Application of nervous system autoantibodies in the preparation of gastric cancer prognosis assessment products

Abstract

The present invention belongs to the field of biotechnology, and specifically relates to the application of nervous system autoantibodies in the preparation of gastric cancer prognosis assessment products. The present invention finds that nervous system autoantibodies have good specificity and sensitivity in the prognosis assessment of gastric cancer. The effect of broad-spectrum nervous system autoantibodies in the prognosis of gastric cancer was detected by the mouse brain TBA method. Patients with negative TBA had a higher overall survival rate and better prognosis of gastric cancer. The area under the ROC curve was 0.784, and the sensitivity and specificity were 0.684 and 0.765, respectively, showing its good characteristics as a marker. Further, the present invention uses antibodies YO, SOX1, Ri, AMPH, PCA2, and Ma2 as marker combinations to evaluate the prognosis of gastric cancer. The area under the ROC dotted line is 0.855, and the sensitivity and specificity are 0.829 and 0.762, respectively, which has better predictive performance.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and specifically relates to the application of nervous system autoantibodies in the preparation of gastric cancer prognosis assessment products. Background Art

[0002] Gastric cancer is one of the most common cancers and one of the most common causes of cancer-related death [1]. Despite some progress in diagnosis and treatment, many patients still have a poor prognosis, which may be related to the biological characteristics of gastric cancer and individual differences [2]. Currently, many studies focus on the prognosis of gastric cancer patients. Various methods have been explored to predict the prognosis of gastric cancer patients. Common clinical tumor markers such as CEA, CA199, CA724, etc. are combined to assist diagnosis and prognosis, and the new clinical biomarker ctDNA is used for disease monitoring [3–5]. In addition, epigenetic changes such as abnormal DNA methylation and histone modification are used as prognostic markers. RT-PCR and molecular pathology methods are used to confirm the expression of specific genes related to tumor invasion, metastasis and staging [6]. Using pathological omics features as prognostic predictors of gastric cancer can improve the prediction effect when combined with TNM staging [7]. In addition, consensus clustering algorithms, CIBERSORT and other algorithms can be used to construct tumor microenvironment features to compare the prognosis of gastric cancer between different subtypes [8]. AI was used to establish a predictive model to assess the response of gastric cancer to drug treatment, lymph node metastasis, and prognosis[9]. Huang et al. developed a deep learning model to diagnose GC and predict the survival outcomes of GC patients by analyzing H&E stained pathological images

[10] .

[0003] However, in recent years, the role of the nervous system has been considered to be a major factor in the development and metastasis of cancer

[11] . From carcinogenesis to malignant growth and metastatic spread, nervous system activity can control the occurrence of cancer and strongly influence the progression and metastasis of cancer

[12] . In addition, neuroimmune interactions also play an important role in the development of gastric cancer. The interaction between nerves and gastric cancer is mediated by many nervous system-related factors, which can not only be synthesized and released by cancer cells and nerve endings, but also participate in regulating cell proliferation, angiogenesis, etc.

[13] . Nervous system autoantibodies are a class of autoimmune antibodies against nervous system tissues or antigens, which can cause nervous system diseases and may also be involved in the occurrence and development of tumors. This antibody has promoted the study of paraneoplastic neurological syndrome (PNS). However, the role of nervous system autoantibodies in predicting the prognosis of gastric cancer has not been reported

[14] . At present, most gastric cancer patients are already in the advanced stage at the time of diagnosis. At this time, their treatment options are limited and their prognosis is poor. Many clinical prognostic indicators are not sensitive and have poor specificity. It is urgent to explore new methods for the diagnosis, prognosis and treatment guidance of gastric cancer.

[0004] [1]IJMS|Free Full-Text|Gastric Cancer:Epidemiology,Risk Factors,Classification,Genomic Characteristics and Treatment Strategies[Internet].[cited 2024Apr 15].Available from:https: / / www.mdpi.com / 1422-0067 / 21 / 11 / 4012.

[0005] [2]Gastric cancer-The Lancet[Internet].[cited 2024 Apr 15].Availablefrom:https: / / www.thelancet.com / article / S0140-6736(20)31288-5 / abstract.

[0006] [3]Shen M,Wang H,Wei K,Zhang J,You C.Five common tumor biomarkers andCEA for diagnosing early gastric cancer:A protocol for a network meta-analysis of diagnostic test accuracy.Medicine.2018;97:e0577.

[0007] [4]Yang J,Gong Y,Lam VK,Shi Y,Guan Y,Zhang Y,et al.Deep sequencing ofcirculating tumor DNA detects molecular residual disease and predictsrecurrence in gastric cancer.Cell Death Dis.2020;11:1–9.

[0008] [5]Chen X-Z,Zhang W-K,Yang K,Wang L-L,Liu J,Wang L,et al.Correlationbetween serum CA724 and gastric cancer:multiple analyses based on Chinesepopulation.Mol Biol Rep.2012;39:9031–9039.

[0009] [6]Yasui W,Oue N,Aung PP,Matsumura S,Shutoh M,Nakayama H.Molecular-pathological prognostic factors of gastric cancer:a review.GastricCancer.2005;8:86–94.

[0010] [7]Chen D,Fu M,Chi L,Lin L,Cheng J,Xue W,et al.Prognostic andpredictive value of a pathomics signature in gastric cancer.Nat Commun.2022;13:6903.

[0011] [8]Zeng D,Li M,Zhou R,Zhang J,Sun H,Shi M,et al.TumorMicroenvironment Characterization in Gastric Cancer Identifies Prognostic andImmunotherapeutically Relevant Gene Signatures.Cancer Immunol Res.2019;7:737–750.

[0012] [9]Jin P,Ji X,Kang W,Li Y,Liu H,Ma F,et al.Artificial intelligence ingastric cancer:a systematic review.J Cancer Res Clin Oncol.2020;146:2339–2350.

[0013]

[10] Huang B,Tian S,Zhan N,Ma J,Huang Z,Zhang C,et al.Accuratediagnosis and prognosis prediction of gastric cancer using deep learning ondigital pathological images:A retrospective multicentre study.eBioMedicine[Internet].2021[cited 2024 Apr 15];73.Available from:https: / / www.thelancet.com / journals / ebiom / article / PIIS2352-3964(21)00424-2 / fulltext.

[0014]

[11] Kuol N,Stojanovska L,Apostolopoulos V,Nurgali K.Role of thenervous system in cancer metastasis.J Exp Clin Cancer Res.2018;37:5.

[0015]

[12] Wang H,Zheng Q,Lu Z,Wang L,Ding L,Xia L,et al.Role of the nervoussystemin cancers:a review.Cell Death Discov.2021;7:1–12.

[0016]

[13] Wang K,Zhao X,Liu J,Zhang R,Li J.Nervous system and gastriccancer.Biochimica et Biophysica Acta(BBA)-Reviews on Cancer.2020;1873:188313.

[0017]

[14] Aurello P, Berardi G, Tierno SM, Rampioni Vinciguerra GL, SocciarelliF, Laracca GG, et al. Influence of perineural invasion in predicting overallsurvival and disease-free survival in patients With locally advanced gastriccancer. The American Journal of Surgery. 2017; 213:748–753. Summary of the invention

[0018] The purpose of the first aspect of the present invention is to provide the use of nervous system autoantibodies in the preparation of products.

[0019] The second aspect of the present invention aims to provide a marker combination.

[0020] The third aspect of the present invention is to provide a kit.

[0021] The fourth aspect of the present invention aims to provide an application.

[0022] The fifth aspect of the present invention aims to provide a method for constructing a model for gastric cancer prognosis assessment.

[0023] The sixth aspect of the present invention aims to provide a system for gastric cancer prognosis assessment.

[0024] In order to achieve the above-mentioned purpose of the present invention, the technical solution adopted by the present invention is:

[0025] The first aspect of the present invention provides the use of nervous system autoantibodies in preparing products.

[0026] The product is used for the diagnosis, treatment and prognosis assessment of gastric cancer.

[0027] Preferably, the gastric cancer includes the full cycle of gastric cancer; further preferably, the gastric cancer includes stage I-IV gastric cancer; further preferably, it is stage III-IV gastric cancer.

[0028] Preferably, the nervous system autoantibodies include: intracellular antigen antibodies, such as AMPH, Ma2, Ri, Yo, Hu, CV2, GAD, etc.; neuron surface antigen antibodies, such as NMDAR, GlyR, mGluR, LGI1, Caspr2, GABAAR, AMPAR, etc.; paraneoplastic syndrome-related antibodies, such as Hu, YO, Ri, CV2, Ma2, AMPH, ANNA-3, Tr (DNER), PCA2, Zic4, Recoverin, etc.; stiff-man syndrome-related antibodies, such as GAD, AMPH, GlyR; autoimmune encephalitis-related antibodies, such as NMDAR, AMPAR1 / 2, GABABR, LGI1, CASPR2, DPPX, IgLON5, GlyR, mGluR1, mGluR5, D2R; antibodies related to neuromyelitis optica spectrum disorder, such as NMO (AQP4), MOG, MBP; antibodies related to immune-mediated peripheral neuropathy, such as GM1, GM2, GM3, GD1a, GD1b, GT1b, GQ1b, MAG, thioesters; antibodies related to immune-mediated neuromuscular junction disease, such as AchR, Musk, LRP4, Titin, SOX1, VGCC; antibodies related to immune-mediated myositis, such as Mi-2α, Mi-2β, TIF1γ, MDA5, NXP2, SAE1, Ku, PM-Scl100, PM-Scl75, Jo-1, SRP, PL-7, PL-12, EJ, OJ, Ro-52.

[0029] Preferably, the nervous system autoantibodies include at least one of YO, SOX1, Ri, AMPH, PCA2, and Ma2.

[0030] The nervous system autoantibodies claimed for protection in the present invention include but are not limited to the above-listed contents. The present invention is a broad-spectrum nervous system autoantibody detected by the TBA method, and ROC curve and other related analyses are performed with TBA positivity (broad-spectrum nervous system autoantibodies) as the standard, which proves the effectiveness, specificity and sensitivity of broad-spectrum nervous system autoantibodies in the prognosis of gastric cancer, so the broad-spectrum nervous system autoantibodies can be supported by the specification. YO, SOX1, Ri, AMPH, PCA2, Ma2 further listed in the present invention belong to a specific preferred embodiment and do not constitute a limitation of the present invention.

[0031] Preferably, the product includes at least one of a reagent, a kit, a test paper, a chip, and a system.

[0032] Preferably, the reagents comprise reagents for one or more detection methods selected from the group consisting of: PCR, ELISA, immunoblotting, chemiluminescence, radioimmunoassay, liquid phase chip method, lateral flow, electrochemistry, flow cytometry, and immunofluorescence.

[0033] Preferably, the immunofluorescence includes indirect immunofluorescence (IIF), cell based assay (CBA), and tissue based assay (TBA).

[0034] Preferably, the immunofluorescence comprises TBA method.

[0035] Preferably, the tissue substrate sources of the TBA method include but are not limited to mouse brain, rabbit brain, and monkey brain.

[0036] Preferably, the reagent comprises at least one of a primer, a probe, an aptamer, and an antibody specific to the marker.

[0037] Preferably, the test sample of the product is selected from at least one of body fluids, tissues and cells of the subject to be tested.

[0038] Preferably, the body fluid includes at least one of blood and digestive fluid.

[0039] Preferably said tissue comprises a gastric biopsy.

[0040] In the first aspect of the present invention, the nervous system autoantibodies refer to antibodies against self-tissues, organs, cells and components thereof in the nervous system.

[0041] In some embodiments of the present invention, the present invention demonstrates the feasibility of nervous system autoantibodies in assessing the prognosis of gastric cancer through the TBA method. Since all natural target antigens are contained in brain tissue (rodents or primates), the antibodies in the test sample will react with brain tissue sections for antigen-antibody. Therefore, TBA can retain the integrity of the original matrix full antigen spectrum and has a better evaluation effect. Although the present invention specifically uses the mouse brain TBA method, in view of the gene homology, it does not mean that the TBA method of other tissue sources cannot achieve the effect of the present invention. In addition, it does not mean that other immunofluorescence methods such as IIF and CBA cannot achieve the effect of the present invention.

[0042] It should be further emphasized that the core of the present invention is the accuracy, sensitivity and specificity of nervous system autoantibodies in the detection, treatment and prognosis of gastric cancer. The methods and means of detecting nervous system autoantibodies should not be a limitation of the present invention. The detection methods at the gene level or the protein level can achieve the expected effect of the invention.

[0043] Specifically, in the first aspect of the present invention, patients who are positive for nervous system autoantibodies have a lower overall survival rate and a worse prognosis.

[0044] According to a second aspect of the present invention, a marker combination is provided, comprising YO, SOX1, Ri, AMPH, PCA2, and Ma2.

[0045] The second aspect of the present invention is a further supplement based on the first aspect of the present invention. In the first aspect of the present invention, the full antibody spectrum of nervous system autoantibodies has been confirmed based on the full antigen spectrum of TBA in the diagnosis, treatment, and prognosis of gastric cancer. The second aspect of the present invention, based on the first aspect, further confirms the effect of nervous system autoantibodies in the diagnosis, treatment, and prognosis of gastric cancer with a group of specific antibody marker combinations. The present invention collected 399 patients with gastric cancer who were initially diagnosed, among whom not all patients underwent detection of all nervous system autoantibodies. In order to obtain sufficient sample data for statistical analysis, the second aspect of the present invention selected the 6 nervous system autoantibodies for bioinformatics analysis. However, it does not mean that only these 6 neural autoantibodies can be used for the diagnosis, treatment, and prognosis of gastric cancer, and other nervous system autoantibodies can also be expected to have similar diagnostic and identification effects. On the basis that the TBA method has proved that a wide spectrum of nervous system autoantibodies can be used for the diagnosis, treatment, and prognosis of gastric cancer, the specific types of nervous system autoantibodies should not be a limitation of the present invention.

[0046] The third aspect of the present invention provides a kit comprising reagents for detecting the marker combination of the second aspect of the present invention.

[0047] Preferably, the kit comprises reagents for detecting the content or abundance of markers in the marker combination.

[0048] Preferably, the reagents comprise reagents for one or more detection methods selected from the group consisting of: PCR, ELISA, immunoblotting, chemiluminescence, radioimmunoassay, liquid phase chip method, lateral flow, electrochemistry, flow cytometry, and immunofluorescence.

[0049] Preferably, the immunofluorescence includes indirect immunofluorescence (IIF), cell based assay (CBA), and tissue based assay (TBA).

[0050] Preferably, the immunofluorescence comprises TBA method.

[0051] Preferably, the tissue substrate sources of the TBA method include but are not limited to mouse brain, rabbit brain, monkey brain, and hippocampus.

[0052] Preferably, the reagent comprises at least one of a primer, a probe, an aptamer, and an antibody specific to the marker.

[0053] Preferably, the test sample of the product is selected from at least one of body fluids, tissues and cells of the subject to be tested.

[0054] Preferably, the body fluid includes at least one of blood and digestive fluid.

[0055] Preferably said tissue comprises a gastric biopsy.

[0056] The fourth aspect of the present invention provides the use of the nervous system autoantibodies in the application of the first aspect of the present invention and the kit described in the third aspect of the present invention in a1) to a3):

[0057] a1) Application in the preparation of products for diagnosing gastric cancer;

[0058] a2) Application in the preparation of products for treating gastric cancer;

[0059] a3) Application in the preparation of products for gastric cancer prognosis assessment;

[0060] Preferably, the product includes at least one of a reagent, a kit, a test paper, a chip, and a system.

[0061] Preferably, the product comprises reagents for detecting the amount or abundance of markers in the marker combination.

[0062] Preferably, the reagents comprise reagents for one or more detection methods selected from the group consisting of: PCR, ELISA, immunoblotting, chemiluminescence, radioimmunoassay, liquid phase chip method, lateral flow, electrochemistry, flow cytometry, and immunofluorescence.

[0063] Preferably, the immunofluorescence includes indirect immunofluorescence (IIF), cell based assay (CBA), and tissue based assay (TBA).

[0064] Preferably, the immunofluorescence comprises TBA method.

[0065] Preferably, the tissue substrate sources of the TBA method include but are not limited to mouse brain, rabbit brain, monkey brain, and hippocampus.

[0066] Preferably, the reagent comprises at least one of a primer, a probe, an aptamer, and an antibody specific to the marker.

[0067] Preferably, the test sample of the product is selected from at least one of body fluids, tissues and cells of the subject to be tested.

[0068] Preferably, the body fluid includes at least one of blood and digestive fluid.

[0069] Preferably said tissue comprises a gastric biopsy.

[0070] Specifically, in the fourth aspect of the present invention, patients with positive nervous system autoantibodies have a lower overall survival rate and a worse prognosis. Patients with positive YO, SOX1, Ri, AMPH, PCA2, and Ma2 have a lower overall survival rate and a worse prognosis.

[0071] A fifth aspect of the present invention provides a method for constructing a model for gastric cancer prognosis assessment, characterized in that:

[0072] The method comprises using b1) to b2) to construct a model;

[0073] b1) Neural autoimmune antibodies for use in the first aspect of the present invention;

[0074] b2) The marker combination according to the second aspect of the present invention.

[0075] Preferably, the algorithm for constructing the model includes at least one of regression analysis, linear discriminant analysis, support vector machine, Bayesian network, and decision tree.

[0076] A sixth aspect of the present invention provides a system for gastric cancer prognosis assessment, characterized in that

[0077] The system comprises a computing device for judging the prognosis of gastric cancer according to the detection results of b1) to b2);

[0078] b1) Neural autoimmune antibodies for use in the first aspect of the present invention;

[0079] b2) The marker combination according to the second aspect of the present invention.

[0080] Preferably, the test result is obtained by testing a test sample from a subject to be tested.

[0081] Preferably, the test sample is selected from at least one of body fluids, tissues and cells of the subject to be tested.

[0082] Preferably, the body fluid includes at least one of blood and digestive fluid.

[0083] Preferably said tissue comprises a gastric biopsy.

[0084] Preferably, the system further comprises a detection device.

[0085] Preferably, the detection device includes at least one of a reagent, a test kit, a test paper, a chip, and a system.

[0086] Preferably, the detection device comprises a reagent for detecting the content or abundance of the markers in the marker combination.

[0087] Preferably, the reagents comprise reagents for one or more detection methods selected from the group consisting of: PCR, ELISA, immunoblotting, chemiluminescence, radioimmunoassay, liquid phase chip method, lateral flow, electrochemistry, flow cytometry, and immunofluorescence.

[0088] Preferably, the immunofluorescence includes indirect immunofluorescence (IIF), cell based assay (CBA), and tissue based assay (TBA).

[0089] Preferably, the immunofluorescence comprises TBA method.

[0090] Preferably, the tissue substrate sources of the TBA method include but are not limited to mouse brain, rabbit brain, monkey brain, and hippocampus.

[0091] Preferably, the reagent comprises at least one of a primer, a probe, an aptamer, and an antibody specific to the marker.

[0092] Preferably, the detection device comprises the kit described in the third aspect of the present invention.

[0093] Preferably, the system further comprises any one or more of the following:

[0094] 1) a detection result collection device, which may also be referred to as a detection result input device, and may specifically be one or more of a mouse, a keyboard, a touch screen display, one or more buttons, one or more switches, one or more triggers, etc.;

[0095] 2) a diagnostic result output device, which may also be referred to as a diagnostic result display device, and may specifically be one or more of a liquid crystal display (LCD), a light emitting diode (LED) display, a plasma display, a projection display, a touch screen display, and the like;

[0096] 3) A diagnostic result sending device, which can send the result of distinguishing whether the subject is a high-risk or low-risk group to an information communication terminal device that can be consulted by the patient or medical staff.

[0097] The beneficial effects of the present invention are:

[0098] The present invention found that nervous system autoantibodies have good specificity and sensitivity in the diagnosis, detection, and prognosis evaluation of gastric cancer, and can be used to prepare products for the diagnosis, detection, and prognosis evaluation of gastric cancer. The effect of broad-spectrum nervous system autoantibodies in the prognosis of gastric cancer was detected by the mouse brain TBA method. Patients with negative TBA had a higher overall survival rate and a better prognosis for gastric cancer. The area under the ROC curve was 0.784, and the sensitivity and specificity were 0.684 and 0.765, respectively, showing its good properties as a marker. Furthermore, the present invention used antibodies YO, SOX1, Ri, AMPH, PCA2, and Ma2 as marker combinations to evaluate the prognosis of gastric cancer. The area under the ROC dotted line was 0.855, and the sensitivity and specificity were 0.829 and 0.762, respectively, with better predictive performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0100] Figure 1 It is a diagram of the experimental detection process results of the present invention; wherein A is the experimental process result, and B is the TBA detection result of some samples in Example 1.

[0101] Figure 2 The total survival rate curve results of gastric cancer patients in the TBA(+) group and the TBA(-) group of the training set of the present invention are shown.

[0102] Figure 3 The overall survival rate curves of patients with gastric cancer in stages III and IV in the TBA(+) group and TBA(-) group of the training set of the present invention are shown.

[0103] Figure 4 This is the survival curvature result of the TBA grouping in the validation set of the present invention for predicting the survival outcomes of gastric cancer patients.

[0104] Figure 5 This is the ROC curve result of TBA combined with TNM in the validation set of the present invention.

[0105] Figure 6 In the validation set of the present invention, logistic regression was used to demonstrate the effectiveness of TBA combined with TNM staging in predicting the survival outcomes of gastric cancer patients.

[0106] Figure 7 The overall survival curve results of gastric cancer patients in the positive and TBA(-) groups of the six known antibodies YO, SOX1, Ri, AMPH, PCA2, Ma2 of the present invention.

[0107] Figure 8This is the ROC curve result of the six known antibodies YO, SOX1, Ri, AMPH, PCA2, and Ma2 combined with TNM grouping in the present invention. DETAILED DESCRIPTION

[0108] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0109] All experiments in this invention were approved by Southern Medical University, and related experiments were approved by the Ethics Review Committee of Nanfang Hospital (NFEC-2024-247). All patients were informed and agreed to the collection and use of relevant data.

[0110] Terminology explanation:

[0111] GC:Gastric cancer.

[0112] TBA: Tissue-based assay, based on tissue detection.

[0113] CBA: Cell based assay, based on cell substrate detection.

[0114] CEA: carcinoembryonic antigen, carcinoembryonic antigen.

[0115] CA-199: Carbohydrate antigen 199, carbohydrate antigen 199.

[0116] CA-724: Carbohydrate antigen 724

[0117] ctDNA: circulating tumor deoxyribonucleic acid, circulating tumor DNA. .

[0118] TNM: Tumor, Node, Metastasis.

[0119] H&E: Hematoxyli, (hematoxylin) and Eosin (bright red).

[0120] PNS: Paraneoplastic syndromes, paraneoplastic syndrome.

[0121] BMI: Body Mass Index, body mass index.

[0122] PD-L1: Gene Expression Profiling Score, programmed death ligand-1.

[0123] HER-2: Human Epidermal Growth Factor Receptor 2, human epidermal growth factor receptor-2.

[0124] S100B: S100 calcium binding protein beta (neural), calcium ion binding protein, β (neural).

[0125] YO: also known as CDR2L, Cerebellar Degeneration-Related Protein 2-Like, cerebellar degeneration-related protein 2.

[0126] SOX1: SRY-Box Transcription Factor 1, sex-determining region of Y chromosome (SRY)-box transcription factor 1.

[0127] Ri: NOVA1 / 2, Neuro-Oncological Ventral Antigen 1 / 2, neuro-oncological ventral antigen 1 / 2.

[0128] AMPH: Amphiphysin, amphotericin.

[0129] PCA2: Purkinje cell antibody 2, anti-Purkinje cell type 2 antibody.

[0130] Ma2: Paraneoplastic Ma Antigen 2, paraneoplastic antigen Ma2

[0131] Example 1 Experimental data collection

[0132] In this example, data of 399 patients initially diagnosed with gastric cancer were collected, and further screening was performed to exclude 76 non-gastric cancer patients with stromal tumors, lymphomas, etc. with similar phenotypes to gastric cancer, resulting in a total of 323 patient data confirmed to be with gastric cancer.

[0133] The serum samples of the 323 patients were tested for mouse brain TBA. The specific process is as follows:

[0134] (1) After anesthetizing adult mice, the heart was perfused with PBS (phosphate buffered saline) to remove circulating blood. The mice were killed by cervical dislocation and the brains were removed. The sagittal frozen sections were then made, fixed with cold acetone for 10 minutes, and air-dried for later use.

[0135] (2) Wash the sections three times with PBST solution (PBS containing 0.05% Tween-20), 3 minutes each time;

[0136] (3) Block with 10% goat serum / PBST solution at room temperature for 1 hour;

[0137] (4) Remove the blocking solution, add serum sample diluted 1:100 with PBST, and incubate at 37°C for 1 hour;

[0138] (5) After incubation, wash with PBST three times, 5 minutes each time;

[0139] (6) Add green fluorescent labeled goat anti-human IgG secondary antibody and incubate at 37°C for 30 min;

[0140] (7) After incubation, wash with PBST three times, 5 minutes each time;

[0141] (8) Remove the PBST solution, seal the slides with an anti-fading sealant, and observe the staining results and collect images under a fluorescence microscope.

[0142] The serum samples of 144 TBA-positive patients were tested for partial paraneoplastic anti-neural antibodies using CBA method. The specific process is as follows:

[0143] (1) 293T cell slides were prepared. When the cells grew to a density of 50% to 90%, the eukaryotic expression plasmids of Hu, YO, Ri, Ma2, CV2 / CRMP5, AMPH, Tr (DNER), SOX1, PCA2, Zic4, KLHL11, Titin, Ma1, Recoverin, PKCγ, and GAD65 were transfected into 293T cells for 24 to 48 hours, respectively, using pcDNA3.1 as the vector, so that the 293T cells overexpressed the above recombinant proteins;

[0144] (2) Wash the cells three times with PBS solution and then fix them with 4% paraformaldehyde / PBS solution for 10 min;

[0145] (3) Treat with 0.3% TritonX-100 / PBS solution for 10 minutes, then wash the slides three times with PBST solution, each time for 3 minutes;

[0146] (4) Block with 10% goat serum / PBST solution at room temperature for 1 hour;

[0147] (5) Discard the blocking solution, add patient serum (1:10 dilution), and incubate at 37°C for 1 hour;

[0148] (6) After incubation, wash three times with PBST, 5 minutes each time;

[0149] (7) Discard the wash solution, add 1:200 diluted green fluorescent labeled goat anti-human IgG secondary antibody, and incubate at 37°C for 30 min;

[0150] (8) Wash three times with PBST, 5 minutes each time;

[0151] (9) Seal the slides with sealing medium and observe the results and collect images using a fluorescence microscope.

[0152] result:

[0153] The results are as follows Figure 1 As shown in the figure, A is the experimental process result. 323 serum samples of gastric cancer patients were tested, of which 144 were TBA positive and 179 were negative. Among the 144 TBA positive samples, 15 were CBA positive and 129 were CBA negative. B is part of the TBA test results. C is part of the CBA test results. The positive indicators detected include YO, SOX1, Ri, AMPH, PCA2, and Ma2. D is the antibody titer and subcellular localization of TBA positive serum samples, as well as CBA screening antibody positive samples and corresponding positive indicators.

[0154] Example 2 Collection and collation of clinical data of gastric cancer patients

[0155] A total of 323 gastric cancer patients were included, including 144 in the TBA-positive group and 179 in the TBA-negative group. The baseline characteristics of the two groups were basically the same (as shown in Table 1).

[0156] Among them, the average age of the TBA(+) group was 60.71 years, and the average age of the TBA(-) group was 59.47 years (p>0.05); 96 TBA(+) patients (66.7%) and 137 TBA(-) patients (76.5%) underwent radical surgery; pTNM staging, TBA(+) group had 46 cases (31.9%) of stage IV, 31 cases (21.5%) of stage III, 24 cases (16.7%) of stage II, and 43 cases (29.9%) of stage 0 or I; TBA(-) group had 40 cases (22.9%) of stage IV, 54 cases (30.2%) of stage III, 29 cases (16.2%) of stage II, and 56 cases (31.3%) of stage 0 or I; S100 protein was not detected in most patients for various reasons. In the TBA(+) group, only 40 patients underwent S100 testing, of which 24 (16.1%) were S100 positive and 16 (11.1%) were negative. In the TBA(-) group, 39 patients underwent S100 testing, of which 18 (10%) were S100 positive and 21 (11.7%) were S100 negative. PD-L1 GPS score ≥ 5, 60 (41.7%) in the TBA(+) group and 62 (34.6%) in the TBA(-) group. GPS score < 5, 83 (57.6%) in the TBA(+) group and 113 (63.1%) in the TBA(-) group, of which 1 (0.7%) was lost in the TBA(+) group and 4 (2.2%) were lost in the negative group. The Lauren grade and HER-2 characteristics of the two groups showed statistical differences (p = 0.049). Among them, 68 cases (47.2%) were intestinal type or mixed type, 23 cases (16%) were intestinal type, and 53 cases (36.8%) were mixed type; 88 cases (49.2%) were intestinal type or mixed type, 40 cases (22.3%) were intestinal type, and 51 cases (28.5%) were mixed type in the TBA(-) group; 26 cases (18.1%) were in the HER-2 2+ / 3+TBA positive group, 20 cases (11.2%) were in the HER-2 negative group, 115 cases (79.9%) were in the HER-2 0 / 1+TBA(+) group, and 152 cases (84.9%) were in the HER-2 negative group.

[0157] Table 1 Clinical data characteristics of gastric cancer patients

[0158] Clinicopathological features in gastric cancer patients

[0159]

[0160]

[0161] Example 3 Nervous system autoantibodies are closely related to the survival prognosis of gastric cancer patients

[0162] According to the data of 323 cases after TBA screening, there were 144 TBA-positive cases and 179 TBA-negative cases. After excluding patients who were lost to follow-up and lacked survival data, a total of 306 patients were obtained, including 136 TBA-positive cases and 170 TBA-negative cases.

[0163] The correlation analysis between nervous system autoantibodies and gastric cancer survival prognosis was conducted for this data set, among which, Figure 2 The overall survival rate curves of gastric cancer patients in the TBA(+) group and the TBA(-) group are shown. The TBA(+) group had a lower overall survival rate than the TBA(-), p=0.014. Figure 3 The overall survival rate curves of patients with gastric cancer in stages III and IV in the TBA(+) group and the TBA(-) group are shown. The TBA(+) group had a significantly lower overall survival rate than the TBA(-), p=0.0072.

[0164] Example 4 Validation set confirms the specificity and sensitivity of neural autoantibodies in assessing gastric cancer survival prognosis

[0165] In this embodiment, the training set (n=226) and the validation set (n=97) were randomly selected from the total data set in a ratio of 7:3. Since the training set needs to be subjected to survival analysis, the training set includes 214 cases of data after excluding the lost data in the training set, of which 99 people are in the TBA(+) group and 115 people are in the TBA(-) group. The specificity and sensitivity of neural autoantibodies in the survival prognosis evaluation of gastric cancer were verified. The validation set is an independent sample different from the test set data, and the two do not have data overlap.

[0166] The results are as follows Figure 4 , 5 As shown, Figure 4 The survival curvature of TBA grouping in the training set (n=214) for predicting the survival outcome of gastric cancer patients is shown. In the training set, it can still be reflected that the TBA(+) group has a lower overall survival rate than the TBA(-), p=0.014.

[0167] Figure 5 The ROC curve of TBA combined with TNM in the validation set was shown, showing an AUC of 78.4%. The cut-off value was 0.162, and the sensitivity and specificity were 0.684 and 0.765, respectively. The results showed that TBA grouping (TBA positive and TBA negative) combined with TNM staging (stage I-IV) is indeed effective in predicting the survival outcomes of gastric cancer patients.

[0168] Example 5 Prediction of survival outcomes of gastric cancer patients by combining nervous system autoantibodies with TNM staging

[0169] Figure 6The effectiveness of TBA combined with TNM staging in predicting the survival outcomes of gastric cancer patients in the total data (323 cases) was demonstrated by logistic regression. The ROC curve of TBA combined with TNM staging level and outcome showed an AUC of 86.8%. The cut-off value was 0.117, and the sensitivity and specificity were 0.735 and 0.881, respectively, indicating that the model was better than random guessing and had certain value. The specific regression model was: y = -7.2168 + 0.5935 * TBA_GROUP + 1.6213 * pTNM

[0170] Example 6 Survival curves and receiver operating characteristic curves for known nervous system autoantibodies and antibody negative

[0171] This example further analyzes the evaluation effect of known neural autoantibodies based on the predictive evaluation effect of a broad spectrum of neural autoantibodies.

[0172] Based on the clinical test data of the patients, this example selected 6 known antibodies (antibodies YO, SOX1, Ri, AMPH, PCA2, Ma2) and performed CBA testing, of which 19 samples were CBA (+) positive for all 6 antibodies.

[0173] CBA-positive samples (19 cases) and TBA-negative samples (170 cases) were analyzed.

[0174] The results are as follows Figure 7 , 8 As shown, Figure 7 The overall survival curves of 170 TBA(-) and 19 CBA(+) gastric cancer patients are shown.

[0175] Figure 8 The ROC curve of CBA grouping (19+170) combined with TNM grouping was shown. By comparing the data of 6 known antibodies (antibodies YO, SOX1, Ri, AMPH, PCA2, Ma2) with antibody-negative data, the AUC was 85.5%. The cut-off value was 0.139, and the sensitivity and specificity were 0.829 and 0.762, respectively. This shows that the expression of autoantibodies in the nervous system is indeed effective in predicting the survival outcomes of gastric cancer patients.

Claims

1. Application of nervous system autoantibodies in product preparation; The product is used for prognosis assessment of gastric cancer; The nervous system autoantibodies are TBA-positive nervous system autoantibodies.

2. The use according to claim 1, characterized in that: The nervous system autoantibodies are autoantibodies to YO, SOX1, Ri, AMPH, PCA2 and Ma2.

3. The use according to claim 1, characterized in that: The product includes at least one of a reagent, a test kit, a test paper, a chip, and a system.

4. The use according to claim 3, characterized in that: The reagents include reagents for one or more detection methods selected from the group consisting of ELISA, immunoblotting, chemiluminescence, radioimmunoassay, liquid phase chip method, lateral flow, electrochemistry, flow cytometry, and immunofluorescence; The immunofluorescence includes indirect immunofluorescence, immunofluorescence based on cell substrates, and immunofluorescence based on tissue substrates; The reagent comprises at least one of an aptamer and an antibody; The test sample of the product is selected from blood.

5. A method for constructing a model for gastric cancer prognosis assessment, characterized in that: The method comprises using b1) to b2) to construct a model; b1) TBA-positive nervous system autoantibodies for use according to claim 1; b2) autoantibodies against YO, SOX1, Ri, AMPH, PCA2 and Ma2 for use according to claim 2; The algorithm for constructing the model includes at least one of regression analysis, linear discriminant analysis, support vector machine, Bayesian network, and decision tree.

6. A system for gastric cancer prognosis assessment, characterized in that: The system comprises a computing device for judging the prognosis of gastric cancer according to the detection results of b1) to b2); b1) TBA-positive nervous system autoantibodies for use according to claim 1; b2) Autoantibodies against YO, SOX1, Ri, AMPH, PCA2 and Ma2 for use according to claim 2.

7. The system according to claim 6, characterized in that: The system further comprises one or more of c1) to c3): c1) Test result collection device; c2) Evaluation result output device; c3) Evaluation result sending device.

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