Molecular network-based identification of competing endogenous RNAs and mRNA signatures that predict survival in prostate cancer

J Transl Med. 2018 Oct 4;16(1):274. doi: 10.1186/s12967-018-1637-x.

Abstract

Background: The aim of the study is described the regulatory mechanisms and prognostic values of differentially expressed RNAs in prostate cancer and construct an mRNA signature that predicts survival.

Methods: The RNA profiles of 499 prostate cancer tissues and 52 non-prostate cancer tissues from TCGA were analyzed. The differential expression of RNAs was examined using the edgeR package. Survival was analyzed by Kaplan-Meier method. microRNA (miRNA), messenger RNA (mRNA), and long non-coding RNA (lncRNA) networks from the miRcode database were constructed, based on the differentially expressed RNAs between non-prostate and prostate cancer tissues.

Results: A total of 773 lncRNAs, 1417 mRNAs, and 58 miRNAs were differentially expressed between non-prostate and prostate cancer samples. The newly constructed ceRNA network comprised 63 prostate cancer-specific lncRNAs, 13 miRNAs, and 18 mRNAs. Three of 63 differentially expressed lncRNAs and 1 of 18 differentially expressed mRNAs were significantly associated with overall survival in prostate cancer (P value < 0.05). After the univariate and multivariate Cox regression analyses, 4 mRNAs (HOXB5, GPC2, PGA5, and AMBN) were screened and used to establish a predictive model for the overall survival of patients. Our ROC curve analysis revealed that the 4-mRNA signature performed well.

Conclusion: These ceRNAs may play a critical role in the progression and metastasis of prostate cancer and are thus candidate therapeutic targets and potential prognostic biomarkers. A novel model that incorporated these candidates was established and might provide more powerful prognostic information in predicting survival in prostate cancer.

Keywords: 4-mRNA signature; Competing endogenous RNAs; Prostate cancer; Survival.

MeSH terms

  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic
  • Gene Ontology
  • Gene Regulatory Networks*
  • Humans
  • Male
  • Multivariate Analysis
  • Prognosis
  • Proportional Hazards Models
  • Prostatic Neoplasms / genetics*
  • RNA, Long Noncoding / genetics
  • RNA, Long Noncoding / metabolism
  • RNA, Messenger / genetics*
  • RNA, Messenger / metabolism
  • RNA, Neoplasm / genetics*
  • RNA, Neoplasm / metabolism
  • Survival Analysis

Substances

  • RNA, Long Noncoding
  • RNA, Messenger
  • RNA, Neoplasm