Multi-Omics Analysis Delineates Molecular Signatures of Spinal Ependymal Tumor

Multi-Omics Analysis Delineates Molecular Signatures of Spinal Ependymal Tumor | 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 Multi-Omics Analysis Delineates Molecular Signatures of Spinal Ependymal Tumor Weihao Liu, Chao Ning, Xiaohan Geng, Bo Wang, Yaowu Zhang, Chong Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5761045/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Cellular Oncology → Version 1 posted 11 You are reading this latest preprint version Abstract Spinal ependymal tumors are a diverse group of neoplasms encompassing three subtypes: spinal ependymoma (SP-EPN), spinal myxopapillary ependymoma (SP-MPE), and spinal subependymoma (SP-SE). However, the molecular differences among these subtypes remain largely unknown. Here, we identified the distinct molecular characteristics of each subtype through a multi-omics analysis. In grade-2 SP-EPN, abnormal enrichment of ciliary signaling, particularly involving the MKS complex and Hedgehog (Hh) pathway, was evident, suggesting potential therapeutic targets. SP-MPE exhibited significant dysregulation of mitochondrial metabolism, reflecting a metabolic profile aligned with the Warburg effect. SP-SE tumors showed enhanced activity of immune-related pathways, including interferon signaling and extracellular vesicle dynamics, suggesting a distinct tumor microenvironment. This study underscores the molecular diversity of spinal ependymal tumors, offering novel insights into their pathobiology, and highlighting promising therapeutic avenues tailored to each subtype. Spinal Ependymoma Myxopapillary ependymoma Subependymoma Multi-omics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Spinal ependymal tumors account for approximately 60% of intramedullary spinal cord tumors, with an annual incidence ranging from 0.22 to 0.32 cases per 100,000 individuals. 1 Although surgical resection is the primary treatment, many patients suffer post-operative neurological impairments that affect sensory and motor functions. 2 Additionally, 50–70% of patients who undergo subtotal resection experience local recurrence or distant metastasis. 3 Current clinical classifications divide these tumors into three subtypes: SP-EPN, SP-MPE, and SP-SE primarily based on histopathological features. 4 However, such classifications fail to capture the molecular diversity of these tumors. 5 This underscores the need for molecular profiling to refine tumor classification and improve therapeutic strategies. To establish a molecular classification, a pioneer study profiled DNA methylation across 500 central nervous system (CNS) ependymal tumors, encompassing all anatomical compartments of the CNS, including the spine, posterior fossa, and supratentorial regions. 6 Nine distinct molecular subgroups were identified in this study. Among these, specific genetic alterations were characteristic of spinal ependymal tumor subgroups: 6q deletions were associated with SP-SE, chromosomal instability was recurrent in SP-MPE, and NF2 mutations were observed in SP-EPN. 6 Neyazi et al. refined the classification of SP-EPN by integrating transcriptomic, DNA methylation, and clinical data, and identified two molecular subtypes based on the presence or absence of NF2 mutations. 7 Further investigations have identified MYCN amplification as a novel molecular subgroup of SP-EPN. 8 , 9 Bockmayr et al. elucidated SP-MPE's morphological and clinical heterogeneity by distinguishing between two molecularly distinct subtypes. 10 However, the above findings heavily rely on the DNA methylation profiling for spinal ependymal tumor classification. Comprehensive studies integrating WGS, transcriptomics, and proteomics across all subtypes remain limited. To deepen our understanding of the molecular patterns of different spinal ependymal tumor subtypes, we conducted multi-omics analyses of a cohort of 25 spinal ependymal tumors encompassing all three subtypes. This comprehensive approach unveiled the molecular characteristics underlying the heterogeneity among subtypes, providing a valuable resource for the rare cancer research community. Materials and methods Clinical Sample Collection All 25 patients provided informed consent for the use of their tumor tissues in this study. Tumor samples were collected during routine clinical treatment. Fresh tumor tissues from 25 patients were snap-frozen in liquid nitrogen within 5 minutes of surgical resection. Relevant clinical data were extracted from patients' electronic medical records. Histology and Immunohistochemistry Two neuropathologists independently evaluated the tumor samples according to H&E-staining and categorized them into histological subtypes: SP-EPN, SP-MPE, SP-SE, or anaplastic SP-EPN. The hospital pathology department performed immunohistochemical analysis of FFPE samples during hospitalization. The primary antibodies used for immunohistochemical analysis were GFAP, Ki-67, EMA, H3K27me3, Olig-2, S100, and SOX10. Staining was performed using the SuperPicture™ 3 rd Gen Immunohistochemistry Detection Kit. WGS Library Construction Genomic DNA was extracted from 10 mg of fresh-frozen tumor tissue using a DNeasy Blood & Tissue Kit (Qiagen, Cat. No. 69504) according to the manufacturer's instructions. WGS libraries were prepared using the MGIEasy PCR-Free DNA Library Prep Set and sequenced using the BGI DNBSEQ-T7 platform. RNA-Seq Library Construction and Fusion Transcript Detection Total RNA was isolated from 2 mg of frozen tumor tissues using Trizol reagent (Thermo Fisher Scientific, Cat. No. 15596018). RNA-seq libraries were constructed using the NEB Next Ultra™ RNA Library Prep Kit (NEB, Cat. No. E7490) and sequenced using the NovaSeq 6000 System platform. Fusion transcript detection was performed using the Arriba algorithm. 11 Mass Spectrometry (MS-spec) Approximately 10 mg of each tumor sample was homogenized in 500 μL of lysis buffer (100 mM TEAB pH 8.0, 1% SDS, supplemented with 1× protease inhibitor cocktail) using a Precellys Evolution tissue homogenizer with dry ice. Lysates were centrifuged at 20,000 × g for 30 min at 4°C and the supernatants were collected. Protein concentrations were measured using a BCA assay. For downstream MS-spec analysis, 100 µg of protein was reduced with 5 mM TCEP at 55°C for 1 hour and alkylated with 10 mM IAA at room temperature for 30 min in the dark. Proteins were precipitated using a standard methanol/chloroform protocol, followed by digestion with 4 µg sequencing-grade modified trypsin (Promega, Cat. No. V5117) at 37°C for 12 h. The resulting peptides were acidified with TFA to pH ~3, desalted using C18 Zip-Tips, and dried using Speed-Vac. The desalted peptides were subjected to LC-MS/MS analysis using an Orbitrap Exploris™ 480 mass spectrometer (Thermo Scientific) coupled to a Proxeon Easy-nLC 1200 system (Thermo Scientific). Data were processed using the SEQUEST HT search engine in the Thermo Proteome Discoverer software (v2.4.1.15) against the UniProt human reference proteome database. Immunofluorescence Staining of FFPE Samples The FFPE sample slides were deparaffinized and rehydrated by sequential immersion in BioDewax and Clear Solution (Servicebio), 50% BioDewax and Clear Solution mixed with 50% ethanol, 100% ethanol, 95% ethanol, 70% ethanol, and 50% ethanol for 10 min each, and deionized water for 30 min. Antigen retrieval was performed by heating slides in 10 mM sodium citrate buffer (pH 6.0) at 95°C for 10 min. The slides were washed twice with permeabilization buffer (1% donkey serum and 0.4% Triton X-100 in PBS) and blocked with 5% donkey serum in PBS for 30 min at room temperature. Primary antibodies diluted 200 folds in 1% donkey serum in PBS were incubated with slides for 1-2 hours at room temperature. Primary antibodies used were rabbit anti-AQP4 (Proteintech, 16473-1-AP), rabbit anti-GFAP (Proteintech, 16825-1-AP), rabbit anti-TCTN1 (Proteintech, 15004-1-AP), rabbit anti-MKS1 (Proteintech, 16206-1-AP), and rabbit anti-ISG15 (Proteintech, 15981-1-AP). Alexa Fluor 488-conjugated secondary antibodies (Life Technologies) were added to the slides at a dilution of 1/1000 and incubated for 1 h at room temperature. Targeted Gene Panel Sequencing Targeted gene panel sequencing was performed on FFPE samples from 8 out of 25 patients. In brief, this panel-seq analyzed 86 genes for point mutations, insertions, and deletions; 28 genes for copy number variations; 44 gene exons; and 88 rearrangement events, focusing on the genomic alternations relevant to brain tumors. Detailed results are provided in the Supplementary Materials section. Data Availability Raw WGS and RNA-seq data were deposited in the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsa-human/) under the accession number HRA008375. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD056112. RESULTS The basic information of the spinal ependymal tumor’s cohort To characterize the molecular landscape of spinal ependymal tumors in China, we conducted a comprehensive analysis that included WGS (n=25), RNA sequencing (RNA-seq) (n=25), targeted sequencing of brain tumor molecualar markers (panel-seq) (n=8), and proteomics analysis (n=21) across the three distinct subtypes of spinal ependymal tumors (Figure 1A). The clinicopathological and immunohistochemical characteristics of patients and their tumors are summarized in Tables S1-S3. Specifically, the cohort comprised 60% male and 40% female patients with a median age of 37 (Table S1). The histopathological classification of the cohort included 4 grade-2 and 4 grade-3 SP-EPN, seven grade-1 SP-SE, and ten grade-2 SP-MPE cases. All samples, except SP-EPN_7, were primary spinal ependymal tumors (Table S2). As shown in Table S3, Ki-67 levels vary significantly. Grade-3 SP-EPN exhibited a higher Ki-67 proliferation index compared to grade-2 ones. SP-SE and SP-MPE cases show a broader range, with some SP-MPE cases exhibiting higher Ki-67 levels (up to 15%). Variability in the expression of EMA and Olig-2 suggests the need for integrative analysis to refine molecular and clinical classification. This cohort contained all known ependymal tumors of the spinal cord, and all were from the Han Chinese population, which is distinct from previous spinal ependymal tumor multi-omics studies. 6,7 The CNAs patterns in Spinal Ependymal Tumors To obtain a comprehensive view of copy number alterations (CNAs) across all chromosomes, we performed WGS and used CNVpytor 12 to analyze CNAs in different spinal ependymal tumor subtypes. In SP-SE, CNAs display a relatively balanced and random distribution, with no discernible focal amplifications or deletion hotspot regions (Figure 1B left). Although a 6q deletion has been reported in brain SE 13 , our analysis of seven SP-SE samples did not identify a similar trend, underscoring the variability and inconsistency of CNAs between the brain and spinal SE. By contrast, SP-MPE and SP-EPN displayed significantly higher CNAs. The most frequent event in SP-EPN tumors was the loss of 22 (Figure 1B middle). Additionally, we observed copy number gains on chromosomes 7, 9, 12, and 15q in SP-EPN and on chromosomes 4, 7, 9, 16, 17, and 18 in SP-MPE (Figure 1B right). The CNAs in SP-SE were significantly fewer than those in SP-EPN and SP-MPE, consistent with the distinctions between benign and malignant tumors as classified by the WHO. These findings demonstrate that spinal ependymal tumors across these three molecular subgroups are genetically distinct. The landscape of r ecurrent somatic mutations in Spinal Ependymal Tumors Next, we identified somatic mutations using MutSigCV algorithm 14 and compared them against the Catalogue of Somatic Mutations in Cancer (COSMIC) and dbSNP databases to filter SNPs and genetic variations prevalent in healthy people. 15 Nine types of protein-coding sequence variations were detected. The top four protein-coding sequence variations were missense mutations, followed by frameshift deletions, in-frame insertions, and in-frame deletions (Figure 1C). The most common point mutations observed were T>C, C>T, and T>A, with frequencies of 28.8%, 28.0%, and 11.7%, respectively (Figure 1D). These point mutations did not show a preferential pattern toward specific subtypes of ependymal tumors (Figure 1E, lower panel). The protein-coding sequence variation events across 25 samples ranged from 96 to 202 (Figure 1E, upper panel). The top ten high-frequency protein-coding sequence variations occurred in FCGBP, CTBP2, ANKRD36, ZNF83, LILRB1, PABPC1, SYN2, ERAP2, and HLA-DRB1 (Figure 1E, middle panel). Previous studies have shown that among the 21 SP-EPN analyzed, 19 exhibited a loss of 22q, where the NF2 gene is located. 6 However, in our cohort, only one out of eight SP-EPNs carried an NF2 mutation (Figure 1E, highlighted in blue), indicating that the mutation frequency of this gene is correlated with the genetic background. Notably, HLA-DRB1 and FCGBP were two immune-related genes (Figure 1E, highlighted in red), the mutations of which were not previously identified in spinal ependymal tumors. HLA-DRB1 plays a central role in the immune system by presenting peptides derived from extracellular proteins 16 , and FCGBP is an immune-related gene. 17 Specifically, in the exon region of chr19:39906139-39906232 in FCGBP, missense mutations occurred with high frequency (Figure 1F). Mutations in these two genes may contribute to the ability of spinal ependymal tumors to evade immune surveillance. Moreover, in the chr2:97,241,313-97,241,339 exon region of ANKRD36, we identified a nonsense mutation at chr2:97,241,317 (T>G) (Figure 1G). Additionally, synapsin II (SYN2), a gene involved in neurotransmitter release regulation, was frequently affected by a 12-nucleotide insertion, resulting in the addition of PAPQ amino acids (Figure 1H). Different Spinal Ependymal Tumor subtypes exhibit distinct features To construct a transcriptomic map of various spinal ependymal tumor subtypes, we performed RNA-seq and conducted pairwise comparisons between the tumor samples. Our analysis revealed distinct transcriptional profiles for the SP-SE and SP-MPE subtypes across different patients, suggesting a unique molecular signature for each tumor type (Figure 2A). In contrast, the transcriptional patterns of SP-EPN varied significantly according to the tumor grade. Specifically, grade-2 SP-EPN (SP-EPN_1/2/3/4) clustered together, while grade-3 SP-EPN_5 exhibited transcriptional similarities to SP-SE. The other three grade-3 SP-EPN (SP-EPN_6/7/8) showed a unique transcription pattern that differed from grade-2 EPN, suggesting that grade-3 SP-EPN is highly heterogeneous. These findings were confirmed by Principal Component Analysis (PCA) results, which aligned with those observed in the RNA-seq heatmap (Figure 2B). Clinical panel-seq data further suggested that MYCN amplification is present in grade-2 SP-EPN (SP-EPN_3/4) but absent in SP-EPN_1/2 (Table S2). Despite this differential amplification, the transcriptional profiles of SP-EPN_1/2 and SP-EPN_3/4 are remarkably similar. This observation implies that MYCN amplification has a minimal impact on the overall transcriptomic landscape of SP-EPN, a point we discuss later in Figure 4Z. To gain deeper insights, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of differentially expressed genes (DEGs), as highlighted by the black square in Figure 2A. GO analysis revealed that the upregulated DEGs in SP-EPN_1/2/3/4 were primarily enriched in terms related to primary cilia, including axoneme assembly, cilium, and MKS complex (Figure 2C). Primary cilia play crucial roles in signaling pathways during spinal development, such as Hh signaling. 19,20 Aberrant activation of Hh signaling via primary cilia has been implicated in the development of supratentorial ependymomas. 21 Consistent with this, Gene Set Enrichment Analysis (GSEA) revealed significant enrichment of the Hh signaling pathway in grade-2 SP-EPN_1/2/3/4 (Supplementary Figure. 1A). These findings suggest that the upregulation of cilia-related genes in grade-2 SP-EPN may contribute to tumor development via activation of Hh signaling, similar to what has been observed in supratentorial ependymomas. In contrast, SP-SE tumors showed significant enrichment of immune-related response pathways, such as the response to interferon-related pathways and complement component C1 complex, as indicated by both GO and GSEA analyses (Figure 2D, Supplementary Figure. 1B). The response to the interferon-beta pathway suggests that SP-SE tumors may actively trigger an immune response, particularly in their interaction with the innate immune system. Additionally, the enrichment of the complement component C1 complex points to the activation of the complement system, a key component of innate immunity that helps clear pathogens and modulate inflammation. This suggests that immune-related mechanisms may either support tumor progression in SP-SE or may reflect an attempt by the immune system to control tumor growth. Interestingly, SP-MPE tumors exhibited preferential enrichment in mitochondrial and cellular metabolic pathways (Figure 2E, Supplementary Figure. 1C). These include processes such as positive regulation of mitochondrial autophagy, mitochondrial ATP synthesis coupled to electron transport, and the tricarboxylic acid (TCA) cycle. These findings suggest that SP-MPE tumors have distinct metabolic characteristics that may be central to their pathophysiology. Notably, this metabolic profile aligns with features of the Warburg effect previously described in other malignancies and could support the increased energy demands of SP-MPE tumors, potentially contributing to their progression. To identify potential biomarkers for the three distinct types of spinal ependymal tumors, we selected the genes that exhibited the most significant fold changes in transcript level. Our analysis revealed that HOXB13, PRAC1, and SPARCL1 were preferentially expressed in SP-MPE, whereas APOE and LARP3 were highly expressed in SP-SE. BANF, C9orf3, CAPS2, CALR3, C4orf50, CAPNS2, and BCAR1 showed a preferential expression in grade-2 SP-EPN (Supplementary Figure. 2). The balloon plot quantifies the top different genes across three subtypes. To identify potential onco-fusions in spinal ependymal tumors, we used Arriba to detect fusion transcripts. As a result, we identified CTBS-GNG5 fusion genes in all SP-MPE samples (Supplementary File, Fusion gene). However, this fusion gene was also found in normal tissues of various types 22 , suggesting that it may not be involved in the development of SP-MPE. Additionally, we identified KIAA0319L-PARK7 fusion in SP-EPN_6 and MAP7-MDFI fusion in SP-SE_1 (Supplementary Figure. 3), although the functions of these two fusion genes remain unclear. We speculated that the fusion of KIAA0319L and PARK7 results in a protein that integrates the endocytosis capabilities of KIAA0319L 23 with PARK7, an enzyme mutated in hereditary Parkinson's disease, 24 potentially influencing SP-EPN_6 cellular responses to oxidative stress (Supplementary Figure. 3A). The breakpoint of MAP7-MDFI was located in the intron between exons 5 and 6 of MAP7 and the intron between exons 4 and 5 of MDFI (Supplementary Figure. 3B). MAP7 is a microtubule-binding protein 25 and MDFI is a transcription factor that negatively regulates other myogenic family proteins 26 . Thus, MAP7-MDFI fusion could potentially influence cellular architecture and differentiation processes in SP-SE_1. Proteomics profiling of spinal ependymal tumors To characterize the proteomic landscape of spinal ependymal tumors, we performed proteomic analyses on 21 samples, including 6 SP-SE, 8 SP-EPN, and 7 SP-MPE specimens. A total of 8,689 proteins were identified (Figure 3A). PCA revealed that SP-EPN displayed characteristics intermediate between SP-SE and SP-MPE (Figure 3B). Differentially expressed proteins, highlighted within the black square in Figure 3A, were subjected to GO analysis. In grade-2 SP-EPN subtypes (SP-EPN_1/2/3/4), upregulated proteins were enriched in cilia-related biological processes, including cilium assembly, cilium movement, and the MKS complex (Figure 3C). In contrast, SP-MPE tumors were characterized by pronounced expression and translation of mitochondrial proteins, indicating a distinct metabolic profile unique to this subtype (Figure 3D), aligning with the observations from RNA-seq analysis (Figure 2). For SP-SE tumors, GO analysis revealed significant enrichment in metabolic processes and extracellular vesicle-related pathways (Figure 3E). This suggests that SP-SE tumors may harbor a unique tumor microenvironment modulated by extracellular vesicles. We hypothesize that these vesicles contribute to inflammatory factor secretion, as previously indicated by transcriptomic analyses of SP-SE tumors. Integrated Analysis of Spinal Ependymal Tumors We combined the RNA-seq and proteomics results to characterize the three tumor subtypes. A total of 461 candidates exhibited consistent upregulation in both the transcriptomic and proteomic datasets, with 234, 134, and 93 candidates specifically upregulated in SP-SE, SP-EPN, and SP-MPE, respectively (Figure 4A, K, S). Grade-3 SP-EPN samples were excluded because of their heterogeneous transcriptomic and proteomic patterns. In SP-SE, upregulated candidates were enriched in glial cell differentiation pathways, in addition to the previously identified extracellular vesicle pathways (Figure 4B). Among these, GFAP and ISG15 drew particular attention (Figure 4C and G). GFAP, a marker of glial differentiation, is associated with less malignant tumors and typical features of brain cell aging. 27,28 ISG15, an interferon-stimulated gene involved in immune pathways, plays an immunoregulatory role in tumors and serves as a potential biomarker for cancer progression. 29,30 Although GFAP was ubiquitously expressed in 84% (21/25) of ependymoma samples across all subtypes (Table S3), IF staining of FFPE slices confirmed preferential expression of GFAP (Figure 4D vs. E/F) and ISG15 (Figure 4H vs. I/G) in SP-SE. In grade-2 SP-EPN, cilium-related genes were prominently expressed (Figure 4L). Among these, MKS1 and TCTN1, key components of the MKS complex, showed strong positive correlations between RNA and protein levels (Pearson’s R = 0.68488 and R = 0.73098, respectively). Both genes demonstrated significantly higher expression in grade-2 SP-EPN compared to other subtypes (Figure 4M). This finding was further validated using an independent SP-EPN cohort, where MKS complex-related genes, including MKS1, TCTN1, and TMEM231, were notably upregulated in grade-2 SP-EPN compared to grade-3 ones (Figure 4N). Immunofluorescence staining confirmed these observations, showing high expression of MKS1 (Figure 4O vs. P) and TCTN1 (Figure 4Q vs. R) in grade-2 SP-EPN, while their expression was markedly reduced in grade 3 SP-EPN. The MKS complex, located in the primary ciliary transition zone, mediates essential developmental processes including ciliary formation and epithelial morphogenesis. 31,32 Low expression of TCTN1 expression the ciliary transition zone, compromises barrier function and impairs Hh signaling. 33,34 This finding partially explained the enrichment of Hh signaling observed in our RNA-seq results. In SP-MPE, upregulated candidates were enriched in mitochondrial translation pathways (Figure 4T). Using the STRING database, we constructed a PPI network of upregulated DEGs, highlighting mitochondrial ribosomal proteins (MRPL58, MRPL15, MRPL17, MRPL37, MRPL16, and MRPS23) as central nodes (yellow dots, Figure 4T). We further analyzed genes downregulated in SP-MPE and focused on Aquaporin-4 (AQP4). Its downregulation in SP-MPE was validated by IF staining (Figure 4W vs. X/Y). Previous studies suggested that AQP4 participated in CNS water balances. 35 T SP-MPE has been reported to present with cerebrospinal fluid dissemination or "drop metastases" at the time of diagnosis. 36 We speculated that AQP4 depletion in SP-MPE may lead to cerebrospinal fluid imbalance, potentially contributing to the spread of SP-MPE to other locations via cerebrospinal fluid imbalance. Endocytosis-related proteins (HIP1R, BIN1, FNBP1, DNM3, MYO6, NES and STON2) were downregulated in SP-MPE (Figure 4V). Notably, HIP1R was almost absent in the SP-MPE group. Among the 25 patients, eight underwent targeted sequencing based on brain glioma indices. However, the copy number amplifications detected in genes such as NTRK2, NTRK3, KRAS, and NOTCH1 did not correlate with their RNA and protein expression levels. This suggests that gene amplification may not reliably represent transcriptional and proteomic changes in spinal ependymal tumors (Figure 4Z). Discussion Spinal ependymal tumors are highly heterogeneous, and understanding their molecular characteristics provides insight into their clinical behavior and potential therapeutic strategies. SP-EPN: Abnormal Ciliary Signaling and Therapeutic Implications In grade-2 SP-EPN tumors, we observed significant enrichment of ciliary signaling pathways, particularly those involving the MKS complex and Hh signaling. Dysregulation of ciliary components, such as TCTN1 and MKS1 may contribute to tumorigenesis through abnormal Hh pathway activation. 37 – 39 These findings suggest the potential therapeutic utility of targeting the Hh signaling pathway. Drugs such as Vismodegib and Sonidegib, which inhibit Hh signaling, could be explored as adjunctive treatments for this subtype. 40 , 41 In contrast, grade-3 SP-EPN tumors exhibit notable intertumor heterogeneity, indicating that additional clinical samples and further stratification are necessary to refine their molecular profiling. SP-MPE: Mitochondrial Metabolism and the Warburg Effect SP-MPE tumors demonstrate strong enrichment of mitochondrial pathways, including the TCA cycle, which distinguishes them from the other two subtypes. This is consistent with previous reports on the Warburg effect in SP-MPE. 42 Thus, targeting SP-MPE metabolism through inhibitors of TCA metabolism, such as glucose analog 2-deoxy-D-glucose 43 , could be a promising therapeutic strategy for SP-MPE. SP-SE: Immune-Related Pathways and Extracellular Vesicles SP-SE tumors were enriched in immune-related pathways, particularly interferon signaling and extracellular vesicle-related genes. High expression of interferon-stimulated genes and extracellular vesicles indicates a unique tumor microenvironment characterized by active immune modulation. Conclusion This study provides a comprehensive characterization of the molecular heterogeneity of spinal ependymal tumors. Grade-2 SP-EPN tumors with enriched ciliary signaling may benefit from targeted therapies such as Hh pathway inhibitors, while the metabolic reprogramming of SP-MPE suggests therapeutic opportunities for disrupting mitochondrial or Warburg pathway metabolism. Future studies with larger cohorts are essential to validate these findings and further refine subtype-specific therapeutic strategies. Declarations Ethical Approval This study was approved by the Institutional Ethics Committee of Beijing Tiantan Hospital, Capital Medical University (KY2022-089-02), and was conducted in accordance with the Declaration of Helsinki. Patients were consented to the informed consent process and consented to publish. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This study was supported by the Beijing Municipal Health Commission (11000023T000002044300-2). Author Contribution W.L. wrote the main manuscript text and conducted the experiments. C.N. and X.G. prepared the figures and analyzed the data. B.W. and Y.Z. prepared the materials for the experiments. C.W., Y.L. and G.Z. collected the clinical data. Y.W., X.W., D.L. and W.J. provided experimental design and guidance. All authors have reviewed the manuscript. Data Availability Raw WGS and RNA-seq data were deposited in the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsa-human/) under the accession number HRA008375. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD056112. References Ostrom, Q.T., Cioffi, G., Gittleman, H., Patil, N., Waite, K., Kruchko, C., and Barnholtz-Sloan, J.S. (2019). CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016. Neuro Oncol 21 , v1-v100. 10.1093/neuonc/noz150. 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Zhang, D., Li, J., Wang, F., Hu, J., Wang, S., and Sun, Y. (2014). 2-Deoxy-D-glucose targeting of glucose metabolism in cancer cells as a potential therapy. Cancer Lett 355 , 176-183. 10.1016/j.canlet.2014.09.003. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx SupplementaryTableS2.docx SupplementaryTableS3.docx sFigure1GESA.png Supplementary Figure 1. GSEA analysis of spinal ependymal tumors. GSEA plots for representative gene sets of SP-EPN 1-4 (A) SP-SE (B) and SP-MPE (C). The y-axis represents the enrichment score (ES) and the x-axis represents genes (vertical black lines) present in the gene sets. NES, normalized enrichment score; FDR, false discovery rate. sFigure2Ballonplots.png Supplementary Figure 2. Balloon plot of representative DEGs in transcriptome. The balloon plot quantifies the top different genes across three subtypes. sFigure3.tif Supplementary Figure 3. Schematic of fusion genes KIAA0319L-PARK7 (A) and MAP7-MDFI (B) that were respectively identified in SP-EPN6 and SP-SE1samples. Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Cellular Oncology → Version 1 posted Editorial decision: Revision requested 22 Apr, 2025 Reviews received at journal 21 Apr, 2025 Reviews received at journal 14 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviews received at journal 30 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 06 Jan, 2025 Submission checks completed at journal 06 Jan, 2025 First submitted to journal 03 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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03:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5761045/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5761045/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13402-025-01122-0","type":"published","date":"2025-10-29T15:58:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73280400,"identity":"58fc353c-2b6d-45fa-87a5-355093cb432a","added_by":"auto","created_at":"2025-01-08 12:30:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1136972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic landscape of three subtypes of spinal ependymal tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A),\u003c/strong\u003e Summary of the spinal ependymal tumor cohort. Left panel, the anatomical distribution and pathological types of tumors in the spinal cord diagram. Right panel: The number of cases and subtypes in the cohorts, along with the types of data included (blue), with gray representing missing data. \u003cstrong\u003e(B),\u003c/strong\u003e Manhattan plot of the read depth signal in chromosome copy number alteration (CNA) profiles in the three types of spinal ependymal tumors. The bin size is 1000 kb. \u003cstrong\u003e(C),\u003c/strong\u003e Nine types of protein-coding sequence variations were identified, with missense mutations being the most common. The number of variants was subdivided using the Human Genome Variation Society (HGVS) classification criteria.\u003csup\u003e18\u003c/sup\u003e \u003cstrong\u003e(D),\u003c/strong\u003e Types and quantities of point mutations, with the top two being T\u0026gt;C and C\u0026gt;T. \u003cstrong\u003e(E),\u003c/strong\u003e Mutated genes are ordered by \u003cem\u003eq\u003c/em\u003e value. Each column denotes an individual sample, and each row represents a gene. Top panel, the protein-coding sequence variation events. Right panel: Percentage of mutations across 25 samples. Mutation types are shown by color as indicated. Genes annotated as “Multi Hit” have more than one type of mutation in the sample. \u003cstrong\u003e(F-H),\u003c/strong\u003e The schematic of mutations in the FCGBP (F, missense), ANKRD36 (G, nonsense), and SYN2 (H, insertion) gene.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/bb6be0a32b48c546eaaac182.png"},{"id":73280398,"identity":"448eb5e2-2bb4-429d-bbe3-c3963d4aec52","added_by":"auto","created_at":"2025-01-08 12:30:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":551290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic Profiling Reveals Subtype-Specific Signatures in Spinal Ependymal Tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A),\u003c/strong\u003e Heatmap of transcriptomics among the three subtypes of spinal ependymal tumors (n=25). \u003cstrong\u003e(B),\u003c/strong\u003e PCA analysis of transcriptomic data. \u003cstrong\u003e(C-E), \u003c/strong\u003eGO analysis indicated the upregulated transcript enriched in pathways in SP-EPN (C), SP-SE (D), and SP-MPE (E).\u003c/p\u003e","description":"","filename":"Figure201.png","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/9f861c3bb44746814c95a6e2.png"},{"id":73281396,"identity":"37408c3c-8122-4c15-b839-8abeefcdabb8","added_by":"auto","created_at":"2025-01-08 12:38:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38306433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic Characterization Highlights Molecular Differences Across Spinal Ependymal Tumor Subtypes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A),\u003c/strong\u003e Heatmap of proteomic among the three types of spinal ependymal tumors (n=21). \u003cstrong\u003e(B), \u003c/strong\u003ePCA of proteomics data. \u003cstrong\u003e(C-E),\u003c/strong\u003e Pathways that were significantly upregulated in SP-EPN (C), SP-MPE (D), and SP-EPN (E).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/9eeb2b625ccdb957358b741a.png"},{"id":73280401,"identity":"0cb800cf-1a7a-4822-923a-5fab1bf1768e","added_by":"auto","created_at":"2025-01-08 12:30:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":556097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrative Proteogenomic Analysis of Spinal Ependymal Tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, K, S),\u003c/strong\u003e The Venn diagram depicts the overlapping upregulated genes in both transcriptome and proteome of SP-SE, grade 2 SP-EPN_1-4, SP-MPE. \u003cstrong\u003e(B, L, T), \u003c/strong\u003eProtein-protein interaction network of the overlapping genes in the three subtypes. \u003cstrong\u003e(C, G, M, U), \u003c/strong\u003eThe transcript (left) or protein abundance (right) of GFAP (C), ISG15 (G), MKS1/TCTN1 (M), and AQP4 (U). \u003cstrong\u003e(D-F, H-J, W-Y\u003c/strong\u003e), Representative IF staining images of GFAP (D-F), ISG15 (H-J), and AQP4 (W-Y) in FFPEsamples from three subtypes. Scale bar, 50 μm. \u003cstrong\u003e(N),\u003c/strong\u003e Boxplots of differential expression of MKS complex genes based on RNA (top) and proteomic data (bottom). \u003cstrong\u003e(O-R), \u003c/strong\u003eRepresentative IF staining images of MKS1 (O, P) and TCTN1 (Q, R) in independent samples of grade-2 and grade-3 SP-EPN. \u003cstrong\u003e(V), \u003c/strong\u003eHeatmaps of down-regulated TCA-related genes in SP-MPE, based on RNA data (left) and protein data (right). \u003cstrong\u003e(Z), \u003c/strong\u003eThe copy numbers, transcript, and protein abundance of eight patients whose panel-seq results are available. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ns, P\u0026gt;0.05.\u003c/p\u003e","description":"","filename":"Figure301.png","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/dbb5fee98cbbf924671fe943.png"},{"id":95040442,"identity":"29d6f7cf-4d14-41e5-b05b-d82592f86b94","added_by":"auto","created_at":"2025-11-03 16:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":49300793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/ea47c9a7-422b-42b4-8a11-323c163056f5.pdf"},{"id":73280428,"identity":"5352bb88-6854-492f-943a-5f1f876cacf2","added_by":"auto","created_at":"2025-01-08 12:30:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/3f0d91cb0727260ca6a3c00a.docx"},{"id":73281394,"identity":"765df50b-6003-4ea4-b6e5-7b98aefd1315","added_by":"auto","created_at":"2025-01-08 12:38:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18381,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/5afcc77c6e1fb748fb546ae5.docx"},{"id":73280408,"identity":"44ef0d65-cb00-4a84-b3ad-01aeb000ac70","added_by":"auto","created_at":"2025-01-08 12:30:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18254,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/b7bf9eef89134a248060c33d.docx"},{"id":73281419,"identity":"e7a9fd57-5ffd-4501-ae17-f1f9afd7d247","added_by":"auto","created_at":"2025-01-08 12:38:54","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":530614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1. GSEA analysis of spinal ependymal tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA plots for representative gene sets of SP-EPN 1-4 (A) SP-SE (B) and SP-MPE (C). The y-axis represents the enrichment score (ES) and the x-axis represents genes (vertical black lines) present in the gene sets. NES, normalized enrichment score; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"sFigure1GESA.png","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/2bde541218260f2df91ad8f0.png"},{"id":73280416,"identity":"53b6d5e8-bbc6-4cbc-af1a-0da56755cb09","added_by":"auto","created_at":"2025-01-08 12:30:53","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":261418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2. Balloon plot of representative DEGs in transcriptome.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe balloon plot quantifies the top different genes across three subtypes.\u003c/p\u003e","description":"","filename":"sFigure2Ballonplots.png","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/0287547f63dca38a6b75922f.png"},{"id":73280414,"identity":"27aa2920-b39c-457c-a6eb-06d1166f2eea","added_by":"auto","created_at":"2025-01-08 12:30:53","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":446592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3.\u003c/strong\u003e Schematic of fusion genes KIAA0319L-PARK7 (A) and MAP7-MDFI (B) that were respectively identified in SP-EPN6 and SP-SE1samples.\u003c/p\u003e","description":"","filename":"sFigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5761045/v1/0d92ea1c7b65c9da3a511919.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Omics Analysis Delineates Molecular Signatures of Spinal Ependymal Tumor","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinal ependymal tumors account for approximately 60% of intramedullary spinal cord tumors, with an annual incidence ranging from 0.22 to 0.32 cases per 100,000 individuals.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Although surgical resection is the primary treatment, many patients suffer post-operative neurological impairments that affect sensory and motor functions.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Additionally, 50\u0026ndash;70% of patients who undergo subtotal resection experience local recurrence or distant metastasis.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCurrent clinical classifications divide these tumors into three subtypes: SP-EPN, SP-MPE, and SP-SE primarily based on histopathological features.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e However, such classifications fail to capture the molecular diversity of these tumors.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e This underscores the need for molecular profiling to refine tumor classification and improve therapeutic strategies.\u003c/p\u003e \u003cp\u003eTo establish a molecular classification, a pioneer study profiled DNA methylation across 500 central nervous system (CNS) ependymal tumors, encompassing all anatomical compartments of the CNS, including the spine, posterior fossa, and supratentorial regions.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Nine distinct molecular subgroups were identified in this study. Among these, specific genetic alterations were characteristic of spinal ependymal tumor subgroups: 6q deletions were associated with SP-SE, chromosomal instability was recurrent in SP-MPE, and NF2 mutations were observed in SP-EPN.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Neyazi \u003cem\u003eet al.\u003c/em\u003e refined the classification of SP-EPN by integrating transcriptomic, DNA methylation, and clinical data, and identified two molecular subtypes based on the presence or absence of NF2 mutations.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Further investigations have identified MYCN amplification as a novel molecular subgroup of SP-EPN.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Bockmayr \u003cem\u003eet al.\u003c/em\u003e elucidated SP-MPE's morphological and clinical heterogeneity by distinguishing between two molecularly distinct subtypes.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, the above findings heavily rely on the DNA methylation profiling for spinal ependymal tumor classification. Comprehensive studies integrating WGS, transcriptomics, and proteomics across all subtypes remain limited.\u003c/p\u003e \u003cp\u003eTo deepen our understanding of the molecular patterns of different spinal ependymal tumor subtypes, we conducted multi-omics analyses of a cohort of 25 spinal ependymal tumors encompassing all three subtypes. This comprehensive approach unveiled the molecular characteristics underlying the heterogeneity among subtypes, providing a valuable resource for the rare cancer research community.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eClinical Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 25 patients provided informed consent for the use of their tumor tissues in this study. Tumor samples were collected during routine clinical treatment. Fresh tumor tissues from 25 patients were snap-frozen in liquid nitrogen within 5 minutes of surgical resection. Relevant clinical data were extracted from patients\u0026apos; electronic medical records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistology and Immunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo neuropathologists independently evaluated the tumor samples according to H\u0026amp;E-staining and categorized them into histological subtypes: SP-EPN, SP-MPE, SP-SE, or anaplastic SP-EPN. The hospital pathology department performed immunohistochemical analysis of FFPE samples during hospitalization. The primary antibodies used for immunohistochemical analysis were GFAP, Ki-67, EMA, H3K27me3, Olig-2, S100, and SOX10. Staining was performed using the SuperPicture\u0026trade; 3\u003csup\u003erd\u003c/sup\u003e Gen Immunohistochemistry Detection Kit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGS Library Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from 10 mg of fresh-frozen tumor tissue using a DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Cat. No. 69504) according to the manufacturer\u0026apos;s instructions. WGS libraries were prepared using the MGIEasy PCR-Free DNA Library Prep Set and sequenced using the BGI DNBSEQ-T7 platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-Seq Library Construction and Fusion Transcript Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was isolated from 2 mg of frozen tumor tissues using Trizol reagent (Thermo Fisher Scientific, Cat. No. 15596018). RNA-seq libraries were constructed using the NEB Next Ultra\u0026trade; RNA Library Prep Kit (NEB, Cat. No. E7490) and sequenced using the NovaSeq 6000 System platform. Fusion transcript detection was performed using the Arriba algorithm.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMass Spectrometry (MS-spec)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 10 mg of each tumor sample was homogenized in 500 \u0026mu;L of lysis buffer (100 mM TEAB pH 8.0, 1% SDS, supplemented with 1\u0026times; protease inhibitor cocktail) using a Precellys Evolution tissue homogenizer with dry ice. Lysates were centrifuged at 20,000 \u0026times; g for 30 min at 4\u0026deg;C and the supernatants were collected. Protein concentrations were measured using a BCA assay. For downstream MS-spec analysis, 100 \u0026micro;g of protein was reduced with 5 mM TCEP at 55\u0026deg;C for 1 hour and alkylated with 10 mM IAA at room temperature for 30 min in the dark. Proteins were precipitated using a standard methanol/chloroform protocol, followed by digestion with 4 \u0026micro;g sequencing-grade modified trypsin (Promega, Cat. No. V5117) at 37\u0026deg;C for 12 h. The resulting peptides were acidified with TFA to pH ~3, desalted using C18 Zip-Tips, and dried using Speed-Vac. The desalted peptides were subjected to LC-MS/MS analysis using an Orbitrap Exploris\u0026trade; 480 mass spectrometer (Thermo Scientific) coupled to a Proxeon Easy-nLC 1200 system (Thermo Scientific). Data were processed using the SEQUEST HT search engine in the Thermo Proteome Discoverer software (v2.4.1.15) against the UniProt human reference proteome database.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence Staining of FFPE Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FFPE sample slides were deparaffinized and rehydrated by sequential immersion in BioDewax and Clear Solution (Servicebio), 50% BioDewax and Clear Solution mixed with 50% ethanol, 100% ethanol, 95% ethanol, 70% ethanol, and 50% ethanol for 10 min each, and deionized water for 30 min. Antigen retrieval was performed by heating slides in 10 mM sodium citrate buffer (pH 6.0) at 95\u0026deg;C for 10 min. The slides were washed twice with permeabilization buffer (1% donkey serum and 0.4% Triton X-100 in PBS) and blocked with 5% donkey serum in PBS for 30 min at room temperature. Primary antibodies diluted 200 folds in 1% donkey serum in PBS were incubated with slides for 1-2 hours at room temperature. Primary antibodies used were rabbit anti-AQP4 (Proteintech, 16473-1-AP), rabbit anti-GFAP (Proteintech, 16825-1-AP), rabbit anti-TCTN1 (Proteintech, 15004-1-AP), rabbit anti-MKS1 (Proteintech, 16206-1-AP), and rabbit anti-ISG15 (Proteintech, 15981-1-AP). Alexa Fluor 488-conjugated secondary antibodies (Life Technologies) were added to the slides at a dilution of 1/1000 and incubated for 1 h at room temperature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted Gene Panel Sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTargeted gene panel sequencing was performed on FFPE samples from 8 out of 25 patients. In brief, this panel-seq analyzed 86 genes for point mutations, insertions, and deletions; 28 genes for copy number variations; 44 gene exons; and 88 rearrangement events, focusing on the genomic alternations relevant to brain tumors. Detailed results are provided in the Supplementary Materials section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw WGS and RNA-seq data were deposited in the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsa-human/) under the accession number HRA008375. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD056112.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eThe basic information of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003espinal ependymal tumor\u0026rsquo;s\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the molecular landscape of spinal ependymal tumors in China, we conducted a comprehensive analysis that included WGS (n=25), RNA sequencing (RNA-seq) (n=25), targeted sequencing of brain tumor molecualar markers (panel-seq) (n=8), and proteomics analysis (n=21) across the three distinct subtypes of spinal ependymal tumors (Figure 1A). The clinicopathological and immunohistochemical characteristics of patients and their tumors are summarized in Tables S1-S3. Specifically, the cohort comprised 60% male and 40% female patients with a median age of 37 (Table S1). The histopathological classification of the cohort included 4 grade-2 and 4 grade-3 SP-EPN, seven grade-1 SP-SE, and ten grade-2 SP-MPE cases. All samples, except SP-EPN_7, were primary spinal ependymal tumors (Table S2).\u0026nbsp;As shown in Table S3, Ki-67 levels vary significantly. Grade-3 SP-EPN exhibited a higher Ki-67 proliferation index compared to grade-2 ones. SP-SE and SP-MPE cases show a broader range, with some SP-MPE cases exhibiting higher Ki-67 levels (up to 15%). Variability in the expression of EMA and Olig-2 suggests the need for integrative analysis to refine molecular and clinical classification. This cohort\u0026nbsp;contained all known ependymal tumors of the spinal cord, and all were from the Han Chinese population, which is distinct from previous spinal ependymal tumor multi-omics studies.\u003csup\u003e6,7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe CNAs patterns in Spinal Ependymal Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain a comprehensive view of copy number alterations (CNAs) across all chromosomes, we performed WGS and used CNVpytor\u003csup\u003e12\u003c/sup\u003e to analyze CNAs in different spinal ependymal tumor subtypes.\u003c/p\u003e\n\u003cp\u003eIn SP-SE, CNAs display a relatively balanced and random distribution, with no discernible focal amplifications or deletion hotspot regions (Figure 1B left). Although a 6q deletion has been reported in brain SE\u003csup\u003e13\u003c/sup\u003e, our analysis of seven SP-SE samples did not identify a similar trend, underscoring the variability and inconsistency of CNAs between the brain and spinal SE. By contrast, SP-MPE and SP-EPN displayed significantly higher CNAs. The most frequent event in SP-EPN tumors was the loss of 22 (Figure 1B middle). Additionally, we observed copy number gains on chromosomes 7, 9, 12, and 15q in SP-EPN and on chromosomes 4, 7, 9, 16, 17, and 18 in SP-MPE (Figure 1B right). The CNAs in SP-SE were significantly fewer than those in SP-EPN and SP-MPE, consistent with the distinctions between benign and malignant tumors as classified by the WHO.\u0026nbsp;These findings demonstrate that spinal ependymal tumors across these three molecular subgroups are genetically distinct.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe landscape of r\u003c/strong\u003e\u003cstrong\u003eecurrent\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;somatic mutations in Spinal Ependymal Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we identified somatic mutations using MutSigCV algorithm\u003csup\u003e14\u003c/sup\u003e and compared them against the Catalogue of Somatic Mutations in Cancer (COSMIC) and dbSNP databases to filter SNPs and genetic variations prevalent in healthy people.\u003csup\u003e15\u003c/sup\u003e Nine types of protein-coding sequence variations were detected. The top four protein-coding sequence variations were missense mutations, followed by frameshift deletions, in-frame insertions, and in-frame deletions (Figure 1C). The most common point mutations observed were T\u0026gt;C, C\u0026gt;T, and T\u0026gt;A, with frequencies of 28.8%, 28.0%, and 11.7%, respectively (Figure 1D). These point mutations did not show a preferential pattern toward specific subtypes of ependymal tumors (Figure 1E, lower panel). The protein-coding sequence variation events across 25 samples ranged from 96 to 202 (Figure 1E, upper panel). The top ten high-frequency protein-coding sequence variations occurred in FCGBP, CTBP2, ANKRD36, ZNF83, LILRB1, PABPC1, SYN2, ERAP2, and HLA-DRB1 (Figure 1E, middle panel). Previous studies have shown that among the 21 SP-EPN analyzed, 19 exhibited a loss of 22q, where the NF2 gene is located.\u003csup\u003e6\u003c/sup\u003e However, in our cohort, only one out of eight SP-EPNs carried an NF2 mutation (Figure 1E, highlighted in blue), indicating that the mutation frequency of this gene is correlated with the genetic background. Notably, HLA-DRB1 and FCGBP were two immune-related genes (Figure 1E, highlighted in red), the mutations of which were not previously identified in spinal ependymal tumors. HLA-DRB1 plays a central role in the immune system by presenting peptides derived from extracellular proteins\u003csup\u003e16\u003c/sup\u003e, and FCGBP is an immune-related gene.\u003csup\u003e17\u003c/sup\u003e Specifically, in the exon region of chr19:39906139-39906232 in FCGBP, missense mutations occurred with high frequency (Figure 1F). Mutations in these two genes may contribute to the ability of spinal ependymal tumors to evade immune surveillance. Moreover, in the chr2:97,241,313-97,241,339 exon region of ANKRD36, we identified a nonsense mutation at chr2:97,241,317 (T\u0026gt;G) (Figure 1G). Additionally, synapsin II (SYN2), a gene involved in neurotransmitter release regulation, was frequently affected by a 12-nucleotide insertion, resulting in the addition of PAPQ amino acids (Figure 1H).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSpinal Ependymal Tumor subtypes exhibit distinct features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo construct a transcriptomic map of various spinal ependymal tumor subtypes, we performed RNA-seq and conducted pairwise comparisons between the tumor samples. Our analysis revealed distinct transcriptional profiles for the SP-SE and SP-MPE subtypes across different patients, suggesting a unique molecular signature for each tumor type (Figure 2A). In contrast, the transcriptional patterns of SP-EPN varied significantly according to the tumor grade. Specifically, grade-2 SP-EPN (SP-EPN_1/2/3/4) clustered together, while grade-3 SP-EPN_5 exhibited transcriptional similarities to SP-SE. The other three grade-3 SP-EPN (SP-EPN_6/7/8) showed a unique transcription pattern that differed from grade-2 EPN, suggesting that grade-3 SP-EPN is highly heterogeneous. These findings were confirmed by Principal Component Analysis (PCA) results, which aligned with those observed in the RNA-seq heatmap (Figure 2B).\u003c/p\u003e\n\u003cp\u003eClinical panel-seq data further suggested that MYCN amplification is present in grade-2 SP-EPN (SP-EPN_3/4) but absent in SP-EPN_1/2 (Table S2). Despite this differential amplification, the transcriptional profiles of SP-EPN_1/2 and SP-EPN_3/4 are remarkably similar. This observation implies that MYCN amplification has a minimal impact on the overall transcriptomic landscape of SP-EPN, a point we discuss later in Figure 4Z.\u003c/p\u003e\n\u003cp\u003eTo gain deeper insights, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of differentially expressed genes (DEGs), as highlighted by the black square in Figure 2A. GO analysis revealed that the upregulated DEGs in SP-EPN_1/2/3/4 were primarily enriched in terms related to primary cilia, including axoneme assembly, cilium, and MKS complex (Figure 2C).\u0026nbsp;Primary cilia play crucial roles in signaling pathways during spinal development, such as Hh signaling.\u003csup\u003e19,20\u003c/sup\u003e Aberrant activation of Hh signaling via primary cilia has been implicated in the development of supratentorial ependymomas.\u003csup\u003e21\u003c/sup\u003e Consistent with this, Gene Set Enrichment Analysis (GSEA) revealed significant enrichment of the Hh signaling pathway in grade-2 SP-EPN_1/2/3/4 (Supplementary Figure. 1A). These findings suggest that the upregulation of cilia-related genes in grade-2 SP-EPN may contribute to tumor development via activation of Hh signaling, similar to what has been observed in supratentorial ependymomas.\u003c/p\u003e\n\u003cp\u003eIn contrast, SP-SE tumors showed significant enrichment of immune-related response pathways, such as the response to interferon-related pathways and complement component C1 complex, \u0026nbsp;as indicated by both GO and GSEA analyses (Figure 2D, Supplementary Figure. 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe response to the interferon-beta pathway suggests that SP-SE tumors may actively trigger an immune response, particularly in their interaction with the innate immune system. Additionally, the enrichment of the complement component C1 complex points to the activation of the complement system, a key component of innate immunity that helps clear pathogens and modulate inflammation. This suggests that immune-related mechanisms may either support tumor progression in SP-SE or may reflect an attempt by the immune system to control tumor growth.\u003c/p\u003e\n\u003cp\u003eInterestingly, SP-MPE tumors exhibited preferential enrichment in mitochondrial and cellular metabolic pathways (Figure 2E, Supplementary Figure. 1C). These include processes such as positive regulation of mitochondrial autophagy, mitochondrial ATP synthesis coupled to electron transport, and the tricarboxylic acid (TCA) cycle. These findings suggest that SP-MPE tumors have distinct metabolic characteristics that may be central to their pathophysiology. Notably, this metabolic profile aligns with features of the Warburg effect previously described in other malignancies and could support the increased energy demands of SP-MPE tumors, potentially contributing to their progression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify potential biomarkers for the three distinct types of spinal ependymal tumors, we selected the genes that exhibited the most significant fold changes in transcript level. Our analysis revealed that HOXB13, PRAC1, and SPARCL1 were preferentially expressed in SP-MPE, whereas APOE and LARP3 were highly expressed in SP-SE. BANF, C9orf3, CAPS2, CALR3, C4orf50, CAPNS2, and BCAR1 showed a preferential expression in grade-2 SP-EPN (Supplementary Figure. 2).\u003c/p\u003e\n\u003cp\u003eThe balloon plot quantifies the top different genes across three subtypes.\u003c/p\u003e\n\u003cp\u003eTo identify potential onco-fusions in spinal ependymal tumors, we used Arriba to detect fusion transcripts. As a result, we identified CTBS-GNG5 fusion genes in all SP-MPE samples (Supplementary File, Fusion gene). However, this fusion gene was also found in normal tissues of various types\u003csup\u003e22\u003c/sup\u003e, suggesting that it may not be involved in the development of SP-MPE. Additionally, we identified KIAA0319L-PARK7 fusion\u0026nbsp;in SP-EPN_6 and MAP7-MDFI fusion in SP-SE_1 (Supplementary Figure. 3), although the functions of these two fusion genes remain unclear. We speculated that the fusion of KIAA0319L and PARK7 results in a protein that integrates the endocytosis capabilities of KIAA0319L\u003csup\u003e23\u003c/sup\u003e with PARK7, an enzyme mutated in hereditary Parkinson\u0026apos;s disease,\u003csup\u003e24\u003c/sup\u003e potentially influencing SP-EPN_6 cellular responses to oxidative stress (Supplementary Figure. 3A). The breakpoint of MAP7-MDFI was located in the intron between exons 5 and 6 of MAP7 and the intron between exons 4 and 5 of MDFI (Supplementary Figure. 3B). MAP7 is a microtubule-binding protein\u003csup\u003e25\u003c/sup\u003e and MDFI is a transcription factor that negatively regulates other myogenic family proteins\u003csup\u003e26\u003c/sup\u003e. Thus, MAP7-MDFI fusion could potentially influence cellular architecture and differentiation processes in SP-SE_1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;profiling of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003espinal ependymal tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the proteomic landscape of spinal ependymal tumors, we performed proteomic analyses on 21 samples, including 6 SP-SE, 8 SP-EPN, and 7 SP-MPE specimens. A total of 8,689 proteins were identified (Figure 3A). PCA revealed that SP-EPN displayed characteristics intermediate between SP-SE and SP-MPE (Figure 3B).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentially expressed proteins, highlighted within the black square in Figure 3A, were subjected to GO analysis. In grade-2 SP-EPN subtypes (SP-EPN_1/2/3/4), upregulated proteins were enriched in cilia-related biological processes, including cilium assembly, cilium movement, and the MKS complex (Figure 3C). In contrast, SP-MPE tumors were characterized by pronounced expression and translation of mitochondrial proteins, indicating a distinct metabolic profile unique to this subtype (Figure 3D), aligning with the observations from RNA-seq analysis (Figure 2).\u003c/p\u003e\n\u003cp\u003eFor SP-SE tumors, GO analysis revealed significant enrichment in metabolic processes and extracellular vesicle-related pathways (Figure 3E). This suggests that SP-SE tumors may harbor a unique tumor microenvironment modulated by extracellular vesicles. We hypothesize that these vesicles contribute to inflammatory factor secretion, as previously indicated by transcriptomic analyses of SP-SE tumors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated Analysis of Spinal Ependymal Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe combined the RNA-seq and proteomics results to characterize the three tumor subtypes. A total of 461 candidates exhibited consistent upregulation in both the transcriptomic and proteomic datasets, with 234, 134, and 93 candidates specifically upregulated in SP-SE, SP-EPN, and SP-MPE, respectively (Figure 4A, K, S). Grade-3 SP-EPN samples were excluded because of their heterogeneous transcriptomic and proteomic patterns.\u003c/p\u003e\n\u003cp\u003eIn SP-SE, upregulated candidates were enriched in glial cell differentiation pathways, in addition to the previously identified extracellular vesicle pathways (Figure 4B). Among these, GFAP and ISG15 drew particular attention (Figure 4C and G). GFAP, a marker of glial differentiation, is associated with less malignant tumors and typical features of brain cell aging.\u003csup\u003e27,28\u003c/sup\u003e ISG15, an interferon-stimulated gene involved in immune pathways, plays an immunoregulatory role in tumors and serves as a potential biomarker for cancer progression.\u003csup\u003e29,30\u003c/sup\u003e Although GFAP was ubiquitously expressed in 84% (21/25) of ependymoma samples across all subtypes (Table S3), IF staining of FFPE slices confirmed preferential expression of GFAP (Figure 4D vs. E/F) and ISG15 (Figure 4H vs. I/G) in SP-SE.\u003c/p\u003e\n\u003cp\u003eIn grade-2 SP-EPN, cilium-related genes were prominently expressed (Figure 4L). Among these, MKS1 and TCTN1, key components of the MKS complex, showed strong positive correlations between RNA and protein levels (Pearson\u0026rsquo;s R = 0.68488 and R = 0.73098, respectively). Both genes demonstrated significantly higher expression in grade-2 SP-EPN compared to other subtypes (Figure 4M). This finding was further validated using an independent SP-EPN cohort, where MKS complex-related genes, including MKS1, TCTN1, and TMEM231, were notably upregulated in grade-2 SP-EPN compared to grade-3 ones (Figure 4N). Immunofluorescence staining confirmed these observations, showing high expression of MKS1 (Figure 4O vs. P) and TCTN1 (Figure 4Q vs. R) in grade-2 SP-EPN, while their expression was markedly reduced in grade 3 SP-EPN.\u003c/p\u003e\n\u003cp\u003eThe MKS complex, located in the primary ciliary transition zone, mediates essential developmental processes including ciliary formation and epithelial morphogenesis.\u003csup\u003e31,32\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLow expression of TCTN1 expression the ciliary transition zone, compromises barrier function and impairs Hh signaling.\u003csup\u003e33,34\u003c/sup\u003e This finding partially explained the enrichment of Hh signaling observed in our RNA-seq results.\u003c/p\u003e\n\u003cp\u003eIn SP-MPE, upregulated candidates were enriched in mitochondrial translation pathways (Figure 4T). Using the STRING database, we constructed a PPI network of upregulated DEGs, highlighting mitochondrial ribosomal proteins (MRPL58, MRPL15, MRPL17, MRPL37, MRPL16, and MRPS23) as central nodes (yellow dots, Figure 4T). We further analyzed genes downregulated in SP-MPE and focused on Aquaporin-4 (AQP4). Its downregulation in SP-MPE was validated by IF staining (Figure 4W vs. X/Y). Previous studies suggested that AQP4 participated in CNS water balances.\u003csup\u003e35\u003c/sup\u003e T SP-MPE has been reported to present with cerebrospinal fluid dissemination or \u0026quot;drop metastases\u0026quot; at the time of diagnosis. \u003csup\u003e36\u003c/sup\u003e We speculated that AQP4 depletion in SP-MPE may lead to cerebrospinal fluid imbalance, potentially contributing to the spread of SP-MPE to other locations via cerebrospinal fluid imbalance. Endocytosis-related proteins (HIP1R, BIN1, FNBP1, DNM3, MYO6, NES and STON2) were downregulated in SP-MPE (Figure 4V). Notably, HIP1R was almost absent in the SP-MPE group.\u003c/p\u003e\n\u003cp\u003eAmong the 25 patients, eight underwent targeted sequencing based on brain glioma indices. However, the copy number amplifications detected in genes such as NTRK2, NTRK3, KRAS, and NOTCH1 did not correlate with their RNA and protein expression levels. This suggests that gene amplification may not reliably represent transcriptional and proteomic changes in spinal ependymal tumors (Figure 4Z).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSpinal ependymal tumors are highly heterogeneous, and understanding their molecular characteristics provides insight into their clinical behavior and potential therapeutic strategies.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSP-EPN: Abnormal Ciliary Signaling and Therapeutic Implications\u003c/h2\u003e \u003cp\u003eIn grade-2 SP-EPN tumors, we observed significant enrichment of ciliary signaling pathways, particularly those involving the MKS complex and Hh signaling. Dysregulation of ciliary components, such as TCTN1 and MKS1 may contribute to tumorigenesis through abnormal Hh pathway activation.\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e These findings suggest the potential therapeutic utility of targeting the Hh signaling pathway. Drugs such as Vismodegib and Sonidegib, which inhibit Hh signaling, could be explored as adjunctive treatments for this subtype.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e In contrast, grade-3 SP-EPN tumors exhibit notable intertumor heterogeneity, indicating that additional clinical samples and further stratification are necessary to refine their molecular profiling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSP-MPE: Mitochondrial Metabolism and the Warburg Effect\u003c/h2\u003e \u003cp\u003eSP-MPE tumors demonstrate strong enrichment of mitochondrial pathways, including the TCA cycle, which distinguishes them from the other two subtypes. This is consistent with previous reports on the Warburg effect in SP-MPE.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Thus, targeting SP-MPE metabolism through inhibitors of TCA metabolism, such as glucose analog 2-deoxy-D-glucose\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, could be a promising therapeutic strategy for SP-MPE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSP-SE: Immune-Related Pathways and Extracellular Vesicles\u003c/h2\u003e \u003cp\u003eSP-SE tumors were enriched in immune-related pathways, particularly interferon signaling and extracellular vesicle-related genes. High expression of interferon-stimulated genes and extracellular vesicles indicates a unique tumor microenvironment characterized by active immune modulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive characterization of the molecular heterogeneity of spinal ependymal tumors. Grade-2 SP-EPN tumors with enriched ciliary signaling may benefit from targeted therapies such as Hh pathway inhibitors, while the metabolic reprogramming of SP-MPE suggests therapeutic opportunities for disrupting mitochondrial or Warburg pathway metabolism. Future studies with larger cohorts are essential to validate these findings and further refine subtype-specific therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of Beijing Tiantan Hospital, Capital Medical University (KY2022-089-02), and was conducted in accordance with the Declaration of Helsinki. Patients were consented to the informed consent process and consented to publish.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Beijing Municipal Health Commission (11000023T000002044300-2).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eW.L. wrote the main manuscript text and conducted the experiments. C.N. and X.G. prepared the figures and analyzed the data. B.W. and Y.Z. prepared the materials for the experiments. C.W., Y.L. and G.Z. collected the clinical data. Y.W., X.W., D.L. and W.J. provided experimental design and guidance. All authors have reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eRaw WGS and RNA-seq data were deposited in the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsa-human/) under the accession number HRA008375. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD056112.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOstrom, Q.T., Cioffi, G., Gittleman, H., Patil, N., Waite, K., Kruchko, C., and Barnholtz-Sloan, J.S. (2019). 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Cancer Lett \u003cem\u003e355\u003c/em\u003e, 176-183. 10.1016/j.canlet.2014.09.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cellular-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ceon","sideBox":"Learn more about [Cellular Oncology](http://link.springer.com/journal/13402)","snPcode":"13402","submissionUrl":"https://submission.nature.com/new-submission/13402/3","title":"Cellular Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Spinal, Ependymoma, Myxopapillary ependymoma, Subependymoma, Multi-omics","lastPublishedDoi":"10.21203/rs.3.rs-5761045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5761045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpinal ependymal tumors are a diverse group of neoplasms encompassing three subtypes: spinal ependymoma (SP-EPN), spinal myxopapillary ependymoma (SP-MPE), and spinal subependymoma (SP-SE). However, the molecular differences among these subtypes remain largely unknown. Here, we identified the distinct molecular characteristics of each subtype through a multi-omics analysis. In grade-2 SP-EPN, abnormal enrichment of ciliary signaling, particularly involving the MKS complex and Hedgehog (Hh) pathway, was evident, suggesting potential therapeutic targets. SP-MPE exhibited significant dysregulation of mitochondrial metabolism, reflecting a metabolic profile aligned with the Warburg effect. SP-SE tumors showed enhanced activity of immune-related pathways, including interferon signaling and extracellular vesicle dynamics, suggesting a distinct tumor microenvironment. This study underscores the molecular diversity of spinal ependymal tumors, offering novel insights into their pathobiology, and highlighting promising therapeutic avenues tailored to each subtype.\u003c/p\u003e","manuscriptTitle":"Multi-Omics Analysis Delineates Molecular Signatures of Spinal Ependymal Tumor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 12:30:46","doi":"10.21203/rs.3.rs-5761045/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T11:00:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T19:51:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T15:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13842084685510101956191004956307280976","date":"2025-03-31T06:21:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T23:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51096128245640318858437551742532171194","date":"2025-03-29T22:26:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259657676930498492590718220122337522933","date":"2025-03-27T11:19:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T09:34:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-06T13:04:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-06T13:03:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cellular Oncology","date":"2025-01-04T02:52:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cellular-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ceon","sideBox":"Learn more about [Cellular Oncology](http://link.springer.com/journal/13402)","snPcode":"13402","submissionUrl":"https://submission.nature.com/new-submission/13402/3","title":"Cellular Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0d5a62a3-ae53-4c1c-a87d-5b3e0876aaaa","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:04:00+00:00","versionOfRecord":{"articleIdentity":"rs-5761045","link":"https://doi.org/10.1007/s13402-025-01122-0","journal":{"identity":"cellular-oncology","isVorOnly":false,"title":"Cellular Oncology"},"publishedOn":"2025-10-29 15:58:52","publishedOnDateReadable":"October 29th, 2025"},"versionCreatedAt":"2025-01-08 12:30:46","video":"","vorDoi":"10.1007/s13402-025-01122-0","vorDoiUrl":"https://doi.org/10.1007/s13402-025-01122-0","workflowStages":[]},"version":"v1","identity":"rs-5761045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5761045","identity":"rs-5761045","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}