IntroductionCirculating microRNAs (miRNAs) are easily accessible and have already proven to be useful as prognostic markers in cancer patients. However, their origin and function in the circulation is still under discussion. In the present study we analyzed changes in the miRNAs in blood plasma of head and neck squamous cell carcinoma (HNSCC) patients in response to radiochemotherapy and compared them to the changes in a cell culture model of primary HNSCC cells undergoing simulated anti-cancer therapy.Materials and methodsMiRNA-profiles were analyzed by qRT-PCR arrays in paired blood plasma samples of HNSCC patients before therapy and after two days of treatment. Candidate miRNAs were validated by single qRT-PCR assays. An in vitro radiochemotherapy model using primary HNSCC cell cultures was established to test the possible tumor origin of the circulating miRNAs. Microarray analysis was performed on primary HNSCC cell cultures followed by validation of deregulated miRNAs via qRT-PCR.ResultsUnsupervised clustering of the expression profiles using the six most regulated miRNAs (miR-425-5p, miR-21-5p, miR-106b-5p, miR-590-5p, miR-574-3p, miR-885-3p) significantly (p = 0.012) separated plasma samples collected prior to treatment from plasma samples collected after two days of radiochemotherapy. MiRNA profiling of primary HNSCC cell cultures treated in vitro with radiochemotherapy revealed differentially expressed miRNAs that were also observed to be therapy-responsive in blood plasma of the patients (miR-425-5p, miR-21-5p, miR-106b-5p, miR-93-5p) and are therefore likely to stem from the tumor. Of these candidate marker miRNAs we were able to validate by qRT-PCR a deregulation of eight plasma miRNAs as well as miR-425-5p and miR-93-5p in primary HNSCC cultures after radiochemotherapy.ConclusionChanges in the abundance of circulating miRNAs during radiochemotherapy reflect the therapy response of primary HNSCC cells after an in vitro treatment. Therefore, the responsive miRNAs (miR-425-5p, miR-93-5p) may represent novel biomarkers for therapy monitoring. The prognostic value of this exciting observation requires confirmation using an independent patient cohort that includes clinical follow-up data.

Multimodal therapy of glioblastoma (GBM) reveals inter-individual variability in terms of treatment outcome. Here, we examined whether a miRNA signature can be defined for the a priori identification of patients with particularly poor prognosis.FFPE sections from 36 GBM patients along with overall survival follow-up were collected retrospectively and subjected to miRNA signature identification from microarray data. A risk score based on the expression of the signature miRNAs and cox-proportional hazard coefficients was calculated for each patient followed by validation in a matched GBM subset of TCGA. Genes potentially regulated by the signature miRNAs were identified by a correlation approach followed by pathway analysis.A prognostic 4-miRNA signature, independent of MGMT promoter methylation, age, and sex, was identified and a risk score was assigned to each patient that allowed defining two groups significantly differing in prognosis (p-value: 0.0001, median survival: 10.6 months and 15.1 months, hazard ratio = 3.8). The signature was technically validated by qRT-PCR and independently validated in an age- and sex-matched subset of standard-of-care treated patients of the TCGA GBM cohort (n=58). Pathway analysis suggested tumorigenesis-associated processes such as immune response, extracellular matrix organization, axon guidance, signalling by NGF, GPCR and Wnt. Here, we describe the identification and independent validation of a 4-miRNA signature that allows stratification of GBM patients into different prognostic groups in combination with one defined threshold and set of coefficients that could be utilized as diagnostic tool to identify GBM patients for improved and/or alternative treatment approaches.

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Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify miRNAs involved in metastasis of lung adenocarcinoma as prognostic biomarkers and therapeutic targets. To that end, lymph node metastasis-associated miRNAs were identified in The Cancer Genome Atlas lung adenocarcinoma patient cohort (sequencing data; = 449) and subsequently validated by qRT-PCR in an independent clinical cohort ( = 108). Overexpression of miRNAs located on chromosome 14q32 was associated with metastasis in lung adenocarcinoma patients. Importantly, Kaplan-Meier analysis and log-rank test revealed that higher expression levels of individual 14q32 miRNAs (mir-539, mir-323b, and mir-487a) associated with worse disease-free survival of never-smoker patients. Epigenetic analysis including DNA methylation microarray data and bisulfite sequencing validation demonstrated that the induction of 14q32 cluster correlated with genomic hypomethylation of the 14q32 locus. CRISPR activation technology, applied for the first time to functionally study the increase of clustered miRNA levels in a coordinated manner, showed that simultaneous overexpression of 14q32 miRNAs promoted tumor cell migratory and invasive properties. Analysis of individual miRNAs by mimic transfection further illustrated that miR-323b-3p, miR-487a-3p, and miR-539-5p significantly contributed to the invasive phenotype through the indirect regulation of different target genes. In conclusion, overexpression of 14q32 miRNAs, associated with the respective genomic hypomethylation, promotes metastasis and correlates with poor patient prognosis in lung adenocarcinoma. This study points to chromosome 14q32 miRNAs as promising targets to inhibit tumor cell dissemination and to predict patient prognosis in lung adenocarcinoma. .

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: .