Neural–tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients’ survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients’ plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

A significant proportion of mammalian genomes corresponds to genes that transcribe long non-coding RNAs (lncRNAs). Throughout the last decade, the number of studies concerning the roles played by lncRNAs in different biological processes has increased considerably. This intense interest in lncRNAs has produced a major shift in our understanding of gene and genome regulation and structure. It became apparent that lncRNAs regulate gene expression through several mechanisms. These RNAs function as transcriptional or post-transcriptional regulators through binding to histone-modifying complexes, to DNA, to transcription factors and other DNA binding proteins, to RNA polymerase II, to mRNA, or through the modulation of microRNA or enzyme function. Often, the lncRNA transcription itself rather than the lncRNA product appears to be regulatory. In this review, we highlight studies identifying lncRNAs in the homeostasis of various cell and tissue types or demonstrating their effects in the expression of protein-coding or other non-coding RNA genes.

Multifactorial diseases such as cancer, cardiovascular conditions and neurological, immunological and metabolic disorders are a group of diseases caused by the combination of genetic and environmental factors. High-throughput RNA sequencing (RNA-seq) technologies have revealed that less than 2% of the genome corresponds to protein-coding genes, although most of the human genome is transcribed. The other transcripts include a large variety of non-coding RNAs (ncRNAs), and the continuous generation of RNA-seq data shows that ncRNAs are strongly deregulated and may be important players in pathological processes. A specific class of ncRNAs, the long non-coding RNAs (lncRNAs), has been intensively studied in human diseases. For clinical purposes, lncRNAs may have advantages mainly because of their specificity and differential expression patterns, as well as their ideal qualities for diagnosis and therapeutics. Multifactorial diseases are the major cause of death worldwide and many aspects of their development are not fully understood. Recent data about lncRNAs has improved our knowledge and helped risk assessment and prognosis of these pathologies. This review summarizes the involvement of some lncRNAs in the most common multifactorial diseases, with a focus on those with published functional data.

Extracellular vesicles (EVs) act as carriers of biological information from tumors to the bloodstream, enabling the detection of circulating tumor material and tracking of disease progression. This is particularly crucial in glioblastoma, a highly aggressive and heterogeneous tumor that is challenging to monitor. Using imaging flow cytometry (IFCM), we conducted an immunophenotyping analysis of eight glioma-associated antigens and tetraspanins in plasma EVs from 37 newly diagnosed glioblastoma patients (pre- and post-surgery), 11 matched individuals with recurrent glioblastoma, and 22 healthy donors (HD). Tenascin-C (TNC) positive EVs displayed the strongest differences in newly diagnosed and recurrent glioblastoma patients, when compared to non-tumor subjects. Among dual-positive subpopulations, TNC⁺/CD9⁺ EVs were the most elevated in newly diagnosed (FC = 7.6, p <0.0001, AUC = 81%) and recurrent patients (FC = 16.5, p <0.0001; AUC = 90%) than HD. In comparison with other CNS tumors (n = 25), this subpopulation was also 34.5-fold higher in glioblastoma than in meningioma cases (p <0.01). Additionally, TNC⁺/CD9⁺ EV levels were 3.3-fold elevated in cerebrospinal fluid from glioblastoma patients (n = 6) than controls (p <0.05). Aberrant TNC levels were further observed in glioblastoma EVs from different sources and purified via different methods. Immunohistochemical analysis revealed high levels of TNC in tumor tissues. Spatial transcriptomic analysis indicated a TNC overexpression in malignant cell populations of glioblastoma resections, particularly in cells with mesenchymal-like signatures and chromosomal aberrations. Lastly, we purified TNC⁺ EVs from plasma of 21 glioblastoma patients by magnetic sorting and detected the oncogenic mutation TERT*C228T by droplet digital PCR. The mutant allele frequency was higher in TNC⁺ EVs vs TNC-negative EVs (FC = 32, p <0.001), total EVs (FC = 5.3, p <0.001) or cell-free DNA (FC = 5.3, p <0.01). In conclusion, circulating TNC⁺ EVs may have potential as clinical biomarkers in glioblastoma, and their purification could improve the identification of tumor-specific mutations in liquid biopsies.