Urine protein marker related to biliary tract cancer immunotherapy and application thereof

Abstract

The invention provides a urine protein marker related to biliary tract cancer immunotherapy and application of the urine protein marker, and belongs to the technical field of biomedicine. The urine protein marker related to biliary tract cancer immunotherapy comprises a non-receptor tyrosine phosphatase 13 type (PTPN13), an activation RNA polymerase II transcription coactivator p15 (SUB1), an MICAL-like 1 protein (MICALL1) and a valyl-tRNA synthetase 1 (VARS1), and a combination established by the four urine proteins can be used for predicting the curative effect and (or) prognosis of the biliary tract cancer immunotherapy. One or more of the four urine protein markers can be used for prediction and / or prognosis analysis of the immunotherapy curative effect of the biliary tract cancer through mass spectrometric detection or immunodetection.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to urinary protein biomarkers related to immunotherapy for biliary tract cancer and their applications. Background Technology

[0002] Biliary tract cancers (BTC) are a rare but highly aggressive malignancy with an increasing global incidence. Approximately 70% of BTC patients are diagnosed at an advanced stage, at which point combination therapy based on immune checkpoint inhibitors (ICIs) becomes the first-line treatment. However, most BTC patients do not achieve durable benefits from combination immunotherapy, resulting in a poor prognosis with a median survival of only about one year (Scott AJ, et al., Precision Medicine in Biliary Tract Cancer. J Clin Oncol 2022;40:2716-2734; Kelley RK, et al. Pembrolizumab in combination with gemcitabine and cisplatin compared with gemcitabine and cisplatin alone for patients with advanced biliary tract cancer (KEYNOTE-966): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2023;401:1853-1865). Therefore, this clinical situation highlights the urgent need for predictive biomarkers to stratify patients before treatment and screen those who can obtain lasting clinical benefits from immunotherapy.

[0003] Currently, most biomarkers are tissue-based static biomarkers, which often exhibit inconsistent predictive value and are limited by invasiveness, spatiotemporal heterogeneity, and the inability to capture dynamic immune-tumor interactions. Furthermore, the efficacy of ICIs is intricately linked to the composition and dynamics of the tumor immune microenvironment (TME) (Holder AM, et al., Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumors. Nat Rev Cancer 2024;24:498-512; Bernatowicz K, et al., Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction. J Immunother Cancer 2025;13). However, dynamic biomarkers for predicting and monitoring the reactivity of ICIs and the plasticity of the TME are still in their infancy (Lesterhuis WJ, et al., Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat Rev Drug Discov 2017;16:264-272). Compared to clinical research on other tumors, studies specifically targeting biomarkers for predicting the efficacy of immunotherapy for BTC are relatively scarce. Screening and identifying predictive biomarkers for tumor immunotherapy in body fluids using various omics methods is currently a standard strategy in tumor immunotherapy-related research (Nabet BY, et al., Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition. Cell 2020;183:363-376 e313).

[0004] Proteomics is a high-throughput research method for detecting and quantifying the expression of various proteins in biological samples, with mass spectrometry being its primary research technique. Therefore, utilizing mass spectrometry and employing quantitative proteomics strategies to screen for immunotherapy-related protein biomarkers from clinical samples will be beneficial for predicting the efficacy of immunotherapy in patients with biliary tract cancer. Furthermore, the non-invasive nature of urine sampling facilitates long-term patient monitoring. Summary of the Invention

[0005] In view of this, the present invention provides urinary protein biomarkers related to immunotherapy of biliary tract cancer and their applications to solve the above problems.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides, in one aspect, the application of an identification reagent for urinary protein biomarkers related to biliary cancer immunotherapy in the preparation of products for predicting, detecting, and / or predicting the efficacy of biliary cancer immunotherapy, wherein the protein biomarkers are one or four of the following: non-receptor tyrosine phosphatase type 13 (PTPN13), activated RNA polymerase II transcriptional coactivator p15 (SUB1), MICAL-like 1 protein (MICALL1), and valine-tRNA synthetase 1 (VARS1).

[0007] As used in this invention, the term prediction refers to predicting whether a subject can obtain a lasting clinical benefit from immunotherapy based on changes in the protein markers in the subject's urine; the term monitoring refers to determining the effectiveness of treatment based on changes in the protein markers in the patient's urine during treatment.

[0008] In some embodiments of the above-described application of the present invention, the process for predicting the efficacy of immunotherapy for biliary tract cancer includes the following steps: 1) Obtain urine samples from the subjects; 2) Optionally, isolate urinary protein from the urine sample; 3) Determine the expression levels of one or four of the following proteins in the subject's urine sample: non-receptor tyrosine phosphatase type 13 (PTPN13), activated RNA polymerase II transcriptional coactivator p15 (SUB1), MICAL-like 1 protein (MICALL1), and valine-tRNA synthetase 1 (VARS1).

[0009] In some embodiments of the above-described application of the present invention, the prediction of immunotherapy efficacy is to predict durable clinical benefit and early (within 3 months after treatment) treatment response before treatment.

[0010] In some embodiments of the above-described application of the present invention, the subject is a human being.

[0011] In some embodiments of the above-described applications of the present invention, the product for predicting and / or monitoring and / or predicting the prognosis of biliary tract cancer immunotherapy is a kit or a chip.

[0012] Preferably, mass spectrometry is used to determine the expression level of the urinary protein, and the process may further include a digestion step of the urinary protein after obtaining the urine sample. Specifically, the proteins extracted from the urine sample are digested with trypsin.

[0013] In some embodiments of the above-described applications of the present invention, the identification reagent is one or four of the following mass spectrometry detection reagents: non-receptor tyrosine phosphatase type 13 (PTPN13), activated RNA polymerase II transcriptional coactivator p15 (SUB1), MICAL-like 1 protein (MICALL1), and valine-tRNA synthetase 1 (VARS1).

[0014] A tag peptide is a peptide segment that can represent a certain protein and is specifically present only in the amino acid sequence of the target protein to be monitored.

[0015] Furthermore, in some embodiments of the above-described applications of the present invention, the mass spectrometry detection reagent is used through a data-independent acquisition method or parallel reaction monitoring.

[0016] Furthermore, in some embodiments of the above-described applications of the present invention, the amino acid sequence of the tag peptide of non-receptor tyrosine phosphatase type 13 (PTPN13) is ASWEEKPR, the amino acid sequence of the tag peptide of activated RNA polymerase II transcriptional coactivator p15 (SUB1) is GISLNPEQWSQLK, the amino acid sequence of the tag peptide of MICAL-like 1 protein (MICALL1) is LQELASPPAGRPTPAPR, and the amino acid sequence of the tag peptide of valine-tRNA synthetase 1 (VARS1) is SSAQDPQAVLGALGR.

[0017] Data-independent detection methods divide the entire scan range of mass spectrometry into several windows, and then rapidly and cyclically select, fragment, and detect all ions in each window, thereby obtaining complete fragmentation information of all ions in the sample without omission or difference. Data-independent detection is like carpet bombing, hitting all targets without omission.

[0018] Parallel reaction monitoring (PRM) is a target-based mass spectrometry quantitative analysis technique based on secondary mass spectrometry signals. It detects the tagged peptide of a target protein by acquiring all daughter ions corresponding to the precursor ion of the tagged peptide and quantifying based on the signal intensity of the daughter ions. Compared to traditional selected reaction monitoring (SRM), PRM eliminates the need for pre-designing precursor / daughter ion pairing information for the target protein, simplifying experimental design and saving operation time. It also offers higher selectivity and better sensitivity, better reproducibility, and stronger resistance to interference in complex backgrounds. Compared to immunoassays, it is not limited by commercially available antibodies, overcoming the limitations of antibody specificity and titer inherent in immunoassays. Furthermore, PRM can simultaneously perform qualitative and quantitative analysis of multiple proteins.

[0019] Methods for quantitative detection of the two proteins mentioned above in urine can use their corresponding standards to establish a baseline for these two proteins in the population, thereby predicting the pretreatment efficacy of biliary tract cancer patients based on the content range of the durable clinical benefit and non-durable clinical benefit groups.

[0020] In some embodiments of the product for predicting the efficacy and / or prognosis of immunotherapy for biliary tract cancer in this invention, the prediction of the efficacy of immunotherapy for biliary tract cancer is a pre-treatment prediction.

[0021] In some embodiments of the product described above for predicting the efficacy and / or prognosis of immunotherapy for biliary tract cancer, the product is a kit or a chip.

[0022] By adopting the above technical solution, the present invention has the following beneficial effects: The urinary protein biomarkers related to biliary tract cancer immunotherapy of the present invention include non-receptor tyrosine phosphatase type 13 (PTPN13), activated RNA polymerase II transcriptional coactivator p15 (SUB1), MICAL-like 1 protein (MICALL1), and valine-tRNA synthetase 1 (VARS1). The combination of these four urinary proteins can predict the efficacy and / or prognosis of biliary tract cancer immunotherapy. By detecting one or more of the above four urinary protein biomarkers by mass spectrometry or immunoassay, it can be used for predicting the efficacy and / or prognostic analysis of biliary tract cancer immunotherapy. Attached Figure Description

[0023] Figure 1 Comparison of protein mass spectrometry signal abundance between the non-benefit and benefit groups in DIA-based urinary proteinome.

[0024] Figure 2 ROC curves predicting early treatment response to immunotherapy in different categories of biliary tract cancer in a DIA-based urinary proteinome discovery cohort. (A) Tumor enlargement vs. tumor shrinkage, (B) Tumor progression vs. no progression, (C) Partial remission vs. no remission. The four proteins are identified by database accession numbers P26640, P53999, Q12923, and Q8N3F8, respectively.

[0025] Figure 3 ROC curves were generated to differentiate between beneficial and non-beneficial immunotherapy for biliary tract cancer based on four protein combinations using PRM technology. The ROC curves were based on analyses of the training set (A), validation set (A), and test cohort (A), respectively.

[0026] Figure 4 The ROC curves predict different responses to early treatment using four protein combinations based on PRM technology. The different treatment responses are tumor enlargement / shrinkage (top), tumor progression (middle), and partial remission or progression (bottom). Detailed Implementation

[0027] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention. Unless otherwise specified, the materials, reagents, etc. used in the embodiments are commercially available. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0028] Example 1: Detection of proteins related to biliary tract cancer immunotherapy in urine Screening for proteins related to biliary cancer immunotherapy in urine using data-independent acquisition (DIA) methods.

[0029] Materials and reagents 1) Instrument: TimsTOF pro2 mass spectrometer (Bruker).

[0030] 2) Main reagents: trypsin (Promega); C18 Ziptip C18 chromatography column (Millipore); C18 reversed-phase chromatography column (75 µm × 15 cm, C18, Omitech).

[0031] 3) Samples: Urine from 35 patients in the durable clinical benefit group and 29 patients in the non-durable clinical benefit group, obtained from Peking Union Medical College Hospital.

[0032] 1.1 Collection of human urine samples and enrichment of urinary protein Collect fasting morning urine, centrifuge at 5000g for 30 minutes to remove precipitate. Precipitate the supernatant with ethanol. o Incubate overnight at C to precipitate proteins. Redissolve the protein precipitate with lysis buffer. The concentration of collected human urinary proteins was measured using the Bradford method. Protein samples were analyzed by SDS-PAGE.

[0033] 1.2 Proteolytic activity Protein digestion was performed using a membrane-based enzymatic digestion method. The protein sample was first reduced with 20 mM DTT (95%). o C 5 min), then alkylate with 50 mM IAA (room temperature, protected from light for 45 min), and load onto a 10 KD filter membrane. Centrifuge and discard the lower layer. Wash the protein sample on the membrane twice with UA solution (containing 8 M urea) and twice with 25 mM ammonium bicarbonate solution. Wash the protein sample on the membrane with 1:50 trypsin 37. o Enzymatic digestion was performed overnight at C18, followed by centrifugation and collection of the digested peptides. The digested peptides were then purified using a Ziptip C18 syringe and vacuum dried. The resulting peptides were quantified using the BCA method after drying.

[0034] 1.3 Liquid Chromatography-Mass Analyzer All urine samples, representing both the durable clinical benefit group and the non-durable clinical benefit group for biliary tract cancer (BTC), were pooled in equal volumes and used as samples for method construction and subsequent quality control. All 64 samples were analyzed by LC-MS / MS. Data for each sample was acquired via DIA (Data-independent acquisition), and the raw data from DIA were analyzed using Spectronaut software for database matching and quantification. The main search parameters were as follows: database: SwissProt human (downloaded from Uniprot website); enzyme digestion method: trypsin; maximum of two missed cleavage sites allowed; fixed modification: cysteine ​​formylation; variable modification: methionine oxidation, lysine deamination, and carbamylation; protein and peptide FDR <1%, and each protein containing at least one unique peptide. Protein quantification reports were exported. The 64 samples (discovery cohort) were randomly split into a training set (42 cases) and a validation set (22 cases) at a 2:1 ratio. Multivariate statistical analysis software was used, and statistical differences (p < 0.05) were used to analyze the training set data to screen for differentially expressed proteins (DEPs). Figure 1 Information on the mass spectrometry signal abundance values ​​of the specific proteolytic products of four proteins in the urine of patients with biliary tract cancer after immunotherapy was provided in both the non-benefit and benefit groups. Compared with the non-benefit group, the median signal of all four proteins was significantly increased in the benefit group.

[0035] 1.4 Constructing a portfolio of biomarkers to predict durable clinical benefit from immunotherapy for biliary tract cancer A multi-level screening and analysis strategy was employed to optimize the biomarker combination: ① Defective protein expression (DEP) samples with a missing value >35% or an expression abundance below 10% of the total were removed; ② DEP samples with an area under the ROC curve (AUC) >0.70 in the training set were further analyzed; ③ The top 20 key features were selected using the AdaBoost algorithm on the OmicLearn platform. Finally, 14 overlapping proteins were selected for model generation. Logistic regression was used to screen for protein biomarkers or combinations of protein biomarkers with high sensitivity and specificity. Finally, the biomarker combination was optimized, establishing a protein combination based on non-receptor tyrosine phosphatase type 13 (PTPN13), activated RNA polymerase II transcriptional coactivator p15 (SUB1), MICAL-like 1 protein (MICALL1), and valine-tRNA synthetase 1 (VARS1) as relevant to the evaluation of biliary tract cancer immunotherapy. ROC curves of the quantification results of these four proteins in urine predicting early treatment responses in different categories of biliary tract cancer immunotherapy are detailed below. Figure 2The four proteins have database accession numbers of P26640, P53999, Q12923, and Q8N3F8, respectively. Figure 2 The results showed that when analyzing tumor enlargement and shrinkage (A), tumor progression and non-progression (B), partial remission and non-remission (C) respectively, using mass spectrometry signal values ​​as the distinguishing factor, these four proteins all showed good sensitivity and specificity for the above observation angles.

[0036] Example 2: Targeted mass spectrometry detection of urinary proteins: non-receptor tyrosine phosphatase type 13 (PTPN13), activated RNA polymerase II transcriptional coactivator p15 (SUB1), MICAL-like 1 protein (MICALL1), and valine-tRNA synthetase 1 (VARS1). To better apply the differential proteomics analysis results from Example 1 to a larger scale of clinical samples, the identified differential proteins were validated using targeted mass spectrometry analysis in the discovery cohort and independent testing cohort, and a mass spectrometry detection method for subsequent large-scale clinical testing was established.

[0037] Parallel reaction monitoring (SRM) is a target-based mass spectrometry quantitative analysis technique based on secondary mass spectrometry signals. Compared to traditional selected reaction monitoring (SRM), it eliminates the need for pre-designing precursor / daughter ion pairing information for the target protein, saving experimental design and operation time. It also offers higher selectivity, better sensitivity, better reproducibility, and stronger resistance to interference in complex backgrounds. Compared to immunoassay methods, it is not limited by commercially available antibodies, overcoming the limitations of antibody specificity and titer inherent in immunoassay methods. Furthermore, to achieve absolute quantification, a stable isotope-labeled (SIL) standard peptide is added, and an internal standard-triggered parallel reaction monitoring (IS-PRM) detection method is employed. IS-PRM can perform both relative and absolute quantification simultaneously.

[0038] Materials and reagents 1) Instrument: Orbitrap Exploris™ 480 mass spectrometer (Thermo Scientific).

[0039] 2) Main reagents: Trypsin (Promega); C18 chromatography column (Millipore); C18 reversed-phase analytical column (75 µm × 15 cm, C18, Omitech). 3) Samples: Urine samples from 53 patients with biliary tract cancer who passed quality control in the discovery cohort and urine samples from 24 patients in the test cohort were collected from Peking Union Medical College Hospital.

[0040] 2.1 Sample Preparation Four milliliters of urine were collected from both the discovery and testing cohorts and subjected to trypsin digestion. After digestion, the peptide concentration of the samples was determined using the BCA method. An equal mass of each sample was added to an iRT standard peptide as an internal control. Equal volumes of these samples were then mixed to create a control / initiative pooled sample.

[0041] 2.2 PRM peptide screening Proteins to be validated were selected from the differentially expressed proteins in Example 1 for PRM validation. Skyline 21 software was used for PRM peptide screening. First, a mixed peptide screening was performed, selecting no more than three peptides for each differentially expressed protein. Peptides with good spectra in the urinary proteome database and those that could be identified in the mixed sample or had high signal-to-noise ratio peaks were selected for subsequent validation.

[0042] 2.3 PRM Verification PRM analysis was performed on the peptides selected in section 2.2 that could be used for PRM validation. The 77 samples were validated individually, with each sample analyzed using the scheduled mode for the peptide to be validated. To ensure data quality, a pooled sample analysis was performed before and after loading all samples, and again between every 50 samples as quality control to observe the stability of the instrument signal throughout the analysis. To further ensure data quality, iRT standard peptides were added to each sample for analysis, and the stability of the chromatographic retention time was observed during the analysis. Each sample underwent two technical replicate analyses. Mass spectrometry analyses of different groups of samples were performed in a shuffled order to reduce systematic errors.

[0043] 2.4 PRM Data Analysis and Application Model Construction PRM data analysis was performed using Skyline 21 software. All results were imported into Skyline software, and correct peaks were manually selected. All peptide results for all samples were exported. The total ion current intensity (TIC) for +2 to +5 charge was extracted for each sample using Progenesis software. The mass spectrometry results for each peptide in each sample were normalized using the total ion current intensity of that sample, correcting for errors in sample loading and mass spectrometry signal intensity. The efficacy of four urinary protein combinations in predicting durable clinical benefit was validated based on IS-PRM data. ROC curve analysis was performed using the statistical analysis tool MedCalc.

[0044] The results showed that the expression trends of the four protein subunit tag peptides were consistent in both the durable clinical benefit group and the non-durable clinical benefit group in both the discovery and testing cohorts. This result is consistent with the trend obtained in Example 1. The sequence information of the characteristic peptides (tag peptides) of the four proteins is shown in Table 1. Then, based on PRM data, machine learning models were reconstructed for these four proteins, and receiver operating characteristic (ROC) curve analysis was performed to determine the ability of these four proteins to predict durable clinical benefit from immunotherapy for biliary tract cancer. The results showed that the content of these four proteins in the four protein combinations was consistent in the training set ( Figure 3 A in the validation set Figure 3 B in the middle) and test queue ( Figure 3 The ROC curves in C) of the study on the predictive value of immunotherapy for biliary tract cancer in distinguishing between benefit and non-benefit outcomes showed that the area under the curve (AUC) was greater than 0.7, indicating that the studies have diagnostic value.

[0045] Table 1. List of characteristic peptides of four proteins in urine Protein name and label Protein numbering Characteristic peptides Serial Number Valyl-tRNA synthetase 1 (VARS1) P26640 SSAQDPQAVLGALGR SEQ ID NO.1 Activation of RNA polymerase II transcriptional coactivator p15 (SUB1) P53999 GISLNPEQWSQLK SEQ ID NO.2 Non-receptor tyrosine phosphatase type 13 (PTPN13) Q12923 ASWEEKPR SEQ ID NO.3 MICAL-like protein (MICALL1) Q8N3F8 LQELASPPAGRPTPAPR SEQ ID NO.4 Further analysis was conducted to determine whether the four protein combinations based on PRM technology could predict different early treatment responses to immunotherapy for biliary tract cancer. This was achieved by comparing tumor growth / shrinkage, tumor progression, and partial remission versus progression—different early treatment responses—based on ROC curves. Figure 4 It is evident that these four protein combinations have a discriminative predictive effect. The specific model construction and thresholds are as follows: A model was built using the PRM detection results from the training set. Based on the tag peptides of the four proteins and the detection values ​​after mass spectrometry signal homogenization, the probability p-value was calculated. The prediction model for tumor progression is as follows: logit(p) = log(p / (1-p)). =-1.6023+1.67825*VARS1(SSAQDPQAVLGALGR)-0.43632*SUB1(GISLNPEQWSQLK)+1.07164*PTPN13(ASWEEKPR)+2.15938*MICALL1(LQELASPPAGRPTPAPR).

[0046] Note: In the formula, VARS1, SUB1, PTPN13, and MICALL1 are protein tags, and the parentheses following them contain the tag peptide sequence corresponding to that protein.

[0047] The effectiveness of the model was further evaluated using a validation set. Predictions were made based on the calculated p-values, with a threshold of 0.2889. A p-value greater than this indicated tumor progression. The model demonstrated a sensitivity of 94.74%, a specificity of 60.00%, and a Youden index of 0.5474.

[0048] Based on the above findings, these four proteins can be used to predict the efficacy and / or prognostic analysis of immunotherapy for biliary tract cancer.

[0049] As can be seen from the above embodiments, the present invention provides urinary protein biomarkers related to immunotherapy of biliary tract cancer and their applications. The four urinary protein biomarkers described in the present invention can be used alone or in combination for predicting the efficacy and / or prognostic analysis of immunotherapy for biliary tract cancer.

[0050] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. Urinary protein markers related to immunotherapy for biliary tract cancer, characterized in that, The urinary protein markers are any one or more of the following: non-receptor tyrosine phosphatase type 13, activated RNA polymerase II transcriptional coactivator p15, MICAL-like 1 protein, and valine-tRNA synthetase 1.

2. The use of the identification reagent for urinary protein markers related to biliary cancer immunotherapy as described in claim 1 in the preparation of products for predicting the efficacy and / or prognosis of biliary cancer immunotherapy.

3. A kit for predicting the efficacy of immunotherapy for biliary tract cancer, characterized in that, The reagent for identifying urinary protein markers related to biliary cancer immunotherapy as described in claim 2.

4. A method for constructing a predictive model for the efficacy of immunotherapy for biliary tract cancer, characterized in that, Includes the following steps: S1. The levels of non-receptor tyrosine phosphatase type 13, activated RNA polymerase II transcriptional coactivator p15, MICAL-like 1 protein, and valine-tRNA synthetase 1 in urine samples were detected. S2. Based on the test results of S1, if the above four proteins are lower than the test signals before treatment, it indicates that the tumor volume has increased and progressed, and the prognosis is poor. Alternatively, the following methods can be used to determine whether a tumor has progressed: Based on the tag peptides corresponding to non-receptor tyrosine phosphatase 13, activated RNA polymerase II transcriptional coactivator p15, MICAL-like 1 protein, and valine-tRNA synthetase 1, the probability p-value is calculated based on the normalized detection values ​​of the peptide mass spectrometry signals. The formula for calculating the p-value is as follows: logit(p) = log(p / (1-p)) =-1.6023+1.67825*VARS1(SSAQDPQAVLGALGR)-0.43632*SUB1(GISLNPEQWSQLK)+1.07164*PTPN13(ASWEEKPR)+2.15938*MICALL1(LQELASPPAGRPTPAPR); Predictions are made based on the calculated p-value. The threshold for the p-value is 0.2889. When the p-value is greater than this value, it indicates tumor progression.

5. The construction method according to claim 4, characterized in that, The detection is based on mass spectrometry or immunoassay; the mass spectrometry detection is for the identification of one or more of the following four proteins: non-receptor tyrosine phosphatase type 13, activated RNA polymerase II transcriptional coactivator p15, MICAL-like 1 protein, and valine-tRNA synthetase 1 tag peptide.

6. The construction method according to claim 5, characterized in that, The mass spectrometry detection is performed using a data-independent acquisition method or parallel reaction monitoring.

7. The construction method according to claim 6, characterized in that, When applied in parallel reaction monitoring mass spectrometry mode, the amino acid sequence of the tag peptide of non-receptor tyrosine phosphatase 13 is ASWEEKPR, the amino acid sequence of the tag peptide of activated RNA polymerase II transcriptional coactivator p15 is GISLNPEQWSQLK, the amino acid sequence of the tag peptide of MICAL-like 1 protein is LQELASPPAGRPTPAPR, and the amino acid sequence of the tag peptide of valine-tRNA synthetase 1 is SSAQDPQAVLGALGR.

8. The construction method according to claim 5, characterized in that, The reagents for the immune detection are antibody-associated kits or chips that identify one or more of the four proteins.

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