Dragūnaitė, Rugilė

Introduction Glioblastoma (GB) is the most common and the most aggressive form of brain cancer. Although these days the treatment for glioblastoma is improving, the median survival of patients still remains 12-15 months. Glioma stem cells contribute to such a short survival rate. These cells are responsible for glioblastoma initiation, tumor development, and resistance to treatment. Aim The aim of this study was to identify mRNAs that are related to glioma stem cells to find new molecular biomarkers for gliomas. Methods Glioblastoma and glioma stem cell lines were used to find potentially related mRNA to glioma stem cells. Differential gene expression analysis (DESeq2) of total 7903 genes, identified 121 significantly stemness-associated target genes according to Nanopore sequencing data. The top three candidates were subsequently selected for validation in glioma tumor tissues. Total RNA was isolated from 100 mg of snap-frozen patients’ tumor tissue using TRIzol according to the manufacturers’ instructions. The quality, quantity, and purity were measured on a 1.5% agarose gel and Nanodrop 2000 spectrophotometer. After, cDNA was synthesized. mRNA expression levels were detected using RTqPCR using glioblastoma (n= 31), II (n=12), and III (n=5) grade glioma patients’ samples.  Results After dRNA Nanopore sequencing data analysis, we identified top 3 the most significant stemness- related mRNAs (SERPINE1, ITGA3, and PMP2). mRNA expression levels significantly differed in glioma tumor samples (p<0.05) according to their pathology, which is proposing suitability of target mRNAs for glioma stratification. High expression of the analyzed target genes was associated with poorer prognosis in glioma patients. Conclusions In this study, we identified glioma stem cells specific mRNA target genes: SERPINE1, ITGA3, and PMP2 that have an impact on gliomagenesis. Although SERPINE1 and ITGA3 genes have been confirmed to be associated with stemness, validation in tumor tissues is still lacking. And to our knowledge, validation of the PMP2 gene in different glioma tumor grades has not been performed at all. Finding new clinical biomarkers can help to reflect patient prognosis and better predict therapeutic response.

Glioblastoma (GBM) is an aggressive brain tumor marked by rapid growth, frequent recurrence, and resistance to treatment. Emerging research shows that GBM cells interact with neural stem cells (NSCs) through extracellular vesicles (EVs). Understanding how GBM-derived EVs influence NSCs is key to revealing tumor–microenvironment dynamics and identifying novel therapeutics. Because calcium signaling governs cell proliferation, migration, and differentiation, changes in intracellular calcium may provide an early indicator of EV-driven NSC reprogramming. This study examined how GBM cell lines from different origins affect NSC behavior, focusing on calcium-signaling changes and validating findings with next-generation sequencing (NGS). Human neural stem cells (NSC H9 line) were treated with EVs isolated from GBM cell lines A172 and U87-MG conditioned media using 12% polyethylene glycol (PEG) precipitation. Cells were exposed to GBM-derived EVs (~1250 EVs per cell) for 17 h for Ca2+ signalling analysis and for 24 h for NGS. For Ca²⁺ imaging, NSCs were loaded with 2 µM Oregon Green-488 BAPTA-1 dye and imaged at 2 Hz for 200 s using a 40× objective. Nifedipine (2 µM) and caffeine (5 mM) were applied sequentially to assess L-type Ca²⁺ channel activity and ryanodine receptor (RyR)-mediated Ca²⁺ release, respectively. The pilot study revealed that GBM EVs influence Ca²⁺ signaling in NSCs, with effects dependent on the cell line. A172 EVs increased Ca²⁺ signal frequency in affected NSCs, which was blocked by nifedipine, suggesting elevated L-type Ca²⁺ channel activity. In U87 EV– treated NSCs, signal amplitude increased with nifedipine and further rose with caffeine, indicating enhanced RyR activity. NGS analysis revealed changes in RyR, SLC8B1 and LETM1 gene expression, suggesting increased Ca²⁺ concentration in mitochondria leading to augmented metabolic activity in mitochondria. Changes in Ca²⁺ signaling induced by EVs point to potential cross-talk between cytosolic and mitochondrial Ca²⁺ pathways. The study shows that A172 EVs enhance Ca²⁺ signaling, potentially promoting NSC proliferation and differentiation, whereas U87-MG EVs strongly elevate RyR levels, increasing NSC sensitivity to environmental cues such as growth factors or inflammatory interleukins. These functional effects are further supported by NGS analysis, which confirms the underlying molecular changes driving these EV-mediated responses.

Glioblastoma (GB) are highly aggressive and genetically complex brain tumors that infiltrate surrounding brain tissue, making them extremely difficult to treat effectively. Recent research has revealed that m6A modification is abnormally activated in glioma, underscoring its key role in various processes of glioma development. m6A modifications in mRNA of glioma stem cells NCH421k (GSCs) and in glioblastoma tumor tissues were investigated using methylation-based RNA immunoprecipitation (MeRIP) and direct Nanopore RNA sequencing (dRNA-seq). Data analysis amongst GSCs demonstrated 740 hypermethylated and 830 hypomethylated genes after MeRIP-seq. dRNA-seq data presented 4,340 RRACH motifs associated with hyper-methylated up-regulated genes and 9,106 motifs associated with hyper-methylated down-regulated genes across samples taken from glioma patients. We identified eight statistically significant RRACH motifs across seven genes — AAACA|2129|OS9, AGACA|1210|PAGR1, GGACA|2173|OS9, GGACT|2187|TOB1, AAACC|3283|PIK3R2, GAACC|3068|GP1BB, GGACA|3110|RETREG1, and GGACT|3122|LUC7L3 — to narrow down potential gene candidates. Findings indicated that m6A in target motifs is modified approximately 3.4-fold more in LGG than in GB, distinguishing patient samples by pathology. Next, we found that global m6A methylation score was significantly lower in GB cluster compared to LGG cluster (p=0.0002). Furthermore, lower m6A methylation scores were associated with shorter survival in glioma patients (p=0.016) while the expression levels did not differ among m6A-based clusters (p=0.08) or in a Kaplan-Meier survival analysis (p=0.79). In summary, m6A modifications in mRNA may emerge as future-defining biomarkers capable of reshaping gliomas biology.

Glioblastoma (GB) continue to be a devastating disease despite advances in treatment therapies. High invasiveness, intra-tumoral heterogeneity and poor prognosis are key barriers to successful GB treatment. The most prevalent m6A modification in RNA serves as a biomarker for cancer diseases. Recent findings implicate the valuable role of m6A modification in tumor progression and tumorigenesis of gliomas. We profiled m6A modifications within mRNA across glioma stem cells NCH421k (GSCs) using methylated RNA immunoprecipitation (MeRIP) technique and glioblastoma tumor tissues using direct Nanopore RNA sequencing (dRNA-seq). A total of 1,570 distinct differentially modified genes were found after MeRIP-seq data analysis of which 740 genes were hypermethylated, and 830 genes were hypomethylated in GSCs. Four-quadrant plot was produced to evaluate the distribution of differentially expressed mRNAs with statistically significant m6A modifications. After, dRNA-seq data showed 4,340 RRACH motifs linked to hyper-methylated upregulated genes and 9,106 linked to hyper-methylated downregulated genes across all patients’ samples. To find statistically significant 8 RRACH motifs through seven genes and narrow down the list we used logistic regression model and Chi-square test: AAACA|2129|OS9, AGACA|1210|PAGR1, GGACA|2173|OS9, GGACT|2187|TOB1, AAACC|3283|PIK3R2, GAACC|3068|GP1BB, GGACA|3110|RETREG1 and GGACT|3122|LUC7L3. Analysis showed that adenosine in selected motifs in LGG samples undergo modifications about 3.4 times more frequently compared to GB, and subsequent hierarchical clustering analysis classified patient samples according to pathology. Next, based on 8 RRACH motifs methylation we constructed total m6A methylation score which significantly differed between LGG and GB clusters (p=0.0002) being higher in LGG cluster. Low m6A methylation score was associated with a worse survival prognosis in glioma patients (p=0.016). The expression patterns of selected genes did not show any significant differences in m6A-based clusters (p=0.08), as well as the Kaplan-Meier survival analysis (p=0.79). To summarize, m6a modifications in mRNA can serve as potential biomarkers in gliomas.

Gene expression is a quantitative estimate reflecting how much the information encoded in a gene is turned into a functional “outcome”, usually proteins or RNA molecules. Evaluation of gene expression levels can reveal peculiarities of cellular processes related with particular pathogenic mechanisms. The construction of biomarkers of certain diseases can be based on gene expression estimates. However, processing, analyzing and generalizing the gene expression data is challenging due to the inescapable variation of technical conditions in sophisticated biochemical process. It results in so called “batch effects” in estimates, when artifact differences appear not due to the sought genetic differences, but due to the variety of technical conditions. The normalization of gene expression levels is the solution and a crucial step in obtaining reliable results. The expression of so called „House-keeping“ genes, ensuring the basic functions of the cells can serve as normalizing values in such cases. We propose Principal Component Analysis [1] approach to concentrate the correlated variety of gene expression values into uncorrelated principal components. In that way variety of gene expression estimates concentrated in the same principal component as the ones from “House

• keeping” genes will be concerned as technical artifacts, while the others will reflect the sought epigenetic phenomena. The proposed method was used to validate expression levels of targeted gene set related to various complexity brain tumors. Data were collected by Oxford Nanopore [2] direct RNA Sequencing. Potential biomarker genes were identified as showing statistically different in Glioblastoma vs Low-grade-glioma cases.

Šio tyrimo tikslas nustatyti gliomoms specifines iRNR N6-metiladenozino modifikacijas gliomos kamieninėse ląstelėse ir navikuose, siekiant atrinkti naujus, kliniškai reikšmingus gliomų molekulinius žymenis. Uždaviniai: 1. Nustatyti gliomos kamieninių ląstelių (NCH421k) iRNR N6-metiladenozino (m6A) modifikacijų profilį lyginant su glioblastomos ląstelėmis (U87-MG) ir atrinkti potencialias, m6A metilintas iRNR, susijusias su gliomos kamienišku ir progresavimu. 2. Apibūdinti iRNR molekulių rinkinį su būdingomis m6A epitranskriptominėmis modifikacijomis, susijusiomis su gliomos patogeneze ir paciento prognoze. 3. Ištirti gliomos kamieninėms ląstelėms specifinius m6A metilintus iRNR transkriptus gliomos navikuose, siekiant įvertinti jų poveikį naviko patologijai bei pacientų klinikinėms charakteristikoms. Tyrimai rodo, kad m6A modifikacijos galėtų būti potencialus taikinys gliomų atveju, o terapinis epitranskriptomo modifikacijų reguliavimas gliomos kamieninėse ląstelėse galėtų padėti kontroliuoti jų augimą, atsinaujinimą ir naviko vystymąsi.

Background: Efforts to understand the interplay between m6A (N6-methyladenosine) modification and long noncoding RNAs (lncRNAs) in the pathogenesis of various diseases, including cancer, have recently attracted considerable attention. Methods: Herein, we profiled epitranscriptome-wide m6A modifications within lncRNAs at single m6A site resolution across different grades of gliomas (Glioblastomas (GB): n = 17, Low grade gliomas (LGG): n= 9) using direct RNA long-read sequencing. Results: Our analysis demonstrated that, 1) 98.5% of m6A-modified RRACH motifs were present within mRNA transcripts, while only 1.16% were conspicuous within lncRNAs. Importantly, LGGs exhibited a higher m6A abundance (23.73%) compared to the GB transcriptome (15.84%). 2) The m6A profiles of lncRNAs differed significantly between gliomas, with unsupervised cluster analysis revealing two clusters (C1, C2). LGG dispersed between C1 and C2 clusters while GB stayed mainly in C1. Clinical feature association analysis between m6A clusters showed the tendency of m6A to be associated with higher malignancy grade (p = 0.053), while significant association was observed with higher Ki-67 proliferation index (p = 0.04), and tumor location (p < 0.01). Specifically, brain tumors located in cerebellum (n = 3) were highly m6A modified on lncRNAs as compared to tumors in other locations (frontal lobe, n = 5, p = 0.003; frontotemporal lobe, n = 2, p = 0.08; occipital,n = 2, p = 0.038; parietal, n = 2, p = 0.007; temporal, n = 11, p < 0.001). Cox regression analysis showed that the status of lncRNAs m6A modifications had no significant value in predicting post-surgical survival time in our GB or LGG cohorts. The trend of higher lncRNA expression in m6A methylated group was observed for the majority of lncRNAs, while only MIR9-1HG (r = 0.439, p = 0.028) and ZFAS1 (r = 0.609, p < 0.05) m6A showed statistically significant positive correlations in gliomas. A high-resolution m6A study revealed that mRNA levels of m6A writers and erasers in gliomas do not reflect global m6A methylation. Conclusions: Overall, we provide evidence that m6A lncRNAs are strongly modulated in gliomas, representing biologically distinct subgroups. Ten novel differentially methylated lncRNAs were identified in gliomas, which might exert regulatory role in glioma cells. These findings may provide a basis for further deeper research on the role of m6A lncRNAs in gliomas.

Increasing evidence suggests that the beneficial abilities of neural stem cells (NSCs) can be attributed to their paracrine secretion of extracellular vesicles (EVs). These EVs may offer promising therapies for neurological disorders due to their capacity to induce neuroprotection and neurogenesis, migrate to damaged brain areas, including tumours, and modulate inflammation and the aggregation of misfolded proteins. [...].

Interest in long-non-coding RNAs (lncRNAs) during oncogenesis is constantly increasing. lncRNA plays an important role in gene regulation via chromatin remodelling, RNA processing, transcriptional activation, in protein function and activity alteration via direct interaction with proteins etc. Recently developed methods for RNA chemical modification identification, promoted interest in human epitranscriptome. N6-methyladenosine (m6A) is the most prominent and widely studied RNA modification, which affects RNA stability, location, and translation. However, information on m6A effect to lncRNAs and other non-coding RNAs (ncRNAs) is lacking. Here we compare transcriptome wide m6A modifications of lncRNAs in human glioblastoma and healthy brain samples applying direct RNA sequencing method from Oxford Nanopore Technology (ONT dRNA-seq). The analysis of ONT dRNA-seq identified lncRNA targets that are differentially modified between glioblastoma and healthy human brain specimen. Previously reported glioma associated lncRNAs like NEAT1, SOX2-OT, SNGH6, OIP5-AS1, etc. in present study showed decreased levels of m6A modification in glioblastoma specimens compared to healthy brain samples indicating that m6A demethylation of lncRNAs transcripts might be characteristic during gliomagenesis.

Alterations of N6-methyladenosine (m6A) RNA modifications are responsible for a platter of structural and functional changes in healthy and cancerous cell states. Even though several studies have linked m6A to the onset and development of tumors, very few have examined transcriptome-wide m6A landscape of long non-coding RNAs (lncRNAs) in cancerous cells, including gliomas. This work aims to explore and elucidate lncRNA m6A alterations and expression in glioma stem cells, U87-MG cells and glioma patients. We utilized four cell lines (NCH421k, NCH644, NCH690, U-87 MG), along with 16 glioblastomas (GB) and 9 low grade (LGG) gliomas. m6A RNA modifications were detected using MeRIP-seq and dRNA-seq. First, we provided transcriptome-wide lncRNAs m6A peak distribution in GSCs. MERIP-seq analysis between U-87 MG and GSCs showed 76.4% overlap of lncRNAs m6A peaks, while 19.4% of peaks were exclusively identified in GSCs. Next, m6A was observed uniquely in GSCs for previously identified glioma-associated lncRNAs, including LINC000461, HOTTIP, CRNDE, TUG1, and XIST. Further, dRNA-seq transcript hierarchical clustering analysis revealed differentially expressed lncRNAs capable to separate LGG, GB, and GSCs (NEAT1, ANAPC1P2, CYTOR, ZNF8-ERVK3-1, MIR4435-2HG, LINC01116, and MIR222HG). Subsequently, PCA analysis based on lncRNA expression pattern identified clusters attributed to GSCs-GB and LGG. Additionally, we discovered that a set of lncRNAs were associated with stemness transcript activation in gliomas and worse patient survival (p<0.05). Our findings offer a foundation and understanding for further research on m6A modifications and expression patterns in the non-coding epitranscriptome of GSCs and glioma.