Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network

Hu,Daojun; Zhang,Boke; Yu,Miao; Shi,Wenjie; Zhang,Li

doi:10.3892/mmr.2020.11171

Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network

Authors:
- Daojun Hu
- Boke Zhang
- Miao Yu
- Wenjie Shi
- Li Zhang
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Affiliations: Department of Clinical Laboratory, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Chongming Branch, Shanghai 202150, P.R. China, Department of Clinical Laboratory, The First Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui 230031, P.R. China, Department of Gynecology, Pius‑Hospital of University Medicine Oldenburg, D‑26121 Oldenburg, Germany
Published online on: May 22, 2020 https://doi.org/10.3892/mmr.2020.11171
Pages: 620-632
Copyright: © Hu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Colorectal cancer is one of the commoner digestive tract malignant tumor types, and its incidence and mortality rate are high. Accumulating evidence indicates that long‑chain non‑coding RNAs (lncRNAs) and protein‑coding RNAs interact with each other by competing with the same micro(mi)RNA response element (MREs) and serve an important role in the regulation of gene expression in a variety of tumor types. However, the regulatory mechanism and prognostic role of lncRNA‑mediated competing endogenous (ce)RNA networks in colon cancer have yet to be elucidated. The expression profiles of mRNAs, lncRNAs and miRNAs from 471 colon cancer and 41 paracancerous tissue samples were downloaded from The Cancer Genome Atlas database. A lncRNA‑miRNA‑mRNA ceRNA network in colon cancer was constructed and comprised 17 hub lncRNAs, 87 hub miRNA and 144 hub mRNAs. The topological properties of the network were analyzed, and the random walk algorithm was used to identify the nodes significantly associated with colon cancer. Survival analysis using the UALCAN database indicated that 2/17 lncRNAs identified [metastasis‑associated lung adenocarcinoma transcript (MALAT1) and maternally expressed gene 3 (MEG3)] and 5/144 mRNAs [FES upstream region (FURIN), nuclear factor of activated T‑cells 5 (NFAT5), RNA Binding Motif Protein 12B (RBM12B), Ras related GTP binding A (RRAGA) and WD repeat domain phosphoinositide‑interacting protein 2 (WIPI2)] were significantly associated with the overall survival of patients with colon cancer, and may therefore be used as potential prognostic biomarkers of colon cancer. According to extracted lncRNA‑miRNA‑mRNA interaction pairs, the GSE26334 dataset was used to confirm that the lncRNA MALAT1/miR‑129‑5p/NFAT5 axis may represent a novel regulatory mechanism concerning the progression of colon cancer. The clusterProfiler package was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in colon cancer. Finally, drugs that significantly interact with the core genes identified in colon cancer were predicted using a hypergeometric test. Of these, fostamatinib was identified to be a targeted drug for colon cancer therapy. The present findings provide a novel perspective for improved understanding of the lncRNA‑associated ceRNA network and may facilitate the development of novel targeted therapeutics in colon cancer.

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