BackgroundMicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.MethodsThe human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.ResultsUsing this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.ConclusionsOur analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Acute myeloid leukemia (AML) in children is a complex and heterogeneous disease. The identification of reliable and stable molecular biomarkers for diagnosis, especially early diagnosis, remains a significant therapeutic challenge. Aberrant microRNA expression could be used for cancer diagnosis and treatment selection. Here, we describe a novel bioinformatics model for the prediction of microRNA biomarkers for the diagnosis of paediatric AML based on computational functional analysis of the microRNA regulatory network substructure. microRNA-196b, microRNA-155 and microRNA-25 were identified as putative diagnostic biomarkers for pediatric AML. Further systematic analysis confirmed the association of the predicted microRNAs with the leukemogenesis of AML. In vitro q-PCR experiments showed that microRNA-155 is significantly overexpressed in children with AML and microRNA-196b is significantly overexpressed in subgroups M4-M5 of the French-American-British classification system. These results suggest that microRNA-155 is a potential diagnostic biomarker for all subgroups of paediatric AML, whereas microRNA-196b is specific for subgroups M4-M5.

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

BackgroundProstate 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.MethodsIn 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.ResultsWith 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.ConclusionsFive 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.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1506-7) contains supplementary material, which is available to authorized users.

The indifferent mammalian embryonic gonad generates an ovary or testis, but the factors involved are still poorly known. The Wnt-4 signal represents one critical female determinant, since its absence leads to partial female-to-male sex reversal in mouse, but its signalling is as well implicated in the testis development. We used the Wnt-4 deficient mouse as a model to identify candidate gonadogenesis genes, and found that the Notum, Phlda2, Runx-1 and Msx1 genes are typical of the wild-type ovary and the Osr2, Dach2, Pitx2 and Tacr3 genes of the testis. Strikingly, the expression of these latter genes becomes reversed in the Wnt-4 knock-out ovary, suggesting a role in ovarian development. We identified the transcription factor Runx-1 as a Wnt-4 signalling target gene, since it is expressed in the ovary and is reduced upon Wnt-4 knock-out. Consistent with this, introduction of the Wnt-4 signal into early ovary cells ex vivo induces Runx-1 expression, while conversely Wnt-4 expression is down-regulated in the absence of Runx-1. We conclude that the Runx-1 gene can be a Wnt-4 signalling target, and that Runx-1 and Wnt-4 are mutually interdependent in their expression. The changes in gene expression due to the absence of Wnt-4 in gonads reflect the sexually dimorphic role of this signal and its complex gene network in mammalian gonad development.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA–mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.

Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis.

Gastric cancers (GC) have the high morbidity and mortality rates worldwide and there is a need to identify sufficiently sensitive biomarkers for GC. MicroRNAs (miRNAs) could be promising potential biomarkers for GC diagnosis. We employed a systematic and integrative bioinformatics framework to identify GC-related microRNAs from the public microRNA and mRNA expression dataset generated by RNA-seq technology. The performance of the 17 candidate miRNAs was evaluated by hierarchal clustering, ROC analysis, and literature mining. Fourteen have been found to be associated with GC and three microRNAs (miR-211, let-7b, and miR-708) were for the first time reported to associate with GC and may be used for diagnostic biomarkers for GC.