Background: Long noncoding RNAs (lncRNAs) are important regulators of gene expression and are frequently dysregulated in cancer. The mitotically associated lncRNA MANCR is highly expressed in aggressive cancers and contributes to genomic instability in triple-negative breast cancer (TNBC), but the molecular mechanisms underlying its activity remain poorly defined. Methods: Here we integrated computational and experimental approaches to examine the structure and regulatory interactions of MANCR isoforms. RNA-sequencing was performed in MDA-MB-231 and MCF10A cells and integrated with publicly available breast cancer transcriptomic datasets. MANCR-associated chromatin was characterized by ChIRP-seq in MDA-MB-231 cells. Computational analyses of patient data, sequence motifs, and molecular modeling suggested an interaction between MANCR and p53, which was investigated using RNA immunoprecipitation sequencing (RIP-seq) and a p53-responsive luciferase reporter assay. Results: Analysis of transcriptomic datasets revealed tumor-type-specific expression patterns for seven MANCR isoforms in breast cancer cell lines. Computational prediction of RNA secondary structures identified conserved structural features across isoforms, suggesting potential functional specialization. We identify p53 as a MANCR-associated protein through computational docking and RNA immunoprecipitation sequencing (RIP-seq) and demonstrate that MANCR depletion reduces p53-dependent transcriptional activity. Chromatin isolation by RNA purification sequencing (ChIRP-seq) revealed 1250 genomic regions associated with MANCR, including enrichment of p53 consensus motifs and GC-rich sequence elements. Motif analysis further identified candidate sequence features associated with MANCR-occupied chromatin regions. Conclusions: Together, these results suggest that MANCR may contribute to transcriptional regulation in TNBC in an isoform-specific manner through interactions with chromatin and p53 signaling pathways.
Background/Objectives: Tick-borne encephalitis virus (TBEV) is a vector-borne flavivirus that targets the CNS and causes severe encephalitis with long-term neurological sequelae. As the geographic range of its vectors expands and climate conditions shift, the incidence of tick-borne encephalitis continues to rise, yet no specific antiviral therapy has been developed to date. Understanding the molecular mechanisms of tissue-specific immune responses during TBEV infection is essential for identifying novel therapeutic targets and biomarkers. Methods: In this study, quantitative reverse transcription PCR with stem-loop primers was used to examine changes in the levels of seven immunoregulatory miRNAs (let-7i, miR-9-5p, miR-21a-5p, miR-30a-5p, miR-146b-5p, miR-181a-5p, miR-486-5p) in blood plasma, peripheral organs (spleen, liver), and four central nervous system structures (olfactory bulb, cerebral hemispheres, cerebellum, spinal cord) of BALB/c mice. Three experimental groups were compared: (1) infection with the Aina strain (Siberian subtype), (2) infection with the Sofyin strain (Far Eastern subtype), and (3) vaccination with the vaccine “Tick-E-Vak”. Results: It was shown that both infection and vaccination induce pronounced tissue-specific changes in the miRNA profile. MiR-181a-5p is a negative regulator of TLR4/NF-κB signaling. Its downregulation was the most reproducible change observed across all compartments and in all three groups, suggesting a possible universal mechanism for derepression of the proinflammatory cascade. Two miRNAs (miR-30a-5p and miR-146b-5p) were altered in all examined tissues and may be considered candidate markers of antigenic stimulation. Conclusions: The direction of their changes in plasma and tissues was frequently opposite, suggesting redistribution of miRNAs between the tissue and circulating pools, which is important for the interpretation of plasma biomarkers. Infection-specific changes were identified for let-7i and miR-9-5p in the CNS. The most pronounced changes were observed in the structures most affected by neuroinvasion (olfactory bulb, spinal cord), making them candidate markers associated with TBEV infection and CNS involvement. Infection with the Aina strain induced more pronounced miRNA changes in peripheral organs and the CNS than the Sofyin strain, despite the higher epidemiological lethality of the latter. This suggests that the pathogenicity of individual strains in the mouse model may not always correspond to the subtype-based lethality classification in humans and is determined by strain-specific features of tropism and infection kinetics.
Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults and is characterized by pronounced molecular heterogeneity, contributing to poor prognosis and limited therapeutic responsiveness. Distinct clinical subgroups, including short-term survivors (GBM-STS) and long-term survivors (GBM-LTS), differ substantially in disease progression and outcome; however, reliable biomarkers for prognostic stratification remain insufficiently defined. This study aimed to identify microRNA (miRNA) expression patterns capable of distinguishing GBM-STS from GBM-LTS patients. Methods: miRNA expression profiling was performed on glioblastoma tissue samples using next-generation sequencing (NGS). Differentially expressed miRNAs between GBM-STS and GBM-LTS groups were subjected to Gene Ontology (GO) Biological Process enrichment analysis to identify dysregulated pathways associated with their predicted target genes. Selected candidate miRNAs were further validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: Functional enrichment analysis revealed significant overrepresentation of pathways related to neurogenesis, neuronal differentiation, and neuron projection development, supporting the involvement of neural developmental programs in glioblastoma progression and invasion. NGS and RT-qPCR analyses demonstrated significant downregulation of hsa-miR-133a-3p, and significant upregulation of hsa-miR-424-5p and has-miR-216a-5p in GBM-STS tissues compared with GBM-LTS and control samples. Conclusions: Our findings demonstrate that distinct miRNA expression profiles are associated with survival-related glioblastoma subgroups and may contribute to differences in tumor aggressiveness. Hsa-miR-424-5p, has-miR-216a-5p, and hsamiR-133a-3p may represent promising candidate biomarkers for prognostic stratification in glioblastoma.
MicroRNA (miRNA) target prediction is often treated as a single problem, yet it encompasses two distinct computational tasks: identifying whether a miRNA can bind a particular target RNA binding site, and predicting the change in gene expression that follows from miRNA perturbation. Here, we trace the evolution of the field of miRNA target prediction in humans from early seed- and conservation-based algorithms, to deep learning models trained on large-scale interaction datasets. We note that early frameworks obscured binding site recognition and gene-level regulation by aggregating binding site-level scores into gene-level predictions, whereas newer experimental and computational advances have enabled a clearer separation of the tasks. Deep learning has substantially advanced miRNA binding site prediction, but progress in miRNA target gene prediction remains constrained by limited perturbation data, inconsistent data processing and annotations, heterogeneous definitions of regulatory response, uneven predictor coverage, and the absence of comprehensive benchmarks. We also highlight the importance of negative-example construction, which can introduce dataset-specific biases and inflate apparent model performance. We propose two complementary benchmarked community challenges: the prediction of miRNA–binding site interactions, and the genome-wide prediction of miRNA-mediated changes in target gene expression in defined biological contexts. Treating these as distinct computational tasks, while recognising their biological interdependence, would enable more meaningful evaluation of methodological advances and provide a stronger foundation for modelling miRNA-mediated regulatory networks.
Background/Objectives: This study aimed to identify a characteristic miRNA expression profile in the CSF of patients diagnosed with vestibular schwannoma and evaluate its potential for tumor assessment. Methods: In this prospective study, 17 CSF and corresponding tumor samples (seven small tumors—SVS and 10 large tumors—LVS) were collected from patients operated on for VS in a Tertiary Academic Center. The miRNA expression was analyzed using high-throughput RNA sequencing (NovaSeq 6000 Illumina). Data were normalized, and a comparative analysis of miRNA expression rankings was performed between VS patients and a public healthy donor dataset. Functional implications were explored using KEGG pathway enrichment analysis. Results: A total of 1633 miRNAs were identified in all CSF samples derived from VS patients. Comparison with healthy donors revealed a moderate ranking correlation (ρ = 0.39), with significant shifts for specific molecules like hsa-miR-766-3p and hsa-miR-182-5p. Only six miRNAs were found to correlate between CSF and tumor tissue, while 16 exhibited a negative correlation. No statistical correlation was found between tumor size and the CSF miRNA profile. KEGG analysis highlighted enriched pathways, including neurotrophin signaling and focal adhesion. Conclusions: The results of our study support the feasibility of miRNA-based CSF liquid biopsy for VS assessment. However, the results of miRNA expression profiling conducted in tumor tissue cannot be directly transferred into CSF sample analyses. Further studies are warranted to explain this phenomenon and to search for reliable miRNA markers of VS progression in the CSF liquid biopsy specimens.
