H/ACA snoRNAs and snoRNPs Dysregulation Links rRNA Modification to Glioblastoma Progression

H/ACA snoRNAs and snoRNPs Dysregulation Links rRNA Modification to Glioblastoma Progression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article H/ACA snoRNAs and snoRNPs Dysregulation Links rRNA Modification to Glioblastoma Progression Gabriela D. A. Guardia, Xiufen Lei, Christina Middle, Morgan Roos, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8079210/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Small nucleolar RNAs (snoRNAs) are critical players in ribosome biogenesis and have essential roles in rRNA processing and modification (2’O methylation and pseudo-uridylation). snoRNAs define which nucleotides get modified by guiding small nucleolar ribonucleoprotein complexes (snoRNPs) to specific positions in rRNA via base pairing. Altered snoRNA expression has been reported in diverse biological and pathological contexts. In cancer cells, the dysregulation of snoRNAs can alter the rRNA modification landscape, potentially affecting ribosome composition and translation. In aggressive tumors, such as glioblastoma (GBM), snoRNA profiling is crucial for expanding our understanding of ribosome biogenesis and identifying novel biomarkers and therapeutic targets. Methods. We have developed an Ampliseq platform and analysis pipeline to measure the expression of 127 snoRNAs (mostly those containing H/ACA boxes) and Cajal body–specific RNAs (scaRNAs) and conducted a study in a panel of glioma stem cell (GSC), GBM, and normal neuronal lines/tissue. Results. The results of our Ampliseq analysis identified snoRNAs and scaRNAs (snoRNAs/scaRNAs) that were differentially expressed in GBM and GSC cells compared to normal neuronal cells/tissue, as well as snoRNAs/scaRNAs associated with stemness and differentiation. SnoRNAs with elevated expression in GSC and GBM lines (snoRA38, snoRA51, snoRA71, and snoRA75) have been previously implicated in cancer development. Components of the H/ACA snoRNP complex, which regulate snoRNA processing and rRNA pseudouridylation, were also found to be overexpressed in GBM and showed decreased levels during neuronal differentiation. Notably, high expression of Dyskerin (DKC1)—the pseudouridylation enzyme and a key H/ACA snoRNP component—correlates with poor survival in patients with high-grade gliomas. Finally, we assessed the therapeutic potential of targeting snoRNAs in GBM. Knockdown of two upregulated snoRNAs, snoRA46 and snoRA75, using antisense oligonucleotides significantly impaired GBM cell growth. Conclusions snoRNA/scaRNAs profiling revealed distinct alterations in snoRNA expression between glioblastoma and normal neuronal cells. These differences may contribute to the reprogramming of rRNA modification and ribosome composition in cancer cells. Moreover, our findings highlight the potential of antisense oligonucleotide-based targeting of overexpressed snoRNAs in GBM as a promising therapeutic strategy. snoRNAs scaRNAs rRNA modification snoRNPs DKC1 Ampliseq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Ribosome biogenesis is a complex, multi-step process that generates and assembles the RNA and protein components of the 80S ribosome. Cancer cells are highly dependent on this process to sustain their increased and specialized protein demands ( 1 – 4 ). Consistent with this, altered expression of ribosomal proteins and regulators of rRNA transcription, processing, and modification has been linked to cancer development. Targeting ribosome biogenesis has therefore emerged as a promising therapeutic strategy, particularly in aggressive tumors such as glioblastoma (GBM) ( 3 , 4 ). To improve the likelihood of clinical success, it is crucial to conduct focused studies to identify the RNA and protein factors driving rRNA alterations in each tumor type and to evaluate their roles in tumor progression and therapeutic response. Ribosomal RNA (rRNA) modification is a key step in ribosome biogenesis; its impact on RNA-RNA and RNA-protein interactions can ultimately alter ribosome composition and influence translation. Growing evidence suggests that ribosomes are heterogeneous, and "specialized" ribosomes have been described in different scenarios ( 5 – 10 ). A recent study, employing ribosome ratio-omics (RibosomeR) to investigate 80S ribosome proteomics data, identified major variations in ribosome protein stoichiometry across various biological samples, including differences between tissues, developmental stages, and pathological states ( 9 ). In cancer cells, changes in ribosome composition can enhance and/or tailor translation to promote the production of proteins involved in tumor progression, therapy resistance, and metastasis ( 8 ). RNA-protein complexes called snoRNPs regulate the two main types of rRNA modifications, 2’O methylation and pseudo-uridylation. 2’O methylation occurs in all four nucleotides and is catalyzed by Fibrillarin (FBL) while pseudo-uridylation (the conversion of uridine into pseudo-uridine, Ψ) is catalyzed by dyskerin (DKC1) ( 10 ). DKC1 driven pseudo-uridylation has two additional regulatory impacts. Small Cajal body-specific RNAs (scaRNAs) guide DKC1 to pseudo-uridylate specific nucleotides in small nuclear RNAs (snRNAs). snRNAs are essential components of the spliceosome and have important functions in splice site recognition and selection ( 11 ). DKC1 also binds the telomerase RNA (TERC) and modulates its activity ( 12 ). Small nucleolar RNAs (snoRNAs) define which nucleotides in rRNA get modified by guiding snoRNPs to specific positions in rRNA via base pairing. More than 500 snoRNAs, ranging from 60 to 300 nucleotides, have been cataloged in the human genome. There are two classes of snoRNAs: C/D box snoRNAs are implicated in 2’-O-methylation, while snoRNAs with H/ACA boxes regulate pseudo-uridylation (Ψ) ( 13 , 14 ). Most expressed snoRNAs are embedded in introns of lncRNAs and protein-coding genes, including ribosomal proteins and rRNA transcription/processing regulators. However, snoRNA expression levels do not always correlate with those of their host genes, reflecting the influence of specific sequence features and differences in snoRNA maturation ( 15 ). Alterations in snoRNA expression have been observed in various disease states and cancers, with several snoRNAs contributing to cancer-relevant phenotypes and tumor growth ( 16 ). Despite their biological relevance and potential as biomarkers or therapeutic targets, studies on snoRNA and scaRNA profiling remain very scarce. The lack of specific sequencing approaches for snoRNA/scaRNA analysis is certainly a barrier. We developed an Ampliseq panel and analysis pipeline to evaluate H/ACA box snoRNAs and scaRNAs specifically, and conducted the first comprehensive study of snoRNAs and scaRNAs in GBM versus normal cells. Results led to the identification of multiple snoRNAs/scaRNAs aberrantly expressed in tumor cells, defined a snoRNA/scaRNA signature for glioma stem cells, and identified snoRNAs/scaRNAs associated with stemness and differentiation. Our study also demonstrated the potential use of antisense oligonucleotides (ASOs) to target aberrantly expressed snoRNAs in GBM as a potential therapeutic strategy. METHODS Cell culture Human glioblastoma cell line U251 was obtained from Uppsala University (Uppsala, Sweden), while the T98G and LN229 cell lines were obtained from the American Type Culture Collection. Human astrocyte cells (Cat# 1800) were obtained from ScienCell Research Laboratories (Carlsbad, CA, USA). Glioblastoma Stem Cell (GSC) lines 3565 and 1919 were gifts from Drs. Jeremy Rich, Christopher Hubert, and Ichiro Nakano ( 17 , 18 ). GSC lines 031417, 040909, and 040815 were established by Dr. Andrew Brenner’s lab. Sareddy. Neural Progenitor Cells (NPCs) were purchased from Axol Bio (Cat# ax0011). The U251, T98G, LN229, and HEK293T cell lines were cultured in DMEM medium (HyClone; Cat# SH30243.01) supplemented with 10% Fetal Bovine Serum (FBS) (Corning; Cat# 35015CV) and 1% penicillin/streptomycin (Gibco; Cat# 10378016). Human astrocyte cells were cultured in DMEM-F12 media (Thermo Fisher Scientific, Cat# 11320033). supplemented with 10% FBS (Corning) and 1% penicillin/streptomycin (Gibco). All GSCs and NPCs were cultured in Neurobasal-A medium supplemented with B27, glutamine, sodium pyruvate, 20 ng/mL of both epidermal growth factor (EGF) (Thermo Fisher Scientific) and basic fibroblast growth factor (bFGF) (PeproTech). All cells were maintained in a humidified incubator at 37°C with 5% CO 2 . For routine passaging, cells were harvested by using 0.05% Trypsin/0.53 mM EDTA in HBSS (Corning, Cat# 25-051-Cl) and replated for continued culture. For subsequent experiments, cells were harvested and counted with the Countess automated cell counter (Invitrogen) using trypan blue, then plated at specific densities for transfection and the various assays described below. Cell transfection For transient gene knockdown, cells were transfected with small interfering RNA (siRNA) or antisense oligonucleotides (ASOs) using Lipofectamine RNAiMAX (Invitrogen, Carlsbad, CA; Cat# 13778150) and OptiMEM (Thermo Fisher Scientific, Cat# 31985070). Transfected cells were harvested after 72 hours for RNA analysis or incubated for other assays as described below. siRNAs were obtained from Dharmacon (Lafayette, CO) and Millipore-Sigma (Burlington, MA). ON-TARGETplus non-targeting siRNA (Dharmacon; Cat# D-001810-01-05) was used as a negative control. The DKC1 siRNA (siDKC1, SASI_Hs01_00195836) was obtained from Millipore-Sigma. ASOs were purchased from Millipore Sigma. Cell proliferation assay U251-GFP cells were harvested, counted, and seeded at a density of 1200 cells/well into 96-well plates. After transfection with ASOs, plates were transferred to a live-cell imaging system (IncuCyte, Essen BioScience). Cell proliferation was subsequently monitored for 72 consecutive hours, with images acquired and cells counted every four to six hours. MTS assay U251 cells were harvested, counted, and seeded at a density of 1200 cells/well into 96-well plates. Plates were incubated at 37℃ for 72 hours after transfection with ASOs as described above. Next, 20µl of MTS mixture (1,000µl MTS and 50µl PMS) of MTS solution (Promega) was added to each well, and samples were incubated at 37°C for 60 minutes. Optical density was measured at absorbance 490nm with a Synergy HT microplate reader (BioTek). Co-culture assay U251 GFP cells (800 cells/well) and human astrocyte cells (1000 cells/well) were harvested, counted and seeded into a 96-well plate. ASO transfection, as described above, was performed simultaneously with cell seeding, using a final ASO concentration of 40 nM. Cell proliferation and morphology were subsequently monitored for five consecutive days using the IncuCyte automated microscope system (Essen Bioscience), with images acquired every six hours. 2x2 montage images per well were manually analyzed using ImageJ. A digital grid was overlaid, and the number of astrocytes within four fields was counted. RNA preparation Total RNA was extracted from cells using TRIzol™ reagent (Thermo Fisher Scientific, Grand Island, NY; Cat# 15596018), following the manufacturer’s instructions. RNA concentration and purity were determined spectrophotometrically using a NanoDrop 8000 (Thermo Scientific). Only RNA samples with an acceptable A260/A280 ratio were used for subsequent steps. The RNA was stored at -80°C until further use. For complementary DNA (cDNA) synthesis, 300 ng of total RNA from each sample was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific; Cat# 4368814) according to the manufacturer’s protocol. The resulting cDNA was stored at -20°C until further use. Quantitative Real-Time PCR To validate the knockdown efficiency of the DKC1 siRNA and the ASOs against snoRA46 and snoRA75, qRT-PCR was performed on transfected U251 cells. Gene expression was normalized to GAPDH, a housekeeping gene, using the ddCT method. For all qRT-PCR experiments, the PowerUp SYBR Green Master Mix (Applied Biosystems) was used. Sequences of qRT-PCR primers are listed in Table S1 , and sequences of ASOs are presented in Figure S1 . RNA sample preparation and RNA-seq Total RNA from transfected U251 cells (siRNA control vs. siDKC1) was extracted using the TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Libraries for RNA sequencing were prepared using the TruSeq RNA Library Preparation Kit (Illumina, San Diego, CA), following the manufacturer’s instructions. The prepared libraries were then sequenced at UT Health San Antonio Genomic Facility. All experiments were performed in triplicate. Amplicon sequencing (Ampliseq) The snoRNA/scaRNA Ampliseq panel was designed using the HUGO Gene Nomenclature Committee (HGNC; database ( 19 ), which contains 564 entries. Due to design constraints on the length of gene entries (genes < 125 nucleotides were omitted), 213 multiplex primer pairs were designed for 127 snoRNA/scaRNA genes ( Table S2 ) . The RNA sequencing (RNAseq) libraries were constructed using the AmpliSeq for Illumina On-Demand, Custom, and Community Panels reference guide, following the standard workflow listed in the protocol. Briefly, Total RNA (~ 10–100 ng) was reverse transcribed with random primers, then combined with the AmpliSeq snoRNA/scaRNA multiplex primer pairs and amplified by PCR for 17 cycles. The amplicons were partially digested with the FuPa enzyme and ligated to the AmpliSeq CD Indexes for Illumina. The samples were purified with Illumina Purification Beads (IPB) and PCR-amplified a second time for 7 cycles using generic (P5/P7) primers standard to all amplicons. The samples were then purified for a second time prior to quantification using the Qubit high-sensitivity assay. The samples were then normalized and pooled for sequencing. The libraries were then sequenced on a NextSeq 550 or NextSeq 2000 instrument at 2 × 150 paired-end reads ( Figure S2 A) . Ampliseq analysis AmpliSeq RNA sequencing reads were aligned to the human reference genome (GRCh38/hg38) using BWA-MEM with default parameters ( https://github.com/lh3/bwa , Accessed 23 October 2025). Only properly paired reads were retained using SAMtools with parameter -f 0x0002 ( 20 ). To remove ambiguously mapped reads, read pairs exhibiting identical primary (AS) and suboptimal (XS) alignment scores for both mates were filtered out using local scripts. This filtering step ensured that only uniquely aligned read pairs were retained for downstream quantification. Gene-level read counts were quantified using HTSeq-count (parameters: -m intersection-strict and -s no) ( 21 ), based on gene annotations from the NCBI RefSeq (hg38). Differential expression analyses of snoRNA genes between sample groups were performed using DESeq2 ( 22 ). Genes with |log₂FC| ≥ 0.6 and a false discovery rate (FDR) < 0.05 were considered differentially expressed. Splicing analysis To evaluate the impact of DKC1 knockdown on RNA splicing, RNA-Seq reads were pseudo-aligned to the GENCODE human reference transcriptome (version 36) (Frankish et al., 2021) using Kallisto (version 0.48) with default parameters and 100 bootstrap replicates ( 23 ). Normalized expression values (TPM) were then used as input to SUPPA2 (v. 2.3), with default parameters ( 24 ), to detect differential alternative splicing events. Alternative splicing events in each gene were generated with the “generateEvents” function and classified as skipping exon (SE), alternative 5’ (A5S) and 3’ (A3S) splice sites, mutually exclusive exons (MX), or retained intron (RI). Events were considered significant when |ΔPSI| >0.1 and False Discovery Rate (FDR) adjusted p-values < 0.05. RNA-Seq analyses of public datasets To investigate the expression profiles of genes involved in H/ACA snoRNA processing and rRNA modification, as well as snoRNA host genes in healthy brain and glioma samples, we obtained publicly available gene expression datasets (TPM-normalized) from the GTEx (V8) ( https://www.gtexportal.org/ , Accessed 23 October 2025) and TCGA projects ( https://portal.gdc.cancer.gov/ , Accessed 23 October 2025), respectively. Normal (frontal cortex) tissue expression data (n = 464) were retrieved from GTEx, while data from lower-grade gliomas (LGG grade II, n = 249; LGG grade III, n = 265) and glioblastoma (GBM, IDH1/2 wild-type, n = 152) were obtained directly from TCGA. Boxplots were generated using the ggplot2 package (v. 4.0.0) in R, and statistical differences between groups were assessed using the Mann–Whitney U test. For high-grade gliomas, we evaluated the impact of DKC1 gene expression on overall survival by stratifying patients into high- and low-expression groups based on the median TPM value. Survival analyses were performed using the survival (v. 3.8.3), survminer (v. 0.5.1), and forestmodel (v. 0.6.2) R packages. Multivariate analyses were conducted to adjust for tumor histology (GBM or LGG grade III). To compare DKC1 expression levels across multiple tumor types, we analyzed publicly available gene expression data (TPM-normalized) from the GEPIA2 web platform ( http://gepia2.cancer-pku.cn , Accessed 23 October 2025), which integrates GTEx and TCGA datasets. Only tumor types displaying significantly different expression levels between tumor and normal tissues (p < 0.05) were included: CESC (306 tumor and 13 normal samples), CHOL (36 tumor and 9 normal), COAD (275 tumor and 349 normal), DLBC (47 tumor and 337 normal), LUSC (486 tumor and 338 normal), READ (92 tumor and 318 normal), STAD (408 tumor and 211 normal), and THYM (118 tumor and 339 normal). To assess DKC1 expression changes during cortical development, we obtained TPM-normalized gene expression data from the Cortecon database ( 25 ). Boxplots were generated using ggplot2 (v. 4.0.0), and statistical significance was assessed by Mann–Whitney U tests. Finally, to evaluate differences in DKC1 expression levels in neuroblastoma BE( 2 )C cells between day 0 and day 7 of ATRA-induced differentiation, we used RNA-Seq data from a previous study ( 26 ). First, RNA-Seq reads were pseudoaligned to the human reference transcriptome (GENCODE v36, https://www.gencodegenes.org/ , Accessed 23 October 2025) using Kallisto (v. 0.48) ( 23 ). Gene-level count data were then obtained using the tximport R package (v. 1.26.1) and differential gene expression analysis was performed with DESeq2 (v. 1.38.3) ( 22 ). RESULTS Expression of snoRNAs/scaRNAs is altered in glioblastoma. We devised an Ampliseq panel and analysis pipeline ( Figure S2 ) to specifically evaluate the expression of 127 snoRNAs (primarily those containing H/ACA boxes) and scaRNAs. Among the 77 rRNA pseudouridylation sites with known guiding snoRNAs, our Ampliseq platform covers snoRNAs responsible for guiding 67 of these modifications ( 27 , 28 ). We profiled their expression in glioblastoma cells, glioma stem cells (GSC), neuronal precursor cells (NPCs), astrocytes, and the normal human brain. In all samples, we observed that more than 50% of all detected snoRNA and scaRNA expression corresponds to a group of 10–12 snoRNAs. Notably, snoRD3A, snoRA63, snoRA8, snoRA22, snoRD22, and snoRA73A showed consistently high expression across all samples (Fig. 1 A). From this group, snoRD3A, snoRA63, snoRA73A, and snoRD22 participate in specific steps of rRNA processing, while snoRA8 and snoRA22 are implicated in rRNA modification. Their markedly higher expression compared to other snoRNAs suggests that they may have additional regulatory roles. For instance, snoRD3A and snoRA73 interact with chromatin and bind numerous RNA species ( 29 – 32 ). Apart from snoRD3A, all highly expressed snoRNAs are located in the introns of host genes, present on distinct chromosomes ( Table S3 ) . To identify snoRNAs/scaRNAs with altered expression in glioblastoma, we conducted two comparative analyses. First, we compared GSCs against neuronal cells and tissue (astrocytes and normal brain), which revealed two distinct groups of up- and down-regulated snoRNAs and scaRNAs in GSCs (Fig. 1 B-C). Second, we compared GBM cell lines against astrocytes and normal brain (Fig. 2 A). By integrating these two analyses, we identified snoRNAs/scaRNAs that were consistently altered in both GSCs and GBM lines, as well as those with cell-type-specific alterations, either exclusive to GSCs or to GBM cells (Fig. 2 B). Representative box plots of snoRNAs displaying the most prominent expression differences in both GSCs and GBM lines are shown in Fig. 2 C-D. We further analyzed NPCs in comparison with astrocytes/normal human brain tissue. Thirteen snoRNAs/scaRNAs were determined to be upregulated in both NPCs and GSCs in comparison to astrocytes and the normal human brain, defining a stemness-associated subset. On the other hand, eleven snoRNAs/scaRNAs showed increased expression in astrocytes/normal brain relative to NPCs and GSCs, establishing a subset linked to differentiation. Nineteen upregulated and twenty downregulated snoRNAs/scaRNAs were observed exclusively in the GSC analysis ( Figure S3 ) . Differential snoRNA expression can alter the rRNA modification profile, consequently affecting ribosome assembly and composition. In Fig. 3 and Table S4 , we show the snoRNAs displaying altered expression in GSC and/or GBM lines and their respective pseudo-uridylation sites in 18S and 28S rRNAs in the context of ribosome structure. snoRA46 and snoRA75 knockdown affected the growth of glioblastoma cells Targeting aberrantly expressed snoRNAs with ASOs is an effective alternative for impairing the growth of cancer cells ( 34 – 36 ). We designed ASOs to knock down the expression of two snoRNAs, snoRA46 and snoRA75, which are upregulated in GSC and GBM cells, as well as in GBM tumors. Two ASOs were combined to increase knockdown levels. snoRA46 and snoRA75 knockdown affected the proliferation of U251 cells according to Incucyte analysis and cell viability (MTS assay). Additionally, in a co-culture assay, we demonstrated that transfection with snoRNA ASOs inhibited the growth of U251 cells (GFP-labeled) but had no effect on astrocytes (Fig. 4 ). Components of the snoRNP H/ACA are highly expressed in glioblastoma. The snoRNPs in charge of rRNA pseudo-uridylation are composed of four main proteins: DKC1, NOP10, NHP2, and GAR1, with DKC1 being the enzyme catalyzing the conversion of uridine into pseudo-uridine – Fig. 5 A. DKC1 expression levels are elevated in numerous tumor types in relation to their normal counterparts – Fig. 5 B. Expression analysis using data from GTEx for normal brain and TCGA for glioma indicated that DKC1 and other members of the snoRNP H/ACA show increased expression in GBM in comparison to normal brain (cortex) and low-grade glioma (LGG or grade II) – Fig. 5 C, Figure S4 . Interestingly, snoRA36A, which is located in DKC1 intron 8, is also upregulated in GBM. Survival analysis using TCGA’s high-grade glioma (Grades 3 and 4) patient data showed that high DKC1 expression correlates with poor survival – Fig. 5 D. DKC1 also shows expression alterations during neuronal differentiation. Neuroblastoma BE( 2 )C cells treated with retinoic acid (ATRA) to induce differentiation ( 26 ) showed a decrease in DKC1 levels after 7 days– Fig. 5 E. Analysis of the Cortecon dataset ( 25 ) indicated a similar trend, with DKC1 showing high expression in NPCs and a drastic decrease during cortical development – Fig. 5 F. These results corroborate the association between increased DKC1 expression and the GBM poorly differentiated state. Factors influencing snoRNA expression changes in glioblastoma and glioma stem cells Several factors contribute to the observed differences in snoRNA expression levels, including their processing from host transcripts, interactions with associated proteins, and intrinsic sequence and structural features ( 38 , 39 ). Although most snoRNAs/scaRNAs are processed from introns of host genes, the expression levels of the host dictate the levels of its associated snoRNA/scaRNA. Among snoRNAs with decreased expression in GSC and GBM lines, snoRA12, snoRA14B, snoRA35, snoRA47, snoRA49, and snoRA55 are preferentially expressed in neuronal cells. Interestingly, snoRA35 and its host gene, HTRC2, are exclusively expressed in neuronal cells ( 13 ). snoRA35 is predicted to pseudo-uridylate the nucleotide 566 in 18S and could drive an essential difference between neuronal and tumor cells. All 29 snoRNAs/scaRNAs identified as differentially expressed in GSC and GBM cells are located within introns. We performed an expression analysis of GBM vs. normal brain to determine in which cases snoRNA expression levels are potentially influenced by their host genes. Results indicate that 14 host genes display significant differences in expression between brain and GBM, resembling what was observed for their associated snoRNAs/scaRNAs – Fig. 6 A. DKC1 is implicated in the processing of all H/ACA box-containing snoRNAs and scaRNAs and pseudo-uridylation of all sites in rRNA. However, certain snoRNAs and modification sites appear more “sensitive” to DKC1 mutations or changes in its expression levels, as observed in samples from dyskeratosis patients and cancer cells ( 40 – 44 ). We inquired whether high DKC1 expression observed in GBM/GSC cells could increase the levels of specific snoRNAs/scaRNAs. Ampliseq analysis determined that a partial reduction in DKC1 levels in U251 cells decreased the levels of several snoRNAs/scaRNAs determined to be upregulated in GSC and/or GBM cells, including snorA21, snoRA22, snoRA27, snoRA38, snoRA70, snoRA71A, snoRA71D, scaRNA4, scaRNA11, scaRNA18, and scaRNA22 – Fig. 6 B and Table S5 . scaRNAs, pseudouridylation of snRNA, and splicing. Pseudo-uridylation also occurs in snRNAs and is driven by snRNPs containing DKC1 and scaRNAs. snRNAs are critical components of the spliceosome and are responsible for the recognition and selection of splice sites via base pairing. Pseudo-uridylation of snRNAs occurs at critical nucleotide positions, relevant for RNA-RNA and RNA-protein interactions ( 45 , 46 ). We determined that several scaRNAs implicated in snRNA pseudo-uridylation at sites that affect RNA-RNA interactions show increased expression in GSC and GBM cells. The most relevant ones include scaRNA4, scaRNA8, scaRNA11, scaRNA14 and scaRNA23 - Fig. 7 A-B. Alterations in scaRNAs have been observed in congenital heart disease (Tetralogy of Fallot) and shown to affect the splicing of RNAs critical for heart development ( 47 ). In cancer cells, scaRNA15 influences the splicing of transcripts encoding chromatin and transcriptional regulators, thereby impacting the expression and function of ATRX and TP53 ( 48 ). Therefore, the alterations in DKC1 and scaRNA expression observed in tumor cells could ultimately contribute to their splicing profile and constitute a new route implicated in GBM development. In agreement, we determined that a partial DKC1 knockdown affected the splicing profile of U251 GBM cells ( Figure S5 and Table S6 ) . DISCUSSION Using a dedicated Ampliseq platform, we performed the first snoRNA profiling in GBM and GCS cells, identifying sets of snoRNAs that were up- and down-regulated in comparison to normal neuronal cells/tissue and associated with stemness and differentiation. Several snoRNAs that are elevated in GSC and GBM lines have previously been implicated in cancer development. We highlight snoRA38, snoRA51, snoRA71, and snoRA75. snoRA38 is upregulated in breast cancer, and its expression levels correlated with tumor size, lymph node metastasis, and TNM stage ( 49 ). snorA38 and snoRA71 were identified as part of a 20 snoRNA/scaRNA signature associated with breast cancer brain metastases ( 50 ). Increased expression of snoRA38 and snoRA75 was observed in colon cancer metastasis to the liver ( 51 ). High snoRA51 has been observed in different tumor types. In breast cancer, increased snoRA51 expression was connected to worse prognosis, overall survival, and disease-free survival. In addition, snoRA51 enhanced cancer stem cell-like properties via the RPL3/NPM1/c-MYC pathway ( 52 ). snoRA51 is also upregulated in colon cancer and hepatocellular carcinoma (HCC) and is defined as a potential biomarker ( 53 , 54 ). SnoRA75 is among a group of 12 snoRNAs that show significant correlation with tumor microenvironment immune infiltration in melanoma ( 55 ). It is also part of a 14 snoRNA signature that can significantly stratify AML patients into high- and low-risk groups ( 56 ). Five copies of snoRA71 (snoRA71, snoRA71A, snorA71B, snoRA71C, and snoRA71D) are located in various introns of SNHG17 gene. They share strong sequence similarity and are all implicated in pseudo-uridylation of nucleotide 406 in 18S rRNA. snoRA71 is highly expressed and has been identified as a prognostic marker of lung cancer ( 57 , 58 ), hepatocellular carcinoma (HCC) ( 59 ), multiple myeloma ( 60 ), and colorectal cancer ( 61 ). SnoRA71 knockdown affected proliferation, migration, invasion, and tumor growth ( 58 , 61 , 62 ). A Pan-cancer study using TCGA small RNA-seq data from 31 cancer types (excluding glioblastoma) identified 46 clinically relevant snoRNAs ( 63 ), including snoRA71, which showed alterations in at least 12 different tumor types. Interestingly, it has recently been demonstrated that snoRA71 binds multiple chromatin sites, suggesting additional regulatory roles ( 31 ). Several factors can contribute to differences in snoRNA and scaRNA levels ( 15 , 38 ). The majority of snoRNAs and scaRNAs analyzed in our study are located in introns of host genes. We investigated the expression of 29 host genes associated with snoRNAs/scaRNAs in GBM/GSC lines. Fourteen host genes show expression differences between normal brain and GBM that resemble those observed for their respective associated snoRNA/scaRNA. Therefore, in these cases, the host genes are likely relevant contributors to the expression levels of their pertinent snoRNA/scaRNA. Dyskerin (DKC1) is part of a protein complex involved in the processing of H/ACA snoRNAs and scaRNAs. It also catalyzes pseudo-uridylation in rRNA and snRNA, guided by H/ACA snoRNAs or scaRNAs, respectively ( 64 , 65 ). Mutations in DKC1 cause dyskeratosis congenita, a multi-organ syndrome. In these patients, reduced pseudo-uridylation at specific nucleotides in 28S has been observed. These changes could compromise rRNA-ribosomal protein interactions, destabilizing the ribosome ( 40 ). Conversely, DKC1 is often highly expressed in cancers, including GBM as we described. Two recent meta-analyses identified links between elevated DKC1 expression and poorer survival and presence of metastasis in multiple tumor types ( 66 , 67 ) while several studies showed that a reduction in DKC1 expression via siRNA or shRNA affects proliferation, migration, invasion, apoptosis, and tumor growth ( 41 , 68 – 71 ). Additionally, DKC1 promotes cell immortalization by modulating telomerase activity ( 12 , 69 ). Agreeing with results showing that certain snoRNAs and modification sites in rRNA are preferentially affected by DKC1 mutations or changes in DKC1 expression levels ( 40 – 44 ), we determined that a partial DKC1 knockdown in GBM cells reduced the levels of a specific group of snoRNAs/scaRNAs. We hypothesize that elevated DKC1 levels increase the production of specific H/ACA snoRNAs and enhance pseudo-uridylation at critical rRNA sites, ultimately altering ribosome composition and function to support tumor progression. The value of snoRNAs as biomarkers and their potential as targets in cancer therapy have just begun to emerge ( 72 – 75 ). To advance the field, the development of sequencing platforms and dedicated bioinformatics pipelines for accurate quantification and characterization of snoRNAs and scaRNAs, as in this study, is necessary. Targeting “onco-snoRNAs” is still another area under development. Anti-sense oligos (ASOs), which have been successfully used in therapy ( 76 ), have been established as the most effective agents to knock down snoRNAs in cells and in tumors ( 34 , 77 – 81 ). However, selecting and designing effective snoRNA ASOs remains a challenging task. Due to the limited number of studies, there is insufficient information to choose ASOs based on target sequence motifs, locations, or secondary structure. The consequences of differential snoRNA expression on rRNA modification and the subsequent impact on translation are just beginning to be explored. Recent studies suggest that variations in the pattern of rRNA modification across tissues, during development, in disease states, and in response to stimuli create a platform for ribosome heterogeneity ( 10 , 82 ). A notable example comes from a study on Trypanosoma brucei . Knocking out a single snoRNA that guides the pseudo-uridylation of nucleotide 530 decreased the presence of eS12 on ribosomes, affecting the translation of mRNAs encoding proteins regulated during the two life stages of the parasite ( 83 ). This study also found that rRNA in translating polysomes displays a higher Ψ frequency than total rRNA ( 83 ). snoRNAs can regulate additional processes besides rRNA processing and modification. In fact, several snoRNAs known as Orphans are not implicated in rRNA modification and have been shown to interact with chromatin ( 30 , 31 , 84 ). snoRNAs have other non-canonical functions in splicing regulation and RNA silencing ( 85 ). Therefore, characterizing targets and the regulatory impact of “onco-snoRNAs” in cancer cells could be challenging. Fortunately, the advent of novel genomic methods to profile pseudo-uridylation and 2’O methylation ( 28 , 86 , 87 ) as well as platforms to identify snoRNA interactions with other RNA species ( 29 , 88 , 89 ) can greatly facilitate these tasks and advance knowledge on snoRNA function. CONCLUSIONS Using a dedicated Ampliseq platform, we performed the first targeted profiling of snoRNAs and scaRNAs in glioblastoma and glioma stem cells, identifying subsets linked to stemness and differentiation. Our results indicate that changes in snoRNA levels are influenced by both host gene expression and DKC1 activity, suggesting coordinated regulation of snoRNA maturation and function in tumor cells. Elevated DKC1 may enhance the production of specific H/ACA snoRNAs, altering rRNA modification and ribosome composition to support malignancy. These findings highlight the importance of developing specialized sequencing platforms and bioinformatics pipelines for accurate snoRNA quantification, as well as the potential of onco-snoRNAs as biomarkers and therapeutic targets in glioblastoma. Abbreviations snoRNAs : small nucleolar RNAs snoRNPs: small nucleolar ribonucleoprotein complexes rRNA: ribosomal RNA GBM: glioblastoma scaRNAs : Cajal body–specific RNAs Ampliseq: Amplicon sequencing GSC: glioma stem cell DKC1: Dyskerin FBL: Fibrillarin Ψ : pseudo-uridine snRNAs: small nuclear RNAs TERC: telomerase RNA lncRNAs: long non-coding RNA ASOs: antisense oligonucleotides EGF: epidermal growth factor FGF: fibroblast growth factor GFP: Green Fluorescent Protein RNAseq: RNA sequencing AST: astrocyte HBR: human brain NPC: neuronal precursor cell ATRA: retinoic acid CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL: Cholangiocarcinoma COAD: Colon adenocarcinoma DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma LUSC: Lung squamous cell carcinoma READ: Rectum adenocarcinoma STAD: Stomach adenocarcinoma THYM: Thymoma Declarations Availability of data and materials The dataset generated during the current study is available in the European Nucleotide Archive repository (https://www.ebi.ac.uk/ena/, under accession PRJEB101101). FUNDING This work was supported by the NIH grant 1R21CA297561-01 to LOFP. GLF was supported by a fellowship from CAPES. LD and SA were sponsored by The German Academic Exchange Service (DAAD). G.D.A.G. was supported by the Young Scientist Program from Hospital Sírio-Libanês and Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (grant 382424/2025-5), F.G.C.O. was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/PROEX (grant 88887.220499/2025-00), and P.A.F.G. was supported by the grant #2018/15579-8, São Paulo Research Foundation (FAPESP). AUTHOR INFORMATION Authors and Affiliations LOP, XL, LD, SR, SA, GLF and NA: Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX. LOP: Department of Cell Systems and Anatomy, UT Health San Antonio, San Antonio, TX. PAFG, GDAG and FGCO: Hospital Sírio-Libanês, São Paulo, SP FGCO: Departamento de Bioquímica, Universidade de São Paulo, SP. SK, CM, MR, NS, GS and SA: Illumina, Inc., San Diego, CA AB: Mays Cancer Center, UT Health San Antonio, San Antonio, TX. Contributions LOP, PAFG and SK conceived the original idea for the project, analyzed data and wrote most of the manuscript. GDAG developed the pipeline for Ampliseq data analysis, performed most bioinformatics analyses, prepared several figures and helped with text writing. XL performed most of biological experiments and prepared all Ampliseq libraries. CM, MR, NS, GS and SA help with the design of Ampliseq platform and sequencing experiments. LD conducted experiments to analyze snoRNAs and helped with text and figures, FGCO performed analysis of splicing data and helped with text and figures, SR, SA, GLF and NA helped with data analysis and figure preparation, AB was involved in the generation of GSC lines and characterization. ETHICS DECLARATIONS Ethics approval and consent to participate Not applicable. 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We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8079210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548405075,"identity":"0d7faf50-14d4-48d7-ae0c-bbc76d1bff77","order_by":0,"name":"Gabriela D. A. 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A) \u003c/strong\u003eThe topmost expressed snoRNAs in astrocytes (AST), normal brain (HBR), neural progenitor cells (NPC), glioma stem cells (GSC), and glioblastoma (GBM) cells. \u003cstrong\u003eB)\u003c/strong\u003e Volcano plot showing differentially expressed snoRNAs in GSCs vs. normal cells (AST/astrocytes and HBR/human brain). \u003cstrong\u003eC)\u003c/strong\u003e Heatmap showing differentially expressed snoRNAs in GSCs vs. normal cells (AST and HBR).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/5e3f0382307d294fbf11688d.png"},{"id":98424251,"identity":"2a56e11c-51e8-4c43-a68e-ced56e639935","added_by":"auto","created_at":"2025-12-17 16:33:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":743467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esnoRNAs/scaRNAs differentially expressed in glioblastoma cells vs. normal brain. A)\u003c/strong\u003e Heatmap showing differentially expressed snoRNAs in GBM cells vs. normal (AST and HBR). \u003cstrong\u003eB)\u003c/strong\u003e Circos plot showing differentially expressed snoRNAs/scaRNAs in GBM and/or GSC vs. normal brain (astrocyte and HBR). * snoRNAs showing differential expression in GBM cells, but not statistically significant. \u003cstrong\u003eC-D)\u003c/strong\u003eBoxplots showing expression levels of snoRNAs \u003cstrong\u003e(C) \u003c/strong\u003eup-regulated or \u003cstrong\u003e(D) \u003c/strong\u003edown-regulated both in GBM and GSC vs. normal brain. Statistical differences were assessed by DESeq2 p-values (* \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001, **** \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/e875dd2f7d3a1c6f6382d4b7.png"},{"id":98423265,"identity":"b89a39b4-0c5e-4369-a5e0-7d772bdbd956","added_by":"auto","created_at":"2025-12-17 16:32:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2862590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed snoRNAs in GSC and GBM lines and location of pertinent pseudo-uridylation sites in the 80S human ribosome according to Piekna-Przybylska et al., 2008 \u003c/strong\u003e\u003ca href=\"https://paperpile.com/c/Xp16vJ/NPYP\"\u003e(33)\u003c/a\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/494ee3a7cdbb4d3eabda6697.png"},{"id":98423289,"identity":"f4c09e5e-cbab-4343-910c-f0c448bb329d","added_by":"auto","created_at":"2025-12-17 16:32:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1780872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKnockdown of snoRA46 and snoRA75 affects glioblastoma cells. \u003c/strong\u003eU251\u003cstrong\u003e A) \u003c/strong\u003eDifferences in snoRA46 and snoRA75 expression levels measured by qRT-PCR in control and ASO-transfected cells. \u003cstrong\u003eB)\u003c/strong\u003e Knockdown of snoRA46 and snoRA75 in U251 cells decreased viability (MTS assays). Data were analyzed with Student’s t-test and presented as the mean ± standard deviation. *** = p ≤ 0.001. \u003cstrong\u003eC) \u003c/strong\u003eU251 proliferation across time was followed with the Incucyte automated system. A decrease in snoRA46 and snoRA75 levels impaired cell proliferation. \u003cstrong\u003eD) \u003c/strong\u003eU251 cells (GFP labeled) and astrocytes were co-cultured and treated with control, snoRA46, or snoRA75 ASOs. Images show that treatment with snoRA46 or snoRA75 ASOs affected the proliferation of U251 (green cells), corroborating the Incucyte results. White arrows highlight astrocytes.\u003cstrong\u003e \u003c/strong\u003eYellow bars =\u003cstrong\u003e \u003c/strong\u003e400mM\u003cstrong\u003e. \u003c/strong\u003eBar-graphs (bottom) show relative astrocyte counts in days 2 and 4 ½ and indicate that they were not affected by treatment with snoRA46 or snoRA75 ASOs.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/0ead0e0c0161ba758d5883f6.png"},{"id":98423201,"identity":"a23d286a-7b06-46a9-902a-6c3854151379","added_by":"auto","created_at":"2025-12-17 16:31:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":458546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDKC1 is highly expressed in glioblastoma and associated with poor survival in high-grade glioma. A)\u003c/strong\u003e Representation of H/ACA snoRNP. \u003cstrong\u003eB)\u003c/strong\u003eExpression levels of DKC1 in distinct tumor types vs. corresponding normal tissue obtained from TCGA and GTEx, respectively (data source: GEPIA2 database \u003ca href=\"https://paperpile.com/c/Xp16vJ/P4R6\"\u003e(37)\u003c/a\u003e. Abbreviations: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon adenocarcinoma (COAD), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), Lung squamous cell carcinoma (LUSC), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), Thymoma (THYM). \u003cstrong\u003eC)\u003c/strong\u003e Expression levels of DKC1 in normal cortex, gliomas grade II and III, and glioblastoma. Statistical differences were assessed by Mann–Whitney U tests (p-value * \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001, **** \u0026lt; 0.0001). \u003cstrong\u003eD)\u003c/strong\u003e Survival curves of high-grade glioma patients with high/low expression of DKC1 and multivariate analysis adjusted by GBM histology. Statistical differences were assessed by log-rank tests. \u003cstrong\u003eE) \u003c/strong\u003eDKC1 expression levels in BE2C neuroblastoma cells on day 0 and day 7 of ATRA-induced differentiation. Statistical differences were evaluated by DESeq2 p-values (**** \u0026lt; 0.0001).\u003cstrong\u003eF) \u003c/strong\u003eExpression levels of DKC1 during cortex development according to Cortecon \u003ca href=\"https://paperpile.com/c/Xp16vJ/pwDE\"\u003e(25)\u003c/a\u003e. Statistical differences were assessed by Mann–Whitney U tests (p-value * \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001, **** \u0026lt; 0.0001). Non-significant comparisons are not shown.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/5603d29c114845250dc08c8a.png"},{"id":98423929,"identity":"f363e920-9948-44cd-9a04-6d9dcafb231c","added_by":"auto","created_at":"2025-12-17 16:32:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":872029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential factors driving snoRNAs and scaRNAs differential expression. A) \u003c/strong\u003eExpression of host genes of differentially expressed snoRNAs/scaRNAs in GBM vs. normal brain. Only host genes displaying expression differences that resemble what has been observed for their respective associated snoRNA/scaRNA are represented. Statistical differences were assessed by Mann–Whitney U tests (p-value * \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001, **** \u0026lt; 0.0001).\u003cstrong\u003e B)\u003c/strong\u003eBubbleplots with results of an RNAseq analysis showing snoRNAs/scaRNAs upregulated in GSC/GBM lines whose expression decreased after partial DKC1 knockdown in U251 cells. |log₂FC| values are shown only for snoRNAs with FDR \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/089d548bba5354dc833216c0.png"},{"id":97965834,"identity":"1c34cc3f-3daf-4984-ad68-ba414d8208cf","added_by":"auto","created_at":"2025-12-11 09:51:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":864706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escaRNAs differentially expressed in GSCs and snRNA pseudo-uridylation. A) \u003c/strong\u003eBoxplots showing expression levels of up-regulated or down-regulated scaRNAs in GSC vs. normal (brain and astrocytes). Statistical differences were assessed by DESeq2 p-values (* \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001, **** \u0026lt; 0.0001). \u003cstrong\u003eB) \u003c/strong\u003eRepresentation\u003cstrong\u003e \u003c/strong\u003eof relevant RNA-RNA interactions, showing pseudo-uridylation sites and the implicated scaRNAs. Table showing the regulatory relevance of pseudo-uridylation sites. \u003cstrong\u003eC)\u003c/strong\u003eRepresentation of U2, U5, and U6 snRNPs showing RNA-protein interactions, pseudo-uridylation sites, and the implicated scaRNAs.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/c21cfecf16d28ff93ddf8c33.png"},{"id":99679657,"identity":"251448c8-ac2b-4b9a-80f7-12e7f8f0cfcb","added_by":"auto","created_at":"2026-01-07 08:40:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8610963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/68a0baaf-cde4-4ba6-b5b7-60ffd9605d77.pdf"},{"id":97965825,"identity":"c240436a-ffd2-44da-a303-97e87c1eb51b","added_by":"auto","created_at":"2025-12-11 09:51:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4289991,"visible":true,"origin":"","legend":"","description":"","filename":"snoRNAs.manuscript.Sup.docx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/2e8009b51a9c752214a62dd0.docx"},{"id":98423397,"identity":"107a15a0-4d0a-4d3d-b6dc-cf8e7c892104","added_by":"auto","created_at":"2025-12-17 16:32:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8130,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/afddbeea3dc569a4887474be.xlsx"},{"id":97965830,"identity":"ebdecdc6-734b-4697-9319-b5580545cc14","added_by":"auto","created_at":"2025-12-11 09:51:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23229,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/e061eb248cc7da004a2cfdab.xlsx"},{"id":97965832,"identity":"4db3458e-7c95-4a3e-9315-a8923f507113","added_by":"auto","created_at":"2025-12-11 09:51:28","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15149,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/6b38528cc3469be8b82b9647.xlsx"},{"id":98423809,"identity":"c7136a53-797b-43de-9643-76c878c6a9c5","added_by":"auto","created_at":"2025-12-17 16:32:38","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9838,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/bb72dea77ae0a2035554f9e4.xlsx"},{"id":98423679,"identity":"af7f666f-50e4-4629-ad3e-a58e8ada3dbb","added_by":"auto","created_at":"2025-12-17 16:32:31","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9408,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/7f6bab0e2ed07f159be7cbff.xlsx"},{"id":98422932,"identity":"cf57580e-afef-470d-9729-46089f3718ca","added_by":"auto","created_at":"2025-12-17 16:31:39","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":23547,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8079210/v1/1519ba53a765a9087bf6721c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"H/ACA snoRNAs and snoRNPs Dysregulation Links rRNA Modification to Glioblastoma Progression","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRibosome biogenesis is a complex, multi-step process that generates and assembles the RNA and protein components of the 80S ribosome. Cancer cells are highly dependent on this process to sustain their increased and specialized protein demands (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Consistent with this, altered expression of ribosomal proteins and regulators of rRNA transcription, processing, and modification has been linked to cancer development. Targeting ribosome biogenesis has therefore emerged as a promising therapeutic strategy, particularly in aggressive tumors such as glioblastoma (GBM) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). To improve the likelihood of clinical success, it is crucial to conduct focused studies to identify the RNA and protein factors driving rRNA alterations in each tumor type and to evaluate their roles in tumor progression and therapeutic response.\u003c/p\u003e\u003cp\u003eRibosomal RNA (rRNA) modification is a key step in ribosome biogenesis; its impact on RNA-RNA and RNA-protein interactions can ultimately alter ribosome composition and influence translation. Growing evidence suggests that ribosomes are heterogeneous, and \"specialized\" ribosomes have been described in different scenarios (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A recent study, employing ribosome ratio-omics (RibosomeR) to investigate 80S ribosome proteomics data, identified major variations in ribosome protein stoichiometry across various biological samples, including differences between tissues, developmental stages, and pathological states (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In cancer cells, changes in ribosome composition can enhance and/or tailor translation to promote the production of proteins involved in tumor progression, therapy resistance, and metastasis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRNA-protein complexes called snoRNPs regulate the two main types of rRNA modifications, 2\u0026rsquo;O methylation and pseudo-uridylation. 2\u0026rsquo;O methylation occurs in all four nucleotides and is catalyzed by Fibrillarin (FBL) while pseudo-uridylation (the conversion of uridine into pseudo-uridine, Ψ) is catalyzed by dyskerin (DKC1) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). DKC1 driven pseudo-uridylation has two additional regulatory impacts. Small Cajal body-specific RNAs (scaRNAs) guide DKC1 to pseudo-uridylate specific nucleotides in small nuclear RNAs (snRNAs). snRNAs are essential components of the spliceosome and have important functions in splice site recognition and selection (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). DKC1 also binds the telomerase RNA (TERC) and modulates its activity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSmall nucleolar RNAs (snoRNAs) define which nucleotides in rRNA get modified by guiding snoRNPs to specific positions in rRNA via base pairing. More than 500 snoRNAs, ranging from 60 to 300 nucleotides, have been cataloged in the human genome. There are two classes of snoRNAs: C/D box snoRNAs are implicated in 2\u0026rsquo;-O-methylation, while snoRNAs with H/ACA boxes regulate pseudo-uridylation (Ψ) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Most expressed snoRNAs are embedded in introns of lncRNAs and protein-coding genes, including ribosomal proteins and rRNA transcription/processing regulators. However, snoRNA expression levels do not always correlate with those of their host genes, reflecting the influence of specific sequence features and differences in snoRNA maturation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Alterations in snoRNA expression have been observed in various disease states and cancers, with several snoRNAs contributing to cancer-relevant phenotypes and tumor growth (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite their biological relevance and potential as biomarkers or therapeutic targets, studies on snoRNA and scaRNA profiling remain very scarce. The lack of specific sequencing approaches for snoRNA/scaRNA analysis is certainly a barrier. We developed an Ampliseq panel and analysis pipeline to evaluate H/ACA box snoRNAs and scaRNAs specifically, and conducted the first comprehensive study of snoRNAs and scaRNAs in GBM versus normal cells. Results led to the identification of multiple snoRNAs/scaRNAs aberrantly expressed in tumor cells, defined a snoRNA/scaRNA signature for glioma stem cells, and identified snoRNAs/scaRNAs associated with stemness and differentiation. Our study also demonstrated the potential use of antisense oligonucleotides (ASOs) to target aberrantly expressed snoRNAs in GBM as a potential therapeutic strategy.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCell culture\u003c/h2\u003e\u003cp\u003eHuman glioblastoma cell line U251 was obtained from Uppsala University (Uppsala, Sweden), while the T98G and LN229 cell lines were obtained from the American Type Culture Collection. Human astrocyte cells (Cat# 1800) were obtained from ScienCell Research Laboratories (Carlsbad, CA, USA). Glioblastoma Stem Cell (GSC) lines 3565 and 1919 were gifts from Drs. Jeremy Rich, Christopher Hubert, and Ichiro Nakano (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). GSC lines 031417, 040909, and 040815 were established by Dr. Andrew Brenner\u0026rsquo;s lab. Sareddy. Neural Progenitor Cells (NPCs) were purchased from Axol Bio (Cat# ax0011). The U251, T98G, LN229, and HEK293T cell lines were cultured in DMEM medium (HyClone; Cat# SH30243.01) supplemented with 10% Fetal Bovine Serum (FBS) (Corning; Cat# 35015CV) and 1% penicillin/streptomycin (Gibco; Cat# 10378016). Human astrocyte cells were cultured in DMEM-F12 media (Thermo Fisher Scientific, Cat# 11320033). supplemented with 10% FBS (Corning) and 1% penicillin/streptomycin (Gibco). All GSCs and NPCs were cultured in Neurobasal-A medium supplemented with B27, glutamine, sodium pyruvate, 20 ng/mL of both epidermal growth factor (EGF) (Thermo Fisher Scientific) and basic fibroblast growth factor (bFGF) (PeproTech). All cells were maintained in a humidified incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. For routine passaging, cells were harvested by using 0.05% Trypsin/0.53 mM EDTA in HBSS (Corning, Cat# 25-051-Cl) and replated for continued culture. For subsequent experiments, cells were harvested and counted with the Countess automated cell counter (Invitrogen) using trypan blue, then plated at specific densities for transfection and the various assays described below.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCell transfection\u003c/h3\u003e\n\u003cp\u003eFor transient gene knockdown, cells were transfected with small interfering RNA (siRNA) or antisense oligonucleotides (ASOs) using Lipofectamine RNAiMAX (Invitrogen, Carlsbad, CA; Cat# 13778150) and OptiMEM (Thermo Fisher Scientific, Cat# 31985070). Transfected cells were harvested after 72 hours for RNA analysis or incubated for other assays as described below. siRNAs were obtained from Dharmacon (Lafayette, CO) and Millipore-Sigma (Burlington, MA). ON-TARGETplus non-targeting siRNA (Dharmacon; Cat# D-001810-01-05) was used as a negative control. The DKC1 siRNA (siDKC1, SASI_Hs01_00195836) was obtained from Millipore-Sigma. ASOs were purchased from Millipore Sigma.\u003c/p\u003e\n\u003ch3\u003eCell proliferation assay\u003c/h3\u003e\n\u003cp\u003eU251-GFP cells were harvested, counted, and seeded at a density of 1200 cells/well into 96-well plates. After transfection with ASOs, plates were transferred to a live-cell imaging system (IncuCyte, Essen BioScience). Cell proliferation was subsequently monitored for 72 consecutive hours, with images acquired and cells counted every four to six hours.\u003c/p\u003e\n\u003ch3\u003eMTS assay\u003c/h3\u003e\n\u003cp\u003eU251 cells were harvested, counted, and seeded at a density of 1200 cells/well into 96-well plates. Plates were incubated at 37℃ for 72 hours after transfection with ASOs as described above. Next, 20\u0026micro;l of MTS mixture (1,000\u0026micro;l MTS and 50\u0026micro;l PMS) of MTS solution (Promega) was added to each well, and samples were incubated at 37\u0026deg;C for 60 minutes. Optical density was measured at absorbance 490nm with a Synergy HT microplate reader (BioTek).\u003c/p\u003e\n\u003ch3\u003eCo-culture assay\u003c/h3\u003e\n\u003cp\u003eU251 GFP cells (800 cells/well) and human astrocyte cells (1000 cells/well) were harvested, counted and seeded into a 96-well plate. ASO transfection, as described above, was performed simultaneously with cell seeding, using a final ASO concentration of 40 nM. Cell proliferation and morphology were subsequently monitored for five consecutive days using the IncuCyte automated microscope system (Essen Bioscience), with images acquired every six hours. 2x2 montage images per well were manually analyzed using ImageJ. A digital grid was overlaid, and the number of astrocytes within four fields was counted.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRNA preparation\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from cells using TRIzol\u0026trade; reagent (Thermo Fisher Scientific, Grand Island, NY; Cat# 15596018), following the manufacturer\u0026rsquo;s instructions. RNA concentration and purity were determined spectrophotometrically using a NanoDrop 8000 (Thermo Scientific). Only RNA samples with an acceptable A260/A280 ratio were used for subsequent steps. The RNA was stored at -80\u0026deg;C until further use. For complementary DNA (cDNA) synthesis, 300 ng of total RNA from each sample was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific; Cat# 4368814) according to the manufacturer\u0026rsquo;s protocol. The resulting cDNA was stored at -20\u0026deg;C until further use.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQuantitative Real-Time PCR\u003c/h3\u003e\n\u003cp\u003eTo validate the knockdown efficiency of the DKC1 siRNA and the ASOs against snoRA46 and snoRA75, qRT-PCR was performed on transfected U251 cells. Gene expression was normalized to GAPDH, a housekeeping gene, using the ddCT method. For all qRT-PCR experiments, the PowerUp SYBR Green Master Mix (Applied Biosystems) was used. Sequences of qRT-PCR primers are listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, and sequences of ASOs are presented in \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eRNA sample preparation and RNA-seq\u003c/h3\u003e\n\u003cp\u003eTotal RNA from transfected U251 cells (siRNA control vs. siDKC1) was extracted using the TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer\u0026rsquo;s instructions. Libraries for RNA sequencing were prepared using the TruSeq RNA Library Preparation Kit (Illumina, San Diego, CA), following the manufacturer\u0026rsquo;s instructions. The prepared libraries were then sequenced at UT Health San Antonio Genomic Facility. All experiments were performed in triplicate.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAmplicon sequencing (Ampliseq)\u003c/h2\u003e\u003cp\u003eThe snoRNA/scaRNA Ampliseq panel was designed using the HUGO Gene Nomenclature Committee (HGNC; database (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), which contains 564 entries. Due to design constraints on the length of gene entries (genes\u0026thinsp;\u0026lt;\u0026thinsp;125 nucleotides were omitted), 213 multiplex primer pairs were designed for 127 snoRNA/scaRNA genes (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e. The RNA sequencing (RNAseq) libraries were constructed using the AmpliSeq for Illumina On-Demand, Custom, and Community Panels reference guide, following the standard workflow listed in the protocol. Briefly, Total RNA (~\u0026thinsp;10\u0026ndash;100 ng) was reverse transcribed with random primers, then combined with the AmpliSeq snoRNA/scaRNA multiplex primer pairs and amplified by PCR for 17 cycles. The amplicons were partially digested with the FuPa enzyme and ligated to the AmpliSeq CD Indexes for Illumina. The samples were purified with Illumina Purification Beads (IPB) and PCR-amplified a second time for 7 cycles using generic (P5/P7) primers standard to all amplicons. The samples were then purified for a second time prior to quantification using the Qubit high-sensitivity assay. The samples were then normalized and pooled for sequencing. The libraries were then sequenced on a NextSeq 550 or NextSeq 2000 instrument at 2 \u0026times; 150 paired-end reads (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAmpliseq analysis\u003c/h2\u003e\u003cp\u003eAmpliSeq RNA sequencing reads were aligned to the human reference genome (GRCh38/hg38) using BWA-MEM with default parameters (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/lh3/bwa\u003c/span\u003e\u003cspan address=\"https://github.com/lh3/bwa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed 23 October 2025). Only properly paired reads were retained using SAMtools with parameter -f 0x0002 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). To remove ambiguously mapped reads, read pairs exhibiting identical primary (AS) and suboptimal (XS) alignment scores for both mates were filtered out using local scripts. This filtering step ensured that only uniquely aligned read pairs were retained for downstream quantification. Gene-level read counts were quantified using HTSeq-count (parameters: -m intersection-strict and -s no) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), based on gene annotations from the NCBI RefSeq (hg38). Differential expression analyses of snoRNA genes between sample groups were performed using DESeq2 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Genes with |log₂FC| \u0026ge; 0.6 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered differentially expressed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSplicing analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the impact of DKC1 knockdown on RNA splicing, RNA-Seq reads were pseudo-aligned to the GENCODE human reference transcriptome (version 36) (Frankish et al., 2021) using Kallisto (version 0.48) with default parameters and 100 bootstrap replicates (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Normalized expression values (TPM) were then used as input to SUPPA2 (v. 2.3), with default parameters (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), to detect differential alternative splicing events. Alternative splicing events in each gene were generated with the \u0026ldquo;generateEvents\u0026rdquo; function and classified as skipping exon (SE), alternative 5\u0026rsquo; (A5S) and 3\u0026rsquo; (A3S) splice sites, mutually exclusive exons (MX), or retained intron (RI). Events were considered significant when |ΔPSI| \u0026gt;0.1 and False Discovery Rate (FDR) adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRNA-Seq analyses of public datasets\u003c/h2\u003e\u003cp\u003eTo investigate the expression profiles of genes involved in H/ACA snoRNA processing and rRNA modification, as well as snoRNA host genes in healthy brain and glioma samples, we obtained publicly available gene expression datasets (TPM-normalized) from the GTEx (V8) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed 23 October 2025) and TCGA projects (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed 23 October 2025), respectively. Normal (frontal cortex) tissue expression data (n\u0026thinsp;=\u0026thinsp;464) were retrieved from GTEx, while data from lower-grade gliomas (LGG grade II, n\u0026thinsp;=\u0026thinsp;249; LGG grade III, n\u0026thinsp;=\u0026thinsp;265) and glioblastoma (GBM, IDH1/2 wild-type, n\u0026thinsp;=\u0026thinsp;152) were obtained directly from TCGA. Boxplots were generated using the ggplot2 package (v. 4.0.0) in R, and statistical differences between groups were assessed using the Mann\u0026ndash;Whitney U test.\u003c/p\u003e\u003cp\u003eFor high-grade gliomas, we evaluated the impact of DKC1 gene expression on overall survival by stratifying patients into high- and low-expression groups based on the median TPM value. Survival analyses were performed using the survival (v. 3.8.3), survminer (v. 0.5.1), and forestmodel (v. 0.6.2) R packages. Multivariate analyses were conducted to adjust for tumor histology (GBM or LGG grade III).\u003c/p\u003e\u003cp\u003eTo compare DKC1 expression levels across multiple tumor types, we analyzed publicly available gene expression data (TPM-normalized) from the GEPIA2 web platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed 23 October 2025), which integrates GTEx and TCGA datasets. Only tumor types displaying significantly different expression levels between tumor and normal tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included: CESC (306 tumor and 13 normal samples), CHOL (36 tumor and 9 normal), COAD (275 tumor and 349 normal), DLBC (47 tumor and 337 normal), LUSC (486 tumor and 338 normal), READ (92 tumor and 318 normal), STAD (408 tumor and 211 normal), and THYM (118 tumor and 339 normal).\u003c/p\u003e\u003cp\u003eTo assess DKC1 expression changes during cortical development, we obtained TPM-normalized gene expression data from the Cortecon database (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Boxplots were generated using ggplot2 (v. 4.0.0), and statistical significance was assessed by Mann\u0026ndash;Whitney U tests.\u003c/p\u003e\u003cp\u003eFinally, to evaluate differences in DKC1 expression levels in neuroblastoma BE(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)C cells between day 0 and day 7 of ATRA-induced differentiation, we used RNA-Seq data from a previous study (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). First, RNA-Seq reads were pseudoaligned to the human reference transcriptome (GENCODE v36, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gencodegenes.org/\u003c/span\u003e\u003cspan address=\"https://www.gencodegenes.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed 23 October 2025) using Kallisto (v. 0.48) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Gene-level count data were then obtained using the tximport R package (v. 1.26.1) and differential gene expression analysis was performed with DESeq2 (v. 1.38.3) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eExpression of snoRNAs/scaRNAs is altered in glioblastoma.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe devised an Ampliseq panel and analysis pipeline (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e) to specifically evaluate the expression of 127 snoRNAs (primarily those containing H/ACA boxes) and scaRNAs. Among the 77 rRNA pseudouridylation sites with known guiding snoRNAs, our Ampliseq platform covers snoRNAs responsible for guiding 67 of these modifications (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We profiled their expression in glioblastoma cells, glioma stem cells (GSC), neuronal precursor cells (NPCs), astrocytes, and the normal human brain. In all samples, we observed that more than 50% of all detected snoRNA and scaRNA expression corresponds to a group of 10\u0026ndash;12 snoRNAs. Notably, snoRD3A, snoRA63, snoRA8, snoRA22, snoRD22, and snoRA73A showed consistently high expression across all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). From this group, snoRD3A, snoRA63, snoRA73A, and snoRD22 participate in specific steps of rRNA processing, while snoRA8 and snoRA22 are implicated in rRNA modification. Their markedly higher expression compared to other snoRNAs suggests that they may have additional regulatory roles. For instance, snoRD3A and snoRA73 interact with chromatin and bind numerous RNA species (\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Apart from snoRD3A, all highly expressed snoRNAs are located in the introns of host genes, present on distinct chromosomes (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo identify snoRNAs/scaRNAs with altered expression in glioblastoma, we conducted two comparative analyses. First, we compared GSCs against neuronal cells and tissue (astrocytes and normal brain), which revealed two distinct groups of up- and down-regulated snoRNAs and scaRNAs in GSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). Second, we compared GBM cell lines against astrocytes and normal brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). By integrating these two analyses, we identified snoRNAs/scaRNAs that were consistently altered in both GSCs and GBM lines, as well as those with cell-type-specific alterations, either exclusive to GSCs or to GBM cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Representative box plots of snoRNAs displaying the most prominent expression differences in both GSCs and GBM lines are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe further analyzed NPCs in comparison with astrocytes/normal human brain tissue. Thirteen snoRNAs/scaRNAs were determined to be upregulated in both NPCs and GSCs in comparison to astrocytes and the normal human brain, defining a stemness-associated subset. On the other hand, eleven snoRNAs/scaRNAs showed increased expression in astrocytes/normal brain relative to NPCs and GSCs, establishing a subset linked to differentiation. Nineteen upregulated and twenty downregulated snoRNAs/scaRNAs were observed exclusively in the GSC analysis (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eDifferential snoRNA expression can alter the rRNA modification profile, consequently affecting ribosome assembly and composition. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e, we show the snoRNAs displaying altered expression in GSC and/or GBM lines and their respective pseudo-uridylation sites in 18S and 28S rRNAs in the context of ribosome structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003esnoRA46 and snoRA75 knockdown affected the growth of glioblastoma cells\u003c/h2\u003e\u003cp\u003eTargeting aberrantly expressed snoRNAs with ASOs is an effective alternative for impairing the growth of cancer cells (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). We designed ASOs to knock down the expression of two snoRNAs, snoRA46 and snoRA75, which are upregulated in GSC and GBM cells, as well as in GBM tumors. Two ASOs were combined to increase knockdown levels. snoRA46 and snoRA75 knockdown affected the proliferation of U251 cells according to Incucyte analysis and cell viability (MTS assay). Additionally, in a co-culture assay, we demonstrated that transfection with snoRNA ASOs inhibited the growth of U251 cells (GFP-labeled) but had no effect on astrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComponents of the snoRNP H/ACA are highly expressed in glioblastoma.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe snoRNPs in charge of rRNA pseudo-uridylation are composed of four main proteins: DKC1, NOP10, NHP2, and GAR1, with DKC1 being the enzyme catalyzing the conversion of uridine into pseudo-uridine \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. DKC1 expression levels are elevated in numerous tumor types in relation to their normal counterparts \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. Expression analysis using data from GTEx for normal brain and TCGA for glioma indicated that DKC1 and other members of the snoRNP H/ACA show increased expression in GBM in comparison to normal brain (cortex) and low-grade glioma (LGG or grade II) \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e. Interestingly, snoRA36A, which is located in DKC1 intron 8, is also upregulated in GBM. Survival analysis using TCGA\u0026rsquo;s high-grade glioma (Grades 3 and 4) patient data showed that high DKC1 expression correlates with poor survival \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD. DKC1 also shows expression alterations during neuronal differentiation. Neuroblastoma BE(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)C cells treated with retinoic acid (ATRA) to induce differentiation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) showed a decrease in DKC1 levels after 7 days\u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE. Analysis of the Cortecon dataset (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) indicated a similar trend, with DKC1 showing high expression in NPCs and a drastic decrease during cortical development \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF. These results corroborate the association between increased DKC1 expression and the GBM poorly differentiated state.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFactors influencing snoRNA expression changes in glioblastoma and glioma stem cells\u003c/h2\u003e\u003cp\u003eSeveral factors contribute to the observed differences in snoRNA expression levels, including their processing from host transcripts, interactions with associated proteins, and intrinsic sequence and structural features (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Although most snoRNAs/scaRNAs are processed from introns of host genes, the expression levels of the host dictate the levels of its associated snoRNA/scaRNA. Among snoRNAs with decreased expression in GSC and GBM lines, snoRA12, snoRA14B, snoRA35, snoRA47, snoRA49, and snoRA55 are preferentially expressed in neuronal cells. Interestingly, snoRA35 and its host gene, HTRC2, are exclusively expressed in neuronal cells (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). snoRA35 is predicted to pseudo-uridylate the nucleotide 566 in 18S and could drive an essential difference between neuronal and tumor cells. All 29 snoRNAs/scaRNAs identified as differentially expressed in GSC and GBM cells are located within introns. We performed an expression analysis of GBM vs. normal brain to determine in which cases snoRNA expression levels are potentially influenced by their host genes. Results indicate that 14 host genes display significant differences in expression between brain and GBM, resembling what was observed for their associated snoRNAs/scaRNAs \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDKC1 is implicated in the processing of all H/ACA box-containing snoRNAs and scaRNAs and pseudo-uridylation of all sites in rRNA. However, certain snoRNAs and modification sites appear more \u0026ldquo;sensitive\u0026rdquo; to DKC1 mutations or changes in its expression levels, as observed in samples from dyskeratosis patients and cancer cells (\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). We inquired whether high DKC1 expression observed in GBM/GSC cells could increase the levels of specific snoRNAs/scaRNAs. Ampliseq analysis determined that a partial reduction in DKC1 levels in U251 cells decreased the levels of several snoRNAs/scaRNAs determined to be upregulated in GSC and/or GBM cells, including snorA21, snoRA22, snoRA27, snoRA38, snoRA70, snoRA71A, snoRA71D, scaRNA4, scaRNA11, scaRNA18, and scaRNA22 \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cb\u003eTable \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003escaRNAs, pseudouridylation of snRNA, and splicing.\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePseudo-uridylation also occurs in snRNAs and is driven by snRNPs containing DKC1 and scaRNAs. snRNAs are critical components of the spliceosome and are responsible for the recognition and selection of splice sites via base pairing. Pseudo-uridylation of snRNAs occurs at critical nucleotide positions, relevant for RNA-RNA and RNA-protein interactions (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). We determined that several scaRNAs implicated in snRNA pseudo-uridylation at sites that affect RNA-RNA interactions show increased expression in GSC and GBM cells. The most relevant ones include scaRNA4, scaRNA8, scaRNA11, scaRNA14 and scaRNA23 - Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B. Alterations in scaRNAs have been observed in congenital heart disease (Tetralogy of Fallot) and shown to affect the splicing of RNAs critical for heart development (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In cancer cells, scaRNA15 influences the splicing of transcripts encoding chromatin and transcriptional regulators, thereby impacting the expression and function of \u003cem\u003eATRX\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Therefore, the alterations in DKC1 and scaRNA expression observed in tumor cells could ultimately contribute to their splicing profile and constitute a new route implicated in GBM development. In agreement, we determined that a partial DKC1 knockdown affected the splicing profile of U251 GBM cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e and Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUsing a dedicated Ampliseq platform, we performed the first snoRNA profiling in GBM and GCS cells, identifying sets of snoRNAs that were up- and down-regulated in comparison to normal neuronal cells/tissue and associated with stemness and differentiation. Several snoRNAs that are elevated in GSC and GBM lines have previously been implicated in cancer development. We highlight snoRA38, snoRA51, snoRA71, and snoRA75. snoRA38 is upregulated in breast cancer, and its expression levels correlated with tumor size, lymph node metastasis, and TNM stage (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). snorA38 and snoRA71 were identified as part of a 20 snoRNA/scaRNA signature associated with breast cancer brain metastases (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Increased expression of snoRA38 and snoRA75 was observed in colon cancer metastasis to the liver (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). High snoRA51 has been observed in different tumor types. In breast cancer, increased snoRA51 expression was connected to worse prognosis, overall survival, and disease-free survival. In addition, snoRA51 enhanced cancer stem cell-like properties via the RPL3/NPM1/c-MYC pathway (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). snoRA51 is also upregulated in colon cancer and hepatocellular carcinoma (HCC) and is defined as a potential biomarker (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). SnoRA75 is among a group of 12 snoRNAs that show significant correlation with tumor microenvironment immune infiltration in melanoma (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). It is also part of a 14 snoRNA signature that can significantly stratify AML patients into high- and low-risk groups (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Five copies of snoRA71 (snoRA71, snoRA71A, snorA71B, snoRA71C, and snoRA71D) are located in various introns of SNHG17 gene. They share strong sequence similarity and are all implicated in pseudo-uridylation of nucleotide 406 in 18S rRNA. snoRA71 is highly expressed and has been identified as a prognostic marker of lung cancer (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), hepatocellular carcinoma (HCC) (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), multiple myeloma (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), and colorectal cancer (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). SnoRA71 knockdown affected proliferation, migration, invasion, and tumor growth (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). A Pan-cancer study using TCGA small RNA-seq data from 31 cancer types (excluding glioblastoma) identified 46 clinically relevant snoRNAs (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), including snoRA71, which showed alterations in at least 12 different tumor types. Interestingly, it has recently been demonstrated that snoRA71 binds multiple chromatin sites, suggesting additional regulatory roles (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral factors can contribute to differences in snoRNA and scaRNA levels (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The majority of snoRNAs and scaRNAs analyzed in our study are located in introns of host genes. We investigated the expression of 29 host genes associated with snoRNAs/scaRNAs in GBM/GSC lines. Fourteen host genes show expression differences between normal brain and GBM that resemble those observed for their respective associated snoRNA/scaRNA. Therefore, in these cases, the host genes are likely relevant contributors to the expression levels of their pertinent snoRNA/scaRNA.\u003c/p\u003e\u003cp\u003eDyskerin (DKC1) is part of a protein complex involved in the processing of H/ACA snoRNAs and scaRNAs. It also catalyzes pseudo-uridylation in rRNA and snRNA, guided by H/ACA snoRNAs or scaRNAs, respectively (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Mutations in DKC1 cause dyskeratosis congenita, a multi-organ syndrome. In these patients, reduced pseudo-uridylation at specific nucleotides in 28S has been observed. These changes could compromise rRNA-ribosomal protein interactions, destabilizing the ribosome (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Conversely, DKC1 is often highly expressed in cancers, including GBM as we described. Two recent meta-analyses identified links between elevated DKC1 expression and poorer survival and presence of metastasis in multiple tumor types (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) while several studies showed that a reduction in DKC1 expression via siRNA or shRNA affects proliferation, migration, invasion, apoptosis, and tumor growth (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Additionally, DKC1 promotes cell immortalization by modulating telomerase activity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Agreeing with results showing that certain snoRNAs and modification sites in rRNA are preferentially affected by DKC1 mutations or changes in DKC1 expression levels (\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), we determined that a partial DKC1 knockdown in GBM cells reduced the levels of a specific group of snoRNAs/scaRNAs. We hypothesize that elevated DKC1 levels increase the production of specific H/ACA snoRNAs and enhance pseudo-uridylation at critical rRNA sites, ultimately altering ribosome composition and function to support tumor progression.\u003c/p\u003e\u003cp\u003eThe value of snoRNAs as biomarkers and their potential as targets in cancer therapy have just begun to emerge (\u003cspan additionalcitationids=\"CR73 CR74\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). To advance the field, the development of sequencing platforms and dedicated bioinformatics pipelines for accurate quantification and characterization of snoRNAs and scaRNAs, as in this study, is necessary. Targeting \u0026ldquo;onco-snoRNAs\u0026rdquo; is still another area under development. Anti-sense oligos (ASOs), which have been successfully used in therapy (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), have been established as the most effective agents to knock down snoRNAs in cells and in tumors (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR78 CR79 CR80\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). However, selecting and designing effective snoRNA ASOs remains a challenging task. Due to the limited number of studies, there is insufficient information to choose ASOs based on target sequence motifs, locations, or secondary structure.\u003c/p\u003e\u003cp\u003eThe consequences of differential snoRNA expression on rRNA modification and the subsequent impact on translation are just beginning to be explored. Recent studies suggest that variations in the pattern of rRNA modification across tissues, during development, in disease states, and in response to stimuli create a platform for ribosome heterogeneity (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). A notable example comes from a study on \u003cem\u003eTrypanosoma brucei\u003c/em\u003e. Knocking out a single snoRNA that guides the pseudo-uridylation of nucleotide 530 decreased the presence of eS12 on ribosomes, affecting the translation of mRNAs encoding proteins regulated during the two life stages of the parasite (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). This study also found that rRNA in translating polysomes displays a higher Ψ frequency than total rRNA (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). snoRNAs can regulate additional processes besides rRNA processing and modification. In fact, several snoRNAs known as Orphans are not implicated in rRNA modification and have been shown to interact with chromatin (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). snoRNAs have other non-canonical functions in splicing regulation and RNA silencing (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Therefore, characterizing targets and the regulatory impact of \u0026ldquo;onco-snoRNAs\u0026rdquo; in cancer cells could be challenging. Fortunately, the advent of novel genomic methods to profile pseudo-uridylation and 2\u0026rsquo;O methylation (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e) as well as platforms to identify snoRNA interactions with other RNA species (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e) can greatly facilitate these tasks and advance knowledge on snoRNA function.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eUsing a dedicated Ampliseq platform, we performed the first targeted profiling of snoRNAs and scaRNAs in glioblastoma and glioma stem cells, identifying subsets linked to stemness and differentiation. Our results indicate that changes in snoRNA levels are influenced by both host gene expression and DKC1 activity, suggesting coordinated regulation of snoRNA maturation and function in tumor cells. Elevated DKC1 may enhance the production of specific H/ACA snoRNAs, altering rRNA modification and ribosome composition to support malignancy. These findings highlight the importance of developing specialized sequencing platforms and bioinformatics pipelines for accurate snoRNA quantification, as well as the potential of onco-snoRNAs as biomarkers and therapeutic targets in glioblastoma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003esnoRNAs\u003c/strong\u003e: small nucleolar RNAs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003esnoRNPs:\u003c/strong\u003e small nucleolar ribonucleoprotein complexes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003erRNA:\u003c/strong\u003e ribosomal RNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGBM:\u003c/strong\u003e glioblastoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escaRNAs\u003c/strong\u003e: Cajal body\u0026ndash;specific RNAs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmpliseq:\u0026nbsp;\u003c/strong\u003eAmplicon sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSC:\u0026nbsp;\u003c/strong\u003eglioma stem cell\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDKC1:\u003c/strong\u003e Dyskerin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFBL:\u003c/strong\u003e Fibrillarin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Psi;\u003c/strong\u003e: pseudo-uridine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003esnRNAs:\u003c/strong\u003e small nuclear RNAs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTERC:\u003c/strong\u003e telomerase RNA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elncRNAs:\u003c/strong\u003e long non-coding RNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASOs:\u003c/strong\u003e antisense oligonucleotides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEGF:\u003c/strong\u003e epidermal growth factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFGF:\u003c/strong\u003e fibroblast growth factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGFP:\u0026nbsp;\u003c/strong\u003eGreen Fluorescent Protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNAseq:\u003c/strong\u003e RNA sequencing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAST:\u0026nbsp;\u003c/strong\u003eastrocyte\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHBR:\u0026nbsp;\u003c/strong\u003ehuman brain\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNPC:\u0026nbsp;\u003c/strong\u003eneuronal precursor cell\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eATRA:\u003c/strong\u003e retinoic acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCESC:\u003c/strong\u003e Cervical squamous cell carcinoma and endocervical adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHOL:\u003c/strong\u003e Cholangiocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOAD:\u003c/strong\u003e Colon adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDLBC:\u003c/strong\u003e Lymphoid Neoplasm Diffuse Large B-cell Lymphoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eLUSC:\u003c/strong\u003e Lung squamous cell carcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eREAD:\u0026nbsp;\u003c/strong\u003eRectum adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTAD:\u003c/strong\u003e Stomach adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTHYM:\u003c/strong\u003e Thymoma\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during the current study is available in the European Nucleotide Archive repository (https://www.ebi.ac.uk/ena/, under accession PRJEB101101).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the NIH grant 1R21CA297561-01 to LOFP. GLF was supported by a fellowship from CAPES. LD and SA were sponsored by The German Academic Exchange Service (DAAD).\u0026nbsp;G.D.A.G. was supported by the Young Scientist Program from Hospital S\u0026iacute;rio-Liban\u0026ecirc;s and Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico - CNPq (grant 382424/2025-5), F.G.C.O. was supported by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior/PROEX (grant 88887.220499/2025-00), and P.A.F.G. was supported by the grant #2018/15579-8, S\u0026atilde;o Paulo Research Foundation (FAPESP).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOP, XL, LD, SR, SA, GLF and NA:\u0026nbsp;\u003c/strong\u003eChildren\u0026rsquo;s Cancer Research Institute, UT Health San Antonio, San Antonio, TX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOP:\u003c/strong\u003e Department of Cell Systems and Anatomy, UT Health San Antonio, San Antonio, TX.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePAFG, GDAG and FGCO:\u0026nbsp;\u003c/strong\u003eHospital S\u0026iacute;rio-Liban\u0026ecirc;s, S\u0026atilde;o Paulo, SP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFGCO:\u0026nbsp;\u003c/strong\u003eDepartamento de Bioqu\u0026iacute;mica, Universidade de S\u0026atilde;o Paulo, SP.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSK, CM, MR, NS, GS and SA:\u0026nbsp;\u003c/strong\u003eIllumina, Inc., San Diego, CA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAB:\u003c/strong\u003e Mays Cancer Center, UT Health San Antonio, San Antonio, TX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLOP, PAFG and SK conceived the original idea for the project, analyzed data and wrote most of the manuscript. GDAG developed the pipeline for Ampliseq data analysis, performed most bioinformatics analyses, prepared several figures and helped with text writing. XL performed most of biological experiments and prepared all Ampliseq libraries.\u0026nbsp;CM, MR, NS, GS and SA help with the design of Ampliseq platform and sequencing experiments. LD conducted experiments to analyze snoRNAs and helped with text and figures, FGCO performed analysis of splicing data and helped with text and figures, SR, SA, GLF and NA helped with data analysis and figure preparation, AB was involved in the generation of GSC lines and characterization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eETHICS DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not include human participants/data and vertebrate animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eD\u0026ouml;rner K, Ruggeri C, Zemp I, Kutay U. Ribosome biogenesis factors-from names to functions. EMBO J. 2023;42(7):e112699.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZisi A, Bartek J, Lindstr\u0026ouml;m MS. Targeting ribosome biogenesis in cancer: Lessons learned and way forward. 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Genome Biol. 2025;26(1):39.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"snoRNAs, scaRNAs, rRNA modification, snoRNPs, DKC1, Ampliseq","lastPublishedDoi":"10.21203/rs.3.rs-8079210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8079210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmall nucleolar RNAs (snoRNAs) are critical players in ribosome biogenesis and have essential roles in rRNA processing and modification (2’O methylation and pseudo-uridylation). snoRNAs define which nucleotides get modified by guiding small nucleolar ribonucleoprotein complexes (snoRNPs) to specific positions in rRNA via base pairing. Altered snoRNA expression has been reported in diverse biological and pathological contexts. In cancer cells, the dysregulation of snoRNAs can alter the rRNA modification landscape, potentially affecting ribosome composition and translation. In aggressive tumors, such as glioblastoma (GBM), snoRNA profiling is crucial for expanding our understanding of ribosome biogenesis and identifying novel biomarkers and therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have developed an Ampliseq platform and analysis pipeline to measure the expression of 127 snoRNAs (mostly those containing H/ACA boxes) and Cajal body–specific RNAs (scaRNAs) and conducted a study in a panel of glioma stem cell (GSC), GBM, and normal neuronal lines/tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of our Ampliseq analysis identified snoRNAs and scaRNAs (snoRNAs/scaRNAs) that were differentially expressed in GBM and GSC cells compared to normal neuronal cells/tissue, as well as snoRNAs/scaRNAs associated with stemness and differentiation. SnoRNAs with elevated expression in GSC and GBM lines (snoRA38, snoRA51, snoRA71, and snoRA75) have been previously implicated in cancer development. Components of the H/ACA snoRNP complex, which regulate snoRNA processing and rRNA pseudouridylation, were also found to be overexpressed in GBM and showed decreased levels during neuronal differentiation. Notably, high expression of Dyskerin (DKC1)—the pseudouridylation enzyme and a key H/ACA snoRNP component—correlates with poor survival in patients with high-grade gliomas. Finally, we assessed the therapeutic potential of targeting snoRNAs in GBM. Knockdown of two upregulated snoRNAs, snoRA46 and snoRA75, using antisense oligonucleotides significantly impaired GBM cell growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esnoRNA/scaRNAs profiling revealed distinct alterations in snoRNA expression between glioblastoma and normal neuronal cells. These differences may contribute to the reprogramming of rRNA modification and ribosome composition in cancer cells. Moreover, our findings highlight the potential of antisense oligonucleotide-based targeting of overexpressed snoRNAs in GBM as a promising therapeutic strategy.\u003c/p\u003e","manuscriptTitle":"H/ACA snoRNAs and snoRNPs Dysregulation Links rRNA Modification to Glioblastoma Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 09:51:23","doi":"10.21203/rs.3.rs-8079210/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"289b6359-c991-4553-b4b6-1c6f3754e811","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T08:40:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 09:51:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8079210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8079210","identity":"rs-8079210","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}