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Poster display session

134P - Identification, development and validation of a circulating miRNA-based diagnostic signature for early detection of gastric cancer


23 Nov 2019


Poster display session


Tumour Site

Gastric Cancer


Daisuke Izumi


Annals of Oncology (2019) 30 (suppl_9): ix42-ix67. 10.1093/annonc/mdz422


D. Izumi1, F. Gao2, Y. Chen3, T. Ishimoto4, K. Horino5, S. Shimada5, Y. Kodera6, H. Baba7, J. Chen3, X. Wang2, A. Goel8

Author affiliations

  • 1 Gastroenterological Surgery, Kumamoto University, 866-8660 - Kumamoto/JP
  • 2 Biomedical Sciences, City University of Hong Kong, Hong Kong/CN
  • 3 Department Of Oncology, Nanjing Medical University, Nanjin/CN
  • 4 The International Research Center For Medicine Sciences, Kumamoto University, Kumamoto/JP
  • 5 Surgery, JCHO Kumamoto General Hospital, Yatsushiro/JP
  • 6 Gastroenterological Surgery, Nagoya University, Graduate School of Medicine, 466-8550 - Nagoya/JP
  • 7 Gastroenterological Surgery, Kumamoto University, Kumamoto/JP
  • 8 Center For Translational Genomics And Oncology, Baylor University Medical Center, Dallas/US


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Abstract 134P


Although endoscopic surveillance remains the gold standard for diagnosing asymptomatic gastric cancer (GC) patients, associated costs and its invasive nature render it inadequate as a screening approach. Development of less invasive tests is needed for surveillance of early stage GCs. Over the last decade, tumor-derived miRNAs in peripheral blood are emerging as promising disease biomarkers. Herein we have conducted a comprehensive miRNA expression profiling, followed by bioinformatic analysis to establish a novel serum-based miRNA signature for the diagnosis of patients with GC.


We analyzed tissue miRNA expression profiles in three patient cohorts (n = 602) in an in-silico discovery step, during which the robustness of candidate biomarkers was tested and validated. The performance of this miRNA signature was evaluated in a serum training cohort (n = 327). Using a logistic regression model, the panel was further refined, and this circulating miRNA signature was validated in two prospective cohorts (n = 174, 175).


Genome-wide analysis of miRNA expression data resulted in identification of 10-miRNAs that distinguished cancer tissues from normal mucosa in three independent datasets (AUC = 0.984, 0.939 and 1.000). Using a serum training cohort, the miRNA candidates were further refined to six-circulating-miRNA signature. This miRNA signature demonstrated a robust diagnostic value in the training cohort. Subsequently we demonstrated robustness of the signature in two prospective cohorts (AUC = 0.87, 0.86). Remarkably, the 6-circulating-miRNA signature was able to detect early stage GC patients robustly (AUC = 0.855). Furthermore, the signature was significantly superior at identifying patients with GC to conventional tumor markers, CEA (P = 0.0001) and CA19-9 (P = 0.0001).


Using a comprehensive data analysis followed by substantial clinical validations, involving over 1600 GC tissue and serum specimens across 7 independent cohorts, we developed a novel 6-circulating-miRNA signature, which demonstrated an unprecedented diagnostic value and a great promise for early non-invasive detection of GC.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Daisuke Izumi.




All authors have declared no conflicts of interest.

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