Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model

Abstract

Background: Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases.

Methods: In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets.

Results: With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis.

Conclusions: Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Keywords: Bioinformatics model; MicroRNA biomarkers; Network vulnerability analysis; Prostate cancer metastasis.

Fig. 1

The schematic pipeline for MPCa microRNA biomarker identification. LIMMA linear models for microarray data analysis; eBayes the empirical bayes; adj.p value adjusted p value; FC fold change; DE differentially expressed; MicroRNA-BD microRNA biomarker discovery; NSR number of single-line regulation; TFP transcription factor gene percentage; UTP percentage of transcription factor genes in microRNA unique targets; ROC receiver operating characteristic curve; GO Gene Ontology; MPCa metastatic prostate cancer

Fig. 2

Schematic description of microRNA-mRNA regulatory types. Four types were defined here, i.e., TF or non-TF genes regulated by multiple or single microRNAs. For example, G_1 was uniquely regulated by M_1 whereas TF gene G_5 was co-regulated by M_2 and M_3. The co-regulatory sites are robust since one of the regulations altered can be compensated by others. Here the unique regulatory sites, i.e., single-line regulations, are considered as the vulnerable structure in the network. Meanwhile, microRNAs that target more TF genes seem to be functionally important. M microRNA; G gene; TF transcription factor

Fig. 3

Topological and functional characterization of reported PCa microRNA biomarkers. a NSR distribution of reported PCa microRNA biomarkers and all microRNAs in human microRNA-mRNA network. b TFP distribution of reported PCa microRNA biomarkers and all microRNAs in human microRNA-mRNA network. The statistical significance was calculated using Kolmogorov–Smirnov test. NSR number of single-line regulation; TFP transcription factor gene percentage; PCa prostate cancer

Fig. 4

ROC analysis for the identified microRNA biomarkers. The AUC distribution in the prediction set GSE21036 and another independent validation set GSE26964 ranged from 0.70 to 0.99 and from 0.71 to 0.93, respectively. Red curve: GSE21036; blue curve: GSE26964. PPCa primary prostate cancer; MPCa metastatic prostate cancer; ROC receiver operating characteristic curve; AUC area under the curve

Fig. 5

Identified biomarker microRNAs and their targets in MPCa-specific microRNA-mRNA network. Elliptic, triangular and rectangular nodes represent microRNAs, TF genes and non-TF genes, respectively. Nodes in grey represent genes that are uniquely regulated by single microRNAs in the network. MPCa metastatic prostate cancer; TF transcription factor

Fig. 6

Pathway enrichment analysis for targets of the identified microRNA biomarkers. The statistical significance level (adj. p value) was negative 10-based log transformed. a The top ten significant KEGG terms. b The top ten significant IPA terms. adj.p value adjusted p value; KEGG Kyoto Encyclopedia of Genes and Genomes; IPA ingenuity pathway analysis

Fig. 7

The prostate cancer pathway enriched in KEGG. Objects with pentagrams are acting locus by mapped genes. KEGG Kyoto Encyclopedia of Genes and Genomes

Fig. 8

The ERK/MAPK signaling enriched in IPA. Objects with purple circles or triangles are acting locus by mapped genes. ERK extracellular signal-regulated kinases; MAPK mitogen-activated protein kinase; IPA ingenuity pathway analysis