://doi.org/10.3892/ijmm.2017.2888
Copy and paste a formatted citation
x
Spandidos Publications style
Pan J, Zhang Y, Wang J, Xu P and Wang W: lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia. Int J Mol Med 39: 663-671, 2017.
APA
Pan, J., Zhang, Y., Wang, J., Xu, P., & Wang, W. (2017). lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia. International Journal of Molecular Medicine, 39, 663-671. https://doi.org/10.3892/ijmm.2017.2888
MLA
Pan, J., Zhang, Y., Wang, J., Xu, P., Wang, W."lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia". International Journal of Molecular Medicine 39.3 (2017): 663-671.
Chicago
Pan, J., Zhang, Y., Wang, J., Xu, P., Wang, W."lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia". International Journal of Molecular Medicine 39, no. 3 (2017): 663-671. https://doi.org/10.3892/ijmm.2017.2888
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