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

• Published in: Journal of translational medicine Vol. 16; no. 1; p. 274
• Main Authors: Xu, Ning, Wu, Yu-Peng, Yin, Hu-Bin, Xue, Xue-Yi, Gou, Xin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.10.2018; BioMed Central; BMC
• Subjects: 4-mRNA signature; Biomarkers; Cancer therapies; Candidates; Cell cycle; Competing endogenous RNAs; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Ontology; Gene Regulatory Networks; Genomes; Humans; Male; Medical prognosis; Messenger RNA; Metastases; Metastasis; MicroRNAs; miRNA; mRNA; Multivariate Analysis; Non-coding RNA; Ontology; Prognosis; Proportional Hazards Models; Prostate cancer; Prostatic Neoplasms - genetics; Regression analysis; RNA; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; RNA, Messenger - genetics; RNA, Messenger - metabolism; RNA, Neoplasm - genetics; RNA, Neoplasm - metabolism; Studies; Survival; Survival Analysis; Systematic review; Therapeutic applications; Tissues; Prostate cancer; Survival; 4-mRNA signature; Competing endogenous RNAs
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-018-1637-x

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. 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. 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. 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.