A serum protein marker for early screening and diagnosis of pancreatic ductal adenocarcinoma, applications and corresponding analysis methods

Abstract

This invention provides a serum protein biomarker for early screening and diagnosis of pancreatic ductal adenocarcinoma. The serum protein biomarker is one or a combination of two or more autoantibodies against tumor-associated antigens HEXB, TXLNA, and SLAMF6. The invention also provides the application of reagents, kits, and analytical methods for detecting the serum protein biomarker. This invention screens for autoantibodies against pancreatic ductal adenocarcinoma-associated antigens. By measuring the expression levels of these indicators in the serum of patients with pancreatic ductal adenocarcinoma, healthy controls, and controls with benign pancreatic diseases, the screened anti-HEXB, anti-TXLNA, and anti-SLAMF6 autoantibodies can distinguish between pancreatic ductal adenocarcinoma, healthy controls, and benign pancreatic diseases. Furthermore, this invention designs and optimizes combinations of tumor-associated antigens for the diagnosis of pancreatic ductal adenocarcinoma. Applying the optimized antigens to an ELISA kit and detecting serum anti-TAA autoantibodies using an indirect enzyme-linked immunosorbent assay (ELISA) can accurately identify patients with pancreatic ductal adenocarcinoma, healthy controls, and patients with benign pancreatic diseases.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to a serum protein biomarker for early screening and diagnosis of pancreatic ductal adenocarcinoma, its application, and corresponding analytical methods. Background Technology

[0002] Pancreatic ductal adenocarcinoma, the seventh leading cause of cancer death worldwide, is a highly malignant tumor with a mortality rate almost identical to its incidence rate. Therefore, exploring molecular markers for the early diagnosis of pancreatic ductal adenocarcinoma has significant potential clinical value.

[0003] During tumor progression, patients produce specific protein products called tumor-associated antigens (TAAs). These TAAs activate the body's immune response, leading to the generation of corresponding tumor-associated antigen autoantibodies (TAAbs). These TAAbs may act as early messengers recognizing abnormal or dysregulated cellular mechanisms during tumorigenesis. Due to the cascade amplification effect of the immune system, TAAbs can reach high levels in the body and be detected before the appearance of clinical symptoms. They also exhibit high stability and minimal invasiveness during detection. Therefore, TAAbs hold great potential as biomarkers for early tumor diagnosis.

[0004] Common methods for identifying tumor-associated antigens and their autoantibodies include serum recombinant cDNA library analysis, phage display technology, mapping technology, SERPA technology, and protein chip technology. Compared with other technologies, protein chip technology has advantages such as high throughput, automation, rapid analysis, simultaneous detection of multiple proteins, and high sensitivity. Therefore, it is widely used in the screening of biomarkers for various types of tumors, providing excellent technical support for cancer diagnosis and treatment research. The screening principle of protein chips utilizes the specific binding of antigens and antibodies in an immune response, immobilizing the identified antigen proteins on a pre-prepared solid surface.

[0005] This technique measures the expression levels of different types of specific antibodies in the blood through a series of immune responses. Several cancers have already been identified using this technology, revealing tumor-associated antigen autoantibodies with high diagnostic value, such as those in esophageal, gastric, and liver cancer. (HuProt) TMHuman proteome microarrays contain over 20,000 newly sequenced recombinant human proteins, covering 16,152 genes with a coverage rate of over 80%. They are currently the highest-throughput protein microarrays. They can be used to study the relationships between proteins, between proteins and nucleic acids, or between proteins and other small molecules. They can also be used to systematically discover tumor-associated antigen autoantibodies, thereby making preliminary discoveries of diagnostic biomarkers. Summary of the Invention

[0006] The present invention aims to provide a serum protein biomarker for early screening and diagnosis of pancreatic ductal adenocarcinoma, and also to provide the application of reagents, kits and analytical methods for detecting the serum protein biomarker.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A serum protein biomarker for early screening and diagnosis of pancreatic ductal adenocarcinoma, wherein the serum protein biomarker is one or a combination of two or more autoantibodies to tumor-associated antigens HEXB, TXLNA, and SLAMF6.

[0009] The serum protein markers are a combination of autoantibodies against tumor-associated antigens HEXB, TXLNA, and SLAMF6 (referred to as anti-HEXB, anti-TXLNA, and anti-SLAMF6 autoantibodies, respectively).

[0010] Application of reagents for detecting the serum protein markers in the preparation of products for early screening and diagnosis of pancreatic ductal adenocarcinoma.

[0011] The product used for early screening and diagnosis of pancreatic ductal adenocarcinoma is a kit.

[0012] The kit includes reagents for detecting the serum protein markers.

[0013] The reagent is an antigen used to detect the serum protein marker.

[0014] The antigen is one or a combination of two or more of the following proteins: HEXB protein, TXLNA protein, and SLAMF6 protein.

[0015] The antigen is a combination of HEXB protein, TXLNA protein and SLAMF6 protein; the antigen is coated on a solid support.

[0016] The solid support is made of polyvinyl chloride, polystyrene, polyacrylamide, or cellulose; the kit also includes one or more of the following: blocking solution, sample diluent, second antibody, second antibody diluent, washing solution, colorimetric solution, or stop solution.

[0017] Using analytical methods employed with kits for early screening and diagnosis of pancreatic ductal adenocarcinoma, a parallel joint detection model for serum protein biomarkers was constructed by calculating a logistic regression model, and the combined diagnostic results were calculated.

[0018] The detection method using reagents for detecting serum protein markers used in the early screening and diagnosis of pancreatic ductal adenocarcinoma includes the following steps:

[0019] 1) Each antigen was coated, blocked, and washed separately;

[0020] 2) Incubate with the diluted serum for testing with primary antibody, wash, then incubate with secondary antibody, and wash.

[0021] 3) After the colorimetric system has developed color, terminate the reaction and measure the absorbance value;

[0022] 4) Substitute the absorbance value into the following formula to calculate the prediction probability P-value.

[0023] PRE(P=PDAC, 3TAAbs)=1 / (1+EXP(-(-2.207-2.813×OD HEXB +3.671×OD TXLNA +5.265×OD SLAMF6 )));

[0024] In the formula, OD HEXB OD TXLNA OD SLAMF6 The absorbance values ​​are calculated by subtracting the blank control from the OD values ​​of each serum protein marker.

[0025] When the P-value is greater than 0.5, the sample is preliminarily identified as pancreatic ductal adenocarcinoma; when the P-value is less than or equal to 0.5, the sample is preliminarily identified as healthy.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1) This invention uses HuProt TM Human proteome chip V3.1 was used to screen for autoantibodies against pancreatic ductal adenocarcinoma-associated antigens. By measuring the expression levels of these indicators in the serum of pancreatic ductal adenocarcinoma, healthy controls, and benign pancreatic diseases, the selected anti-HEXB, anti-TXLNA, and anti-SLAMF6 autoantibodies were able to distinguish between pancreatic ductal adenocarcinoma, healthy controls, and benign pancreatic diseases.

[0028] 2) A combination of tumor-associated antigens for the diagnosis of pancreatic ductal adenocarcinoma was designed and optimized. The optimized antigens were applied to an ELISA kit, and serum anti-TAA autoantibodies were detected by indirect enzyme-linked immunosorbent assay (ELISA). This method can accurately distinguish between patients with pancreatic ductal adenocarcinoma, healthy controls, and patients with benign pancreatic diseases. The detection accuracy is significantly higher than that of other biomarkers. The method is convenient, fast, and effective, and can be used for the immunological diagnosis of pancreatic ductal adenocarcinoma in clinical practice. Attached Figure Description

[0029] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 The expression levels of 15 pancreatic ductal adenocarcinoma-associated antigens and autoantibodies in the serum of pancreatic ductal adenocarcinoma, benign pancreatic diseases, and healthy individuals were determined.

[0031] Figure 2 To enhance the ability of Logistic regression models to distinguish between pancreatic ductal adenocarcinoma and healthy pancreas;

[0032] Figure 3 To enhance the ability of Logistic regression models to distinguish between pancreatic ductal adenocarcinoma and benign pancreatic diseases.

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described in detail below. However, the following embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0034] Example 1: Screening of candidate anti-TAAs autoantibodies for pancreatic ductal adenocarcinoma using human proteome microarrays.

[0035] 1.1 Protein chip detection

[0036] Using HuProt TM Human proteome microarray was used to detect expression levels in 10 mixed case specimens (3 of which were 2 pancreatic ductal adenocarcinoma specimens mixed into 1 mixed specimen, and the remaining 7 were 3 pancreatic ductal adenocarcinoma specimens mixed into 1 mixed specimen) and 10 healthy control specimens (6 of which were mixed specimens and 3 healthy control specimens mixed into 1 mixed specimen).

[0037] 1.1.1 Reagents required for human proteome microarray experiments:

[0038] (1) Blocking solution: 3 ml 10% BSA, add 7 ml 1 × PBST solution, mix well and place on ice;

[0039] (2) Incubation solution: 1 ml of 10% BSA, add 9 ml of 1 × PBST solution, mix well and place on ice;

[0040] (3) Cleaning solution: 1 × PBST, prepared in advance and placed in a 4°C refrigerator.

[0041] 1.1.2 Experimental Procedure:

[0042] (1) Blocking: Using a chip incubation box, add 10 mL of blocking solution, take the chip out from -80 ℃ and place it face up in the incubation box; side-shaking at 50-60 rpm, room temperature, 1 hr.

[0043] (2) Serum sample incubation: After blocking, pour out the blocking solution and then quickly add the prepared serum incubation solution. Shake the sample on the side at 20 rpm and incubate overnight at 4 ℃ (the sample is first placed in a 4 ℃ chromatography cabinet for freeze-thaw and diluted at a ratio of 1:200).

[0044] (3) Cleaning: After incubation, the chip is removed and placed in a chip cleaning box with cleaning solution. The chip is then cleaned three times at 80 rpm at room temperature for 10 minutes each time on a horizontal shaker.

[0045] (4) Secondary antibody incubation: Transfer the chip to an incubation box containing 3 mL of secondary antibody incubation solution (diluted at a ratio of 1:1000), and incubate at room temperature for 1 hour with a side-shaking incubator at 40 rpm, protected from light.

[0046] (5) Cleaning: Remove the chip (be careful not to touch or scratch the top surface of the chip), place it in a chip cleaning box containing cleaning solution, and place it on a horizontal shaker at 80 rpm for 3 times, 10 minutes each time. After completion, clean it twice with ddH2O for 10 minutes each time;

[0047] (6) Drying;

[0048] (7) Scanning: Operate according to the scanner's operating procedures and user manual;

[0049] (8) Data extraction: Open the corresponding GAL file, align the chip image and each array in the GAL file as a whole, press the automatic alignment button, extract the data and save the GPR.

[0050] 1.2 Data Processing

[0051] 1.2.1 Data Preprocessing

[0052] Before performing the analysis, we normalized the protein chip data to remove systematic errors between samples and different chips.

[0053] (1) Normalization process inside the protein chip

[0054] Due to inconsistent background values, signal inhomogeneity may exist between different protein spots within the same chip. To address this, a background normalization method is used to remove the inhomogeneity. First, the ratio of the foreground value to the background value (F / B) of each protein is calculated. Then, the mean F / B of two repeating proteins is obtained, which is the signal-to-noise ratio (SNR).

[0055] (2) Normalization processing between different chips

[0056] Because experimental samples and operations may differ, systematic errors may arise. Directly comparing data between chips would introduce uncertainty into the results. Therefore, a median-based linear normalization method is used to normalize the SNR between chips.

[0057] 1.2.2 Data Analysis

[0058] Based on the normalized chip data, statistical analysis was performed on 20 groups of samples to screen out highly responsive specific antibodies that distinguish the pancreatic ductal adenocarcinoma group from the healthy control group.

[0059] (1) Assuming that the two groups of samples come from two completely identical populations, the Mann-Whitney U rank-sum test (sample size limit, one-tailed) is used to test the non-normal distribution, and the p-value is used to represent the result. When p < 0.05, the null hypothesis is rejected, that is, there is a significant difference between the pancreatic ductal adenocarcinoma group and the healthy control group;

[0060] (2) Calculate the fold change between the pancreatic ductal adenocarcinoma group and the healthy control group, i.e., fold change = mean of the cancer group / mean of the control group, which is used to indicate the degree to which the cancer group is higher than the control group; when fold change ≥ 1.2, it is considered that there is a potential difference. It is generally believed that the larger the fold change, the more obvious the difference between the two groups.

[0061] (3) To avoid comparing negative proteins, a threshold for determining positive proteins was first set based on the distribution of SNR values ​​of protein spots on the chip after normalization: when IgG-SNR>4, it was determined to be a positive protein. Based on this, for any protein, an appropriate cutoff threshold (cutoff-IgG≥4) was set based on the SNR of the control group on that protein, and the positive rates of the pancreatic ductal adenocarcinoma group and the healthy control group were calculated respectively. The minimum positive rate of the cancer group was defined as 50%, that is, the number of positive samples of the cancer group on that protein was not less than 5 (0~10); the maximum positive rate of the healthy control group was defined as 10%, that is, the number of positive samples of the healthy control group on that protein was not more than 1 (0~10).

[0062] (4) Proteins that meet conditions 1, 2, and 3 are called priority candidate biomarkers.

[0063] (5) Open the String database website, select Multiple proteins, enter all proteins from the priority candidate biomarkers into the List of Name, double-click Organization and select Homosapiens, then double-click Search. Extract proteins related to cancer and immunity through Gene Ontology (GO) enrichment analysis.

[0064] (6) The differentially expressed genes involved in the above union proteins were validated using Gene Expression Profiling Interactive Analysis (GEPIA), an online visualization website that can analyze the expression differences of target genes in specific cancer types and healthy control tissues. At the same time, the candidate biomarkers for this study were determined by searching relevant literature related to the genes on the PubMed website.

[0065] 1.2.3 Results of screening for tumor-associated antigen autoantibodies

[0066] A total of 167 priority candidate biomarkers were identified through human protein microarray screening. GO enrichment analysis on the String website showed that GO:0002252, GO:0002376, and GO:0006955 were involved in immune-related pathways. Thirteen proteins common to these three pathways were selected. Predictive analysis using the GEPIA website and a review of relevant literature ultimately determined the 15 TAAbs to be included in this study: tumor-associated antigens FUCA2, LTF, HEXB, OSCAR, PSMD2, TXLNA, LILRB2, SLAMF6, TP53, P62, TRIM21, BNIP3L, DBNL, GLB1, and RAC1 autoantibodies.

[0067] Example 2: Indirect ELISA detection of serum expression levels of anti-TAAs autoantibodies

[0068] 2.1 Sample Source

[0069] Enzyme-linked immunosorbent assay (ELISA) was used to further detect the expression levels of candidate anti-TAAs autoantibodies in plasma from a large sample population. This application included 170 cases of pancreatic ductal adenocarcinoma, 170 healthy controls, and 137 cases of benign pancreatic diseases, all from the First Affiliated Hospital of Zhengzhou University, Henan Cancer Hospital, and Anyang Cancer Hospital. All cancer cases were diagnosed as pancreatic ductal adenocarcinoma by clinicians in conjunction with pathological histology, and patients had not undergone any treatment such as radiotherapy, chemotherapy, or immunotherapy. The benign disease cases were patients with benign pancreatic diseases, including pancreatitis and benign pancreatic tumors. Healthy controls were obtained from healthy samples excluding pancreatic ductal adenocarcinoma, pancreatic-related diseases, and immune system diseases. There were no statistically significant differences in gender and age among the study subjects.

[0070] This application was approved by the Ethics Committee of Zhengzhou University, and all research subjects signed informed consent forms. Five mL of peripheral blood was collected from each research subject while fasting, placed in a blood collection tube without anticoagulant, and allowed to stand at room temperature for 1 hour. Then, the tube was centrifuged at 4°C, 3000 rpm for 10 minutes. The serum from the top of the blood collection tube was then aspirated and aliquoted into 1.5 mL EP tubes. The EP tubes were labeled with sample numbers on the top and side, and stored at -80°C. The blood collection date and storage location were recorded. Before use, the serum was thawed at 4°C and aliquoted to avoid repeated freeze-thaw cycles.

[0071] 2.2 Indirect ELISA Experiment

[0072] 2.2.1 Reagents required for the experiment:

[0073] (1) 10 × PBST washing solution (1L)

[0074] Preparation method: Take 81.8 g of NaCl, 28.8 g of Na2HPO4•12H2O, 3.1 g of NaH2PO4•2H2O, and 5 mL of Tween 20, and place them in 800 mL of deionized water. Then, dilute the solution to 1 L with deionized water. Mix thoroughly on a magnetic stirrer, mark the date and name with a marker, and store at room temperature for later use.

[0075] (2) Coating solution (1 L)

[0076] Preparation method: Place 1.5 g of Na₂CO₃ and 2.9 g of NaHCO₃ in a 1 L beaker, and add 800 mL of deionized water. Mix thoroughly on a magnetic stirrer, and then bring the volume to 1 L with deionized water. Store the solution at 4°C for later use.

[0077] (3) Sealing solution (1 L)

[0078] Preparation method: Weigh 20 g bovine serum albumin (BSA) and dissolve it in 800 mL of 1×PBST solution. Make up the volume to 1 L with 1×PBST solution and mix thoroughly using a magnetic stirrer. Label the solution with the date and name and store it in a refrigerator at 4°C for later use.

[0079] (4) Second antibody diluent (1 L)

[0080] Preparation method: Weigh 10 g of BSA and dissolve it in 800 mL of 1×PBST solution. Then, bring the volume to 1 L with 1×PBST. After thorough mixing, label the solution with the name and date, and store it in a refrigerator at 4°C for later use.

[0081] (5) Substrate solution A (100 mL)

[0082] Preparation method: Weigh 20 mg of TMB•2HCl and place it in a brown bottle. Add 80 mL of deionized water. Mix thoroughly and then bring the volume to 100 mL with deionized water. Label with the date and name, store away from light, and prepare fresh before use.

[0083] (6) 0.75% H2O2 (10 mL)

[0084] Preparation method: Weigh 75 mg of hydrogen peroxide urea and place it in a centrifuge tube wrapped with aluminum foil. Add 8 mL of deionized water and mix well on a magnetic stirrer. Then, bring the volume to 10 mL with deionized water. Label the tube with the date and name, and store it in a refrigerator at 4°C away from light for later use.

[0085] (7) Substrate solution B (100 mL)

[0086] Preparation method: Weigh 3.7 g of Na₂HPO₄•12H₂O, 0.92 g of citric acid, and 800 μL of 0.75% H₂O₂, and dissolve them in 80 mL of deionized water. Mix thoroughly and then dilute to 100 mL with deionized water. Label with the date and name, and store in a refrigerator at 4°C protected from light for later use.

[0087] (8) Termination solution (100 mL)

[0088] Preparation method: Take a 150 mL beaker, add 80 mL of deionized water, measure 10 mL of concentrated sulfuric acid, and slowly add it to the deionized water along a glass rod while stirring constantly to prevent splashing. After thoroughly mixing, add deionized water to bring the volume to 100 mL. Label the beaker with the date and name, and store it after cooling to room temperature for later use.

[0089] 2.2.2 Experimental Procedure:

[0090] (1) Coating antigens: Seven human recombinant proteins (DBNL, HEXB, OSCAR, TRIM21, BNIP3L, LTF and SLAMF6) were purchased from Wuhan Huamei Biotechnology Co., Ltd., six recombinant proteins (FUCA2, GLB1, PSMD2, TXLNA, RAC1 and LILRB2) were purchased from Wuhan Yunclone Technology Co., Ltd., and two recombinant proteins (TP53 and P62) were purified proteins from our laboratory.

[0091] Fifteen recombinant proteins were diluted to their respective final concentrations using coating buffer (Table 1). The diluted proteins were then sequentially added to 96-well high-binding ELISA plates (made of polystyrene) using a multi-row pipette, with 50 μL added to each well. The plates were then placed back into their original bags, both pre-labeled with protein names using a marker. The plates were incubated overnight (or more than 16 hours) at 4°C.

[0092] (2) Blocking: Take out the coated microplate, warm it to room temperature, pour out the coating solution in the microplate, and pat dry any remaining liquid on absorbent paper. Add 100 μL of blocking solution to each well using a multi-pipette, then put the microplate back into the original bag and place it in a water bath at 37°C for 2 hours.

[0093] (3) Diluting serum: Take out the deep well plate, dilute the sample diluent (1% BSA) and the pre-amplified serum at a ratio of 1:100, and place them in a refrigerator at 4°C for later use.

[0094] (4) Primary antibody incubation: Remove the sealed ELISA plate from the water bath and drain the liquid from the plate. Place the ELISA plate in a 96-needle automated ELISA washer and wash it 3 times. Add 350 μL of 1×PBST washing buffer to each well, and wash for 10 s each time. After washing, pat dry on absorbent paper. Add the sample diluent to the ELISA plate sequentially, according to Table 2. In addition, to detect whether the ELISA plate was contaminated during the experiment, each ELISA plate was set with 5 replicate serums and 3 blank wells. Finally, put the ELISA plate back into the original bag and place it in a water bath at 37°C for 2 hours.

[0095] (5) Secondary antibody incubation: After the primary antibody incubation is complete, remove the ELISA plate from the water bath and drain the liquid from the plate. Place the ELISA plate in a 96-needle automated ELISA washer and wash it 5 times. Add 350 μL of 1×PBST washing buffer to each well, and wash for 10 s each time. After washing, pat the plate dry on absorbent paper (use fresh absorbent paper for each pat to prevent cross-contamination). Then dilute the horseradish peroxidase-labeled secondary antibody (mouse anti-human IgG monoclonal antibody) according to the secondary antibody concentration in Table 1, and add 100 μL of the diluted secondary antibody to each well of the ELISA plate. Then put the ELISA plate back into the original bag and place it in a water bath at 37°C for 1 hour.

[0096] (6) Color development and termination reaction: After the secondary antibody incubation, drain the liquid from the plate. Wash the plate 5 times in a 96-needle automated plate washer. Add 350 μL of 1×PBST washing buffer to each well, and wash for 10 s each time. After washing, pat dry on absorbent paper. Take out the prepared base solution A and base solution B from the refrigerator and prepare the color development solution in a 1:1 ratio. After mixing well, use a multi-row pipette to add 50 μL of the color development solution to each well. Incubate at room temperature in the dark for 5-15 minutes, then add 25 μL of the stop solution to each well.

[0097] (7) Scanning: To prevent foreign matter from affecting the final scan results, the bottom of the ELISA plate should be wiped clean with absorbent paper before each scan. Load the ELISA plate into the microplate reader sequentially, click Start to begin scanning and reading, and read the absorbance (optical density OD) at 450nm and 620nm. The difference between the two absorbance values ​​(the absorbance at 620nm is the background value) is taken as the final absorbance value. If the value of the blank well in the ELISA plate is less than 0.05, it indicates that the plate has not been contaminated.

[0098] (8) Data preprocessing: The average value of the duplicate proteins was standardized to ensure consistency in the expression of the same TAAb. The specific steps were as follows: For any TAAb, the average value and standard deviation of the duplicate sera in each ELISA plate were calculated, and the plate with the smallest standard deviation was selected as the standard plate. Then, the average value of the duplicate sera in that plate was divided by the average value of the control sera in other plates, and this was used as the adjustment factor for the ELISA plate. Finally, the readings of each ELISA plate were adjusted based on the adjustment factor.

[0099] Table 1. Coating concentrations of 15 proteins

[0100]

[0101] Table 2 Layout of 96-well microplate

[0102]

[0103] 2.3 Statistical Analysis

[0104] The study samples were randomly divided into a training set (containing 100 patients with pancreatic ductal adenocarcinoma, 80 patients with benign pancreatic diseases, and 100 healthy controls) and a validation set (containing 70 patients with pancreatic ductal adenocarcinoma, 57 patients with benign pancreatic diseases, and 70 healthy controls).

[0105] According to the Kolmogorov-Smirnova test, the expression level of TAAb in the serum of the study subjects did not conform to a normal distribution. P <0.05, therefore, the 25th percentile (P25), median (P50), and 75th percentile (P75) were used to describe the distribution of expression levels. A non-parametric test (Mann-Whitney U) was used to compare whether there were differences in the expression levels of autoantibodies in pancreatic ductal adenocarcinoma, benign pancreatic diseases, and healthy groups. ROC curves were plotted using GraphPad Prism 9.0 to analyze the diagnostic value of TAAbs for pancreatic ductal adenocarcinoma. The cutoff value was the OD value at which the Youden index (YI) was maximized and the specificity was greater than 85%. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, concordance rate, AUC, and 95% confidence interval were calculated for each TAAb in diagnosing pancreatic ductal adenocarcinoma.

[0106] Using the presence or absence of pancreatic ductal adenocarcinoma in the study subjects as the dependent variable, a diagnostic model was constructed in the training set using binary conditional logistic regression analysis, and the diagnostic value of the model was evaluated in the validation set.

[0107] 2.4 Experimental Results

[0108] When comparing the cancer group and the healthy control group, except for the expression levels of TRIM21, BNIP3L, DBNL, GLB1 and RAC1 autoantibodies in the pancreatic ductal adenocarcinoma group and the healthy control group which showed no statistical difference (P>0.05), the expression levels of the other 10 TAAbs (FUCA2, LTF, HEXB, OSCAR, PSMD2, TXLNA, LILRB2, SLAMF6, TP53 and P62) showed statistical differences (P<0.05).

[0109] The expression levels of the 10 TAAbs mentioned above in the training set were used as independent variables, and whether it was a pancreatic ductal adenocarcinoma event was used as the dependent variable. The independent variables were included by stepwise forward and stepwise backward methods. The p-value for entering the equation was set to 0.05 for stepwise forward and the p-value for removing variables was set to 0.1 for stepwise backward. When the predicted probability P was greater than 0.5, it was determined to be pancreatic ductal adenocarcinoma; otherwise, it was considered healthy.

[0110] Ultimately, the modeling results from both methods were identical. The model incorporated three TAAbs: anti-HEXB autoantibody, anti-TXLNA autoantibody, and anti-SLAMF6 autoantibody. The discriminant model was: PRE(P=PDAC, 3TAAbs)=1 / (1+EXP(-(-2.207-2.813×OD)) HEXB +3.671×OD TXLNA +5.265×OD SLAMF6 Then, the predicted probability P-value corresponding to the research subjects in this application is calculated according to this formula. The ROC curve of the diagnostic model for differentiating pancreatic ductal adenocarcinoma from healthy controls is shown below. Figure 2 The AUC was 0.805 (95% CI: 0.746–0.864), with sensitivity and specificity of 58.0% and 86.0%, respectively.

[0111] The diagnostic value of the constructed logistic regression model containing the three TAAbs (anti-HEXB autoantibody, anti-TXLNA autoantibody, and anti-SLAMF6 autoantibody) was evaluated on the validation set. The results showed that the AUC of the model in diagnosing pancreatic ductal adenocarcinoma was 0.780 (95% CI: 0.703-0.857), with a sensitivity of 65.0% and a specificity of 85.0%.

[0112] Logistic regression models can differentiate between pancreatic ductal adenocarcinoma and benign pancreatic diseases. Figure 3 The model's AUC for distinguishing pancreatic ductal adenocarcinoma from benign pancreatic diseases in the training set was 0.797 (95% CI: 0.733-0.861), with sensitivity and specificity of 61.0% and 85.0%, respectively. In the validation set, the AUC for distinguishing pancreatic ductal adenocarcinoma from benign pancreatic diseases was 0.799 (95% CI: 0.720-0.878), with sensitivity and specificity of 58.6% and 96.5%, respectively. Therefore, using a combination of autoantibodies against tumor-associated antigens HEXB, TXLNA, and SLAMF6 for the diagnosis of pancreatic ductal adenocarcinoma significantly improves both sensitivity and specificity.

Claims

1. Use of reagents for detecting serum protein markers in the manufacture of a product for the early screening and diagnosis of pancreatic ductal adenocarcinoma, characterized in that, The serum protein marker is a combination of autoantibodies of tumor-associated antigens HEXB, TXLNA and SLAMF6.

2. Use according to claim 1, wherein The product for early screening and diagnosis of pancreatic ductal adenocarcinoma is a kit.

3. Use according to claim 2, wherein the compound is ###0002### The kit comprises reagents for detecting the serum protein marker of claim 1 or 2.

4. The use according to claim 3, wherein the compound is ###0002### The reagent is an antigen for detecting the serum protein marker.

5. The use according to claim 4, wherein the compound is ###0002### The antigen is a combination of HEXB protein, TXLNA protein and SLAMF6 protein; the antigen is coated on a solid carrier.

6. Use according to claim 5, wherein The solid carrier is made of polyvinyl chloride, polystyrene, polyacrylamide or cellulose; the kit further comprises one or more than two combinations of a blocking solution, a sample diluent, a secondary antibody, a secondary antibody diluent, a washing solution, a developing solution or a termination solution.

Images