(1) Background: Glioblastoma (GBM) is one of the most aggressive brain tumors with a poor prognosis. Therefore, new insights into GBM diagnosis and treatment are required. In addition to differentially expressed mRNAs, miRNAs may have the potential to be applied as diagnostic biomarkers. (2) Methods: In this study, profiling of human miRNAs in combination with mRNAs was performed on total RNA isolated from tissue samples of five control and five GBM patients, using a high-throughput RNA sequencing (RNA-Seq) approach. (3) Results: A total of 35 miRNAs and 365 mRNAs were upregulated, while 82 miRNAs and 1225 mRNAs showed significant downregulation between tissue samples of GBM patients compared to the control samples using the iDEP tool to analyze RNA-Seq data. To validate our results, the expression of five miRNAs (hsa-miR-196a-5p, hsa-miR-21-3p, hsa-miR-10b-3p, hsa-miR-383-5p, and hsa-miR-490-3p) and fourteen mRNAs (E2F2, HOXD13, VEGFA, CDC45, AURKB, HOXC10, MYBL2, FABP6, PRLHR, NEUROD6, CBLN1, HRH3, HCN1, and RELN) was determined by RT-qPCR assay. The miRNet tool was used to build miRNA–target interaction. Furthermore, a protein–protein interaction (PPI) network was created from the miRNA targets by applying the NetworkAnalyst 3.0 tool. Based on the PPI network, a functional enrichment analysis of the target proteins was also carried out. (4) Conclusions: We identified an miRNA panel and several deregulated mRNAs that could play an important role in tumor development and distinguish GBM patients from healthy controls with high sensitivity and specificity using total RNA isolated from tissue samples.

Background/Objectives: Brain metastases (BM) are the most common type of intracranial malignant tumor and are associated with high mortality. More than 50% of BM cases originate from lung cancer, and lung adenocarcinoma (LUAD) is most commonly associated with the development of BM (25%). The differential diagnosis of solitary BM and glioblastoma (GBM)—one of the most aggressive and fatal malignant brain tumors—remains a considerable challenge. Given the major role of microRNAs (miRNAs) in regulating gene expression, their clinical potential as biomarkers for tumor diagnosis and prognosis offers significant promise. Methods: Next-generation RNA Sequencing (RNA-seq) was used to assess the miRNA expression profiles of 6 LUAD-BM, 6 GBM, and 6 control (non-tumoral brain tissue samples) human brain tissue samples. miRNAs exhibiting the most significant differential expression in LUAD-BM patients in comparison to both control subjects and GBM patients were selected for validation through RT-qPCR. Results: The analysis of RNA-seq data revealed the presence of 229 differentially expressed miRNAs in the comparison between LUAD-BM and control samples and 46 in the comparison between LU-AD-BM and GBM samples. Eight miRNAs were selected for further analysis, four of which were upregulated and four downregulated, based on the significant differences in their expression levels observed between the LUAD-BM samples and the other two groups, as confirmed with the Mann–Whitney U test. Functional enrichment analysis was also conducted based on a miRNA-centered target analysis performed using the miRNet tool. To assess the diagnostic potential of these differentially expressed miRNAs, we performed a receiver operating characteristic (ROC) curve analysis. Conclusions: A panel of eight miRNAs was identified in human brain tissue samples, exhibiting high accuracy in distinguishing LUAD-BM from both GBM and control samples.

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.