A machine learning approach identified a diagnostic model for pancreatic cancer through using circulating microRNA signatures.

Journal: Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
Published Date: Sep 1, 2020

Abstract

Late diagnosis of pancreatic cancer (PC) due to the limited effectiveness of modern testing approaches, causes many patients to miss the chance of surgery and consequently leads to a high mortality rate. Pivotal improvements in circulating microRNA expression levels in PC patients make it possible to diagnose and treat patients at earlier stages. A list of circulating miRNAs was identified in this study using bioinformatics methods in association with pancreatic cancer through analyzing four GEO microarray datasets. The value of top miRNAs was then assessed via using a machine learning method. Taking the advantage of a combinatorial approach consisting of Particle Swarm Optimization (PSO) + Artificial Neural Network (ANN) and Neighborhood Component Analysis (NCA) iterations on a collection of top differentially expressed circulating miRNAs in PC patients, facilitated ranking them by significance. MiRNA's functional analysis in the final index was performed by predicting target genes and constructing PPI networks. Remarkably, the final model consist of miR-663a, miR-1469, miR-92a-2-5p, miR-125b-1-3p and miR-532-5p showed great diagnostic results on investigated cases and the validation set (Accuracy: 0.93, Sensitivity: 0.93, and Specificity: 0.92). Kaplan-Meier survival assessments of the top-ranked miRNAs revealed that three miRNAs, hsa-miR-1469, hsa-miR-663a and hsa-miR-532-5p, had meaningful associations with the prognosis of patients with pancreatic cancer. This miRNA index may serve as a non-invasive and potential PC diagnostic model, although experimental testing is needed.

Authors

  • Behrouz Alizadeh Savareh
    PhD in Medical Informatics, National Agency for Strategic Research in Medical Education, Tehran, Iran; Department of health information management, school of management and medical information sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Hamid Asadzadeh Aghdaie
    Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ali Behmanesh
    Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. Electronic address: aa.behmanesh@gmail.com.
  • Azadeh Bashiri
    Department of health information management, school of management and medical information sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Amir Sadeghi
    Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mohammadreza Zali
    Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Roshanak Shams
    Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: Shams.rosha.86@gmail.com.

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