The expression of microRNA 574-3p as a predictor of postoperative outcome in patients with esophageal squamous cell carcinoma

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Abstract

Background: Despite advances in radical esophagectomies and adjuvant therapy, the postoperative prognosis in esophageal squamous cell carcinoma (ESCC) patients remains poor. The aim of this study was to identify a molecular signature to predict postoperative favorable outcomes in patients with ESCC. Methods: As a training data set, total RNA was extracted from formalin-fixed paraffin-embedded samples of surgically removed specimens from 19 ESCC patients who underwent curative esophagectomy. The expression of microRNA (miRNA) was detected using a miRNA oligo chip on which 885 genes were mounted. As a validation data set, we obtained frozen samples of surgically resected tumors from 12 independent ESCC patients and the expression of miR-574-3p was detected by quantitative real-time PCR. Results: Our microarray analysis in the training set patients identified three miRNAs (miR-574-3p, miR-106b, and miR-1303) and five miRNAs (miR-1203, miR-1909, miR-204, miR-371-3p, miR-886-3p) which were differentially expressed between the patients with (n=14) and without (n=5) postoperative tumor relapse (p<0.01 and p<0.05, respectively). Higher expression of miR-574-3p, which showed the most significant association with non-relapse (p=0.001), was associated with favorable overall survival (p=0.016). Real-time PCR experiments on the validation set patients confirmed that higher expression of miR-574-3p was associated with non-tumor relapse (p=0.029) and better overall survival (p=0.004). Conclusions: Our results suggest that the aberrant expression of the miRNAs identified in this study plays key roles in the progression of ESCC. miR-574-3p was suggested to have a tumor suppressor effect, and thus, to be a predictor of postoperative outcome in patients with ESCC.

Journal

  • World Journal of Surgical Oncology

    World Journal of Surgical Oncology (15), 24, 2017-01-14

    BioMed Central Ltd.

Codes

  • NII Article ID (NAID)
    120005997654
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    1477-7819
  • Data Source
    IR 
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