International Journal of Colorectal Disease

, Volume 26, Issue 11, pp 1415–1422 | Cite as

MicroRNA signature analysis in colorectal cancer: identification of expression profiles in stage II tumors associated with aggressive disease

  • Kah Hoong Chang
  • Nicola Miller
  • Elrasheid A. H. Kheirelseid
  • Christophe Lemetre
  • Graham R. Ball
  • Myles J. Smith
  • Mark Regan
  • Oliver J. McAnena
  • Michael J. Kerin
Original Article

Abstract

Purpose

Colorectal cancer (CRC) is a clinically diverse disease whose molecular etiology remains poorly understood. The purpose of this study was to identify miRNA expression patterns predictive of CRC tumor status and to investigate associations between microRNA (miRNA) expression and clinicopathological parameters.

Methods

Expression profiling of 380 miRNAs was performed on 20 paired stage II tumor and normal tissues. Artificial neural network (ANN) analysis was applied to identify miRNAs predictive of tumor status. The validation of specific miRNAs was performed on 102 tissue specimens of varying stages.

Results

Thirty-three miRNAs were identified as differentially expressed in tumor versus normal tissues. ANN analysis identified three miRNAs (miR-139-5p, miR-31, and miR-17-92 cluster) predictive of tumor status in stage II disease. Elevated expression of miR-31 (p = 0.004) and miR-139-5p (p < 0.001) and reduced expression of miR-143 (p = 0.016) were associated with aggressive mucinous phenotype. Increased expression of miR-10b was also associated with mucinous tumors (p = 0.004). Furthermore, progressively increasing levels of miR-10b expression were observed from T1 to T4 lesions and from stage I to IV disease.

Conclusion

Association of specific miRNAs with clinicopathological features indicates their biological relevance and highlights the power of ANN to reliably predict clinically relevant miRNA biomarkers, which it is hoped will better stratify patients to guide adjuvant therapy.

Keywords

Colorectal cancer MicroRNA Expression signature Artificial neural networks 

Supplementary material

384_2011_1279_MOESM1_ESM.doc (48 kb)
ESM 1(DOC 48 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Kah Hoong Chang
    • 1
  • Nicola Miller
    • 1
    • 3
  • Elrasheid A. H. Kheirelseid
    • 1
  • Christophe Lemetre
    • 2
  • Graham R. Ball
    • 2
  • Myles J. Smith
    • 1
  • Mark Regan
    • 1
  • Oliver J. McAnena
    • 1
  • Michael J. Kerin
    • 1
  1. 1.Department of SurgeryNational University of IrelandGalwayIreland
  2. 2.John Van Geest Cancer Research Centre, School of Science and TechnologyNottingham Trent UniversityNottinghamUK
  3. 3.Department of Surgery, Clinical Science InstituteUniversity College Hospital GalwayGalwayIreland

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