Use of reagent for detecting biomarker in preparation of product for early auxiliary diagnosis of oral squamous cell carcinoma

Abstract

The application discloses application of a reagent for detecting biomarkers in preparation of a product for early auxiliary diagnosis of oral squamous cell carcinoma, the biomarkers being one or a combination of two or more of autoantibodies of ARHGAP28 antigen polypeptides, autoantibodies of ARMC5 antigen polypeptides, autoantibodies of ZNF710 antigen polypeptides, autoantibodies of CUX1 antigen polypeptides, autoantibodies of first ISM2 antigen polypeptides, autoantibodies of second ISM2 antigen polypeptides and autoantibodies of PIANP antigen polypeptides. The expression levels of the autoantibodies of the antigen polypeptides in serum of patients with oral squamous cell carcinoma are all significantly higher than those of normal persons, and oral squamous cell carcinoma and normal persons can be effectively distinguished by detecting the expression levels in human serum. It has been verified that when the biomarkers are combined to distinguish oral squamous cell carcinoma and normal persons, the AUC value of the ROC curve is 0.78, the distinguishing effect is better, and the diagnosis effect is better.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to the application of reagents for detecting biomarkers in the preparation of products for the early auxiliary diagnosis of oral squamous cell carcinoma. Background Technology

[0002] Oral cancer is a common type of head and neck cancer, a malignant tumor that occurs in multiple subsites of the oral cavity and adjacent anatomical structures. According to global cancer statistics, an estimated 878,454 new cases of oral cancer were diagnosed worldwide in 2022, with 452,551 deaths, accounting for approximately 4.4% of cancer incidence and 4.6% of cancer deaths. The distribution of oral cancer shows significant regional differences, with countries at lower socioeconomic levels having higher incidence and mortality rates. In 2022, my country had 65,183 new cases of oral cancer and 35,177 deaths, making it one of the countries with the highest number of oral cancer patients in the world. Approximately 90% of oral cancers are squamous cell carcinomas originating from squamous cells on the mucosal surface, namely oral squamous cell carcinoma (OSCC).

[0003] OSCC patients often present with no obvious symptoms in the early stages. Although some oral malignancies initially manifest as leukoplakia or erythema and can progress to invasive cancer, the vast majority of OSCC patients are already in an advanced stage at diagnosis. In recent years, with advancements in treatment methods, the 5-year survival rate for OSCC has improved, but in my country, it remains only around 50%. For advanced-stage OSCC, drug treatment is less effective, and surgery can impair speech function and organs required for swallowing; compared to early-stage patients, the survival rate is less than half. Even after treatment, 30%-60% of patients diagnosed at an advanced stage will still develop recurrent localized cancer or a second primary cancer. Therefore, early diagnosis of OSCC is of great significance.

[0004] Currently, the main methods for detecting OSCC (occlusive cranial tumor) are oral clinical examination and histopathological biopsy. However, early lesions and precancerous lesions are not easily detected, and oral clinical examination is insufficient for differentiating benign lesions. Histopathological biopsy is an invasive procedure with disadvantages such as high cost, inconvenience, and time consumption. In addition, histopathological biopsy is limited in both time and space, and usually can only provide brief information on a single region of heterogeneous tumors.

[0005] Therefore, identifying specific biomarkers for OSCC is crucial for early diagnosis and treatment. Tumor development and progression are often accompanied by abnormal protein expression, known as tumor-associated antigens (TAAs). TAAs are detected by the human immune system, stimulating the body to produce autoantibodies against tumor-associated antigens (TAAbs). TAAbs can serve as early diagnostic biomarkers for tumors due to their advantages, including early detection, long duration of presence, high titer, ease of detection, and non-invasive screening. Numerous studies have demonstrated the feasibility and rationale for using TAAbs as biomarkers in tumor diagnosis.

[0006] Phage immunoprecipitation sequencing (PhIP-seq) is a technique that combines phage display technology with next-generation sequencing and has been widely used in fields such as autoantibody analysis, infectious disease-related antibodies, and vaccine development. Compared to traditional serological techniques, PhIP-seq offers high throughput, allowing for the simultaneous evaluation of antibody responses to hundreds of thousands of antigens, providing a new technical pathway for the systematic discovery of TAAbs. However, there is currently a lack of research on large-scale TAAb screening using PhIP-seq technology in the serum of patients with oral squamous cell carcinoma, and no reports have been found on the further development of early diagnostic biomarker combinations for oral squamous cell carcinoma based on this technology. Systematic research on "antigen peptide-autoantibody" biomarkers based on PhIP-seq technology could significantly improve the specificity and sensitivity of detection, providing new insights for improving the early diagnosis rate of oral squamous cell carcinoma and expanding the application potential of antigen peptides in precision oncology. Summary of the Invention

[0007] The purpose of this invention is to provide a reagent for detecting biomarkers in the preparation of products for the early auxiliary diagnosis of oral squamous cell carcinoma.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: The application of reagents for detecting biomarkers in the preparation of products for the early auxiliary diagnosis of oral squamous cell carcinoma, wherein the biomarkers are one or a combination of two or more of the following: autoantibodies against ARHGAP28 antigen peptide, ARMC5 antigen peptide, ZNF710 antigen peptide, CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide, and PIANP antigen peptide.

[0009] The reagent is an antigen.

[0010] The antigen is one or a combination of two or more of the following: ARHGAP28 antigenic peptide, ARMC5 antigenic peptide, ZNF710 antigenic peptide, CUX1 antigenic peptide, first ISM2 antigenic peptide, second ISM2 antigenic peptide, and PIANP antigenic peptide.

[0011] The amino acid sequence of the ARHGAP28 antigenic polypeptide is SGLESEGIFRLSGCTAKVKQYRE; The amino acid sequence of the ARMC5 antigen peptide is SPAPPTSLRAPRTQRTPGRSPA. The amino acid sequence of the ZNF710 antigen polypeptide is LSAFSRKPRTLRHLPRTPRPELNV. The amino acid sequence of the CUX1 antigen polypeptide is SVSDMLSRPKPWSKLTQKGREPF; The amino acid sequence of the first ISM2 antigen polypeptide is HQHGCWTVTEPAALTPGN; The amino acid sequence of the second ISM2 antigen polypeptide is LTPGNATPPRTQEVTPLLLEL; The amino acid sequence of the PIANP antigen polypeptide is RAPPPSRSPRVPRSRR.

[0012] The biomarker is one or a combination of two or more of the following: autoantibodies against CUX1 antigen peptide, autoantibodies against first ISM2 antigen peptide, autoantibodies against second ISM2 antigen peptide, and autoantibodies against PIANP antigen peptide.

[0013] The biomarker is a combination of autoantibodies against CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide, and PIANP antigen peptide; the reagent detects the biomarker in the sample by enzyme-linked immunosorbent assay (ELISA), protein chip, immunoblotting, or microfluidic immunoassay.

[0014] The product is a protein chip, reagent kit, or formulation.

[0015] The kit is an ELISA detection kit, which includes an antigen for detecting biomarkers, the antigen being coated on a solid-phase carrier.

[0016] The ELISA test kit further includes any one or more combinations of positive control serum, negative control serum, blocking solution, sample diluent, secondary antibody, secondary antibody diluent, washing solution, coating solution, colorimetric solution, or stop solution; the solid phase carrier is made of polyvinyl chloride, polystyrene, polyacrylamide, or cellulose.

[0017] The sample may be serum, plasma, saliva, interstitial fluid, or urine.

[0018] The second antibody is a horseradish peroxidase-labeled mouse anti-human IgG monoclonal antibody.

[0019] The method for preparing the solid-phase carrier coated with antigen peptides in an ELISA detection kit includes the following steps: (1) Coating antigen peptides: CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide and PIANP antigen peptide were diluted to the specified concentrations using coating solution (the coating concentration of CUX1 antigen peptide and first ISM2 antigen peptide was 0.5 μg / mL, and the coating concentration of second ISM2 antigen peptide and PIANP antigen peptide was 1 μg / mL), and then added to the solid support at 50 μL / well and coated at 4℃ for more than 14 h. (2) Blocking: After incubation, the solid support was warmed to room temperature and the coating solution was removed. Then, the blocking solution was added to the solid support at 100 μL / well and blocked at 37°C for 2 h. (3) After sealing, remove the liquid in the solid carrier, wash with washing solution, and pat dry to obtain the solid carrier coated with antigen peptide.

[0020] The washing conditions were 300 μL washing solution / well, 10 s / cycle, and repeated 3 times.

[0021] The detection method of the ELISA test kit includes the following steps: (1) Primary antibody incubation: The primary antibody was mixed and diluted with the sample diluent at a ratio of 1:100, and 50 μL / well was added to the solid-phase carrier coated with the antigen peptide. A blank control well was set up and incubated at 37°C for 2 h. The primary antibody was the serum sample to be tested, and the blank control was the sample diluent. (2) Secondary antibody incubation: After the primary antibody incubation is completed, remove the liquid in the solid phase carrier, wash with washing solution, pat dry, dilute the secondary antibody with secondary antibody diluent, add 50 μL / well to the solid phase carrier, and incubate at 37°C for 1 h; the dilution ratio of the secondary antibody is 1:5000. (3) Color development and termination: After the second antibody incubation is completed, remove the liquid in the solid support, wash with washing solution, pat dry, add 50 μL of color development solution to the solid support, react at room temperature in the dark for 5-15 min, and then add 25 μL of termination solution to the solid support. (4) Scanning: Wipe the bottom of the solid-phase carrier to remove foreign matter, place the solid-phase carrier into the microplate reader, start the scanning program, and record the absorbance difference of the above four polypeptide autoantibodies. If the OD value of the blank control well is higher than 0.1, it needs to be detected again; absorbance difference = OD 450nm -OD 620nm .

[0022] The washing conditions were 300 μL washing solution / well, 10 s / cycle, and repeated 5 times.

[0023] Compared with the prior art, the present invention has the following beneficial effects: (1) Based on PhIP-Seq technology, this invention first screens out potential antigenic peptides that can be used to detect oral squamous cell carcinoma. Then, it detects the serum marker levels of oral squamous cell carcinoma patients through peptide microarray and further verifies them through ELISA experiments. Finally, it obtains a set of serum markers for the combined detection of oral squamous cell carcinoma that can be used for early screening and diagnosis of oral squamous cell carcinoma, including autoantibodies against CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide, and PIANP antigen peptide. These markers can be used to assist in the early detection of oral squamous cell carcinoma and have good reference value. (2) This invention is the first to discover that the expression levels of autoantibodies against CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide, and PIANP antigen peptide in the serum of patients with oral squamous cell carcinoma are significantly higher than those in normal individuals, and the differences are statistically significant. P <0.05), by detecting the expression level in human serum, oral squamous cell carcinoma can be effectively diagnosed and differentiated from normal individuals; it has been verified that when the four markers of CUX1 antigen peptide autoantibody, first ISM2 antigen peptide autoantibody, second ISM2 antigen peptide autoantibody, and PIANP antigen peptide autoantibody are combined to diagnose and differentiate oral squamous cell carcinoma from normal individuals, the AUC value of the ROC curve is 0.78, indicating good differentiation and diagnostic effects; (3) The kit of the present invention detects the expression levels of autoantibodies of CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide and PIANP antigen peptide in human serum by indirect ELISA method. It can accurately distinguish oral squamous cell carcinoma patients from healthy controls, and provides new reference for clinicians to diagnose oral squamous cell carcinoma. (4) The test sample of the kit of the present invention is serum, which can avoid invasive diagnosis. The risk of oral squamous cell carcinoma can be obtained by taking serum through minimally invasive means. The detection method has the characteristics of high sensitivity, strong specificity and low cost. It is also simple and quick to operate, which can provide a basis for the early diagnosis of oral squamous cell carcinoma. Attached Figure Description

[0024] Figure 1 Volcano plot showing the up- and down-regulation results of edgeR of the antigenic peptide; Figure 2 Heatmap of 36 candidate antigenic peptides screened by PhIP-Seq technology in oral squamous cell carcinoma and healthy controls; OSCC represents the oral squamous cell carcinoma group, and HC represents the healthy control group; Figure 3 This image shows the expression of 10 peptide autoantibodies screened by the peptide microarray in oral squamous cell carcinoma and healthy controls; SNR represents the signal-to-noise ratio, OSCC represents the oral squamous cell carcinoma group, and HC represents the healthy control group. P< 0.05 indicates that the difference is statistically significant; Figure 4 ROC curves for diagnosing oral squamous cell carcinoma with autoantibodies of 10 peptides in a peptide microarray; AUC is the area under the receiver operating characteristic curve, 95% CI is the 95% confidence interval, sensitivity is the sensitivity, and specificity is the specificity. Figure 5 The graph shows the expression levels of nine polypeptide autoantibodies (validation group); OD represents optical density, OSCC represents oral squamous cell carcinoma, and HC represents the healthy control group. P< 0.05 indicates that the difference is statistically significant; Figure 6 ROC curves for diagnosing oral squamous cell carcinoma with 9 types of peptide autoantibodies (validation group); AUC is the area under the receiver operating characteristic curve, 95% CI is the 95% confidence interval, sensitivity is the sensitivity, and specificity is the specificity. Figure 7 This image shows the expression levels of eight peptide autoantibodies (test group); OD represents optical density, OSCC represents oral squamous cell carcinoma, and HC represents the healthy control group. P< 0.05 indicates that the difference is statistically significant; Figure 8 ROC curves (test) for diagnosing oral squamous cell carcinoma with autoantibodies of 8 peptides; AUC is the area under the receiver operating characteristic curve, 95% CI is the 95% confidence interval, sensitivity is the sensitivity, and specificity is the specificity; Figure 9In the middle, from left to right, are the ROC curves of the oral squamous cell carcinoma diagnostic model on the training set, internal validation set, and test set. AUC is the area under the receiver operating characteristic curve, 95% CI is the 95% confidence interval, sensitivity is the sensitivity, and specificity is the specificity. Detailed Implementation

[0025] The present invention will be further described below with reference to specific embodiments and accompanying drawings.

[0026] Example 1: Screening of antigenic peptides based on PhIP-Seq technology 1. Experimental Samples Serum samples were collected from 50 patients with oral squamous cell carcinoma (OCC) at the First Affiliated Hospital of Zhengzhou University (OCC group) and 50 healthy controls (health control group). The 50 OCC patients were diagnosed pathologically and had not undergone surgery, radiotherapy, chemotherapy, immunotherapy, or traditional Chinese medicine-based anti-tumor treatments. Their diagnoses occurred between June and December 2021. Of the 50 OCC patients, 30 were male (60.0%) and 20 were female (40.0%), with a median age of 65.5 years. The 50 healthy controls were selected from healthy subjects who participated in an annual health checkup and had no symptoms of malignant tumors, oral-related diseases, or autoimmune diseases. Of the 50 healthy controls, 25 were male (50.0%) and 25 were female (50.0%), with a median age of 61.0 years. This study was approved by the institutional review committee and the hospital ethics committee, and all participants signed informed consent forms.

[0027] Serum collection: 5 mL of fasting venous blood was collected from the subjects in an anticoagulant blood collection tube, allowed to stand at room temperature for 1 hour, and then centrifuged at 3000 rpm for 5 minutes. The supernatant serum was aliquoted into 1.5 mL EP tubes, labeled with sample numbers, and stored at -80℃. The date of blood collection and storage location were recorded.

[0028] 2. PhIP-Seq (phage immunoprecipitation sequencing) technology detection (1) Experimental methods 1) Serum pretreatment: Place each serum sample at 4℃ and centrifuge at 12000 rpm for 20 min. Take the supernatant and dilute it to 1 mL with PBST buffer at a ratio of 1:1000. Add the solution to a pre-sealed 96-well plate and set up negative control, blank control and positive control wells in each 96-well plate.

[0029] 2) Phage display peptide library incubation: Diluted and centrifuged phage display peptide library (Humanproteome, Shanghai Anticode Biotechnology Co., Ltd.) was added to each well, ensuring a library diversity of 16000× phages per well. The 96-well plate was then sealed with the matching sealing cap and incubated overnight at 4°C by rotation.

[0030] 3) Magnetic bead precipitation enrichment: After incubation, add 20 μL of washed Protein G magnetic beads to each well and incubate at 4℃ for 4 h; after incubation, centrifuge at 500g for 3 min, place the reaction plate on a magnetic rack to adsorb Protein G magnetic beads, discard the original solution, add 200 μL of washing TBS (containing 0.1% IPEGAL CA630) to each well, resuspend and transfer to a new 96-well PCR plate; repeat the washing process twice, and finally add 40 μL of sterile water to resuspend the magnetic beads and transfer to a new 96-well PCR plate.

[0031] 4) PCR library preparation: After sealing the plate, centrifuge briefly for 10 seconds and heat at 95°C for 10 minutes in a PCR instrument; sequencing library preparation uses two rounds of PCR, using universal primers that match the phage sequence and primers with barcodes and sequencing adaptation sequences for amplification, respectively. The amplified products are monitored for quality by gel electrophoresis. Mix the PCR products from 1-3 96-well plates (3 μL per well) and obtain the target band by gel electrophoresis and gel excision.

[0032] 5) High-throughput sequencing: Based on Illumina sequencers and kits, the library-constructed samples are sequenced with a read length of 150 bp. The full-length sequence is obtained through bidirectional sequencing results, and the number of reads for each peptide is used as the basic data.

[0033] (2) Data processing 1) Data Normalization: Sequencing volumes for different samples were normalized (the sequencing volume of each sample was proportionally adjusted to 1.25M reads) to obtain the normalized reads (NR) for each peptide in each sample. Based on the background sequencing abundance of each peptide, i.e., the sequencing data combined with blank beads (beads that have undergone the same process as the test sample after phage incubation, excluding those without the test sample, are called mock-IPs), the enhancement factor (EF) of different peptides in each sample was further calculated, i.e., the normalized reads / mock-IP ratio of the sample. Finally, the distribution trend of peptides with the same abundance after immunoprecipitation was fitted using a generalized Poisson distribution model to calculate the signal strength (EF) of each peptide. P The value is determined based on the set threshold criteria (NR>15, EF>5). P <0.05) to screen for enriched peptides in each sample.

[0034] 2) Differential Analysis: The edgeR method was used to analyze the differences in peptide reads between the case group and the control group. This method normalizes the sequencing depth between samples and uses a negative binomial distribution model to compare the differences between the two groups. When the peptide reads are corrected... P <0.05, and a difference fold log2FC ≥1.2 was considered to indicate a difference in expression. The results are as follows: Figure 1 As shown in the figure. Based on the edgeR method, peptides that showed a positive rate more than 10% higher in the case group than in the control group were considered candidate OSCC-related antigen peptides. The results are as follows: Figure 2 As shown.

[0035] (3) Experimental results Based on the fact that the positive rate in the OSCC group was 10% higher than that in the healthy control group, the correction of the edgeR method... P With a fold change <0.05 and a difference fold log2FC ≥1.2, 36 antigenic peptides with potential diagnostic value were identified: #65592 (ADGRL1), #542805 (ARHGAP28), #429988 (ARMC5), #26438 (BTAF1), #138961 (CUX1), #203745 (DAZ2), #203746 (DAZ2), #518663 (DAZ3), #328369 (DAZ4), #328372 (DAZ4), #393752 (DNAH3), #600829 (EMCN), #446736 (FAM161B), #446738 (FAM161B), #446739 (FAM161B), #153455 (IDH3A), #592963 (IRAG). 1) #286475 (ISM2), #296282 (LARP1), #296284 (LARP1), #296287 (LARP1), #296289 (LARP1), #508621 (NCOA5), #517980 (PARP11), #626481 (PARP11), #352236 (PIANP), #4949 (PSMB11), #578797 (R3HDM2), #578798 (R3HDM2), #35469 (SART1), #587232 (SHANK1), #587233 (SHANK1), #244957 (SPHKAP), #503087 (VIPAS39), #30430 (ZBTB22) and #359253 (ZNF710).

[0036] Example 2: Prediction and Design of B-cell Epitopes for Candidate Related Antigen Peptides in Oral Squamous Cell Carcinoma Based on Bioinformatics For the 36 antigenic peptides obtained in Example 1, the B-cell epitopes of each antigenic peptide were predicted using the six prediction methods shown in Table 1. Combining the prediction results of the six bioinformatics tools, sequences predicted by at least three methods were defined as potential B-cell epitopes. Finally, the secondary structure of the sequences was appropriately adjusted and optimized to suitable lengths to design the final B-cell epitopes. A total of 56 peptide sequences with lengths ranging from 16 to 25 amino acids were obtained.

[0037] The source antigens and sequence ranges of the 56 polypeptide sequences are as follows: ADDRL1 (700-722), ARHGAP28 (412-434, 430-446), ARMC5 (473-494, 490-512, 506-526), ​​BTAF1 (945-965, 956-972), CUX1 (977-999, 1003-1025), DAZ2 / DAZ3 / DAZ4 (316-33). 9), DNAH3 (2119-2235, 2158-2174), EMCN (85-101, 120-137), FAM161B (568-588, 595-616, 622-64 4), IDH3A (67-83), IRAG1 (648-664, 614-634), ISM2 (141-158, 157-174, 170-190), LARP1 (734-750 , 761-781, 768-789, 793-815), NCOA5 (323-341, 348-364), PARP11 (94-110), PIANP (63-78, 91-11 2), PSMB11 (94-110, 59-75), R3HDM2 (418-438, 448-464), SART1 (309-332, 329-348, 339-361), SH ANK1 (464-481, 477-498, 486-506, 506-528), SPHKAP (1541-1557, 1565-1587, 1580-1596), VIPAS 39 (87-108, 99-121, 113-137), ZBTB22 (253-273, 270-291, 283-306), ZNF710 (161-184, 177-196).

[0038] Example 3 uses a polypeptide microarray chip to screen biomarkers for the diagnosis of oral squamous cell carcinoma. The expression levels of autoantibodies against the 56 antigenic peptides obtained in Example 2 were detected in serum using the peptide microarray method.

[0039] 1. Experimental Samples Serum samples were collected from the First Affiliated Hospital of Zhengzhou University (using the same selection criteria as in Example 1), including 240 oral squamous cell carcinoma samples (oral squamous cell carcinoma group) and 240 healthy control samples (healthy control group). Serum collection occurred from June 2021 to June 2024. Of the 240 newly diagnosed oral squamous cell carcinoma patients, 170 were male (78.83%) and 70 were female (29.17%), with a median age of 59.0 years. Of the 240 normal serum samples, 175 were male (72.92%) and 65 were female (27.08%), with a median age of 58.0 years. Among the 204 oral squamous cell carcinoma samples, 114 were TNM stage I-II and 126 were stage III-IV.

[0040] Serum collection: 5 mL of fasting venous blood was collected from the subjects in an anticoagulant blood collection tube, allowed to stand at room temperature for 1 hour, and then centrifuged at 3000 rpm for 5 minutes. The supernatant serum was aliquoted into 1.5 mL EP tubes, labeled with sample numbers, and stored at -80℃. The date of blood collection and storage location were recorded.

[0041] 2. Experimental materials and reagents: (1) Peptide microarray blocking solution: 1×PBST buffer containing 3% bovine serum albumin (BSA); (2) Antibody dilution solution: 1×PBST buffer containing 1% BSA; (3) Washing solution: 1×PBST; (4) Secondary antibody incubation solution: Cy3-labeled anti-human IgG secondary antibody, diluted 1:100 with antibody dilution buffer; (5) 56 antigenic polypeptides: synthesized by Sangon Biotech (Shanghai) Co., Ltd. using the Fmoc solid-phase synthesis method.

[0042] 3. Experimental Methods (1) Preparation of peptide microarray chips: 1) Prepare antigen peptide samples: Dilute the dissolved antigen peptide with ultrapure water to the same concentration. Take the same volume of CrystalCore® protein chip spotting solution B and dilute the antigen peptide at a 1:1 ratio. Transfer 120µL to a 96-well PP plate and store at 4°C in the dark for later use.

[0043] 2) Spotting instrument preparation: Replace with ultrapure water and turn on the humidifier and temperature regulator 30 minutes in advance. Set the humidity of the chip spotting instrument to 60% and the temperature to 25℃. After the humidity and temperature stabilize to the set values, run the spotting instrument to ensure that the instrument's robotic arm, height detector, liquid circuit, etc. are operating normally.

[0044] 4) Preparation of epoxy-based slides: Take the epoxy-based slides out of the 4℃ refrigerator in advance, allow them to reach room temperature for 30 minutes, then open the vacuum packaging tape. Use tweezers to pick up the slides and place them in a Z-shape from the top left into the dustproof glass cover. Then place the 96-well PP plate containing the antigen peptide sample into the spotting instrument and cover it with the dustproof glass cover.

[0045] 5) Spotting: After the temperature and humidity inside the dustproof glass cover are balanced and stable, run the pre-set spotting program. After spotting is completed, the epoxy sheet remains in the same position in the spotting instrument. After standing in the dustproof glass cover for 2 hours, ensure that the spotting points are fully bonded to the substrate.

[0046] 6) Apply the fence: Use the Crystal® chip fence application tool and clamping tool to apply the 12-block fence to the sample surface, ensuring that the fence is applied to the sample surface. During the application process, be careful not to let the fence come into contact with the sample points.

[0047] 7) Chip preservation: The peptide chip can be used immediately. If serum testing cannot be performed on the prepared chip in time, it should be vacuum-sealed and placed in a bag with desiccant and stored in a -80℃ refrigerator to ensure a longer shelf life. (2) Peptide array serum testing: 1) Chip temperature equilibration: Take the peptide chip out of the -80℃ freezer and place it in the -20℃ and 4℃ freezers for 30 minutes in sequence to gradually transition to avoid water vapor condensation on the chip surface; after taking it out, equilibrate it for 30 minutes under sealed conditions at room temperature, ensuring that the chip is sealed well during this process, and open the vacuum packaging bag after equilibration is completed.

[0048] 2) Hybridization box preparation: Take out the CrystalCore® protein chip reaction box, place a piece of filter paper of appropriate size at the bottom, and add 100-150µL of ultrapure water on both sides to maintain the necessary humidity for the reaction.

[0049] 3) Blocking: Slowly add 25µL of 3% BSA to each block using a pipette, cover and seal the hybridization box, and place it in a chip hybridization instrument. Block hybridize at room temperature in the dark for 2 hours.

[0050] 4) Chip cleaning and drying: Use tweezers to remove the chip from the hybridization box and place it in the cleaning chamber. Wash it 3 times with washing solution I (1×PBST) for 5 minutes each time, with the cleaning intensity set to 5. After cleaning, put the chip into the centrifugation drying chamber for centrifugation and drying.

[0051] 5) Serum hybridization: Remove the serum sample from the -80℃ freezer and thaw it slowly in the 4℃ freezer. After vortexing, dilute the serum with 1% BSA at a dilution ratio of 1:50. Use a pipette to slowly add 25µL of diluted serum to each block and place it in a chip hybridization instrument for hybridization reaction at 37℃ and 10rpm for 1 hour.

[0052] 7) Chip cleaning and drying: Repeat step 4).

[0053] 8) Secondary antibody reaction: Under light-protected conditions, add 25µL of secondary antibody solution (Cy3-IgG) diluted with 1% BSA (1:100) to each block, and place it in a chip hybridization instrument for hybridization reaction at 37℃ and 10rpm for 1 hour in the dark.

[0054] 9) Cleaning and drying: After removing the chip from the hybridization box, place it in the chip cleaner. First, clean it 3 times with washing solution I (1×PBST), then clean it once with washing solution II (ultrapure water), and immediately place it in the centrifuge drying chamber for centrifugation and drying.

[0055] 10) Scanning: Preheat the chip scanner for 30 minutes beforehand, place the dried chip in for scanning, adjust the appropriate light wavelength, photomultiplier (PMT) and laser power, use LuxScan3.0 software to extract signal values, and finally save the image and original file, and record the scanning results.

[0056] 4. Data Processing: (1) Chip quality control 1) Substrate quality control: First, the chip detection signal value is quality controlled to identify abnormal slides. If the foreground value of the positive control point is ≥8000 and the foreground value of the negative control point is ≤background value, the chip is considered qualified. Otherwise, the experiment is repeated.

[0057] 2) Spotting quality control: Each antigenic peptide is spotted three times, and the coefficient of variation (CV) between the foreground and background values ​​is calculated. If the CV value of the foreground or background value in the same serum sample exceeds 40%, the test result of the sample is considered invalid and the experiment needs to be repeated.

[0058] 3) Repeatability test: Peptide array detection was performed on the quality control serum both within the same batch and between different batches. The stability and repeatability of the prepared peptide array were evaluated using Pearson analysis.

[0059] (2) Normalization within the peptide array: In order to eliminate the signal deviation caused by inconsistency with the background value, the background normalization method is used for data processing. The signal-to-noise ratio (SNR) = foreground value / background value is calculated for each point. Then, the mean value of the SNR of the three repeated sampling points is calculated as the detection signal value of the autoantibody of the corresponding antigen peptide.

[0060] (3) Standardization between different chips: Even for the same serum test, there are systematic errors. A reference dataset is constructed using the detection signal values ​​from all Block 12 chips. The detection signal values ​​from each Block 12 chip are linearly fitted to the reference dataset to calculate the normalization factor (NF) for each chip. NF is used to adjust the SNR value of each chip to be consistent with the reference dataset, thereby achieving data standardization between different chips. The specific method is as follows: 1) Construct a reference dataset: For the 12th block of each chip, calculate the SNR value of the autoantibody for each antigenic peptide. Then calculate the average SNR of the autoantibody for each antigenic peptide in the 12th block of all chips, and summarize them as the reference dataset.

[0061] 2) Exclusion of low-signal antigen peptides: Low signal may affect the standardization effect, so antigen peptides with a mean SNR of less than 1.50 are excluded for subsequent standardization.

[0062] 3) Calculate the correlation coefficient and eliminate outliers: Calculate the Pearson correlation coefficient between the reference dataset and the data of the 12th block of each chip. If the correlation coefficient is less than 0.96, eliminate outliers until the correlation coefficient is not less than 0.96.

[0063] 4) Linear fitting and NF calculation: The `lm` function in R is used to fit and calculate the linear relationship between each chip and the reference dataset, and the `coef()` function is used to obtain the correction factor. The detailed calculation formula is as follows: Model Ni=lm (Reference Signal Intensities∼Slide i Block - 1) Factor Ni = coef (Model Ni) Where: Reference Signal Intensities are the SNR values ​​of the reference dataset, and Slide i Block is the SNR value of the 12th Block in the i-th chip.

[0064] 5) Standardize the signal intensity of different peptide arrays: For the SNR value in each peptide array, multiply it by the NF value calculated by the corresponding chip to obtain the standardized data.

[0065] To obtain autoantibodies against antigenic peptides with diagnostic potential for OSCC, the following conditions must be met: 1) Immunogenicity: The median SNR in the OSCC group must be ≥1.5; 2) Differentiality: The median SNR difference between the OSCC group and the healthy control group must be statistically significant, and the FC must be ≥1.2; 3) Diagnostic efficacy: The AUC of the autoantibody against a single antigenic peptide must be statistically significant (AUC>0.5). P <0.05). Autoantibodies against oral squamous cell carcinoma-associated antigenic peptides that meet the criteria were screened.

[0066] 5. Experimental Results: After screening, 10 autoantibodies against antigenic peptides with potential early diagnostic value were obtained: anti-Pep02, anti-Pep05, anti-Pep09, anti-Pep23, anti-Pep24, anti-Pep33, anti-Pep37, anti-Pep43, anti-Pep47, and anti-Pep55. The autoantibodies against these antigenic peptides are Pep02 (derived from ARHGAP28, sequence interval 412-434), Pep05 (derived from ARMC5, sequence interval 490-512), and Pep09 (derived from C...). Autoantibodies against UX1 (sequence interval 977-999), Pep23 (from ISM2, sequence interval 157-174), Pep24 (from ISM2, sequence interval 170-190), Pep33 (from PIANP, sequence interval 63-78), Pep37 (from R3HDM2, sequence interval 418-438), Pep43 (from SHANK1, sequence interval 477-498), Pep47 (from SPHKAP, sequence interval 1565-1587), and Pep55 (from ZNF710, sequence interval 161-184) were expressed. The expression of these 10 antigenic peptide autoantibodies in the oral squamous cell carcinoma group and the healthy control group is as follows: Figure 3 As shown, the expression levels of autoantibodies against these 10 antigenic peptides in the serum of the oral squamous cell carcinoma group were higher than those in the healthy control group, and the differences were statistically significant. P <0.05). Meanwhile, the ROC curves of autoantibodies against 10 antigenic peptides in the oral squamous cell carcinoma group and the healthy control group are shown below. Figure 4 As shown, the AUC of autoantibodies against the 10 antigenic peptides was >0.5, and this was statistically significant. P <0.05).

[0067] Example 4: ELISA detection of serum expression levels of autoantibodies against oral squamous cell carcinoma-associated antigens. The expression levels of the 10 oral squamous cell carcinoma-related antigen autoantibodies screened in Example 3 were further verified in human serum using an indirect enzyme-linked immunosorbent assay (ELISA).

[0068] 1. Experimental Samples The same samples as in Example 3 were used as the validation group. Samples collected at different times from the First Affiliated Hospital of Zhengzhou University were used as the test group (external validation group), including 85 patients with oral squamous cell carcinoma and 85 healthy controls. Serum samples for the test group were collected from January 2025 to October 2025. Among the 85 newly diagnosed oral squamous cell carcinoma patients, there were 55 males (64.71%) and 30 females (35.29%), with a median age of 61.0 years. Among the 85 normal serum samples, there were 61 males (71.76%) and 24 females (28.24%), with a median age of 58.0 years. Among the 85 oral squamous cell carcinoma samples, 35 were TNM stage I-II and 49 were stage III-IV.

[0069] Serum collection: 5 mL of fasting venous blood was collected from the subjects in an anticoagulant blood collection tube, allowed to stand at room temperature for 1 hour, and then centrifuged at 3000 rpm for 5 minutes. The supernatant serum was aliquoted into 1.5 mL EP tubes, labeled with sample numbers, and stored at -80℃. The date of blood collection and storage location were recorded.

[0070] 2. Experimental reagents (1) Coating solution: an aqueous solution containing 0.15% sodium carbonate (Na2CO3) and 0.29% sodium bicarbonate (NaHCO3); (2) ELISA blocking solution: 1×PBST buffer containing 2% BSA; (3) Antibody dilution solution: 1×PBST buffer containing 1% BSA; (4) Washing solution: 1×PBST; (5) Secondary antibody incubation solution: HRP-labeled mouse anti-human IgG, diluted 1:5000 with antibody dilution solution; (6) Colorimetric solution: The colorimetric solution consists of colorimetric solution A and colorimetric solution B. Colorimetric solution A is an aqueous solution of 20% tetramethylbenzidine dihydrochloric acid, and colorimetric solution B is an aqueous solution containing 3.7% Na2HPO4•12H2O, 0.92% citric acid and 0.75% urea peroxide. When using, mix colorimetric solution A and colorimetric solution B in equal volumes at a ratio of 1:1 and prepare fresh each time. (7) Termination solution: 10% dilute sulfuric acid.

[0071] 3. Experimental Methods (1) Coating: The 10 antigenic peptides screened in Example 3 above (synthesized by Sangon Biotech (Shanghai) Co., Ltd. using the Fmoc solid-phase synthesis method) were coated separately. The following antigenic peptides were coated at a concentration of 2 µg / mL: Pep05, Pep43, Pep47, Pep55; the following antigenic peptides were coated at a concentration of 1 µg / mL: Pep24, Pep33; and the following antigenic peptides were coated at a concentration of 0.5 µg / mL: Pep02, Pep09, Pep23, Pep37. 50 µL of each peptide was added to a 96-well microplate. The microplate was then sealed and coated at 4°C for 14 hours.

[0072] (2) Blocking: Take out the coated microplate and shake off the coating solution; add 100µL of 2% BSA to each well and reseal the microplate, then block it in a constant temperature drying oven at 37℃ for 2 hours.

[0073] (3) Primary antibody incubation: Dilute the aliquoted serum with 1% BSA at a ratio of 1:100 in a 96-well deep plate in advance and store it in a refrigerator at 4°C for later use; after blocking, shake off the blocking solution from the microplate; wash the microplate with a plate washer, using 1×PBST as the washing solution (300 μl / well), wash 3 times, and then pat the microplate dry; add the diluted serum to the microplate at a volume of 50 µL per well as designed, add serum-free diluent to the 2 blank wells, then seal the microplate and incubate it in a constant temperature drying oven at 37°C for 1 hour.

[0074] (4) Secondary antibody incubation: After the primary antibody incubation is completed, the primary antibody liquid is shaken out; the microplate is washed with 1×PBST as washing buffer (300 μl / well), and washed 5 times. Different microplates are patted dry on absorbent paper; 50 µL of diluted secondary antibody (dilution ratio of 1:5000) is added to each well and the microplate is sealed and incubated in a constant temperature drying oven at 37°C for 1 hour.

[0075] (5) Color development and termination reaction: After the secondary antibody incubation is completed, shake off the liquid and wash the microplate with 1×PBST as washing buffer (300 μl / well). Wash 5 times and pat dry. Take out the prepared color development solution A and solution B from the 4℃ refrigerator, mix the color development solution 1:1, add 50 µL of color development solution to each well of the microplate, and react in the dark for 5-15 minutes. After the reaction is completed, add 25 µL of termination solution to each well of the microplate, mix it thoroughly with the color development solution, and terminate the color development.

[0076] (6) Scanning: Turn on the microplate reader 30 minutes in advance for preheating and self-testing. Place the microplate into the microplate reader in sequence and read the optical density (OD) at wavelengths of 450nm and 620nm. Use the difference between the two optical densities for subsequent analysis.

[0077] 4. Data Processing (1) Sample quality control: Two blank control wells are set in each ELISA plate to monitor background noise and non-specific binding. If the blank well reading is greater than 0.1, the ELISA plate is considered to be contaminated and the corresponding ELISA plate needs to be retested.

[0078] (2) Standardization: Each ELISA plate has 6 control wells for standardizing OD values ​​between different ELISA plates. For autoantibodies against the same antigenic peptide, the average OD value of the same control serum is calculated. The standardized average and coefficient of variation within the ELISA plate are then calculated. If the coefficient of variation is greater than 0.1, outliers are removed until the coefficient of variation is less than 0.1. The serum OD value between each plate is standardized using the final standardized average.

[0079] (3) Statistical analysis: The diagnostic value of autoantibodies against single antigenic peptides for oral squamous cell carcinoma was evaluated using ROC curves. The cutoff value was the OD value at which specificity was greater than 90% and the Youden index was maximized. The corresponding AUC, sensitivity, specificity, accuracy, and Youden index were calculated and reported. Furthermore, the Delong test was used to compare the differences in AUC values ​​between the two groups. P A value <0.05 indicates a statistically significant difference in AUC between the two groups.

[0080] 5. Experimental Results In the validation group, autoantibodies against 10 antigenic peptides were detected by ELISA. The expression levels of autoantibodies against 9 antigenic peptides in the oral squamous cell carcinoma group were higher than those in the healthy control group, which was statistically significant. P <0.05), such as Figure 5 As shown, the autoantibodies against the nine antigenic peptides are: anti-Pep02, anti-Pep05, anti-Pep09, anti-Pep23, anti-Pep24, anti-Pep37, anti-Pep47, and anti-Pep54; while the expression level of anti-Pep43 did not differ statistically between the two groups.

[0081] Autoantibodies against nine antigenic peptides with statistically significant differences in expression levels between the two groups were used to construct ROC curves to distinguish between oral squamous cell carcinoma and healthy controls. The results are as follows: Figure 6As shown in Table 2, the OD value at which specificity was greater than 90% and the Youden index was the highest was set as the cutoff value. Anti-Pep37 showed poor diagnostic performance in the high-specificity range (>90%), with a Youden index less than 0, and was therefore not analyzed further. The diagnostic value of autoantibodies against eight other antigenic peptides for oral squamous cell carcinoma (validation group) was analyzed, as shown in Table 2. The AUC of autoantibodies against individual antigenic peptides ranged from 0.56 to 0.67, with anti-Pep24 having the highest AUC of 0.67 (95% CI: 0.62–0.72), followed by anti-Pep33 at 0.66 (95% CI: 0.61–0.70). Furthermore, the AUCs of anti-Pep05, anti-Pep09, anti-Pep23, and anti-Pep55 exceeded 0.60. The sensitivity range of autoantibodies against single antigenic peptides is 10.83%-33.75%, the specificity range is 90.42%-92.50%, and the accuracy range is 50.83%-62.08%.

[0082] Note: AUC, Area under the Receiver Operating Characteristic curve; 95% CI, 95% confidence interval.

[0083] In the test group, ELISA was used to detect autoantibodies against eight antigenic peptides. The expression of anti-Pep02, anti-Pep05, anti-Pep09, anti-Pep23, anti-Pep24, anti-Pep33, anti-Pep47, and anti-Pep55 in the oral squamous cell carcinoma group was significantly higher than that in the healthy control group. P <0.05), the result is as follows Figure 7 As shown.

[0084] Autoantibodies against eight antigenic peptides with statistically significant differences in expression levels between the two groups were used to construct ROC curves to distinguish between oral squamous cell carcinoma and healthy controls. The results are as follows: Figure 8As shown in Table 3, the OD value at which specificity was greater than 90% and the Youden index was the highest was set as the cutoff value. Anti-Pep47 showed poor diagnostic performance in the high-specificity range (>90%), with a Youden index less than 0, and was therefore not analyzed further. The diagnostic value of autoantibodies against the other seven antigenic peptides for oral squamous cell carcinoma (test group) was analyzed, as shown in Table 3. The AUC of autoantibodies against individual antigenic peptides ranged from 0.59 to 0.70. Among them, anti-Pep33 had the highest AUC of 0.70 (95% CI: 0.62-0.77), followed by anti-Pep24 at 0.68 (95% CI: 0.60-0.76). In the test group, except for anti-Pep02, the AUCs of all other indicators exceeded 0.60. The sensitivity range of autoantibodies against single antigenic peptides is 18.82%-28.24%, the specificity range is 90.59%-95.29%, and the accuracy range is 57.06%-60.00%.

[0085] Note: AUC, Area under the Receiver Operating Characteristic curve; 95% CI, 95% confidence interval.

[0086] Example 5: Construction and Evaluation of a Diagnostic Model for Oral Squamous Cell Carcinoma Training and validation set samples: Serum samples from the test group in Example 3 were randomly divided into a training set and an internal validation set at a ratio of 7:3. Test group samples: Serum samples from the test group in Example 4 were used for external validation of the model.

[0087] In the training set, seven polypeptide autoantibodies (anti-Pep02, anti-Pep05, anti-Pep09, anti-Pep23, anti-Pep24, anti-Pep33, and anti-Pep55) that showed statistically significant differences in expression levels and had diagnostic value after being screened in Example 4 following ELISA validation were further screened using a recursive feature selection algorithm, resulting in four polypeptide autoantibodies (anti-Pep09, anti-Pep23, and anti-Pep24). Modeling was performed with anti-Pep33, and anti-Pep09, anti-Pep23, anti-Pep24, and anti-Pep33 were identified as autoantibodies against Pep09 antigen peptide (derived from CUX1, sequence interval 977-999), Pep23 antigen peptide (derived from ISM2, sequence interval 157-174), Pep24 antigen peptide (derived from ISM2, sequence interval 170-190), and Pep33 antigen peptide (derived from PIANP, sequence interval 63-78), respectively.

[0088] The amino acid sequence of the anti-Pep02 antigen polypeptide is: SGLESEGIFRLSGCTAKVKQYRE; The amino acid sequence of the anti-Pep05 antigen peptide is: SPAPPTSLRAPRTQRTPGRSPA; The amino acid sequence of the anti-Pep55 antigen peptide is: LSAFSRKPRTLRHLPRTPRPELNV; The amino acid sequence of the anti-Pep09 antigen peptide is: SVSDMLSRPKPWSKLTQKGREPF. The amino acid sequence of the anti-Pep23 antigen peptide is: HQHGCWTVTEPAALTPGN; The amino acid sequence of the anti-Pep24 antigen peptide is: LTPGNATPPRTQEVTPLLLEL; The amino acid sequence of the anti-Pep33 antigenic polypeptide is: RAPPPSRSPRVPRSRR.

[0089] Subsequently, a neural network model was used to construct a model of autoantibodies against the four antigenic peptides, resulting in a diagnostic model for oral squamous cell carcinoma.

[0090] The ELISA detection values ​​of anti-Pep09, anti-Pep23, anti-Pep24, and anti-Pep33 in serum samples from the training set were used to construct a diagnostic model for oral squamous cell carcinoma. The construction method is as follows: (1) Construct a dataset of expression levels of anti-Pep09, anti-Pep23, anti-Pep24 and anti-Pep33 for each sample in the training set; (2) Construct a binary label for each sample in the training set (whether or not the patient has oral squamous cell carcinoma, 0 indicates healthy, 1 indicates disease). (3) The training set samples were sampled 50 times using the Bootstrap sampling method to determine the optimal parameters of the random forest model: size=5, decay=0.1, thus obtaining the model for diagnosing oral squamous cell carcinoma. The input features of the oral squamous cell carcinoma diagnostic model are the expression levels of anti-Pep09, anti-Pep23, anti-Pep24 and anti-Pep33; the output label is whether the sample is suspected of having oral squamous cell carcinoma.

[0091] Since the results obtained by the method in this embodiment can only serve as intermediate information and cannot directly determine whether a patient has oral squamous cell carcinoma, they are mainly used for auxiliary diagnosis. Therefore, further analysis of clinical symptoms, imaging, and histopathological information is needed to ultimately determine the patient's condition.

[0092] The ROC curves of the oral squamous cell carcinoma diagnostic model of this invention on the training set, validation set, and test set are as follows: Figure 9 As shown.

[0093] Depend on Figure 9 The model's AUCs on the training set, internal validation set, and test set were 0.78 (95% CI: 0.73-0.83), 0.70 (95% CI: 0.61-0.79), and 0.73 (95% CI: 0.65-0.80), respectively, indicating that the model can be used effectively to assist in the diagnosis of oral squamous cell carcinoma.

Claims

1. The application of reagents for detecting biomarkers in the preparation of products for the early auxiliary diagnosis of oral squamous cell carcinoma, characterized in that, The biomarker is one or a combination of two or more of the following: autoantibodies against ARHGAP28 antigenic peptide, ARMC5 antigenic peptide, ZNF710 antigenic peptide, CUX1 antigenic peptide, first ISM2 antigenic peptide, second ISM2 antigenic peptide, and PIANP antigenic peptide.

2. The application as described in claim 1, characterized in that, The reagent is an antigen.

3. The application as described in claim 2, characterized in that, The antigen is one or a combination of two or more of the following: ARHGAP28 antigenic peptide, ARMC5 antigenic peptide, ZNF710 antigenic peptide, CUX1 antigenic peptide, first ISM2 antigenic peptide, second ISM2 antigenic peptide, and PIANP antigenic peptide.

4. The application as described in claim 3, characterized in that, The amino acid sequence of the ARHGAP28 antigenic polypeptide is SGLESEGIFRLSGCTAKVKQYRE; The amino acid sequence of the ARMC5 antigen peptide is SPAPPTSLRAPRTQRTPGRSPA. The amino acid sequence of the ZNF710 antigen polypeptide is LSAFSRKPRTLRHLPRTPRPELNV. The amino acid sequence of the CUX1 antigen polypeptide is SVSDMLSRPKPWSKLTQKGREPF; The amino acid sequence of the first ISM2 antigen polypeptide is HQHGCWTVTEPAALTPGN; The amino acid sequence of the second ISM2 antigen polypeptide is LTPGNATPPRTQEVTPLLLEL; The amino acid sequence of the PIANP antigen polypeptide is RAPPPSRSPRVPRSRR.

5. The application as described in claim 4, characterized in that, The biomarker is one or a combination of two or more of the following: autoantibodies against CUX1 antigen peptide, autoantibodies against first ISM2 antigen peptide, autoantibodies against second ISM2 antigen peptide, and autoantibodies against PIANP antigen peptide.

6. The application as described in claim 5, characterized in that, The biomarker is a combination of autoantibodies against CUX1 antigen peptide, first ISM2 antigen peptide, second ISM2 antigen peptide, and PIANP antigen peptide; the reagent detects the biomarker in the sample by enzyme-linked immunosorbent assay (ELISA), protein chip, immunoblotting, or microfluidic immunoassay.

7. The application as described in claim 6, characterized in that, The product is a protein chip, reagent kit, or formulation.

8. The application as described in claim 7, characterized in that, The kit is an ELISA detection kit, which includes an antigen for detecting biomarkers, the antigen being coated on a solid-phase carrier.

9. The application as described in claim 8, characterized in that, The ELISA test kit further includes any one or more combinations of positive control serum, negative control serum, blocking solution, sample diluent, secondary antibody, secondary antibody diluent, washing solution, coating solution, colorimetric solution, or stop solution; the solid phase carrier is made of polyvinyl chloride, polystyrene, polyacrylamide, or cellulose.

10. The application as described in claim 9, characterized in that, The sample may be serum, plasma, saliva, interstitial fluid, or urine.

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