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Identifying pancreatic cancer-associated miRNAs using weighted gene co-expression network analysis. | LitMetric

Identifying pancreatic cancer-associated miRNAs using weighted gene co-expression network analysis.

Oncol Lett

Department of General Surgery, Shanxi Tumor Hospital, Taiyuan, Shanxi 030000, P.R. China.

Published: September 2022

AI Article Synopsis

  • * The study analyzed patient data from the Gene Expression Omnibus database to find different miRNAs that could be used in liquid biopsy diagnostics for pancreatic cancer.
  • * A total of 11 miRNAs were identified, with miR-4668-5p showing the most potential as a diagnostic biomarker for pancreatic cancer due to its strong association with disease modules.

Article Abstract

Pancreatic cancer is a common type of gastrointestinal tumour throughout the world and is characterised by high malignancy rates and poor prognosis. Studies indicated that early and effective diagnosis is key to prolonging patients' overall survival, particularly in the case of fluid biopsy. Given this, the present study was designed to evaluate the expression profile arrays of patients with pancreatic cancer from the Gene Expression Omnibus database in an effort to identify differentially expressed microRNAs (miRNAs/miRs) that may be suitable for application in liquid biopsy-based diagnostics. Suitable miRNA candidates were identified using a weighted correlation network analysis (WGCNA) and key differentially expressed miRNAs were verified using reverse transcription-quantitative PCR. WGCNA identified 11 differentially expressed miRNAs (miR-155-5p, miR-4668-5p, miR-3613-3p, miR-3201, miR-548ac, miR-486-5p, miR-548a-3p, miR-8084, miR-455-3p, miR-6068 and miR-1246). Of these, miR-4668-5p was indicated to have the highest number of associated modules, making it most likely to be of diagnostic value. Thus, the present analysis identified 11 miRNAs associated with pancreatic cancer and further identified miR-4668-5p as a potential biomarker for pancreatic cancer diagnosis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353221PMC
http://dx.doi.org/10.3892/ol.2022.13417DOI Listing

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