A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer

Bai,Xue; Lu,Donglan; Lin,Yan; Lv,Yufeng; He,Liusheng

doi:10.3892/etm.2019.7728

A seven‑miRNA expression‑based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer

Authors:
- Xue Bai
- Donglan Lu
- Yan Lin
- Yufeng Lv
- Liusheng He
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Affiliations: Department of Medical Oncology, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi 541199, P.R. China, Department of Medical Oncology, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Department of Medical Oncology, Affiliated Langdong Hospital of Guangxi Medical University, Nanning, Guangxi 530029, P.R. China, Department of Surgery 1, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530001, P.R. China
Published online on: July 3, 2019 https://doi.org/10.3892/etm.2019.7728
Pages: 1601-1608
Copyright: © Bai et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study aimed to establish a microRNA (miRNA/miR) signature to predict the prognosis of patients with pancreatic cancer (PC) at the early stage and to investigate the involvement of competing endogenous RNAs (ceRNAs) in PC. Using mature miRNA expression profiles from The Cancer Genome Atlas, differentially expressed miRNAs in tissues derived from patients exhibiting early PC and tissues from healthy individuals were compared. The least absolute shrinkage and selection operator regression method was used to construct a miRNA‑based signature for predicting prognosis. The miRNet tool, gene set enrichment analysis (GSEA) and the LncRNADisease database were utilized to explore the mechanistic involvement of ceRNAs. A total of seven downregulated miRNAs in PC (miR‑424‑5p, miR‑139‑5p, miR‑5586‑5p, miR‑126‑3p, miR‑3613‑5p, miR‑454‑3p and miR‑1271‑5p) were selected to generate a signature. Based on this seven‑miRNA signature, it was possible to stratify patients with PC into low‑ and high‑risk groups. The overall survival of the low‑risk group was significantly longer than that of the high‑risk group (P<0.001). The seven‑miRNA signature was able to predict the 2‑year‑survival rate of patients with early PC with an area under the curve of 0.750. Furthermore, as opposed to routine clinicopathological features, this seven‑miRNA signature was an independent prognostic factor according to multivariate Cox regression analysis. GSEA indicated that the extracellular matrix receptor interaction pathway and the transforming growth factor‑β signaling pathway were enriched in the high‑risk group. A ceRNA network of the seven‑miR signature was constructed. In conclusion, the present study provided a seven‑miRNA signature, according to which patients with early PC may be divided into high‑ and low‑risk groups. The ceRNA network of the prognostic signature was preliminarily explored.

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