Glioma operation boundary identification marker and application thereof

Abstract

The invention discloses a glioma operation boundary identification marker and application thereof. The invention confirms that SERPINA3 can be used as a marker for identifying a glioma invasion boundary for the first time, can effectively assist operation planning and tumor excision before or during glioma operation, improves the accuracy of operation excision, provides an effective molecular tool for clinical diagnosis and treatment of glioma, and has a remarkable application prospect.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a surgical boundary identification marker for gliomas and its application. Background Technology

[0002] Gliomas are the most common and deadliest primary brain tumors, with a poor prognosis. Even with multimodal therapy, including surgery, radiotherapy, and chemotherapy, the two-year survival rate after a diagnosis of glioblastoma (GBM) is less than 10%. GBM is the most common and most aggressive primary brain tumor, and one of the most challenging malignant tumors of all. GBM is characterized by high invasiveness and indistinct borders, with few early symptoms. By the time it is clinically discovered, the tumor has often already extensively infiltrated the body, making complete surgical removal difficult, resulting in a high recurrence rate and poor patient prognosis. Adjuvant therapies such as radiotherapy and chemotherapy have limited effectiveness in prolonging survival, highlighting the urgent need for new surgical adjuvant methods to improve treatment outcomes.

[0003] Glial cells are the most numerous cell type in the nervous system, participating in neural support, myelination, and immune regulation. Early GBM development is primarily driven by molecular mutations, while later progression is influenced by the tumor microenvironment. Glial cells play a crucial role in the GBM invasion boundary and the tumor-nerve interface, and their phenotypic diversity and functional characteristics are key to tumor invasion and microenvironment regulation.

[0004] Traditional surgical resection of gliomas relies primarily on intraoperative visual observation by the surgeon or conventional imaging (such as MRI or CT), which has significant limitations in identifying tumor boundaries. The boundary between the tumor and surrounding glial tissue is often indistinct, especially in invasive border areas, leading to increased risks of tumor residue and postoperative recurrence. Current methods struggle to accurately locate microinvasive lesions, and the lack of real-time, visual intraoperative aids further limits surgical resection effectiveness. Accurate identification of tumor boundaries remains an unresolved challenge in clinical practice. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art. To this end, the present invention proposes the application of a reagent for detecting SERPINA3 in the preparation of products for identifying the invasive boundaries of gliomas.

[0006] The present invention also proposes a product comprising a reagent for detecting SERPINA3.

[0007] The present invention also proposes a detection system.

[0008] According to a first aspect of the invention, the application of a reagent for detecting SERPINA3 in the preparation of products that identify the invasion boundaries of gliomas is proposed.

[0009] In some embodiments of the present invention, the glioma includes glioblastoma.

[0010] In some embodiments of the present invention, identifying the glioma invasion boundary includes distinguishing different glioma invasion boundary types.

[0011] In some embodiments of the present invention, the different invasion boundary types of the glioma include a tumor core area, a transition area, and a normal adjacent area.

[0012] In some embodiments of the present invention, SERPINA3 is highly expressed in glial cells at the tumor margin, which can accurately locate the glioma invasion boundary and is related to patient prognosis.

[0013] In some embodiments of the present invention, SERPINA3 is highly expressed in GFAP. + Glial cell cytoplasm and OLIG2 + Oligodendrocytes.

[0014] In some embodiments of the present invention, SERPINA3 expression is concentrated in the tumor-adjacent border region.

[0015] In some embodiments of the present invention, the reagent is capable of detecting the expression level of SERPINA3 protein in a sample.

[0016] In some embodiments of the present invention, the reagent is selected from: Specific identification SERPINA3 Gene probes; or Specific amplification SERPINA3 Gene primers; or A binding agent that specifically binds to the SERPINA3 protein.

[0017] In some embodiments of the present invention, the SERPINA3 The gene's NCBI number is NG_012879.1.

[0018] In some embodiments of the present invention, the binding agent includes an antibody that specifically binds to the SERPINA3 protein, an antibody functional fragment, or a conjugated antibody.

[0019] In some embodiments of the present invention, the product includes reagents for detecting the expression level of the SERPINA3 protein using nucleic acid hybridization technology, nucleic acid amplification technology, protein immunoassay technology, sequencing technology, chromatography technology, and mass spectrometry technology.

[0020] In some embodiments of the present invention, the protein immunotherapy technique includes immunohistochemistry (IHC) or multiplex immunostaining (mIHC).

[0021] In some embodiments of the present invention, the nucleic acid hybridization technique includes in situ hybridization.

[0022] In some embodiments of the present invention, the sequencing technology includes spatial transcriptome sequencing using the 10× Genomics Visium platform.

[0023] In some embodiments of the present invention, the product includes a chip, kit, or nucleic acid membrane strip capable of detecting the expression level of SERPINA3 protein.

[0024] In some embodiments of the present invention, the reagent is used to detect tissue or blood.

[0025] In some embodiments of the present invention, the product also has any one of the following functions: A1) Detection of gliomas; A2) Predicting prognostic risk in glioma patients; A3) Assist in surgical resection of glioma.

[0026] In some embodiments of the present invention, the method of using the product includes: using the reagent for detecting SERPINA3 to detect tumor tissue in order to determine the boundaries of tumor invasion.

[0027] In some embodiments of the present invention, the method of using the product specifically includes: quantifying the expression level of SERPINA3 in tissue or blood samples using PCR or RT-qPCR combined with specific primers targeting SERPINA3; locating SERPINA3-positive cells in tissue sections by immunohistochemistry (IHC) or multiplex immunostaining (mIHC), and assessing their number and distribution by combining image analysis; and also using in situ hybridization (ISH) or RNAscope methods to achieve in situ expression detection of SERPINA3 at high spatial resolution, thereby comprehensively evaluating the expression characteristics of SERPINA3 in tumor tissue and surrounding tissues.

[0028] In some embodiments of the present invention, the product can be confirmed for use in determining the extent of tumor invasion during surgery and assisting surgeons in identifying boundary areas under a microscope.

[0029] In some embodiments of the present invention, the assisted surgical resection of glioma includes real-time display of tumor boundaries before or during glioma resection surgery, reducing tumor residue and lowering the risk of postoperative nerve damage.

[0030] In some embodiments of the present invention, the auxiliary surgical resection of glioma includes detecting SERPINA3 expression in tissue or imaging samples obtained before or during glioma resection surgery. This can assist surgeons in assessing tumor boundaries and microinvasive areas, achieving maximum resection of tumor tissue while protecting adjacent nerve tissue.

[0031] In some embodiments of the present invention, the specific method for assisting surgical resection of glioma includes: using the reagent for detecting SERPINA3 to detect tumor tissue to determine the tumor invasion boundary, and then performing surgical resection of the glioma.

[0032] In some embodiments of the present invention, the specific method for assisting surgical resection of glioma is used to guide surgeons in performing glioma surgery, achieving maximum tumor resection while protecting adjacent neurons.

[0033] According to a second aspect of the invention, a product is provided comprising a reagent for detecting SERPINA3.

[0034] In some embodiments of the present invention, the reagent is capable of detecting the expression level of SERPINA3 protein in a sample.

[0035] In some embodiments of the present invention, the reagent is selected from: Probes that specifically recognize the SERPINA3 gene; or Primers for specific amplification of the SERPINA3 gene; or A binding agent that specifically binds to the SERPINA3 protein.

[0036] In some embodiments of the present invention, the SERPINA3 The gene's NCBI number is NG_012879.1.

[0037] In some embodiments of the present invention, the binding agent includes an antibody that specifically binds to the SERPINA3 protein, an antibody functional fragment, or a conjugated antibody.

[0038] In some embodiments of the present invention, the product includes reagents for detecting the expression level of the SERPINA3 protein using nucleic acid hybridization technology, nucleic acid amplification technology, protein immunoassay technology, sequencing technology, chromatography technology, and mass spectrometry technology.

[0039] In some embodiments of the present invention, the protein immunotherapy technique includes immunohistochemistry (IHC) or multiplex immunostaining (mIHC).

[0040] In some embodiments of the present invention, the nucleic acid hybridization technique includes in situ hybridization.

[0041] In some embodiments of the present invention, the product includes a chip, kit, or nucleic acid membrane strip capable of detecting the expression level of the SERPINA3 protein.

[0042] In some embodiments of the present invention, the reagent is used to detect tissue or blood.

[0043] In some embodiments of the present invention, the product also has any one of the following functions: A1) Detection of gliomas; A2) Predicting prognostic risk in glioma patients; A3) Assist in surgical resection of glioma.

[0044] In some embodiments of the present invention, the method of using the product includes: using the reagent for detecting SERPINA3 to detect tumor tissue in order to determine the boundaries of tumor invasion.

[0045] In some embodiments of the present invention, the method of using the product specifically includes: quantifying the expression level of SERPINA3 in tissue or blood samples using PCR or RT-qPCR combined with specific primers targeting SERPINA3; locating SERPINA3-positive cells in tissue sections by immunohistochemistry (IHC) or multiplex immunostaining (mIHC), and assessing their number and distribution by combining image analysis; and also using in situ hybridization (ISH) or RNAscope methods to achieve in situ expression detection of SERPINA3 at high spatial resolution, thereby comprehensively evaluating the expression characteristics of SERPINA3 in tumor tissue and surrounding tissues.

[0046] In some embodiments of the present invention, the product can be confirmed for use in determining the extent of tumor invasion during surgery and assisting surgeons in identifying boundary areas under a microscope.

[0047] In some embodiments of the present invention, the assisted surgical resection of glioma includes real-time display of tumor boundaries before or during glioma resection surgery, reducing tumor residue and lowering the risk of postoperative nerve damage.

[0048] In some embodiments of the present invention, the auxiliary surgical resection of glioma includes detecting SERPINA3 expression in tissue or imaging samples obtained before or during glioma resection surgery. This can assist surgeons in assessing tumor boundaries and microinvasive areas, achieving maximum resection of tumor tissue while protecting adjacent nerve tissue.

[0049] In some embodiments of the present invention, the specific method for assisting surgical resection of glioma includes: using the reagent for detecting SERPINA3 to detect tumor tissue to determine the tumor invasion boundary, and then performing surgical resection of the glioma.

[0050] In some embodiments of the present invention, the specific method for assisting surgical resection of glioma is used to guide surgeons in performing glioma surgery, achieving maximum tumor resection while protecting adjacent neurons.

[0051] According to a third aspect of the present invention, a detection system is provided, comprising: B1 detection module: collects samples from patients to be tested, measures the expression level of the biomarker described in the first aspect of the present invention, and outputs the biomarker expression level data to the analysis module; B2 Analysis Module: Obtain the above detection results and determine the surgical boundary based on the expression level.

[0052] According to some embodiments of the present invention, at least the following beneficial effects are achieved: For the first time, through multi-omics integrated analysis and cell experiments, the present invention screened out highly expressed SERPINA3 in glioma tissue, identified its independently biologically significant glial subpopulations and glioma invasion boundaries, revealed its key role in tumor invasion and proliferation, and confirmed that SERPINA3 can serve as a marker for identifying glioma invasion boundaries. It can effectively assist in preoperative or intraoperative surgical planning and tumor resection of gliomas, improve the accuracy of surgical resection, and provide an effective molecular tool for the clinical diagnosis and treatment of gliomas, with significant application prospects. Attached Figure Description

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein: Figure 1 TSNE plot for identifying common markers of SERPINA3+ neurological injury-associated astrocytes and oligodendrocytes in the single-cell atlas of pan-brain diseases; Figure 2 The graph shows the detection results of SERPINA3+ neurodegenerative glial cell enrichment in brain tumors and its close association with poor prognosis of glioma. A shows the distribution of differentially expressed genes; B shows the Kaplan-Meier overall survival analysis results of the TCGA-GBM / LGG cohort; and C shows the validation results of SERPINA3 as a prognostic / discriminatory indicator for glioma. The left graph is the Kaplan-Meier overall survival curve, dividing patients into high-expression and low-expression groups based on SERPINA3 expression levels. The high-expression group showed significantly shorter overall survival, suggesting that high SERPINA3 expression is associated with poor prognosis. The right graph is the ROC curve analysis, using SERPINA3 expression to distinguish between TCGA tumor samples and GTEx normal brain tissue samples. The area under the curve (AUC) was 0.978, indicating that SERPINA3 has a high ability to distinguish between tumors and normal tissues and can be used as an auxiliary detection and assessment indicator for glioma. Figure 3The image shows the results of mcIHC and mIHC in various clinical cohorts of glioma patients, validating the presence of SERPINA3+ tumor-associated glial cells. Figure 4 This is a graph showing the qPCR test results, where "***" indicates... p <0.001; Figure 5 The results of mIHC revealing the interface between tumor-associated glial cells and normal neural tissue. Figure 6 The figure shows the results of batch transcriptomics analysis showing that the expression level of SERPINA3 in the glioblastoma cohort was significantly higher than that in adjacent normal tissues. Figure 7 The figure shows the results of single-cell sequencing detection of significantly higher SERPINA3 expression levels in glioblastoma cohorts compared to adjacent normal tissues. Figure 8 The figure shows the results of detecting that the chromatin opening level of SERPINA3 in the glioblastoma cohort was significantly higher than that in adjacent normal tissues by single-cell chromatin transposase accessibility assay. Figure 9 The image shows the results of flow cytometry analysis of SERPINA3+ glial cells enriched in tumor tissue compared to adjacent normal tissue. Figure 10 The image shows the results of inferCNV testing, which revealed that tumor-associated glial cells did not exhibit abnormal chromatin copy number variations, suggesting that they were not tumor cells. Figure 11 The image shows the detection results of PDX-10× Visium, revealing SERPINA3 as a unique marker of tumor-associated glial cells, which is almost not expressed by tumor cells. Figure 12 The image shows the results of 10× Visium assay revealing the interface between tumor-associated glial cells and normal neural tissue. Figure 13 The image shows the results of Cosmx single-cell precision spatial transcriptomics analysis revealing the interface between tumor-associated glial cells and normal neural tissue. Figure 14 This image shows the results of flow cytometry analysis revealing the interface between tumor-associated glial cells and normal neural tissue. Detailed Implementation

[0054] The following will describe the concept and technical effects of the present invention clearly and completely with reference to the embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall be followed. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0055] Example 1: Construction of single-cell atlas of glial cells at the boundary of glioma and acquisition of SERPINA3 marker This embodiment utilizes single-cell sequencing data of normal brain / brain disease from the National Center for Biotechnology Information (NCBI) public database, as well as self-tested single-cell sequencing data of gliomas, to comprehensively analyze normal control brain tissue and brain tissue related to various brain diseases. Normal control brain tissue samples were derived from multiple major anatomical brain regions, including the amygdala (AMY), cerebral vasculature (BVasc), prefrontal and temporal cortex (PFC, TC and their associated area PFC-TC), hippocampus (HPC / Hippo), substantia nigra (SN), caudate nucleus (CAUD), putamen (PUT), and subventricular zone (SVZ), totaling approximately 90 normal samples. Regarding brain tissue related to various brain diseases, approximately 200 samples were collected, covering 18 types of brain diseases. These can be broadly categorized into neurodegenerative diseases (such as Alzheimer's disease and Parkinson's disease), cerebrovascular and demyelinating diseases (such as cerebral hemorrhage and multiple sclerosis), epilepsy and trauma-related diseases (such as temporal lobe epilepsy and traumatic brain injury), infection and drug-related diseases (such as COVID-19 infection and opioid use disorder), and primary and secondary brain tumors (including multiple subtypes of glioblastoma, other grades of gliomas, and brain metastases from different primary tumor sources).

[0056] After uniform quality control and removal of low-quality cells from single-cell sequencing data of normal brain / brain diseases and self-tested single-cell sequencing data of gliomas, pan-disease astrocyte atlases consisting of approximately 250,000 cells and pan-disease oligodendrocyte atlases consisting of approximately 550,000 cells were constructed based on the above normal and disease sample data. On this basis, approximately 4,000 genes were selected for subsequent analysis. Subsequently, the data were standardized, normalized, and hypervariable genes were screened. Principal component analysis (PCA) was used for dimensionality reduction, and unsupervised clustering of cells was performed using Louvain or Leiden clustering algorithms. Finally, the UMAP method was used to realize the two-dimensional visualization of different cell subpopulations.

[0057] Cell type annotation, combined with known marker genes and single-cell annotation databases, was used for identification, thereby systematically constructing a pan-disease atlas of tumor-associated astrocytes and oligodendrocytes, such as... Figure 1 As shown in the figure, SERPINA3 exhibits significant and specific high expression in tumor-associated glial cells in brain tumor (including glioma and brain metastases) samples, suggesting that SERPINA3 can serve as an important molecular marker for identifying glial cells in brain tumors and surgical boundaries of gliomas, and has potential application value.

[0058] Example 2: Application of SERPINA3 as a prognostic marker for glioblastoma This embodiment provides the application of SERPINA3 as a prognostic marker for glioblastoma, and the specific verification is shown below: (1) Data source: Transcriptome expression data and corresponding clinical follow-up information of glioblastoma (GBM) or glioma (GBM / LGG) were downloaded from the TCGA database, including overall survival (OS) and survival status.

[0059] (2) Grouping method: Patients were grouped according to the expression level of SERPINA3. The median (or the optimal cutoff value) was used to divide the sample into a high expression group and a low expression group of SERPINA3.

[0060] (3) Survival analysis: The Kaplan-Meier method was used to plot the overall survival curve, and the log-rank test was used to assess the survival differences between the two groups.

[0061] (4) ROC assessment: Using SERPINA3 expression level as the detection index, ROC curves are plotted and the area under the curve (AUC) is calculated to assess the discriminative ability of SERPINA3 for glioblastoma samples; or time-dependent ROC curves are plotted with survival outcome as the endpoint to assess the predictive ability of SERPINA3 for patient survival outcome.

[0062] The results are as follows Figure 2 As shown in Figure A, the differential expression characteristics of cell populations were compared under two conditions: "Disease versus Control" and "Tumor versus Non-tumor." The horizontal axis represents the proportion of differentially expressed cells (diff_pct), and the vertical axis represents the significance level (…). The results showed that SERPINA3 was significantly upregulated in the disease state / tumor region and co-enriched with immune / glial-related markers such as GFAP and HLA-DRA, suggesting that SERPINA3 is closely related to abnormal glioma-associated microenvironment and enhanced glial cell reactivity.

[0063] As shown in Figure B, the Kaplan-Meier overall survival analysis of the TCGA-GBM / LGG cohort compared high and low TAA and TAO scores. The results showed a significantly lower overall survival probability in the high-score group, suggesting that tumor-associated astrocytes (TAAs) and tumor-associated oligodendrocytes (TAOs) are associated with poor prognosis, supporting a significant association between abnormal glial cell status and glioma progression and clinical outcomes.

[0064] As shown in Figure C, the Kaplan-Meier overall survival curves, when patients were divided into high-expression and low-expression groups based on SERPINA3 expression levels, revealed a significantly shorter overall survival in the high-expression group, suggesting that high SERPINA3 expression is associated with poor prognosis. ROC curve analysis using SERPINA3 expression to distinguish between TCGA tumor samples and GTEx normal brain tissue samples showed an area under the curve (AUC) of 0.978, indicating that SERPINA3 has a high ability to distinguish between tumors and normal tissues and can serve as an auxiliary detection and assessment indicator for gliomas.

[0065] In summary, SERPINA3 expression was elevated in glioblastoma samples, and patients in the high SERPINA3 expression group had significantly shorter overall survival (log-rank test, P-value significant), suggesting that high SERPINA3 expression is associated with poor prognosis. Further ROC curve analysis showed that SERPINA3 has good discriminative / predictive ability for glioblastoma (AUC significantly greater than 0.5), indicating that SERPINA3 can serve as a prognostic biomarker for risk stratification and prognostic assessment in glioblastoma.

[0066] Example 3: Application of SERPINA3 as a marker for identifying surgical boundaries in glioblastoma This embodiment demonstrates the application of SERPINA3 as a marker for identifying surgical boundaries in glioblastoma, as shown in the following specific verification: 1. Multiplex immunofluorescence verification To verify the expression characteristics of SERPINA3 in GBM tissue and its invasion boundary, multiplex immunofluorescence assays were performed on surgically resected samples.

[0067] Sample Information: Surgical resection specimens were collected from 40 patients with brain tumors, including 20 cases of glioblastoma (GBM) and 20 cases of brain metastases (BrM). Additionally, 10 adjacent (near) tissue samples matched for glioma cases were collected as controls. All sample acquisition and use were approved by the ethics committee. A summary of clinical information, including patient gender, age, primary disease, and tumor / lesion location, is shown in Table 1.

[0068] Table 1

[0069] The main testing reagents are shown in Table 2, and the testing platform is shown in Table 3.

[0070] Table 2

[0071] Note: All antibodies have been pre-tested to ensure specificity and signal-to-noise ratio.

[0072] Table 3 Detection Platform

[0073] The procedure for multiplex immunofluorescence staining is as follows: (1) Overview of the overall process The samples were sequentially baked, dewaxed to hydration, antigen retrieval (alkaline retrieval solution, pH 9.0), non-specific binding sites were blocked, and cyclic labeling was performed: each round included "primary antibody incubation → HRP secondary antibody binding → XTSA fluorescence development → antibody elution", DAPI counterstaining of cell nuclei, mounting, and scanning imaging. The specific steps are shown in Table 4, and the fluorescence channel allocation is shown in Table 5.

[0074] Table 4

[0075] Table 5 Fluorescence Channel Allocation

[0076] Spectral crosstalk between all channels is corrected using a filter set and a software spectral separation algorithm.

[0077] (2) Image analysis and quantification Single-cell quantitative analysis of multichannel immunofluorescence images was performed using QuPath software (version XX). First, slice images containing DAPI, SERPINA3, GFAP, NeuN, and OLIG2 channels were imported into QuPath, and necessary channel registration (if applicable), background subtraction, and intensity normalization were performed. Two independent observers manually delineated the tumor region (ROI) and adjacent normal region (ROI) based on tissue morphology and immunomarker signals, using the outer contour of the tumor ROI as the tumor boundary. Fixed-width analysis bands were set at the tumor boundary for spatial distribution comparison; for example, the region extending 0–500 μm inward from the boundary was defined as the inner tumor boundary region (Tumor-edge belt), and the region extending 0–500 μm outward from the boundary was defined as the adjacent normal region (Adjacent belt).

[0078] Subsequently, cell nuclei were detected using the DAPI channel, and single-cell segmentation was performed to obtain the nuclear and cytoplasmic regions of each cell. Signal intensity parameters (e.g., mean fluorescence intensity or integrated intensity) for each cell in each channel were extracted, and the spatial coordinates of each cell were recorded. Thresholds were determined using the negative control region or background signal from the same slice. Cells were identified as positive for the marker when the average intensity in the GFAP, NeuN, or OLIG2 channels exceeded the corresponding threshold, defined as GFAP-positive glial cells, NeuN-positive neurons, and OLIG2-positive oligodendrocytes, respectively. After cell type determination, the SERPINA3 positivity threshold was further determined using the same method. Cells were identified as SERPINA3-positive cells when the average intensity in the SERPINA3 channel exceeded the SERPINA3 threshold.

[0079] The positive proportion of SERPINA3 in different cell types was calculated as follows: the number of cells that were also positive for SERPINA3 in GFAP-positive cells was counted and divided by the total number of GFAP-positive cells to obtain the positive proportion of SERPINA3 in GFAP-positive cells; similarly, the positive proportion of SERPINA3 in NeuN-positive cells and OLIG2-positive cells was counted to obtain the expression distribution of SERPINA3 in GFAP-positive glial cells, NeuN-positive neurons and OLIG2-positive oligodendrocytes.

[0080] The spatial distribution of SERPINA3 at the tumor boundary and adjacent normal regions was quantified using a zoned density method: the number of SERPINA3-positive cells was counted in both the tumor-edge belt and the adjacent normal region, and then divided by the corresponding region area to obtain the SERPINA3-positive cell density (cells / mm2). Further, an enrichment index was calculated, which is the ratio of the SERPINA3-positive cell density in the tumor-edge belt to that in the adjacent normal region. An enrichment index greater than 1 indicates a relative enrichment of SERPINA3-positive cells in the tumor boundary region, while an enrichment index less than 1 indicates a relative enrichment of SERPINA3-positive cells in the adjacent normal region. After exporting the single-cell detection results and intensity parameters of each channel from QuPath to a CSV file, statistical software was used to calculate and plot the positive proportion, cell density, and enrichment index.

[0081] 2. Immunohistochemical verification of SERPINA3 expression in GBM tissues To verify the expression characteristics of SERPINA3 in GBM tumors and their invasive boundaries, immunohistochemical detection was performed on surgically resected GBM tissue and adjacent normal tissue, with non-lesioned brain tissue from epilepsy surgery used as a control. The samples were consistent with those used in the multiplex immunofluorescence verification in section 1. All samples were approved by the ethics committee.

[0082] (1) Main reagents and materials Antibodies: SERPINA3 recombinant monoclonal antibody (rabbit anti-human, Abcam, ab205198), GFAP monoclonal antibody (mouse anti-human, Servicebio, GB12100), OLIG2 polyclonal antibody (rabbit anti-human, Servicebio, GB11766), NeuN recombinant monoclonal antibody (mouse anti-human, Abcam, GB15138).

[0083] Staining reagents: DAB staining kit (Sigma-Aldrich), hematoxylin counterstain. Blocking solution (5% BSA or commercial blocking solution), PBS buffer (pH 7.4), 3% H2O2 solution.

[0084] Instruments: Slide burner: 60°C, optical microscope (Leica or Zeiss optional), whole slide scanner (ZEISSAXIOSCAN 7).

[0085] (2) Sample preparation GBM tissue and control brain tissue were fixed in 10% formalin solution for 24–48 hours, and then paraffin-embedded sections (4–5 μm thick) were prepared. The prepared sections were then baked (60°C, 30 min).

[0086] (3) Dewaxing and hydration The dewaxing and hydration steps are shown in Table 6.

[0087] Table 6

[0088] (4) Antigen retrieval Heat-induced antigen retrieval (HIER) was performed using a pH 9.0 alkaline Tris-EDTA (pH 9.0) alkaline antigen retrieval solution. The solution was heated at 98°C for 20 min, followed by natural cooling to room temperature for 20-30 min. The sample was then rinsed with distilled water for 1-2 min.

[0089] (5) Endogenous peroxidase blockade Incubate with 3% H2O2 solution at room temperature for 10 min. Rinse three times with PBS, 2 min each time.

[0090] (6) Closed Incubate in the blocking solution at room temperature for 15–30 minutes. Aspirate excess liquid and keep the tissue moist.

[0091] (7) Primary antibody incubation The antibodies used and the antibody incubation conditions are shown in Table 7.

[0092] Table 7

[0093] (8) Secondary antibody incubation Wash the slides: Rinse three times with PBS, 2 minutes each time.

[0094] Add HRP-labeled secondary antibody (host-specific), incubate at 37°C for 10-20 min; wash with PBS 3 times, 2 min each time.

[0095] (9) Color development and re-dyeing DAB development, 3-10 min at room temperature (optimize according to signal intensity). Rinse with distilled water for 1-2 min. Counterstain with hematoxylin, 1-2 min at room temperature. Rinse with water until pale blue.

[0096] (10) Dehydration and sealing The dehydration and sealing steps are shown in Table 8.

[0097] Table 8

[0098] (11) Microscopic observation and image acquisition The location of SERPINA3 positive signals in tissues was observed using an optical microscope.

[0099] Full-section scanning was performed, and photographs were taken to record the staining of the tumor boundaries and control tissues.

[0100] Typical results: SERPINA3 signals are mainly distributed in GFAP + Glial cell cytoplasm and OLIG2 + Oligodendrocytes, especially concentrated in the area where the tumor meets the adjacent tissue, NeuN + The neuronal signal was weak or no obvious expression was observed.

[0101] Experimental results: The results are as follows Figure 3 As shown in the figure, SERPINA3 exhibits a brownish-yellow cytoplasmic signal in GBM tumor tissue, primarily located in GFAP. + Glial cells and OLIG2 + In oligodendrocytes and adjacent normal tissues, SERPINA3 signaling was significantly reduced and showed a scattered distribution. In control brain tissue, SERPINA3 expression was almost invisible. These results indicate that SERPINA3 can serve as a marker for glial cells at the GBM infiltration boundary, and its expression level is significantly increased in the tumor boundary region.

[0102] 3. Detection of SERPINA3 mRNA expression in brain tissue or cell samples by qPCR To verify the differential expression of SERPINA3 in patients with different brain diseases and normal controls, its mRNA level was detected using a commercially available qPCR kit. The experimental procedure included tissue / cell sample preparation, total RNA extraction, cDNA reverse transcription, qPCR amplification, and data analysis. All patient samples were approved by the ethics committee.

[0103] (1) Sample source and processing Sample sources included tumor tissue and adjacent normal tissue from GBM patients (n=10), brain tissue from Alzheimer's disease (AD) patients (n=5), and non-lesion brain tissue from epilepsy surgeries (normal controls, n=5). Fresh tissue samples were immediately frozen in liquid nitrogen after collection, or placed in RNA protection solution (such as RNAlater) at 4°C overnight and then stored at -80°C. Approximately 30 mg of tissue blocks were taken and homogenized to a fine powder using a pre-cooled mortar and pestle or a tissue homogenizer.

[0104] (2) Total RNA extraction RNA was extracted using a commercial RNA extraction kit (e.g., QIAGEN RNeasy Mini Kit, Cat# 74104). The kit was followed according to the instructions: after homogenizing the tissue, lysis buffer was added to completely lyse the cells and release RNA. The RNA was then bound to a silica gel column, impurities were washed away, and finally, RNA was eluted with RNase-free water. RNA concentration and purity were determined using NanoDrop or Qubit, with an A260 / A280 ratio between 1.8 and 2.0.

[0105] (3) cDNA reverse transcription A commercial reverse transcription kit (such as the Thermo Fisher RevertAid First Strand cDNASynthesis Kit, Cat# K1622) was used. Each reaction volume was 20 μL, including 1 μg RNA template, 1 μL random primers, 1 μL dNTP mixture, 4 μL 5× reverse transcription buffer, 1 μL RevertAid reverse transcriptase, and RNase-free water to a final volume of 20 μL. The reverse transcription procedure was as follows: RNA and primers were pre-denatured at 65°C for 5 minutes, then cooled. The reverse transcription mixture was added, and the reaction was incubated at room temperature for 2 minutes, then at 42°C for 60 minutes, and finally terminated at 70°C for 5 minutes.

[0106] (4) qPCR kit amplification The SERPINA3 quantitative detection kit (SYBR Green method, commercially available, such as Applied Biosystems qPCR Kit, Cat# 4309155) was used, following the manufacturer's instructions. Each reaction volume was 20 μL, and the composition is shown in Table 9 below.

[0107] Table 9

[0108] The primers and Master Mix in the commercial kit have been optimized and validated, and can be directly used for SERPINA3 and internal controls (such as GAPDH) amplification. The qPCR amplification program was performed according to the kit instructions: pre-denaturation at 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 15 seconds, annealing / extension at 60°C for 1 minute, and melting curve analysis was performed to verify amplification specificity. Each sample was tested in triplicate, and an empty template control (NTC) was included to exclude contamination.

[0109] (5) Data Analysis The relative expression level of SERPINA3 was calculated using the ΔΔCt method:

[0110] Test results as follows Figure 4 As shown in the figure, the relative expression level of SERPINA3 was quantitatively analyzed using total RNA from tissue samples (e.g., RT-qPCR). The results showed that SERPINA3 expression was significantly increased in brain tumor tissues compared to adjacent non-tumor brain tissues matched for gliomas. Specifically, SERPINA3 expression in GBM tissues was significantly higher than in adjacent non-tumor tissues, while the increase was even more significant in brain metastases (BrM) tissues. Statistical analysis indicated significant differences, suggesting that SERPINA3 exhibits a stable upregulation pattern in brain tumor tissues and can be used for the detection / aided diagnosis of brain tumor-related molecular markers or as a basis for potential interventional targets.

[0111] 4. Analysis of GBM and normal brain tissue idling based on 10× Visium spatial transcriptomics To verify the spatial expression distribution of SERPINA3 under different brain tissue conditions and the characteristics of related cell type modules, this embodiment uses the 10×Genomics Visium platform to perform spatial transcriptome sequencing on GBM patient tissues and normal brain tissues, and combines the idle algorithm to correct the background signal to improve the accuracy and reproducibility of the results.

[0112] (1) Tissue processing and fragment preparation The spatial transcriptome (ST) samples used in this embodiment were derived from surgically removed tissue and corresponding normal brain tissue (or adjacent non-tumor brain tissue) from glioblastoma (GBM) patients, consistent with those in section 1 and multiplex immunofluorescence validation. After pathological confirmation, the tissue samples were used for 10× Genomics Visium spatial transcriptome sequencing. To ensure spatial sequencing quality, the tissue was immediately cryogenically treated and cryopreserved after surgical removal, with a section thickness of approximately 10 μm (adjustable according to Visium platform standards). Tissue attachment, histological staining, and imaging were performed on Visium spatial transcriptome slides, followed by in situ reverse transcription and library construction.

[0113] The spatial transcriptome (ST) and single-cell transcriptome (scRNA-seq) data used in this embodiment were all obtained from publicly available databases: the ST and scRNA-seq data supporting the results of this study have been stored in the National Genomics Data Center (NGDC) of China, with accession numbers HRA004677 and CRA011176, respectively. To protect personal privacy, access control was implemented for the raw human sequencing data. Processed, reproducible analysis data can be obtained from Figshare (DOI: 10.6084 / m9.figshare.22434341) or the National Genomics Data Center of China (accession number OMIX003593). All analyses in this embodiment were performed based on the aforementioned publicly available processed data.

[0114] (2) RNA capture and sequencing library preparation Following the official 10×Genomics operating procedures, tissue permeation, mRNA capture, and reverse transcription were performed to obtain a cDNA library containing spatial location information. The sequencing platform used was an Illumina NovaSeq 6000 in paired-end sequencing mode with read lengths of 28 bp (read 1, spatial barcode) + 90 bp (read 2, transcribed sequence).

[0115] (3) Raw data processing and idling correction The sequencing data were initially compared and quantified using Space Ranger to generate a gene expression matrix and tissue location file (tissue_positions_list.csv) for each capture spot.

[0116] Based on this, this embodiment introduces the principle of idling algorithm for background removal, specifically including the following steps: 1) Background signal modeling: Using idle slides and spots marked as "non-tissue region (in_tissue = 0)" on each slide, the average expression level of each gene was calculated to obtain an ambient RNA profile.

[0117] This process is based on the assumption that signals in extra-tissue regions mainly originate from free RNA in solution or non-specific adsorption, and their expression matrix can be regarded as a background noise model.

[0118] 2) Pollution coefficient estimation: Assuming the observed expression (X) of each actual tissue spot i ) is expressed by real cells (S) iIt is formed by linear superposition of the background component (B): X i =(1 α i )S i +α i B Where α i The pollution rate coefficient (between 0 and 1).

[0119] α for all spots is estimated using maximum likelihood estimation (MLE) or Bayesian prior estimation. i The algorithm solves for the spatially distributed contamination rate matrix. In principle, this is equivalent to performing a proportional deconvolution on the background component at each spatial point.

[0120] 3) Signal correction and deconvolution: The estimated α_i is applied to the expression matrix to perform background correction on the original expression vector X_i of the i-th sample (or cell), resulting in the corrected expression value. The formula for calculating _i is as follows: \hat{S}_i=\frac{X_i-\alpha_i B}{1-\alpha_i} Where X_i is the observed expression value before correction, B is the background expression value (or background vector), and α_i is the background contribution ratio coefficient. _i represents the corrected expression value.

[0121] This correction process is mathematically equivalent to background deconvolution of the original matrix, thereby recovering the true cellular transcription signal from the observed signal.

[0122] To ensure stability, for high-noise spot (α) i >0.3)) performs low-weight smoothing processing, and suppresses abnormal fluctuations through a local spatial smoothing kernel.

[0123] 4) Spatial smoothing and local weighted estimation: To further enhance spatial continuity, this embodiment introduces spatial constraints based on Markov Random Field (MRF) to jointly model the expression correlation of neighboring spots, thereby eliminating random noise while preserving boundary mutations.

[0124] The basic idea is: assuming that adjacent spots have similar biological states, their expression values ​​follow a joint Gaussian distribution (P(S)∝exp( β∑ i,j wij ∥S i S j ∥ 2 The smooth solution is obtained by minimizing the energy function.

[0125] (4) Module scoring and cell type characteristic analysis For the expression matrix after idle correction and spatial smoothing, the scores for the following modules are calculated using Seurat's AddModuleScore function: TAA (Tumor-associated astrocyte) module score; TAO (Tumor-associated oligodendrocyte) module score; NEURON module rating; MES-like module scoring; AC-like module scoring.

[0126] The list of gene modules was derived from previous single-cell transcriptome analysis. Module scores were standardized using Z-scores for inter-group comparisons.

[0127] (5) Spatial positioning and visualization The module scores are mapped back to the Visium slice coordinate system, and digital images of tissue slices are overlaid. To facilitate boundary identification, this embodiment adds a black location signal layer to the result image to represent the tissue boundary corresponding to the SERPINA3 high-expression region. After correction by the idle algorithm, the signal of this boundary region has obvious spatial continuity and tissue specificity, which can be used to distinguish between the tumor core and reactive areas.

[0128] (6) Statistics and Verification Intergroup comparisons were performed using the module scores of each sample as the unit, and the Wilcoxon rank-sum test was used to calculate the significance of differences.

[0129] The results are as follows Figure 5-13 As shown, from Figure 5As can be seen from the data, multiplex immunofluorescence / multiplex immunohistochemistry (mIHC) staining of GBM surgical sections labeled the cell nucleus (DAPI), astrocyte marker GFAP, oligodendrocyte lineage marker OLIG2, neuronal marker NeuN, and the target molecule SERPINA3. Low-magnification panoramic imaging revealed significant regional heterogeneity in the tumor tissue, distinguishing the proliferative zone, adjacent normal zone, peritumoral brain zone, and invasive margin. Spatially, SERPINA3 signaling was not widely present in the adjacent normal zone or the proliferative core, but rather significantly enriched in the invasive margin region. Numerous SERPINA3-positive cells were observed at the invasive margin, and these cells were associated with GFAP. + Glial cells and OLIG2 + Glial / oligodendrocyte lineage cells highly overlap within the same microregion (exhibiting co-localization / proximity co-occurrence image features), suggesting the presence of SERPINA3 at the infiltration margin. + The tumor-associated glial cell population. In contrast, the adjacent normal area is dominated by NeuN. + Neurons and background glial cells predominate, with relatively weak or sparse SERPINA3 signaling. The tumor core exhibits a different marker composition compared to the infiltration margin, and SERPINA3 does not show the same level of enrichment. Furthermore, magnified views of the infiltration margin reveal that the co-occurrence of SERPINA3 and GFAP / OLIG2 is more concentrated in specific glial cell populations (the TAAs / TAOs region marked in the figure), while it is relatively rare in the peritumoral brain zone and adjacent normal areas, supporting the spatial characteristic of SERPINA3 as exhibiting "margin-specific enrichment."

[0130] from Figure 6 As can be seen from the spatial transcriptome analysis of GBM infiltration margin and adjacent normal regions, multiple genes related to reactive glial cells / inflammation showed higher expression levels in the infiltration margin region. Specifically, SERPINA3 expression was significantly increased in the infiltration margin samples, and it was upregulated along with genes such as CHI3L1, TIMP1, GFAP, VEGFA, HLA-DRA, and ANXA1, suggesting that SERPINA3 is closely related to enhanced glial cell reactivity and changes in the local inflammatory microenvironment. In contrast, the adjacent normal regions showed a generally lower expression intensity, indicating that SERPINA3 and its related glial modules are mainly enriched at the tumor infiltration interface.

[0131] from Figure 7 As can be seen from HepaCAM + Single-cell expression visualization analysis was performed on the invasive border and adjacent normal regions within the astrocyte (Astro) subset. GFAP expression was observed in HepaCAM cells in both regions. + Positive cells were observed in all Astro cells, suggesting that this population has a distinct astrocyte attribute; however, SERPINA3 was present at the infiltration boundary in HepaCAM. + A significantly enhanced proportion and expression intensity of SERPINA3 were observed in Astro cells (the number and percentage of SERPINA3-positive cells were significantly increased), while the proportion of SERPINA3-positive cells was significantly decreased in the adjacent normal region. These results indicate that SERPINA3 is mainly induced to express in astrocytes at the tumor invasion boundary, suggesting that it can be used to label the reactive glial cell status associated with tumor invasion.

[0132] from Figure 8 As can be seen from the genomic trajectory display of chromatin accessibility / transcriptional regulatory signals at the invasion boundary (IM) and adjacent normal (AN) regions, the SERPINA3 locus shows a stronger enrichment peak signal near the IM region, while the signal is relatively weaker in the adjacent normal region. This suggests that SERPINA3 at the tumor invasion interface may be driven by regulatory events such as enhancer / promoter activation, leading to upregulation of transcriptional levels. In contrast, the enrichment peak signal at the SLC1A2 locus is relatively stable or shows different trends, indicating that the enhancement signal of SERPINA3 has region-specific characteristics. These results support that the upregulation of SERPINA3 at the invasion boundary is not only an expression phenomenon but also has a potential upstream regulatory basis.

[0133] from Figure 9 As can be seen, multichannel flow cytometry was used to verify the co-expression of SERPIA3 and glial cell markers. Compared with adjacent normal tissues, GFAP in tumor tissues was significantly higher. + The proportion of SERPINA3 positive cells in astrocytes was significantly increased, and GFAP and SERPINA3 showed a clear co-expression trend; meanwhile, in OLIG2 + A significantly increased proportion of SERPINA3-positive cells was also observed in oligodendrocyte populations. Quantitative statistical results showed that GFAP in tumor tissue... + SERPINA3 + Cells and OLIG2 + SERPINA3 + The proportion of cells was significantly higher in the tumor-associated glial cells than in adjacent normal tissues, indicating that SERPINA3 was enriched and expressed in tumor-associated glial cells, supporting its application value as a marker of glial cells associated with the GBM tumor invasion interface.

[0134] from Figure 10 As can be seen from the analysis of copy number variation (CNV) characteristics across the entire genome, different cell states / modules (including tumor-associated astrocytes (TAA), tumor-associated oligodendrocytes (TAO), and other neural cell lineages) exhibit different genomic aberration profiles. The figure shows widespread amplification or deletion signals in certain chromosomal regions, suggesting significant genomic instability in tumor-associated cell populations; while CNV changes are weaker in relatively normal neural cell lineages. These results support the abnormal state of interface-infiltrating tumor cells / tumor-associated glial cells at the genomic level and provide a basis for subsequent screening of molecular markers for interface regions.

[0135] from Figure 11-13 As can be seen, the TAA and TAO modules significantly increased in GBM samples, while the NEURON module decreased; this trend became clearer after idling correction, indicating that background removal effectively reduced false positive signals.

[0136] 5. Flow cytometry reveals the localization of tumor-associated glial cells at the interface between gliomas and normal neural tissue. Surgical specimens from glioma patients were collected and divided into three regions based on tissue origin: Adjacent Normal (AN), Tumor Core (TC), and Invasive Margin (IM). The sample composition was consistent with that in section 1 and multiplex immunofluorescence validation. Tissue samples from each region were minced and prepared into single-cell suspensions using enzymatic digestion methods (e.g., collagenase / trypsin or commercial neural tissue dissociation kits). These suspensions were filtered through a cell filter to remove tissue debris, and erythrocytes were lysed. To minimize interference from myelin and debris, density gradient centrifugation was used to enrich cell components. After counting, an equal volume of cells was used for flow cytometry staining.

[0137] Cells were resuspended in PBS (containing 1% BSA) and blocked with an Fc receptor blocker. Subsequently, antibodies targeting glial cell / lineage markers and SERPINA3 were added for staining: GFAP was used as an astrocyte marker, OLIG2 as an oligodendrocyte marker, and SERPINA3 signal was detected simultaneously. If the target was an intracellular protein, the cells were fixed / permeabilized after surface staining, followed by staining with the corresponding intracellular antibody. Isotype controls and monostain controls were included to determine the gating threshold and fluorescence compensation.

[0138] Data were acquired using flow cytometry and analyzed using flow cytometry software. The gating strategy was as follows: first, cell debris was excluded from the FSC / SSC plot to select the dominant cell population; then, doublet exclusion was performed using FSC-H and FSC-A to retain single-cell populations; finally, dead cells were excluded using live-cell dyes (if applicable). Thresholds for GFAP positivity, OLIG2 positivity, and SERPINA3 positivity were set using single-stain controls / isotype controls. The proportion of SERPINA3-positive cells was counted in both the GFAP-positive and OLIG2-positive populations, i.e., the GFAP positivity rate was calculated. + SERPINA3 + Astrocytes account for a significant portion of GFAP + The proportion of cells, and OLIG2 + SERPINA3 + Oligodendrocytes account for OLIG2 + Cell proportions. Statistical analysis and comparison were performed on the AN, TC, and IM regions separately.

[0139] Two-dimensional density map of flow cytometry, as shown Figure 14 As shown in the figure, the co-expression of SERPINA3 and glial cell markers was significantly enhanced in the invasive border region (IM): the proportion of double-positive cells for SERPINA3 and GFAP was significantly increased in the IM region (approximately 50%–60%), while the proportion was lower and not significantly different in the adjacent normal region (AN) and tumor core region (TC). Similarly, the proportion of double-positive cells for SERPINA3 and OLIG2 was also significantly increased in the IM region (approximately 50%), with no significant difference between AN and TC. These results indicate that SERPINA3-positive glial cells (including GFAP-positive cells) are highly susceptible to glial cell proliferation. + Astrocytes and OLIG2 + Oligodendrocytes are mainly enriched at the invasive margin of gliomas, i.e., the interface between the tumor and normal nerve tissue, suggesting that there are significant tumor-associated glial cell accumulation and activation characteristics in this area.

[0140] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. Application of reagents for detecting SERPINA3 in the preparation of products for identifying the invasion boundaries of gliomas.

2. The application according to claim 1, characterized in that, The identification of glioma invasion boundaries includes distinguishing different types of glioma invasion boundaries; Preferably, the different invasion boundary types of the glioma include a tumor core area, a transition area, and a normal adjacent area.

3. The application according to claim 1, characterized in that, The reagents are selected from: Specific identification SERPINA3 Gene probes; or Specific amplification SERPINA3 Gene primers; or A binding agent that specifically binds to the SERPINA3 protein; Preferably, the binding agent comprises an antibody that specifically binds to the SERPINA3 protein, an antibody functional fragment, or a conjugated antibody.

4. The application according to claim 1, characterized in that, The product includes reagents for detecting the expression level of the SERPINA3 protein using nucleic acid hybridization technology, nucleic acid amplification technology, protein immunoassay technology, sequencing technology, chromatography technology, and mass spectrometry technology. Preferably, the protein immunotherapy technique includes immunohistochemistry and multiplex immunostaining; Preferably, the nucleic acid hybridization technique includes in situ hybridization; And / or, the product includes a chip, kit, or nucleic acid membrane strip capable of detecting SERPINA3 protein expression levels; And / or, the reagent is targeted at a test sample that is tissue or blood.

5. The application according to claim 1, characterized in that, The product also has any of the following functions: A1) Detection of gliomas; A2) Predicting prognostic risk in glioma patients; A3) Assist in surgical resection of glioma; Preferably, the assisted surgical resection of glioma includes: using the reagent for detecting SERPINA3 as described in claim 1 to detect tumor tissue to determine the tumor invasion boundary, and then performing surgical resection of the glioma.

6. The application according to claim 1, characterized in that, The method of using the product includes: using the reagent for detecting SERPINA3 as described in claim 1 to detect tumor tissue in order to determine the boundaries of tumor invasion.

7. A product characterized in that, The product includes reagents for detecting SERPINA3.

8. The product according to claim 7, characterized in that, The reagents are selected from: Probes that specifically recognize the SERPINA3 gene; or Primers for specific amplification of the SERPINA3 gene; or A binding agent that specifically binds to the SERPINA3 protein; Preferably, the binding agent comprises an antibody that specifically binds to the SERPINA3 protein, an antibody functional fragment, or a conjugated antibody; Preferably, the product includes reagents for detecting the expression level of the SERPINA3 protein using nucleic acid hybridization technology, nucleic acid amplification technology, protein immunoassay technology, sequencing technology, chromatography technology, and mass spectrometry technology.

9. The product according to claim 7 or 8, characterized in that, The product also has any of the following functions: A1) Detection of gliomas; A2) Predicting prognostic risk in glioma patients; A3) Assist in surgical resection of glioma; Preferably, the assisted surgical resection of glioma includes: using the product to detect tumor tissue to determine the boundaries of tumor invasion, and then performing surgical resection of the glioma.

10. A detection system, characterized in that, include: B1 detection module: collects samples from patients to be tested, measures the expression level of SERPINA3 as described in claim 1, and outputs the biomarker expression level data to the analysis module; B2 Analysis Module: Obtains biomarker expression data and determines the surgical boundaries of gliomas based on expression levels.

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