Dataset Information

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.

SUBMITTER: Lin Y

PROVIDER: S-EPMC5963164 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lin Yuxin Y Chen Feifei F Shen Li L Tang Xiaoyu X Du Cui C Sun Zhandong Z Ding Huijie H Chen Jiajia J Shen Bairong B

Journal of translational medicine 20180521 1

<h4>Background</h4>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.<h4>Methods</h4>In ...[more]

PMID: 29784056

Similar Datasets

Project description:Background: Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey. Methods: In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis. Results: Nine miRNAs, i.e., hsa-miR-1-3p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-182-5p, hsa-miR-198, hsa-miR-22-3p, hsa-miR-24-3p, hsa-miR-34a-5p, and hsa-miR-499a-5p, were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining hsa-miR-22-3p and hsa-miR-499a-5p could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here hsa-miR-145-5p was selected as the case and hsa-miR-145-5p/NDRG2/AR and hsa-miR-145-5p/KLF5/AR axis were found to be putative mechanisms during PCa evolution. In addition, Wnt signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis. Conclusion: Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.

Project description:Prostate Cancer (PCa) overdiagnosis and overtreatment, as a consequence of the limited specificity of current detection and prognostication methods, remains a major challenge in clinical practice. Therefore, development and validation of new molecular biomarkers amenable of detecting clinically significant disease is crucial. MicroRNAs (miRNA) deregulation is common in cancer, constituting potential non-invasive biomarkers for PCa detection and prognostication. Herein, we evaluated the screening and prognostic biomarker potential of two onco-microRNAs (miR-182-5p and miR-375-3p) in liquid biopsies (plasma) of PCa patients with clinically localized disease undergoing curative-intent treatment. A first cohort of 98 PCa and 15 normal prostates were used to assess PCa-specificity of miR-182-5p in tissues. A cohort composed of PCa 252 patients and 52 asymptomatic controls allowed for assessment of diagnostic and prognostic value in plasmas. After RNA extraction from tissue and plasma samples, cDNA synthesis specific for miRNAs was performed followed by measurement of miR-182-5p and miR-375-3p relative expression by RT-qPCR, using U6 snRNA gene as reference. MiR-182-5p was significantly overexpressed in PCa tissues (p < 0.0001) and in plasma of PCa patients (p = 0.0020), compared to respective controls. Moreover, miR-182-5p expression identified PCa with AUC = 0.81 (95% CI: 0.725-0.892, p = 0.0001) in tissue and with 77% specificity and 99% NPV (AUC = 0.64, 95% CI: 0.561-0.709, p = 0.0021) in plasma. Both circulating miR-182-5p and miR-375-3p levels associated with more advanced pathologic stage and the former was significantly higher in patients that developed metastasis (p = 0.0145). Indeed, at the time of diagnosis, circulating miR-375-3p levels predicted which patients would develop metastasis, with almost 50% sensitivity, 76% specificity, and a NPV of 89% (AUC = 0.62, 95% CI: 0.529-0.713, p = 0.0149). We conclude that these two circulating miRNAs might be clinical useful as non-invasive biomarkers for detection and prediction of metastasis development at the diagnosis together with clinical variables used in routine practice.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets