Multiomics Integration of Serum Proteome and Autoantibody Profiles Reveals Diagnostic and Prognostic Biomarkers in Glioma

Multiomics Integration of Serum Proteome and Autoantibody Profiles Reveals Diagnostic and Prognostic Biomarkers in Glioma | 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 Multiomics Integration of Serum Proteome and Autoantibody Profiles Reveals Diagnostic and Prognostic Biomarkers in Glioma Wei Meng, Jian Duan, Chengcheng Guo, Jiang Xu, Suyue Zheng, Haibin Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7933102/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Journal of Neuro-Oncology → Version 1 posted 14 You are reading this latest preprint version Abstract Methods: Tandem mass tag (TMT)-based quantitative proteomics was performed on sera from 30 GBM patients and 30 matched healthy controls (HCs) to identify differentially expressed proteins (DEPs). Candidate tumor-associated antigens were used to design a custom peptide microarray assessing IgG/IgM autoantibodies in the discovery (n = 55 GBM patients, 30 HCs) and validation (n = 32 GBM patients, 29 HCs) cohorts. Prognostic value was analyzed via Kaplan–Meier and Cox regression, and findings were integrated with TCGA transcriptomics and single-cell RNA sequencing data to determine immune associations and cellular origins. Results: Proteomics identified 877 proteins, with DEPs enriched in extracellular matrix remodeling, complement/coagulation cascades, and metabolism/oxidative stress pathways. A three-IgM panel (anti-p-APOE-1, anti-p-P53-1, and anti-p-SAA4-1) showed high diagnostic performance (AUC = 0.96; 0.85 validation). IgM-p-SAA4-1 positivity was correlated with longer survival, whereas elevated IgM-p-IL-1β-2 levels predicted poor prognosis and adverse molecular subtypes (IDH1/ATRX wild-type, unmethylated MGMT). APOE and IL1B are expressed predominantly by tumor-associated macrophages, with divergent prognostic implications at the transcript level. Conclusion: Integrated proteomic–autoantibody profiling identified and validated a serum IgM panel with robust diagnostic accuracy and prognostic relevance in GBM. These biomarkers reflect interactions between humoral immunity, tumor gene expression, and the immune microenvironment, supporting their potential for clinical application in GBM detection and patient stratification. Glioma Proteomics Autoantibodies Diagnostic Biomarkers Prognostic Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Glioblastoma (GBM) is the most aggressive type of primary brain tumor and has profound molecular heterogeneity and a poor prognosis despite advances in surgery, radiotherapy, and chemotherapy[ 1 ]. The median survival remains only 14–16 months, and the 5-year survival rate is less than 7%[ 2 ], highlighting the urgent need for novel biomarkers to enable early detection, prognosis, and therapeutic interventions. In recent years, high-throughput omics technologies, particularly proteomics, have offered powerful tools to interrogate the complex biological landscape of cancer[ 3 ]. Comparative proteomics, with its capacity to comprehensively profile protein expression alterations in tumor tissues and biofluids, has emerged as a powerful tool for discovering and validating tumor-associated biomarkers[ 4 ]. Tandem mass tag (TMT)-based quantitative proteomics, in particular, enables precise and accurate quantification of protein abundance across multiple samples, providing a robust platform for identifying differentially expressed proteins (DEPs) that may serve as diagnostic or therapeutic targets [ 5 ]. Such analyses can reveal systemic changes associated with GBM and identify candidate circulating biomarkers. Furthermore, tumorigenesis can disrupt immune tolerance, leading to the generation of autoantibodies against tumor-associated antigens (TAAs)[ 6 ]. These autoantibodies, potentially detectable early in disease and stable in circulation, represent a promising class of biomarkers[ 7 ]. Exploring serum autoantibody signatures specifically targeting proteins altered in GBM could therefore yield valuable diagnostic and prognostic tools. In this study, we hypothesized that GBM induces distinct serum proteomic and autoantibody changes. We applied TMT-based proteomics to identify DEPs in the serum of GBM patients versus healthy controls and performed functional analyses to interpret their significance. On the basis of these results, we designed peptide microarrays to profile IgG/IgM autoantibodies and evaluated their diagnostic and prognostic utility in discovery and validation cohorts. We further integrated our serological findings with tumor gene expression, immune infiltration, and cellular origin data (via TCGA and single-cell RNA-seq data) to determine their biological relevance. Our aim is to identify robust, clinically actionable biomarkers for GBM and to elucidate the interplay between tumor biology, immunity, and patient outcome. Materials and methods Patient enrollment and serum sample collection This prospective study enrolled 117 patients with histopathologically confirmed glioma and 89 age- and sex-matched healthy controls at Sun Yat-sen University Cancer Center (Guangzhou, China) from September 2021 to February 2023. The glioma diagnoses followed the 2016 WHO CNS criteria. Clinical data—including demographic features, tumor grade, molecular markers (IDH1, TERT, ATRX, MGMT promoter methylation, and Ki-67), and treatment history—were collected. The exclusion criteria included prior immunotherapy or acute infection within one month before sampling. Written informed consent was obtained, and the protocol was approved by the Institutional Review Board. Peripheral blood was centrifuged at 3000×g and 4°C for 10 min; serum aliquots were stored at − 80°C. Quantitative Serum Proteomics (TMT Labeling) 2.1.1 Sample Preparation and Depletion Sera from 30 glioblastoma (GBM) patients and 30 healthy controls were collected. For proteomic profiling, samples within each group were randomly divided into three pools, with each pool containing equal protein amounts from 10 individual serum samples. This pooling strategy was applied to minimize individual-specific variation, increase the detection sensitivity for low-abundance proteins, and reduce the degree of technical variability and analysis cost. Each pool was treated as one biological replicate, resulting in three pooled replicates per group for downstream TMT-based LC–MS/MS analysis. High-abundance serum proteins were depleted via Human-14 Multiple Affinity Removal LC columns (Agilent Technologies) according to the manufacturer’s protocol. The low-abundance fractions were concentrated (5 kDa ultrafiltration, Millipore) and quantified via a BCA assay before digestion. 2.1.2 Protein digestion and TMT labeling Aliquots (200 µg) of protein were reduced with 100 mM DTT (100°C, 5 min), processed via filter-aided sample preparation (FASP) with 30 kDa filters, alkylated with 100 mM IAA (30 min, dark, RT), and sequentially washed with 8 M urea and 0.1 M TEAB. Proteins were digested overnight at 37°C with trypsin (1:50, w/w). Peptides (100 µg) per sample were labeled with TMT 6-plex reagents (Thermo Fisher Scientific) per the manufacturer’s protocol. 2.1.3 Peptide fractionation and LC‒MS/MS The TMT-labeled peptides were fractionated via high-pH reversed-phase HPLC (Agilent 1260 Infinity II) with a 0–40% acetonitrile gradient in 10 mM ammonium formate (pH 10.0). Fractions were pooled, dried, and analyzed on an Easy-nLC 1000 system coupled to a Q Exactive Plus Orbitrap (Thermo Fisher Scientific) using an Acclaim PepMap RSLC C18 column (50 µm × 15 cm, 300 nL/min) with 90–120 min linear gradients. MS1 scans: resolution 70,000 (m/z 350–1800), AGC 3e6, max IT 50 ms; top-10 precursors fragmented by HCD (NCE 30), MS2 resolution 35,000, AGC 1e5, max IT 45 ms, isolation width 2.0 m/z. 2.1.4 Data processing and normalization The raw data were processed in Proteome Discoverer v2.4 (Thermo Fisher Scientific) and searched against the UniProt human database (2018_09) with Mascot 2.6, FDR 0.5 and P < 0.05. GO and KEGG enrichment analyses were performed via gseapy (FDR < 0.05). 2.1.5 Custom peptide microarray Peptides (n = 32; 1–3 per antigen) were designed from linear B-cell epitopes of 15 candidate autoantigens predicted by ABCpred, UniProt BLAST, and SVMTriP, selecting the top consensus-scoring sequences. N-terminal–amidated peptides were conjugated to BSA via Sulfo-SMCC, verified by SDS‒PAGE, and printed in triplicate together with positive (human IgG/IgM) and negative (BSA) controls on PATH substrate slides via a Super Marathon microarrayer. Six landmark/system controls were included per slide: human IgG (0.5 mg/mL), human IgM (0.5 mg/mL), human IgG (0.1 mg/mL), human IgM (0.1 mg/mL), Cy3-labeled anti-human IgG, and Cy5-labeled anti-human IgM, following Li et al[ 8 ]. Each slide accommodated 12–14 serum samples. The serum (diluted 1:100 in PBS + 0.1% Tween-20 + 3% BSA) was incubated for 2 h at RT, followed by Cy3–anti-human IgG and Cy5–anti-human IgM detection. After washing, the slides were scanned on a GenePix 4000B scanner. Median foreground minus background fluorescence was extracted via GenePix Pro 6.0, averaged over triplicates, and normalized via intraslide positive controls and block-specific reference serum for interslide linear normalization, as described by Li et al[ 8 ]. The data from the IgG and IgM channels were normalized separately. The positive cutoff was determined by the Youden index from the ROC analysis. 2.1.6 Microarray Data Analysis and Biomarker Evaluation The raw data were quantile normalized after background correction. Differential signals were identified by Welch’s t test (|log2FC| >0.58, P < 0.05). Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis (area under the curve (AUC), sensitivity, specificity), with multimarker panels constructed via logistic regression. All the statistical analyses and data visualizations were performed via Python (version 3.12) and R (version 4.4). 2.1.7 Survival and clinicopathological correlation analysis Associations between autoantibody levels and clinicopathological parameters were assessed via chi-square tests or Fisher’s exact tests. Survival analyses included Kaplan‒Meier curves, log-rank tests, and univariate Cox regression (hazard ratios with 95% CI). 2.1.8 Transcriptomic and single-cell RNA sequencing analyses Bulk RNA-seq (TCGA-GBMLGG) data were analyzed for autoantigen gene expression and immune infiltration via CIBERSORT and Spearman correlation. Public scRNA-seq datasets (GSE131928_10X and GSE148842) were used to identify the cellular origins of autoantigen expression via standard pipelines (UMAP, cell-type annotation, violin plots). Survival analysis on the basis of gene expression employed median-based dichotomization, Kaplan‒Meier, log-rank, and Cox regression models. Results Serum Proteomic Alterations in GBM To characterize systemic protein changes in glioblastoma (GBM), we performed TMT-based quantitative proteomics on sera from 30 GBM patients and 30 matched healthy controls (HCs). Across all the samples, 877 proteins were confidently identified (≥ 1 unique peptide; Supplementary Table 1 ). By applying |log₂FC| >0.58 and P < 0.05, we detected a distinct set of differentially expressed proteins (DEPs) that clearly segregated GBM patients from HCs via hierarchical clustering (Fig. 1 A). A volcano plot illustrates the distribution of significantly upregulated and downregulated proteins (Fig. 1 B). GO enrichment analysis revealed that the DEPs were involved mainly in platelet degranulation, the regulation of hemostasis, and immune effector regulation, which is consistent with the pro-thrombotic, proinflammatory phenotype reported in GBM. In the cellular component category, these genes were enriched in the extracellular matrix (ECM) and endocytic vesicles, whereas the molecular function terms included protease and glycosaminoglycan binding, both of which are critical for ECM remodeling and growth factor signaling. KEGG pathway analysis highlighted ECM–receptor interactions and complement/coagulation cascades (Fig. 1 C). These alterations may facilitate tumor adhesion, invasion, angiogenesis, and immune escape, reflecting the interplay between tumor- and host-derived factors in GBM. PPI network analysis (STRING) identified hub proteins on the basis of degree centrality: HSP90AA1 and TPI1 had the highest connectivity, followed by SOD1, COL1A1, COL1A2, PRDX6, PFN1, LUM, COL3A1, and YWHAE (Fig. 1 D). These hubs clustered into three functional modules: (i) metabolism/oxidative stress (TPI1, SOD1, PRDX6, PFN1), (ii) ECM organization (COL1A1, COL1A2, COL3A1, LUM), and (iii) signaling/regulation (HSP90AA1, YWHAE), suggesting coordinated “metabolism–ECM–signaling” dysregulation in GBM. Identification of Differential Serum Autoantibodies against Glioma-Associated Antigens From the differentially expressed proteins (DEPs) identified via proteomic profiling, fifteen target proteins were selected on the basis of their biological relevance and functional significance. Linear B-cell epitopes within these proteins were predicted via three independent bioinformatics tools—ABCpred, UniProt BLAST, and SVMTriP. Peptide sequences with the highest consensus scores across all three platforms were selected, yielding 1–3 peptides per protein and a total of 32 peptides ( Supplementary Table 2 ). Each peptide was printed in triplicate on a custom peptide microarray to assess its technical reproducibility in the same serum samples. Serum autoantibody profiles were analyzed in two independent cohorts: a discovery cohort (55 GBM patients and 30 healthy controls) and a validation cohort (32 GBM patients and 29 healthy controls). The cohort baseline characteristics are summarized in Table 1 and detailed in Supplementary Tables 3–4 . Hierarchical clustering of the IgG and IgM reactivity patterns revealed clear separation between the GBM and control samples (Fig. 2 B). Applying the criteria |log₂FC| >0.58 and P < 0.05, we identified 18 significantly altered autoantibodies (15 upregulated, 3 downregulated) in GBM (Fig. 2 C), indicating a disease-specific humoral immune response. Table 1 Demographic characteristics of the glioma and healthy control groups. Characteristic Experimental set Validation set Statistical Analysis Glioma (n = 55) Healthy (n = 30) Glioma (n = 32) Healthy (n = 29) Age (years) Mean ± SD 46.2 ± 13.8 50.5 ± 17.9 44.1 ±10.7 45.6 ±11.9 Mann‒Whitney U test p value p = 0.11 p = 0.42 Sex, n (%) Male 30 (54.5) 15 (50.0) 22 (68.8) 17 (58.6) Pearson's χ² test Female 25 (45.5) 15 (50.0) 10 (31.2) 12 (41.4) p value p = 0.51 p = 0.21 Data are presented as the mean ± standard deviation (SD) or number (percentage), as appropriate. Differences in age were assessed via the Mann‒Whitney U test; differences in sex distribution were assessed via Pearson’s χ² test. P < 0.05 was considered statistically significant. Diagnostic Performance of a Serum Autoantibody Panel To evaluate the diagnostic utility of the candidate autoantibodies, we examined their performance in the discovery cohort and subsequently confirmed the results in the validation cohort ( Supplementary Table 4 ). All samples were assayed in technical triplicates to ensure measurement reliability. Unsupervised clustering consistently distinguished GBM patients from controls in both cohorts (Fig. 4 A). Three IgM autoantibodies—IgM-p-APOE-1, IgM-p-P53-1, and IgM-p-SAA4-1—had significantly higher serum levels in GBM patients than in controls (all P < 0.05; Fig. 4 B). In the validation cohort, the individual AUC values for IgM-p-APOE-1, IgM-p-P53-1, and IgM-p-SAA4-1 were 0.88, 0.68, and 0.61, respectively (Fig. 4 C–E). When combined, the three-marker panel achieved an AUC of 0.85, with 81% sensitivity and 76% specificity (Fig. 4 F). These results confirm the high reproducibility and robust diagnostic performance of the IgM panel across independent cohorts, supporting its potential clinical utility for GBM detection. Prognostic and clinicopathological associations of serum autoantibodies We next examined associations between key autoantibodies and clinical outcomes. Kaplan–Meier analysis revealed that IgM-p-SAA4-1 positivity was significantly associated with prolonged overall survival ( P = 0.026; Fig. 5 A), whereas other autoantibodies did not reach statistical significance ( P > 0.05). Higher IgM-p-IL-1β-2 levels were correlated with wild-type IDH1 ( P = 0.035), wild-type ATRX ( P = 0.022), and an unmethylated MGMT promoter ( P = 0.0071), which are molecular features typically linked to poor prognosis (Fig. 5 B). IgM-p-APOE-1 tended to be associated with favorable subtypes (IDH1 mutation, MGMT methylation; P < 0.1). Univariate Cox regression revealed that high IgM-p-IL-1β-2 levels were associated with shorter survival (HR = 1.36, P = 0.031), whereas high IgM-p-P53-1 levels were associated with improved outcomes ( P = 0.062; Fig. 5 C). These findings suggest that serum autoantibody profiles can provide both diagnostic and prognostic information for GBM patients. Tumor Autoantigen Gene Expression and Immune Infiltration The five autoantigen-encoding genes with the strongest diagnostic and prognostic relevance (SAA4, COL2A1, APOE, IL1B, and TP53) were selected for immune infiltration analysis in the TCGA-GBMLGG cohort via CIBERSORT, revealing distinct immune-association patterns. Specifically, APOE was positively correlated with M2 macrophages and negatively correlated with monocytes and T follicular helper (Tfh) cells; IL-1B was associated with monocytes, neutrophils, and activated mast cells; COL2A1 was positively associated with Tfh cells and M0 macrophages but negatively associated with plasma cells and monocytes; TP53 was positively correlated with M0 macrophages and regulatory T cells and negatively correlated with CD8⁺ T cells and monocytes; and SAA4 was linked to memory B cells and activated CD4⁺ memory T cells. When all 15 candidate genes were evaluated (Supplementary Fig. 1), additional antigens, such as S100A8, S100A12, S100A4, S100A6, and MMP3, also demonstrated significant correlations with macrophages or other immune cell subsets, whereas COL5A2 and IGFBP1 exhibited minimal immune associations. Collectively, these findings highlight the heterogeneous and complex immunological roles of glioma-associated antigens within the TME. Cellular Sources of Autoantigen mRNAs (Single-cell RNA-seq Analysis) To further clarify the cellular origins of autoantigen transcripts, we analyzed single-cell RNA-seq datasets (GSE131928_10X and GSE148842). UMAP plots revealed the major tumor and immune cell populations (Fig. 6 C–D, left panels), whereas violin plots revealed that APOE was predominantly expressed in monocytes/macrophages, with additional expression in astrocyte-like and mesenchymal-like malignant cells; IL1B was almost exclusively expressed in myeloid cells; TP53 was broadly but weakly expressed across malignant and nonmalignant cell types, with minimal levels in oligodendrocytes; and COL2A1 and SAA4 showed low to undetectable expression in all major cell types. These findings indicate that monocytes/macrophages are the principal source of APOE and IL1B in the glioma microenvironment, whereas COL2A1 and SAA4 are rarely expressed in tumor-associated cells. Prognostic impact of autoantigen-related gene expression We finally evaluated the prognostic significance of tumor mRNA levels for SAA4, APOE, COL2A1, IL1B, and TP53 in the TCGA-GBMLGG cohort (n = 698). SAA4 was excluded because of insufficient expression (Fig. 7 A). High APOE expression was correlated with improved overall survival (HR = 0.46, P < 0.001; Fig. 7 B). In contrast, elevated IL1B (HR = 1.75, P < 0.001), COL2A1 (HR = 1.93, P < 0.001), and TP53 (HR = 2.15, P < 0.001) were associated with shorter survival (Fig. 7 C–E). Discussion Glioblastoma (GBM) is one of the most lethal primary brain tumors in adults and is characterized by profound intratumoral heterogeneity, a highly immunosuppressive tumor microenvironment (TME), and poor outcomes despite aggressive multimodal treatment[ 9 , 10 ]. Effective management relies on early diagnosis, accurate prognostic assessment, and reliable monitoring [ 11 ]. However, current diagnostic approaches based on neuroimaging and tissue biopsies are invasive, prone to sampling bias, and limited in sensitivity, particularly for early or recurrent disease[ 12 ]. These limitations have driven the search for noninvasive, blood-based biomarkers—such as circulating proteins and autoantibodies—although their clinical utility in GBM remains to be fully defined[ 13 ]. Systemic humoral immune responses, including autoantibody production against tumor-associated antigens (TAAs), can reflect tumor biology and host interactions[ 9 , 14 , 15 ]. However, integrated serum proteomics and autoantibody profiling studies in glioma remain rare. Our study applied a multilayered approach to identify novel biomarkers and explore their relevance in GBM. TMT-based quantitative proteomics revealed significant serum protein alterations in GBM patients compared with controls. Differentially expressed proteins (DEPs) were enriched in platelet degranulation, extracellular matrix (ECM) organization, and complement/coagulation cascades[ 16 – 18 ]—processes known to drive invasion, angiogenesis, and systemic inflammation. ECM–receptor interactions and coagulation pathway disturbances may further modulate cell signaling and immune infiltration in the TME[ 19 , 20 ]. Together, these findings indicate systemic “vascular–stromal–immune” axis dysregulation in GBM, which may underlie disease aggressiveness and offer potential biomarkers and therapeutic targets. Leveraging these discoveries, we developed a custom peptide microarray to profile disease-related IgM autoantibodies in GBM. Our results revealed that a panel of three IgM autoantibodies—against APOE, TP53, and SAA4—achieved high diagnostic accuracy in both the discovery (AUC = 0.96) and independent validation (AUC = 0.85) cohorts. The use of IgM, which is typically associated with early-phase humoral immunity, suggests that this panel may be sensitive to early or primary immune responses against GBM antigens[ 21 , 22 ]. The superiority of the multimarker panel over individual antibodies underscores the value of composite biomarker strategies in capturing the complexity of immune responses and reducing false positives. In addition to its ability to be used for diagnosis, our study demonstrated the prognostic value of certain autoantibodies. Elevated IgM-p-SAA4-1 levels were associated with improved overall survival, perhaps indicating a favorable immune response or a less aggressive tumor phenotype. Conversely, high IgM-p-IL-1β-2 levels are correlated with wild-type IDH1, wild-type ATRX, and unmethylated MGMT promoter status, as well as poorer prognosis, which is consistent with the known tumor-promoting role of IL-1β in the CNS[ 23 , 24 ]. The observed associations between autoantibody profiles and key molecular features suggest that these blood-based signatures could serve as minimally invasive surrogates for tumor genotype, supporting risk stratification and personalized management. To elucidate the biological foundation of these serological markers, we integrated transcriptomic and single-cell RNA sequencing data. APOE and IL1B are expressed predominantly by tumor-associated macrophages and monocytes in the tumor microenvironment, with APOE being particularly enriched in M2-like macrophages, which are known for their immunosuppressive and tumor-promoting functions. In contrast, COL2A1 and SAA4 exhibited limited tumor expression, suggesting that their origin may be peripheral or from alternative sources. Importantly, the expression patterns of these autoantigen genes are linked to distinct immune cell infiltration profiles, supporting the context-dependent immunogenicity of tumor-associated antigens and the complex interplay between tumors and systemic immunity[ 25 , 26 ]. In addition to the potential bias from immunogenicity-based antigen selection, our systematic analysis of all fifteen candidate genes revealed that, in addition to the five key markers, other antigens, such as S100 family members and MMP3, also demonstrated significant associations with various immune cell populations, notably macrophages and neutrophils. The degree and specificity of these associations varied, with some candidates exhibiting broader links to multiple immune cell types, whereas others remained relatively immune independent. This underscores the multifaceted nature of immune relevance among candidate antigens and supports the robustness of our selection strategy while highlighting the need for future functional and spatial validation. Survival analyses in the TCGA cohort revealed that high APOE expression predicted better outcomes, whereas elevated expression of IL1B, COL2A1, and TP53 was correlated with poorer survival, which is consistent with their roles in inflammation, matrix remodeling, and genomic instability[ 27 – 31 ]. Collectively, these results demonstrate the value of integrating serological and tissue-based markers for comprehensive prognostic evaluation in glioblastoma patients. Compared with previous reports, our study stands out for its focus on IgM autoantibodies, comprehensive multiomics integration, and robust validation in independent cohorts. These methodological strengths increase the reliability and interpretability of our results. linically, the validated three-IgM autoantibody panel offers a promising, minimally invasive tool for GBM diagnosis and risk stratification. The simplicity and scalability of blood-based testing support its use in routine practice, potentially facilitating earlier intervention, more frequent monitoring, and improved detection of recurrence. Additionally, the relationships among specific autoantibody patterns, molecular subtypes, and survival indicate their potential to guide therapy selection and predict treatment response, although further studies are warranted. The validated three-IgM autoantibody panel offers a promising, minimally invasive tool for GBM diagnosis and prognostication. Its scalability and technical simplicity support its feasibility for routine clinical application, potentially enabling earlier intervention, closer surveillance, and improved detection of recurrence. Moreover, the observed associations between antibody patterns, tumor molecular subtypes, and survival suggest potential utility in guiding therapeutic decisions and predicting treatment responses. These findings warrant further evaluation of such blood-based immune signatures as adjuncts to current neuro-oncology practices. Limitations Several factors should be considered when these results are interpreted. This was a single-center study, which may introduce site-specific bias; however, we applied rigorous inclusion criteria and standardized sample handling to minimize variability. The biological mechanisms underlying the identified autoantibodies remain to be elucidated, and we have initiated complementary functional assays to address this question. Although some antibodies exhibited only moderate signal differences or AUC values, their performance was substantially enhanced within the composite panel, underscoring the clinical relevance of integrated biomarker strategies. The microarray platform used detects binding to synthetic linear peptides, which may not fully replicate native protein conformations; nevertheless, this high-throughput approach is widely recognized for discovery-phase studies, and we have planned orthogonal validation using native antigens via Western blotting, immunoprecipitation, and immunohistochemistry. Finally, larger multicenter studies are warranted to confirm generalizability, and several collaborative projects are currently underway to achieve this goal. Conclusion In conclusion, our findings highlight the potential of a composite autoantibody panel as a noninvasive and efficient diagnostic tool [disease name]. By integrating discovery-phase screening with planned orthogonal confirmation and multicenter validation, this work lays a strong foundation for translating candidate biomarkers into clinically applicable tools. Future studies combining longitudinal monitoring, mechanistic investigations, and diverse patient cohorts will be critical for fully realizing the diagnostic and prognostic potential of these biomarkers. Declarations 7 Acknowledgments We thank GeneChem Co., Ltd. (Shanghai, China) for the mass spectrometry analysis. 8 Funding Information This work was financially supported by the Natural Science Foundation of Jiangxi Province (Grant 20232BAB206097), the Natural Science Foundation of Jiangxi Province (Grant 20202BABL216049), and the Jiangxi Provincial Health Commission Science and Technology Program (Grant 202210391). 9 Conflict of interest The authors declare that they have no conflicts of interest associated with this manuscript. 10 Ethics Statement This study was conducted in accordance with the Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects). It was approved by the Ethics Committee of Sun Yat-sen University Cancer Center (Guangzhou, China) and the Ethics Committee of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University (Nanchang). Written informed consent was obtained from all patients. 11 Contributor Information H.W. and W.M. conceived and designed the study. H.W., W.M., and J.D. performed the data analysis and interpretation. S.Z., J.X., and Z.C. collected the clinical samples and acquired the data. and W.M. wrote the main manuscript text. All authors reviewed, revised, and approved the final manuscript and agree to be accountable for all aspects of the work. 12 Data availability statement The datasets presented in this study are available in the GEO database under accession numbers GSE138794 and GSE148842. The serum proteomic profiling datasets have been uploaded to the iProX repository (https://www.iprox.cn/page/PCV010.html). The original contributions presented in the study are included in the article and supplementary material. All data supporting the results of this study are available from the corresponding authors upon reasonable request. 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10.1016/j.jconrel.2024.02.029 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Supplementary Table 1. Summary of serum proteomic profiles and differentially expressed proteins in GBM patients versus healthy controls. A comprehensive list of the 877 serum proteins identified by TMT-based quantitative proteomics in samples from GBM patients and healthy controls is provided. For each protein, the quantitative abundance values, fold changes, and p values are reported. The results also highlight the subset of proteins meeting the predefined criteria for differential expression. TableS2.xlsx Supplementary Table 2. Synthetic peptides and their corresponding amino acid sequences. This lists 32 peptides derived from various proteins, specifying the peptide identifier, the amino acid start and end positions within the parent protein, and the peptide sequence used in microarray analysis. TableS3.xlsx Supplementary Table 3. Differential autoantibody reactivities in the discovery cohort. This details IgG and IgM autoantibody responses against glioma-associated antigens in the discovery cohort (55 GBM patients and 30 healthy controls). TableS4.xlsx Supplementary Table 4. Differential autoantibody reactivities in the validation cohort. This summarizes the IgG and IgM autoantibody responses against glioma-associated antigens in the independent validation cohort (32 GBM patients and 29 healthy controls). Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Journal of Neuro-Oncology → Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 27 Oct, 2025 Submission checks completed at journal 27 Oct, 2025 First submitted to journal 23 Oct, 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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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-7933102","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536933604,"identity":"c230f62a-e922-4599-bf77-43fab1acc44f","order_by":0,"name":"Wei Meng","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Meng","suffix":""},{"id":536933605,"identity":"9caea643-6aae-49fb-bd0f-aecfdb12f7ac","order_by":1,"name":"Jian Duan","email":"","orcid":"","institution":"Nanchang 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08:45:00","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134830,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/949a13420e0caf7795188c95.html"},{"id":95183642,"identity":"dfc86227-7299-4da3-91cb-813560e15024","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1268695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional characterization of differentially expressed genes (DEGs) between GBM and \u003c/strong\u003ehealthy control samples.\u003c/p\u003e\n\u003cp\u003e(A) Hierarchical clustering heatmap of DEPs, illustrating clear separation between the GBM and healthy groups on the basis of normalized protein abundance. (B) Volcano plot of DEPs, showing the log2-fold change versus the –log10 P value; red and blue dots indicate significantly up- and downregulated proteins (|log2FC| \u0026gt; 0.58, P \u0026lt; 0.05). (C) Gene Ontology (GO) enrichment analysis, with a bubble plot indicating the top significantly enriched biological process (BP), cellular component (CC), and molecular function (MF) terms; bubble size represents the number of DEPs, and color intensity reflects statistical significance. (D) KEGG pathway enrichment analysis of DEPs; the bubble plot displays the most significantly enriched pathways, with bubble size and color as in C.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/bf18eafbb069e7dffd2a12fc.png"},{"id":95183645,"identity":"783bf906-ddc7-4fed-b517-6d3fb22f2083","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1643344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePeptide microarray analysis revealing differential serum autoantibody signatures between glioma patients and healthy controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Workflow schematic for peptide microarray detection of serum IgG and IgM autoantibodies, including serum incubation, fluorescence labeling, and signal quantification. (B) Hierarchical clustering heatmap showing the signal intensities of differentially reactive autoantibodies across glioma (red) and healthy control (blue) samples; rows represent peptides, and columns represent individual serum samples, with colors indicating relative binding levels. (C) Volcano plot of autoantibody responses, depicting log2-fold change (glioma vs. healthy) versus –log10 P value; red and blue points indicate significantly up- or downregulated autoantibodies in glioma (|log2FC| \u0026gt; 0.58, P \u0026lt; 0.05); gray indicates nonsignificant differences.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/c2e20ab24dfc636dcd3fe1ba.png"},{"id":95227109,"identity":"49d4d034-da75-42d3-9a08-93c53b5f3e94","added_by":"auto","created_at":"2025-11-05 16:32:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":500864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic performance of selected serum IgM autoantibodies in glioma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Receiver operating characteristic (ROC) curves for IgM autoantibodies against p-APOE-1 (AUC = 0.78), p-P53-1 (AUC = 0.69), and p-SAA4-1 (AUC = 0.56) distinguishing glioma patients from healthy controls. (D) ROC curve for the combined three-marker panel (AUC = 0.96). (E) Box plots comparing relative serum IgM autoantibody levels (log2 signal intensity) between healthy controls (blue) and glioma patients (red); P values from statistical testing are indicated.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/4755a6430f1ed6edac9c0072.png"},{"id":95228446,"identity":"ee1bc067-7f3b-425d-bfaf-eb7bc36d0884","added_by":"auto","created_at":"2025-11-05 16:33:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1090903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of differential serum autoantibody profiles and diagnostic value in an independent glioma cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Hierarchical clustering heatmap of significantly differential autoantibody signals across glioma (red) and healthy control (blue) samples. (B) Box plots comparing the relative levels (log2 signal intensity) of six key autoantibodies between groups; P values indicate significance. (C–E) ROC curves for IgM autoantibodies against p-APOE-1 (AUC = 0.88), p-P53-1 (AUC = 0.68), and p-SAA4-1 (AUC = 0.61). (F) ROC curve for the combined three-marker IgM panel (AUC = 0.85).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/2d30f368eaab54843bd66a5f.png"},{"id":95183654,"identity":"2f7eaebb-ab9a-497c-b76c-63ce7838e072","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":518666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic significance and clinicopathological associations of selected serum autoantibodies in glioma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Kaplan‒Meier survival curves for glioma patients stratified by IgM-p-SAA4-1 status; the P value was calculated via the log-rank test. (B) Heatmap of p values for associations between five IgM autoantibodies and clinicopathological features (grade, IDH1, TERT, ATRX, MGMT, and Ki-67), with color intensity reflecting significance. (C) Forest plot of univariable Cox regression for overall survival based on six serum autoantibodies; points indicate hazard ratios (HRs), and horizontal bars represent 95% confidence intervals. IgM-p-IL-1β-2 was significantly associated with poorer OS (HR = 1.36, P = 0.031).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/4222347a266956fc61e9064d.png"},{"id":95183663,"identity":"0c9febc8-bde5-40d6-aba8-8d15ec9a8ae4","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2885500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of autoantigen genes and associations with the glioma immune microenvironment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Lollipop plots showing Spearman’s correlations between the mRNA expression of autoantigen-encoding genes (SAA4, COL2A1, IL1B, APOE, and TP53) and estimated immune cell infiltration in the TCGA-GBMLGG cohort; point color and size indicate p value significance.(B) Heatmap summarizing Spearman’s correlation coefficients between the five genes and immune cell infiltration levels; color represents correlation strength (red: positive, blue: negative), with asterisks indicating significance (p \u0026lt; 0.05). (C) Left: UMAP plots of single-cell RNA-seq data (GSE131928_10X) showing the major cell types identified in glioma. Right: Violin plots displaying the normalized expression of the five autoantigen genes across different cell types in this dataset, illustrating their cellular sources within the tumor microenvironment. (D) Left: UMAP plots of single-cell RNA-seq data (GSE148842) depicting the major cell types in glioma. Right: Violin plots showing the normalized expression of the five autoantigen genes across cell types in this dataset, further indicating their cellular distribution in the tumor microenvironment.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/4bee311dec52c0d0d1eb2e30.png"},{"id":95227156,"identity":"1f3f2f19-1e9a-4145-bc57-4d32391634a8","added_by":"auto","created_at":"2025-11-05 16:32:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1025068,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between autoantigen gene expression and overall survival in the TCGA-GBMLGG glioma cohort.\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier survival curve for all patients in the TCGA-GBMLGG cohort (n = 698); stratification by SAA4 expression was not performed because of its nonuniform distribution. (B–E) Kaplan–Meier survival curves for overall survival comparing patients with low (blue) versus high (red) mRNA expression levels (median split) of APOE, IL1B, COL2A1, and TP53. Each panel displays the hazard ratio (HR), 95% confidence interval (CI), and log-rank p value. Risks are included to indicate the number of patients at risk over time.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/0d7e6f917b54ffd8747ee9b4.png"},{"id":103251739,"identity":"a22d8782-2cc4-4464-8552-e43319f91239","added_by":"auto","created_at":"2026-02-23 16:11:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10235783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/a3fef293-85ff-4ef0-9eaa-f77f93529b16.pdf"},{"id":95183647,"identity":"bf77453c-1d00-4b8a-b3be-abfe70042092","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":174060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary of serum proteomic profiles and differentially expressed proteins in GBM patients versus healthy controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive list of the 877 serum proteins identified by TMT-based quantitative proteomics in samples from GBM patients and healthy controls is provided. For each protein, the quantitative abundance values, fold changes, and \u003cem\u003ep\u003c/em\u003e values are reported. The results also highlight the subset of proteins meeting the predefined criteria for differential expression.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/76d006ffecc09dfed4e265a8.xlsx"},{"id":95183649,"identity":"f6d23493-761b-4a9f-a67f-6179a51fe932","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynthetic peptides and their corresponding amino acid sequences.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis lists 32 peptides derived from various proteins, specifying the peptide identifier, the amino acid start and end positions within the parent protein, and the peptide sequence used in microarray analysis.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/b838898a0011feb47eb31a1b.xlsx"},{"id":95183646,"identity":"2740d953-f66d-409e-9d94-16a41891b522","added_by":"auto","created_at":"2025-11-05 08:45:00","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":109185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential autoantibody reactivities in the discovery cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis details IgG and IgM autoantibody responses against glioma-associated antigens in the discovery cohort (55 GBM patients and 30 healthy controls).\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/781ff80ef85126a6d4550c68.xlsx"},{"id":95227532,"identity":"ee9438ba-048f-4bfb-b892-8215a919876c","added_by":"auto","created_at":"2025-11-05 16:32:35","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":80124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 4.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential autoantibody reactivities in the validation cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis summarizes the IgG and IgM autoantibody responses against glioma-associated antigens in the independent validation cohort (32 GBM patients and 29 healthy controls).\u003c/p\u003e","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7933102/v1/40a513d23ee5e240b4d1244d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiomics Integration of Serum Proteome and Autoantibody Profiles Reveals Diagnostic and Prognostic Biomarkers in Glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GBM) is the most aggressive type of primary brain tumor and has profound molecular heterogeneity and a poor prognosis despite advances in surgery, radiotherapy, and chemotherapy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The median survival remains only 14\u0026ndash;16 months, and the 5-year survival rate is less than 7%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], highlighting the urgent need for novel biomarkers to enable early detection, prognosis, and therapeutic interventions.\u003c/p\u003e\u003cp\u003eIn recent years, high-throughput omics technologies, particularly proteomics, have offered powerful tools to interrogate the complex biological landscape of cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Comparative proteomics, with its capacity to comprehensively profile protein expression alterations in tumor tissues and biofluids, has emerged as a powerful tool for discovering and validating tumor-associated biomarkers[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Tandem mass tag (TMT)-based quantitative proteomics, in particular, enables precise and accurate quantification of protein abundance across multiple samples, providing a robust platform for identifying differentially expressed proteins (DEPs) that may serve as diagnostic or therapeutic targets [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such analyses can reveal systemic changes associated with GBM and identify candidate circulating biomarkers.\u003c/p\u003e\u003cp\u003eFurthermore, tumorigenesis can disrupt immune tolerance, leading to the generation of autoantibodies against tumor-associated antigens (TAAs)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These autoantibodies, potentially detectable early in disease and stable in circulation, represent a promising class of biomarkers[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Exploring serum autoantibody signatures specifically targeting proteins altered in GBM could therefore yield valuable diagnostic and prognostic tools.\u003c/p\u003e\u003cp\u003eIn this study, we hypothesized that GBM induces distinct serum proteomic and autoantibody changes. We applied TMT-based proteomics to identify DEPs in the serum of GBM patients versus healthy controls and performed functional analyses to interpret their significance. On the basis of these results, we designed peptide microarrays to profile IgG/IgM autoantibodies and evaluated their diagnostic and prognostic utility in discovery and validation cohorts. We further integrated our serological findings with tumor gene expression, immune infiltration, and cellular origin data (via TCGA and single-cell RNA-seq data) to determine their biological relevance. Our aim is to identify robust, clinically actionable biomarkers for GBM and to elucidate the interplay between tumor biology, immunity, and patient outcome.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePatient enrollment and serum sample collection\u003c/p\u003e\u003cp\u003eThis prospective study enrolled 117 patients with histopathologically confirmed glioma and 89 age- and sex-matched healthy controls at Sun Yat-sen University Cancer Center (Guangzhou, China) from September 2021 to February 2023. The glioma diagnoses followed the 2016 WHO CNS criteria. Clinical data\u0026mdash;including demographic features, tumor grade, molecular markers (IDH1, TERT, ATRX, MGMT promoter methylation, and Ki-67), and treatment history\u0026mdash;were collected. The exclusion criteria included prior immunotherapy or acute infection within one month before sampling. Written informed consent was obtained, and the protocol was approved by the Institutional Review Board. Peripheral blood was centrifuged at 3000\u0026times;g and 4\u0026deg;C for 10 min; serum aliquots were stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\u003cp\u003eQuantitative Serum Proteomics (TMT Labeling)\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.1 Sample Preparation and Depletion\u003c/div\u003e\u003cp\u003eSera from 30 glioblastoma (GBM) patients and 30 healthy controls were collected. For proteomic profiling, samples within each group were randomly divided into three pools, with each pool containing equal protein amounts from 10 individual serum samples. This pooling strategy was applied to minimize individual-specific variation, increase the detection sensitivity for low-abundance proteins, and reduce the degree of technical variability and analysis cost. Each pool was treated as one biological replicate, resulting in three pooled replicates per group for downstream TMT-based LC\u0026ndash;MS/MS analysis.\u003c/p\u003e\u003cp\u003eHigh-abundance serum proteins were depleted via Human-14 Multiple Affinity Removal LC columns (Agilent Technologies) according to the manufacturer\u0026rsquo;s protocol. The low-abundance fractions were concentrated (5 kDa ultrafiltration, Millipore) and quantified via a BCA assay before digestion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.2 Protein digestion and TMT labeling\u003c/div\u003e\u003cp\u003eAliquots (200 \u0026micro;g) of protein were reduced with 100 mM DTT (100\u0026deg;C, 5 min), processed via filter-aided sample preparation (FASP) with 30 kDa filters, alkylated with 100 mM IAA (30 min, dark, RT), and sequentially washed with 8 M urea and 0.1 M TEAB. Proteins were digested overnight at 37\u0026deg;C with trypsin (1:50, w/w). Peptides (100 \u0026micro;g) per sample were labeled with TMT 6-plex reagents (Thermo Fisher Scientific) per the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.3 Peptide fractionation and LC‒MS/MS\u003c/div\u003e\u003cp\u003e\u003cb\u003eThe\u003c/b\u003e TMT-labeled peptides were fractionated via high-pH reversed-phase HPLC (Agilent 1260 Infinity II) with a 0\u0026ndash;40% acetonitrile gradient in 10 mM ammonium formate (pH 10.0). Fractions were pooled, dried, and analyzed on an Easy-nLC 1000 system coupled to a Q Exactive Plus Orbitrap (Thermo Fisher Scientific) using an Acclaim PepMap RSLC C18 column (50 \u0026micro;m \u0026times; 15 cm, 300 nL/min) with 90\u0026ndash;120 min linear gradients. MS1 scans: resolution 70,000 (m/z 350\u0026ndash;1800), AGC 3e6, max IT 50 ms; top-10 precursors fragmented by HCD (NCE 30), MS2 resolution 35,000, AGC 1e5, max IT 45 ms, isolation width 2.0 m/z.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.4 Data processing and normalization\u003c/div\u003e\u003cp\u003eThe raw data were processed in Proteome Discoverer v2.4 (Thermo Fisher Scientific) and searched against the UniProt human database (2018_09) with Mascot 2.6, FDR\u0026thinsp;\u0026lt;\u0026thinsp;1%. Reporter ion intensities were normalized to the total signal per channel. The data are available at iProX (PXD064034). Differential expression was defined as |log2FC| \u0026gt;0.5 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. GO and KEGG enrichment analyses were performed via gseapy (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.5 Custom peptide microarray\u003c/div\u003e\u003cp\u003ePeptides (n\u0026thinsp;=\u0026thinsp;32; 1\u0026ndash;3 per antigen) were designed from linear B-cell epitopes of 15 candidate autoantigens predicted by ABCpred, UniProt BLAST, and SVMTriP, selecting the top consensus-scoring sequences. N-terminal\u0026ndash;amidated peptides were conjugated to BSA via Sulfo-SMCC, verified by SDS‒PAGE, and printed in triplicate together with positive (human IgG/IgM) and negative (BSA) controls on PATH substrate slides via a Super Marathon microarrayer. Six landmark/system controls were included per slide: human IgG (0.5 mg/mL), human IgM (0.5 mg/mL), human IgG (0.1 mg/mL), human IgM (0.1 mg/mL), Cy3-labeled anti-human IgG, and Cy5-labeled anti-human IgM, following Li et al[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEach slide accommodated 12\u0026ndash;14 serum samples. The serum (diluted 1:100 in PBS\u0026thinsp;+\u0026thinsp;0.1% Tween-20\u0026thinsp;+\u0026thinsp;3% BSA) was incubated for 2 h at RT, followed by Cy3\u0026ndash;anti-human IgG and Cy5\u0026ndash;anti-human IgM detection. After washing, the slides were scanned on a GenePix 4000B scanner. Median foreground minus background fluorescence was extracted via GenePix Pro 6.0, averaged over triplicates, and normalized via intraslide positive controls and block-specific reference serum for interslide linear normalization, as described by Li et al[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The data from the IgG and IgM channels were normalized separately. The positive cutoff was determined by the Youden index from the ROC analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.6 Microarray Data Analysis and Biomarker Evaluation\u003c/div\u003e\u003cp\u003eThe raw data were quantile normalized after background correction. Differential signals were identified by Welch\u0026rsquo;s t test (|log2FC| \u0026gt;0.58, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis (area under the curve (AUC), sensitivity, specificity), with multimarker panels constructed via logistic regression. All the statistical analyses and data visualizations were performed via Python (version 3.12) and R (version 4.4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.7 Survival and clinicopathological correlation analysis\u003c/div\u003e\u003cp\u003eAssociations between autoantibody levels and clinicopathological parameters were assessed via chi-square tests or Fisher\u0026rsquo;s exact tests. Survival analyses included Kaplan‒Meier curves, log-rank tests, and univariate Cox regression (hazard ratios with 95% CI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.8 Transcriptomic and single-cell RNA sequencing analyses\u003c/div\u003e\u003cp\u003eBulk RNA-seq (TCGA-GBMLGG) data were analyzed for autoantigen gene expression and immune infiltration via CIBERSORT and Spearman correlation. Public scRNA-seq datasets (GSE131928_10X and GSE148842) were used to identify the cellular origins of autoantigen expression via standard pipelines (UMAP, cell-type annotation, violin plots). Survival analysis on the basis of gene expression employed median-based dichotomization, Kaplan‒Meier, log-rank, and Cox regression models.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSerum Proteomic Alterations in GBM\u003c/p\u003e\n\u003cp\u003eTo characterize systemic protein changes in glioblastoma (GBM), we performed TMT-based quantitative proteomics on sera from 30 GBM patients and 30 matched healthy controls (HCs). Across all the samples, 877 proteins were confidently identified (\u0026ge;\u0026thinsp;1 unique peptide; \u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e). By applying |log₂FC| \u0026gt;0.58 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we detected a distinct set of differentially expressed proteins (DEPs) that clearly segregated GBM patients from HCs via hierarchical clustering (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). A volcano plot illustrates the distribution of significantly upregulated and downregulated proteins (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis revealed that the DEPs were involved mainly in platelet degranulation, the regulation of hemostasis, and immune effector regulation, which is consistent with the pro-thrombotic, proinflammatory phenotype reported in GBM. In the cellular component category, these genes were enriched in the extracellular matrix (ECM) and endocytic vesicles, whereas the molecular function terms included protease and glycosaminoglycan binding, both of which are critical for ECM remodeling and growth factor signaling.\u003c/p\u003e\n\u003cp\u003eKEGG pathway analysis highlighted ECM\u0026ndash;receptor interactions and complement/coagulation cascades (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). These alterations may facilitate tumor adhesion, invasion, angiogenesis, and immune escape, reflecting the interplay between tumor- and host-derived factors in GBM.\u003c/p\u003e\n\u003cp\u003ePPI network analysis (STRING) identified hub proteins on the basis of degree centrality: HSP90AA1 and TPI1 had the highest connectivity, followed by SOD1, COL1A1, COL1A2, PRDX6, PFN1, LUM, COL3A1, and YWHAE (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). These hubs clustered into three functional modules: (i) metabolism/oxidative stress (TPI1, SOD1, PRDX6, PFN1), (ii) ECM organization (COL1A1, COL1A2, COL3A1, LUM), and (iii) signaling/regulation (HSP90AA1, YWHAE), suggesting coordinated \u0026ldquo;metabolism\u0026ndash;ECM\u0026ndash;signaling\u0026rdquo; dysregulation in GBM.\u003c/p\u003e\n\u003cp\u003eIdentification of Differential Serum Autoantibodies against Glioma-Associated Antigens\u003c/p\u003e\n\u003cp\u003eFrom the differentially expressed proteins (DEPs) identified via proteomic profiling, fifteen target proteins were selected on the basis of their biological relevance and functional significance. Linear B-cell epitopes within these proteins were predicted via three independent bioinformatics tools\u0026mdash;ABCpred, UniProt BLAST, and SVMTriP. Peptide sequences with the highest consensus scores across all three platforms were selected, yielding 1\u0026ndash;3 peptides per protein and a total of 32 peptides (\u003cstrong\u003eSupplementary Table\u0026nbsp;2\u003c/strong\u003e). Each peptide was printed in triplicate on a custom peptide microarray to assess its technical reproducibility in the same serum samples.\u003c/p\u003e\n\u003cp\u003eSerum autoantibody profiles were analyzed in two independent cohorts: a discovery cohort (55 GBM patients and 30 healthy controls) and a validation cohort (32 GBM patients and 29 healthy controls). The cohort baseline characteristics are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and detailed in \u003cstrong\u003eSupplementary Tables\u0026nbsp;3\u0026ndash;4\u003c/strong\u003e. Hierarchical clustering of the IgG and IgM reactivity patterns revealed clear separation between the GBM and control samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Applying the criteria |log₂FC| \u0026gt;0.58 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we identified 18 significantly altered autoantibodies (15 upregulated, 3 downregulated) in GBM (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating a disease-specific humoral immune response.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" style=\"width: 1003px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic characteristics of the glioma and healthy control groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 281px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eExperimental set\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 481.301px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;Validation set\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 137.822px;\" align=\"left\"\u003eGlioma (n\u0026thinsp;=\u0026thinsp;55)\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 143.178px;\" align=\"left\"\u003eHealthy (n\u0026thinsp;=\u0026thinsp;30)\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 156px;\" align=\"left\"\u003eGlioma (n\u0026thinsp;=\u0026thinsp;32)\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 325.301px;\" colspan=\"2\" align=\"left\"\u003eHealthy (n\u0026thinsp;=\u0026thinsp;29)\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 137.822px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 143.178px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 156px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 325.301px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 137.822px;\" align=\"left\"\u003e\n\u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 143.178px;\" align=\"left\"\u003e\n\u003cp\u003e50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 321.301px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e44.1\u0026nbsp;\u0026plusmn;10.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 160px;\" align=\"left\"\u003e\n\u003cp\u003e45.6\u0026nbsp;\u0026plusmn;11.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\n\u003cp\u003eMann‒Whitney U test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 281px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 481.301px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 137.822px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 143.178px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 156px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 325.301px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 137.822px;\" align=\"left\"\u003e\n\u003cp\u003e30 (54.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 143.178px;\" align=\"left\"\u003e\n\u003cp\u003e15 (50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 156px;\" align=\"left\"\u003e\n\u003cp\u003e22 (68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 325.301px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17 (58.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\n\u003cp\u003ePearson's \u0026chi;\u0026sup2; test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 137.822px;\" align=\"left\"\u003e\n\u003cp\u003e25 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 143.178px;\" align=\"left\"\u003e\n\u003cp\u003e15 (50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 156px;\" align=\"left\"\u003e\n\u003cp\u003e10 (31.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 325.301px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e12 (41.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px; width: 175px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 281px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 481.301px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px; width: 194px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px; width: 1131.3px;\" colspan=\"7\"\u003eData are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or number (percentage), as appropriate.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px; width: 1131.3px;\" colspan=\"7\"\u003eDifferences in age were assessed via the Mann‒Whitney U test; differences in sex distribution were assessed via Pearson\u0026rsquo;s \u0026chi;\u0026sup2; test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDiagnostic Performance of a Serum Autoantibody Panel\u003c/p\u003e\n\u003cp\u003eTo evaluate the diagnostic utility of the candidate autoantibodies, we examined their performance in the discovery cohort and subsequently confirmed the results in the validation cohort (\u003cstrong\u003eSupplementary Table\u0026nbsp;4\u003c/strong\u003e). All samples were assayed in technical triplicates to ensure measurement reliability. Unsupervised clustering consistently distinguished GBM patients from controls in both cohorts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Three IgM autoantibodies\u0026mdash;IgM-p-APOE-1, IgM-p-P53-1, and IgM-p-SAA4-1\u0026mdash;had significantly higher serum levels in GBM patients than in controls (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the validation cohort, the individual AUC values for IgM-p-APOE-1, IgM-p-P53-1, and IgM-p-SAA4-1 were 0.88, 0.68, and 0.61, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;E). When combined, the three-marker panel achieved an AUC of 0.85, with 81% sensitivity and 76% specificity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). These results confirm the high reproducibility and robust diagnostic performance of the IgM panel across independent cohorts, supporting its potential clinical utility for GBM detection.\u003c/p\u003e\n\u003cp\u003ePrognostic and clinicopathological associations of serum autoantibodies\u003c/p\u003e\n\u003cp\u003eWe next examined associations between key autoantibodies and clinical outcomes. Kaplan\u0026ndash;Meier analysis revealed that IgM-p-SAA4-1 positivity was significantly associated with prolonged overall survival (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), whereas other autoantibodies did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Higher IgM-p-IL-1\u0026beta;-2 levels were correlated with wild-type IDH1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), wild-type ATRX (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), and an unmethylated MGMT promoter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0071), which are molecular features typically linked to poor prognosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). IgM-p-APOE-1 tended to be associated with favorable subtypes (IDH1 mutation, MGMT methylation; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression revealed that high IgM-p-IL-1\u0026beta;-2 levels were associated with shorter survival (HR\u0026thinsp;=\u0026thinsp;1.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), whereas high IgM-p-P53-1 levels were associated with improved outcomes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). These findings suggest that serum autoantibody profiles can provide both diagnostic and prognostic information for GBM patients.\u003c/p\u003e\n\u003cp\u003eTumor Autoantigen Gene Expression and Immune Infiltration\u003c/p\u003e\n\u003cp\u003eThe five autoantigen-encoding genes with the strongest diagnostic and prognostic relevance (SAA4, COL2A1, APOE, IL1B, and TP53) were selected for immune infiltration analysis in the TCGA-GBMLGG cohort via CIBERSORT, revealing distinct immune-association patterns. Specifically, APOE was positively correlated with M2 macrophages and negatively correlated with monocytes and T follicular helper (Tfh) cells; IL-1B was associated with monocytes, neutrophils, and activated mast cells; COL2A1 was positively associated with Tfh cells and M0 macrophages but negatively associated with plasma cells and monocytes; TP53 was positively correlated with M0 macrophages and regulatory T cells and negatively correlated with CD8⁺ T cells and monocytes; and SAA4 was linked to memory B cells and activated CD4⁺ memory T cells. When all 15 candidate genes were evaluated (Supplementary Fig.\u0026nbsp;1), additional antigens, such as S100A8, S100A12, S100A4, S100A6, and MMP3, also demonstrated significant correlations with macrophages or other immune cell subsets, whereas COL5A2 and IGFBP1 exhibited minimal immune associations. Collectively, these findings highlight the heterogeneous and complex immunological roles of glioma-associated antigens within the TME.\u003c/p\u003e\n\u003cp\u003eCellular Sources of Autoantigen mRNAs (Single-cell RNA-seq Analysis)\u003c/p\u003e\n\u003cp\u003eTo further clarify the cellular origins of autoantigen transcripts, we analyzed single-cell RNA-seq datasets (GSE131928_10X and GSE148842). UMAP plots revealed the major tumor and immune cell populations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC\u0026ndash;D, left panels), whereas violin plots revealed that APOE was predominantly expressed in monocytes/macrophages, with additional expression in astrocyte-like and mesenchymal-like malignant cells; IL1B was almost exclusively expressed in myeloid cells; TP53 was broadly but weakly expressed across malignant and nonmalignant cell types, with minimal levels in oligodendrocytes; and COL2A1 and SAA4 showed low to undetectable expression in all major cell types. These findings indicate that monocytes/macrophages are the principal source of APOE and IL1B in the glioma microenvironment, whereas COL2A1 and SAA4 are rarely expressed in tumor-associated cells.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrognostic impact of autoantigen-related gene expression\u003c/p\u003e\n\u003cp\u003eWe finally evaluated the prognostic significance of tumor mRNA levels for SAA4, APOE, COL2A1, IL1B, and TP53 in the TCGA-GBMLGG cohort (n\u0026thinsp;=\u0026thinsp;698). SAA4 was excluded because of insufficient expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). High APOE expression was correlated with improved overall survival (HR\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). In contrast, elevated IL1B (HR\u0026thinsp;=\u0026thinsp;1.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), COL2A1 (HR\u0026thinsp;=\u0026thinsp;1.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TP53 (HR\u0026thinsp;=\u0026thinsp;2.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with shorter survival (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC\u0026ndash;E).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlioblastoma (GBM) is one of the most lethal primary brain tumors in adults and is characterized by profound intratumoral heterogeneity, a highly immunosuppressive tumor microenvironment (TME), and poor outcomes despite aggressive multimodal treatment[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Effective management relies on early diagnosis, accurate prognostic assessment, and reliable monitoring [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, current diagnostic approaches based on neuroimaging and tissue biopsies are invasive, prone to sampling bias, and limited in sensitivity, particularly for early or recurrent disease[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These limitations have driven the search for noninvasive, blood-based biomarkers\u0026mdash;such as circulating proteins and autoantibodies\u0026mdash;although their clinical utility in GBM remains to be fully defined[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSystemic humoral immune responses, including autoantibody production against tumor-associated antigens (TAAs), can reflect tumor biology and host interactions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, integrated serum proteomics and autoantibody profiling studies in glioma remain rare. Our study applied a multilayered approach to identify novel biomarkers and explore their relevance in GBM.\u003c/p\u003e\u003cp\u003eTMT-based quantitative proteomics revealed significant serum protein alterations in GBM patients compared with controls. Differentially expressed proteins (DEPs) were enriched in platelet degranulation, extracellular matrix (ECM) organization, and complement/coagulation cascades[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u0026mdash;processes known to drive invasion, angiogenesis, and systemic inflammation. ECM\u0026ndash;receptor interactions and coagulation pathway disturbances may further modulate cell signaling and immune infiltration in the TME[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Together, these findings indicate systemic \u0026ldquo;vascular\u0026ndash;stromal\u0026ndash;immune\u0026rdquo; axis dysregulation in GBM, which may underlie disease aggressiveness and offer potential biomarkers and therapeutic targets.\u003c/p\u003e\u003cp\u003eLeveraging these discoveries, we developed a custom peptide microarray to profile disease-related IgM autoantibodies in GBM. Our results revealed that a panel of three IgM autoantibodies\u0026mdash;against APOE, TP53, and SAA4\u0026mdash;achieved high diagnostic accuracy in both the discovery (AUC\u0026thinsp;=\u0026thinsp;0.96) and independent validation (AUC\u0026thinsp;=\u0026thinsp;0.85) cohorts. The use of IgM, which is typically associated with early-phase humoral immunity, suggests that this panel may be sensitive to early or primary immune responses against GBM antigens[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The superiority of the multimarker panel over individual antibodies underscores the value of composite biomarker strategies in capturing the complexity of immune responses and reducing false positives.\u003c/p\u003e\u003cp\u003eIn addition to its ability to be used for diagnosis, our study demonstrated the prognostic value of certain autoantibodies. Elevated IgM-p-SAA4-1 levels were associated with improved overall survival, perhaps indicating a favorable immune response or a less aggressive tumor phenotype. Conversely, high IgM-p-IL-1β-2 levels are correlated with wild-type IDH1, wild-type ATRX, and unmethylated MGMT promoter status, as well as poorer prognosis, which is consistent with the known tumor-promoting role of IL-1β in the CNS[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The observed associations between autoantibody profiles and key molecular features suggest that these blood-based signatures could serve as minimally invasive surrogates for tumor genotype, supporting risk stratification and personalized management.\u003c/p\u003e\u003cp\u003eTo elucidate the biological foundation of these serological markers, we integrated transcriptomic and single-cell RNA sequencing data. APOE and IL1B are expressed predominantly by tumor-associated macrophages and monocytes in the tumor microenvironment, with APOE being particularly enriched in M2-like macrophages, which are known for their immunosuppressive and tumor-promoting functions. In contrast, COL2A1 and SAA4 exhibited limited tumor expression, suggesting that their origin may be peripheral or from alternative sources. Importantly, the expression patterns of these autoantigen genes are linked to distinct immune cell infiltration profiles, supporting the context-dependent immunogenicity of tumor-associated antigens and the complex interplay between tumors and systemic immunity[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition to the potential bias from immunogenicity-based antigen selection, our systematic analysis of all fifteen candidate genes revealed that, in addition to the five key markers, other antigens, such as S100 family members and MMP3, also demonstrated significant associations with various immune cell populations, notably macrophages and neutrophils. The degree and specificity of these associations varied, with some candidates exhibiting broader links to multiple immune cell types, whereas others remained relatively immune independent. This underscores the multifaceted nature of immune relevance among candidate antigens and supports the robustness of our selection strategy while highlighting the need for future functional and spatial validation.\u003c/p\u003e\u003cp\u003eSurvival analyses in the TCGA cohort revealed that high APOE expression predicted better outcomes, whereas elevated expression of IL1B, COL2A1, and TP53 was correlated with poorer survival, which is consistent with their roles in inflammation, matrix remodeling, and genomic instability[\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Collectively, these results demonstrate the value of integrating serological and tissue-based markers for comprehensive prognostic evaluation in glioblastoma patients.\u003c/p\u003e\u003cp\u003eCompared with previous reports, our study stands out for its focus on IgM autoantibodies, comprehensive multiomics integration, and robust validation in independent cohorts. These methodological strengths increase the reliability and interpretability of our results.\u003c/p\u003e\u003cp\u003elinically, the validated three-IgM autoantibody panel offers a promising, minimally invasive tool for GBM diagnosis and risk stratification. The simplicity and scalability of blood-based testing support its use in routine practice, potentially facilitating earlier intervention, more frequent monitoring, and improved detection of recurrence. Additionally, the relationships among specific autoantibody patterns, molecular subtypes, and survival indicate their potential to guide therapy selection and predict treatment response, although further studies are warranted.\u003c/p\u003e\u003cp\u003eThe validated three-IgM autoantibody panel offers a promising, minimally invasive tool for GBM diagnosis and prognostication. Its scalability and technical simplicity support its feasibility for routine clinical application, potentially enabling earlier intervention, closer surveillance, and improved detection of recurrence. Moreover, the observed associations between antibody patterns, tumor molecular subtypes, and survival suggest potential utility in guiding therapeutic decisions and predicting treatment responses. These findings warrant further evaluation of such blood-based immune signatures as adjuncts to current neuro-oncology practices.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eSeveral factors should be considered when these results are interpreted. This was a single-center study, which may introduce site-specific bias; however, we applied rigorous inclusion criteria and standardized sample handling to minimize variability. The biological mechanisms underlying the identified autoantibodies remain to be elucidated, and we have initiated complementary functional assays to address this question. Although some antibodies exhibited only moderate signal differences or AUC values, their performance was substantially enhanced within the composite panel, underscoring the clinical relevance of integrated biomarker strategies. The microarray platform used detects binding to synthetic linear peptides, which may not fully replicate native protein conformations; nevertheless, this high-throughput approach is widely recognized for discovery-phase studies, and we have planned orthogonal validation using native antigens via Western blotting, immunoprecipitation, and immunohistochemistry. Finally, larger multicenter studies are warranted to confirm generalizability, and several collaborative projects are currently underway to achieve this goal.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings highlight the potential of a composite autoantibody panel as a noninvasive and efficient diagnostic tool [disease name]. By integrating discovery-phase screening with planned orthogonal confirmation and multicenter validation, this work lays a strong foundation for translating candidate biomarkers into clinically applicable tools. Future studies combining longitudinal monitoring, mechanistic investigations, and diverse patient cohorts will be critical for fully realizing the diagnostic and prognostic potential of these biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e7 Acknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank GeneChem Co., Ltd. (Shanghai, China) for the mass spectrometry analysis.\u003c/p\u003e\n\u003ch2\u003e8 Funding Information\u003c/h2\u003e\n\u003cp\u003eThis work was financially supported by the Natural Science Foundation of Jiangxi Province (Grant 20232BAB206097), the Natural Science Foundation of Jiangxi Province (Grant 20202BABL216049), and the Jiangxi Provincial Health Commission Science and Technology Program (Grant 202210391).\u003c/p\u003e\n\u003ch2\u003e9 Conflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest associated with this manuscript.\u003c/p\u003e\n\u003ch2\u003e10 Ethics Statement\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects). It was approved by the Ethics Committee of Sun Yat-sen University Cancer Center (Guangzhou, China) and the Ethics Committee of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University (Nanchang). Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003ch2\u003e11 Contributor Information\u003c/h2\u003e\n\u003cp\u003eH.W. and W.M. conceived and designed the study. H.W., W.M., and J.D. performed the data analysis and interpretation. S.Z., J.X., and Z.C. collected the clinical samples and acquired the data. and W.M. wrote the main manuscript text. All authors reviewed, revised, and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003e12 Data availability statement\u003c/h2\u003e\n\u003cp\u003eThe datasets presented in this study are available in the GEO database under accession numbers GSE138794 and GSE148842. The serum proteomic profiling datasets have been uploaded to the iProX repository (https://www.iprox.cn/page/PCV010.html). The original contributions presented in the study are included in the article and supplementary material. All data supporting the results of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLapointe S, Perry A, Butowski NA (2018) Lancet 392:432\u0026ndash;446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(18)30990-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)30990-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2015) Neuro Oncol 17(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/neuonc/nov189\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/nov189\" targettype=\"DOI\" 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368:208\u0026ndash;218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jconrel.2024.02.029\u003c/span\u003e\u003cspan address=\"10.1016/j.jconrel.2024.02.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Glioma, Proteomics, Autoantibodies, Diagnostic Biomarkers, Prognostic Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7933102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7933102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eTandem mass tag (TMT)-based quantitative proteomics was performed on sera from 30 GBM patients and 30 matched healthy controls (HCs) to identify differentially expressed proteins (DEPs). Candidate tumor-associated antigens were used to design a custom peptide microarray assessing IgG/IgM autoantibodies in the discovery (n\u0026thinsp;=\u0026thinsp;55 GBM patients, 30 HCs) and validation (n\u0026thinsp;=\u0026thinsp;32 GBM patients, 29 HCs) cohorts. Prognostic value was analyzed via Kaplan\u0026ndash;Meier and Cox regression, and findings were integrated with TCGA transcriptomics and single-cell RNA sequencing data to determine immune associations and cellular origins.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eProteomics identified 877 proteins, with DEPs enriched in extracellular matrix remodeling, complement/coagulation cascades, and metabolism/oxidative stress pathways. A three-IgM panel (anti-p-APOE-1, anti-p-P53-1, and anti-p-SAA4-1) showed high diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.96; 0.85 validation). IgM-p-SAA4-1 positivity was correlated with longer survival, whereas elevated IgM-p-IL-1β-2 levels predicted poor prognosis and adverse molecular subtypes (IDH1/ATRX wild-type, unmethylated MGMT). APOE and IL1B are expressed predominantly by tumor-associated macrophages, with divergent prognostic implications at the transcript level.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eIntegrated proteomic\u0026ndash;autoantibody profiling identified and validated a serum IgM panel with robust diagnostic accuracy and prognostic relevance in GBM. These biomarkers reflect interactions between humoral immunity, tumor gene expression, and the immune microenvironment, supporting their potential for clinical application in GBM detection and patient stratification.\u003c/p\u003e","manuscriptTitle":"Multiomics Integration of Serum Proteome and Autoantibody Profiles Reveals Diagnostic and Prognostic Biomarkers in Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 08:44:55","doi":"10.21203/rs.3.rs-7933102/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-19T11:35:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T15:41:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T10:55:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T06:48:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101178287900127203454233167594082212383","date":"2025-11-10T12:40:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129556682277466984246557582975543921775","date":"2025-11-10T08:05:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297368963274815823009190885278700956224","date":"2025-10-29T13:29:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101358943260560329824234001833638639904","date":"2025-10-28T13:47:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62701161263821324197592119216057530988","date":"2025-10-27T22:12:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94574374111945117627408711398992124875","date":"2025-10-27T22:05:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T13:05:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-27T06:18:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T06:16:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuro-Oncology","date":"2025-10-23T14:08:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"49b072a6-6bc8-46f1-b23d-18639678ed14","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:08:36+00:00","versionOfRecord":{"articleIdentity":"rs-7933102","link":"https://doi.org/10.1007/s11060-025-05410-5","journal":{"identity":"journal-of-neuro-oncology","isVorOnly":false,"title":"Journal of Neuro-Oncology"},"publishedOn":"2026-02-17 15:58:11","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-11-05 08:44:55","video":"","vorDoi":"10.1007/s11060-025-05410-5","vorDoiUrl":"https://doi.org/10.1007/s11060-025-05410-5","workflowStages":[]},"version":"v1","identity":"rs-7933102","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7933102","identity":"rs-7933102","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}