Use of anti-gsdmd autoantibodies in the preparation of a product for diagnosing lung adenocarcinoma

Abstract

The application discloses use of anti-GSDMD autoantibody in preparation of a product for diagnosing lung adenocarcinoma. The biomarker for auxiliary diagnosis of lung adenocarcinoma provided by the application is anti-GSDMD autoantibody. By detecting the level of anti-GSDMD autoantibody in human plasma, the application can effectively distinguish lung adenocarcinoma patients from healthy people and lung adenocarcinoma patients from benign pulmonary nodule patients. The novel anti-GSDMD autoantibody in plasma can improve the differential diagnosis efficiency for lung adenocarcinoma patients when combined with tumor markers such as CEA, CA199, AFP and CA125, and has important significance for management and identification of lung adenocarcinoma patients.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, specifically to the use of anti-GSDMD autoantibodies in the preparation of auxiliary diagnostic products for lung adenocarcinoma (LUAD). Background Technology

[0002] This section provides only background information relevant to this disclosure and is not necessarily prior art.

[0003] Lung cancer (LC) is a major public health problem, causing numerous deaths worldwide. The International Agency for Research on Cancer (IARC) GLOBOCAN (Global Cancer Watch) 2020 estimates of cancer incidence and mortality show that lung cancer remains the leading cause of cancer death, causing an estimated 1.8 million deaths (18%) in 2020. Based on histopathology, lung cancer is mainly divided into two categories: small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer includes squamous cell carcinoma (squamous cell carcinoma), adenocarcinoma, and large cell lung cancer. Lung cancer seriously threatens human health. In my country, multiple studies show that the incidence and mortality rates of lung cancer are continuously increasing, making it the leading cause of malignant tumor incidence and death in China.

[0004] Currently, conventional treatments for lung cancer include surgical resection, radiotherapy, and chemotherapy. However, these therapies are not suitable for all lung cancer patients. Among various treatment methods, surgery has the best efficacy, but only 20%–30% of patients are clinically suitable for surgery. Chemotherapy was the earliest and most widely used treatment, and is currently the most mature treatment method, but it has significant clinical side effects and a poor quality of life for patients. Radiotherapy is a local treatment method, less effective for distant metastases, and also has significant side effects. Clinical studies have found that most lung cancer patients are diagnosed at an intermediate or advanced stage, and most are not eligible for surgery, making treatment difficult and less effective. Early-stage lung cancer often has no obvious clinical symptoms or signs, and most lung cancer patients are already in an advanced stage when they first seek medical attention. The 5-year survival rate for advanced lung cancer is extremely low. Therefore, exploring effective methods for early diagnosis of lung cancer is of great significance for improving the survival prognosis of lung cancer patients.

[0005] Imaging and biomarker examinations are commonly used methods for the early diagnosis of lung cancer. However, among the diagnostic methods currently used in clinical practice, imaging examinations lack specificity and can only serve as auxiliary diagnostic indicators; serum tumor marker detection has low sensitivity and specificity, limiting its diagnostic value; low-dose computed tomography (LDCT) has a high false-positive rate due to the structure, size, number, and development characteristics of lung nodules, as well as the principles of CT imaging, leading to overdiagnosis and posing radiation risks that threaten human health, thus proving insufficient for early lung cancer screening. Therefore, it is necessary to actively explore more effective methods for the early diagnosis of lung cancer.

[0006] Tumor-associated autoantibody profiling, or combined detection with tumor autoantigens, has shown good specificity and sensitivity in the early screening and diagnosis of lung cancer. After tumor cells appear in the body, the autoimmune system can mount a specific immune response against tumor-associated antigens (TAAs), producing a series of TAA autoantibodies. TAA autoantibodies can be detected in the early stages of lung cancer, thus serving as biomarkers for early lung cancer diagnosis. To seek better diagnostic methods, highly specific and accurate lung cancer autoantibodies have become a current research hotspot. Summary of the Invention

[0007] Purpose of the invention: The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing the use of anti-GSDMD autoantibodies in the preparation of products for diagnosing lung adenocarcinoma.

[0008] To solve the above-mentioned technical problems, the present invention discloses the following technical solution:

[0009] In a first aspect, the present invention discloses a plasma biomarker for diagnosing lung adenocarcinoma.

[0010] The diagnostic markers for lung adenocarcinoma include anti-GSDMD autoantibodies.

[0011] This invention is the first to discover that the expression level of IgG type autoantibodies against GSDMD in the plasma of patients with lung adenocarcinoma is significantly higher than that in healthy controls and patients with benign lung nodules, and the difference is statistically significant.

[0012] Secondly, this invention discloses a reagent for diagnosing lung adenocarcinoma.

[0013] The lung adenocarcinoma markers include anti-GSDMD autoantibodies.

[0014] The autoantibody is an IgG type autoantibody.

[0015] The autoantibodies mentioned are autoantibodies found in peripheral blood plasma.

[0016] Furthermore, the product also includes serum tumor markers.

[0017] The serum tumor markers include traditional tumor markers CEA, CA199, AFP, CA125, or any combination thereof. This invention uses logistic regression to detect IgG autoantibodies in GSDMD in combination with traditional tumor markers CEA, CA199, AFP, and CA125, finding that the combined use of these markers demonstrates superior diagnostic efficacy for lung adenocarcinoma compared to using any of the markers alone.

[0018] The diagnosis of lung adenocarcinoma includes detecting autoantibodies via ELISA; further, it also includes detecting the expression levels of traditional tumor markers CEA, CA199, AFP, CA125, or any combination thereof.

[0019] Thirdly, the present invention discloses the use of the diagnostic markers for lung adenocarcinoma described in the first aspect or the reagents described in the second aspect in the preparation of products for diagnosing lung adenocarcinoma.

[0020] The diagnosis of lung adenocarcinoma includes detecting autoantibodies via ELISA; further, it also includes detecting the expression levels of traditional tumor markers CEA, CA199, AFP, CA125, or any combination thereof.

[0021] Fourthly, this invention discloses a reagent kit.

[0022] The kit includes the lung adenocarcinoma diagnostic markers described in the first aspect or the reagents described in the second aspect.

[0023] The kit in question is an ELISA detection kit.

[0024] The kit includes the GSDMD protein.

[0025] The basic information of the tumor-associated antigen GSDMD in this invention is as follows: it is located on human chromosome 8, encodes 484 amino acids, and contains 14 exons. This protein is a very important downstream target protein of both classical and non-classical inflammatory pathways. Cleavage by caspases induces pyroptosis, making it a key molecule regulating inflammatory necrosis (pyroptosis). GSDMD contains a cytotoxic N-terminal domain and a C-terminal repressive domain, which are connected by a flexible linker. Upon hydrolysis of the linker protein between these two domains, the intramolecular repression of the cytotoxic domain is released, allowing the N-terminal domain to insert into the cell membrane, forming a large oligomeric pore, thereby disrupting cellular ion homeostasis and inducing cell death. GSDMD-induced pyroptosis plays an important role in many genetic diseases, autoinflammatory diseases, and cancers.

[0026] The method for screening lung adenocarcinoma markers described in this invention specifically includes the following steps: (1) collecting blood samples from lung adenocarcinoma patients, healthy individuals, and patients with benign pulmonary nodules, and obtaining plasma samples after separation and processing; (2) screening and identifying plasma protein markers from lung adenocarcinoma patients, healthy individuals, and patients with benign pulmonary nodules.

[0027] The method for verifying lung adenocarcinoma markers in this invention specifically involves screening and identifying plasma protein markers in patients with lung adenocarcinoma, healthy individuals, and patients with benign pulmonary nodules in the aforementioned step (2), and then verifying the differential plasma protein markers between patients with lung adenocarcinoma, healthy individuals, and patients with benign pulmonary nodules using ELISA.

[0028] The differential plasma protein ELISA validation includes: determining the optimal protein coating concentration of GSDMD and the optimal dilution concentration of the secondary antibody; performing ELISA detection on plasma samples according to the kit instructions; and performing statistical analysis on the obtained data.

[0029] The biomarker for the auxiliary diagnosis of lung adenocarcinoma provided by this invention is anti-

[0030] The autoantibody of Gasdermin-D (GSDMD) was expressed at a higher level in the plasma of patients with lung adenocarcinoma than that of patients with benign lung nodules and healthy controls, and the difference was statistically significant.

[0031] The present invention provides a novel plasma anti-GSDMD that, when combined with serum tumor markers such as carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), alpha-fetoprotein (AFP), and carbohydrate antigen 125 (CA125), exhibits good efficacy in the differential diagnosis of lung adenocarcinoma, and is of great significance for the management and differentiation of lung adenocarcinoma patients.

[0032] The diagnosis of lung adenocarcinoma described in this invention includes differentiating lung adenocarcinoma from healthy individuals and / or differentiating lung adenocarcinoma from benign lung nodules.

[0033] The diagnosis of lung adenocarcinoma described in this invention includes auxiliary diagnosis of lung adenocarcinoma.

[0034] In this invention, serum CEA, CA199, CA125 and AFP were detected using electrochemiluminescence immunoassay, with units of ng / mL.

[0035] Beneficial effects:

[0036] This invention uses enzyme-linked immunosorbent assay (ELISA) to screen and identify novel plasma biomarkers (anti-GSDMD autoantibodies) for the differential diagnosis of lung adenocarcinoma patients and healthy individuals, as well as patients with benign pulmonary nodules. A differential diagnostic model for lung adenocarcinoma patients and healthy individuals, as well as patients with benign pulmonary nodules, is constructed by combining GSDMD with tumor markers CEA, CA199, CA125, and AFP. This model demonstrates good diagnostic efficacy for differentiating between lung adenocarcinoma patients and healthy individuals, and patients with benign pulmonary nodules, providing assistance for better clinical management of lung adenocarcinoma patients. Furthermore, the reagent kit of this invention uses plasma as the test sample, avoiding invasive diagnosis. Early detection of lung adenocarcinoma risk can be achieved through minimally invasive plasma collection and testing. It requires a small blood volume, causes minimal discomfort to the test subjects, and has high compliance. Moreover, the operation is simple, and the test results are available quickly, indicating broad market prospects and social benefits. Attached Figure Description

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0038] Figure 1 SBI scatter plot and ROC curve of anti-GSDMD IgG autoantibodies in lung adenocarcinoma and healthy controls in training sample set.

[0039] Figure 2To validate the use of IgG autoantibodies against GSDMD in the sample set for diagnosing lung adenocarcinoma and benign lung nodules, and to evaluate the SBI scatter plot and ROC curve of healthy controls.

[0040] Figure 3 The ROC of combining anti-GSDMD IgG type autoantibodies with traditional tumor marker CEA for the diagnosis of lung adenocarcinoma and benign lung nodules, and healthy controls.

[0041] Figure 4 The ROC of combining anti-GSDMD IgG type autoantibody with the traditional tumor marker CA199 for the diagnosis of lung adenocarcinoma and benign lung nodules, and healthy controls.

[0042] Figure 5 The ROC of combining anti-GSDMD IgG type autoantibody with the traditional tumor marker AFP for the diagnosis of lung adenocarcinoma and benign lung nodules, and healthy controls.

[0043] Figure 6 The ROC of combining GSDMD's IgG autoantibody with the traditional tumor marker CA125 for the diagnosis of lung adenocarcinoma and benign lung nodules, and healthy controls. Detailed Implementation

[0044] The present invention can be better understood from the following embodiments. However, those skilled in the art will readily understand that the descriptions in the embodiments are for illustrative purposes only and should not, and will not, limit the invention as detailed in the claims.

[0045] Unless otherwise specified, the experimental methods described in the following examples are conventional methods; the reagents and materials described are commercially available unless otherwise specified.

[0046] Example: ELISA detection of plasma expression levels of anti-GSDMD autoantibodies

[0047] 1. Experimental Samples

[0048] This study included a training set of 136 participants (68 patients with lung adenocarcinoma and 68 healthy controls) and a validation set of 900 participants (309 patients with lung adenocarcinoma, 300 patients with benign pulmonary nodules, and 291 healthy individuals). All samples were collected from November 2019 to May 2022 at a tertiary hospital in Henan Province. Basic information, pathological stage, and levels of traditional clinical tumor markers for all participants were compiled and organized through the hospital's internal system with patient consent and approval from the institutional review committee and hospital ethics committee. Specific basic information of the study participants is shown in Tables 1 and 2.

[0049] Table 1. Basic characteristics of the research subjects in the training set samples.

[0050]

[0051] Table 2. Basic characteristics of the research subjects in the validation set sample.

[0052]

[0053] (1) Patient sample and information collection process

[0054] 1) For patients who were hospitalized for this cause after CT scans detected suspicious lung nodules, blood samples were collected on the first day or the day after hospitalization when no treatment was received (for some lung cancer patients, blood samples were collected after treatment). The samples were numbered, packaged, and their basic information was registered before being entered into the blood sample bank.

[0055] 2) After the patient is diagnosed, the relevant clinical information is retrieved and collected in the hospital patient management system using the patient's hospital number, gender, age, etc., including smoking history, drinking history, detection values ​​and cutoff values ​​of traditional tumor markers (CEA, CA199, AFP, CA125, etc.) for patients with lung adenocarcinoma and benign lung nodules, and a patient clinical information database is constructed.

[0056] (2) Healthy control sample and information collection process

[0057] The study data was collected from individuals undergoing physical examinations at a tertiary hospital in Henan Province. Information such as the subjects' examination numbers, genders, and ages was retrieved from the hospital's patient management system, and relevant clinical information was collected. Blood samples were collected, numbered, packaged, and registered in the blood sample database.

[0058] (3) Inclusion and exclusion criteria for study subjects

[0059] The inclusion criteria for patients with lung adenocarcinoma included: 1) no history of chemotherapy, radiotherapy, surgery or other interventions; 2) clear histopathological results, diagnosed as lung adenocarcinoma according to the guidelines for the diagnosis and treatment of primary lung cancer, and pathological histological type (including adenocarcinoma, carcinoma, large cell carcinoma, sarcomatoid carcinoma and mixed carcinoma); 3) no history of other malignant tumors; 4) complete medical history; 5) no autoimmune diseases; 6) all were newly diagnosed cases; 7) patients were older than 18 years.

[0060] The inclusion criteria for patients with benign pulmonary nodules include: 1) a clear histopathological biopsy to confirm whether the nodules are benign or malignant, and the histopathological type; 2) no history of malignant tumors in the lungs or other parts of the body; 3) all cases are newly diagnosed and have not received any treatment; and 4) the patient is older than 18 years.

[0061] The inclusion criteria for healthy controls included: 1) no history of malignant tumors in the lungs or other parts of the body; 2) no autoimmune diseases; and 3) age > 18 years.

[0062] Exclusion criteria: Research subjects that do not meet the above inclusion criteria.

[0063] (4) Plasma collection:

[0064] Five ml of peripheral blood was collected from the subjects while they were fasting and placed in a blood collection tube without anticoagulant. After standing at room temperature for 1 hour, the tube was centrifuged at 3000 rpm for 10 minutes at 4°C. The serum from the top of the blood collection tube was then aspirated and aliquoted into 1.5 ml EP tubes. The sample numbers were marked on the top and side of the EP tubes, and the tubes were frozen at -80°C. The blood collection date and storage location were recorded. Before use, the plasma was thawed at 4°C and aliquoted to avoid repeated freeze-thaw cycles.

[0065] 2. Experimental materials and reagents

[0066] (1) GSDMD protein, purchased from Wuhan Yunclone Technology Service Co., Ltd.;

[0067] (2) 96-well microplate (8 rows × 12 columns);

[0068] (3) Coating solution: an aqueous solution containing 0.15% sodium carbonate (Na2CO3) and 0.29% sodium bicarbonate (NaHCO3);

[0069] (4) Blocking solution: PBST buffer containing 2% (v / v) bovine serum albumin (BSA) in 0.2% (v / v) Tween 20;

[0070] (5) Plasma sample diluent: PBST buffer containing 1% (w / v) BSA;

[0071] (6) Enzyme-labeled second antibody: horseradish peroxidase (HRP) labeled recombinant protein A;

[0072] (7) Antibody dilution buffer: PBST buffer containing 1% (w / v) BSA;

[0073] (8) Washing buffer: PBST buffer containing 0.2% (v / v) Tween 20;

[0074] (9) Developing solution: The developing solution consists of developing solution A and developing solution B. Developing solution A is an aqueous solution of 20% tetramethylbenzidine dihydrochloric acid, and developing solution B is an aqueous solution containing 3.7% Na2HPO4·12H2O, 0.92% citric acid, and 0.75% (v / v) hydrourea peroxide. When using, developing solution A and developing solution B are mixed evenly in a 1:1 ratio and prepared fresh for each use. (10) Quality control plasma: 100 blood samples from normal controls are thoroughly mixed in equal volumes to prepare quality control blood samples for quality control (QC) to eliminate errors between different ELISA plates and represent the general level of the population. If the CV of the quality control samples from all ELISA plates prepared on the same day is <20%, the data is considered usable for subsequent analysis.

[0075] (11) Termination solution: 10% sulfuric acid.

[0076] 3. Experimental Methods

[0077] (1) Protein pretreatment and protein concentration determination: Dilute the protein to an appropriate concentration using the protein dilution buffer recommended in the protein instruction manual and then perform SDS-PAGE electrophoresis to confirm the protein concentration.

[0078] (2) Preliminary experiment to explore the optimal protein coating concentration of GSDMD and the dilution concentration of secondary antibody IgG: Plasma from 4 patients with lung adenocarcinoma and 4 healthy controls were randomly selected for preliminary experiment. Two concentration gradients were set for protein coating concentration and secondary antibody dilution concentration. After the experiment, the optimal multiple was selected by combining the OD values ​​of the sample and the standard to ensure that the color development time of the sample and the standard was consistent and that the OD values ​​of the sample were between the highest and lowest values ​​of the standard OD values.

[0079] (3) Training set sample experiment: After determining the optimal protein coating concentration of GSDMD and the dilution concentration of secondary antibody IgG, plasma from lung adenocarcinoma patients and healthy controls was randomly selected for a small sample experiment. Among them, there were 68 lung adenocarcinoma patients and 68 healthy controls.

[0080] (4) Validation set experiment: Plasma samples from patients with lung adenocarcinoma, benign lung nodules and healthy controls were randomly selected for a large-sample experiment. Among them, there were 309 patients with lung adenocarcinoma, 291 healthy controls and 300 benign lung nodules.

[0081] (5) Plate preparation: Calculate the number of samples, sample volume, protein volume, and sample layout in advance, following the layout of a 96-well microplate. Calculate the total amount of reagents required for subsequent steps based on the sample volume and prepare them in advance. Use 1% BSA in blank wells, with a sample volume of 50 μL / well. The sample layout is shown in Tables 3 and 4.

[0082] Table 3 Training Set Sample Layout (Taking the First Board as an Example)

[0083] NCl NC2 NC3 NC4 NC5 NC6 NC7 NC8 NC9 NC10 NC11 Quality control NC12 NC13 NC14 NC15 NC16 NC17 NC18 NC19 NC20 NC21 NC22 Quality control NC23 NC24 NC25 NC26 NC27 NC28 NC29 NC30 NC31 NC32 NC33 blank NC34 NC35 NC36 NC37 NC 38 NC 39 NC 40 NC 41 NC 42 NC 43 NC 44 blank LC4 LC48 LC104 LC165 LC200 LC230 LC254 LC332 LC373 LC439 LC464 blank LC6 LC59 LC107 LC174 LC204 LC231 LC269 LC337 LC376 LC446 LC474 blank LC9 LC72 LC120 LC175 LC205 LC238 LC272 LC341 LC378 LC449 LC478 blank LC13 LC80 LC122 LC177 LC206 LC239 LC283 LC348 LC380 LC450 LC486 blank

[0084] Table 4. Validation set sample layout (taking the first board as an example)

[0085] LC1 LC2 LC4 LC5 LC7 LC8 LC11 LC12 LC15 LC16 LC18 Quality control LC19 LC20 LC21 LC22 LC23 LC24 LC25 LC27 LC28 LC29 LC30 Quality control LC36 LC38 LC39 LC40 LC41 LC42 LC44 LC45 LC46 LC47 LC48 blank BPN1 BPN2 BPN3 BPN4 BPN5 BPN6 BPN7 BPN8 BPN9 BPN10 BPN11 blank BPN12 BPN13 BPN14 BPN15 BPN16 BPN17 BPN18 BPN19 BPN20 BPN21 BPN22 blank NC1 NC2 NC3 NC4 NC5 NC6 NC7 NC8 NC9 NC10 NC11 blank NC12 NC13 NC14 NC15 NC16 NC17 NC18 NC19 NC20 NC21 NC22 blank NC23 NC24 NC25 NC26 NC27 NC28 NC29 NC30 NC31 NC32 NC33 blank

[0086] (6) Sample aliquoting and dilution: After selecting the required experimental samples from the sample library, thaw them slowly at 4°C the day before the experiment. The next day, aliquot the samples into 96-well pointed-bottom PCR plates according to the previously arranged plate layout and the required sample volume. Then, according to the required sample dilution ratio, add the antibody diluent to the corresponding layout in the 96-well deep-well plates, cover the surface with a membrane for sealing, and place in a 4°C freezer for later use.

[0087] (7) Protein coating: After diluting the protein to the optimal coating concentration (0.125 μg / mL) determined in the pre-experiment using coating buffer, the prepared GSDMD protein solution was added to the reaction wells in columns 1-12 of a 96-well microplate after shaking. The sample volume was 50 μL / well. The plate was sealed with plastic wrap to prevent evaporation and coated overnight at 4°C.

[0088] (8) Blocking: Add blocking solution to each well of the coated 96-well microplate at a volume of 100 μL / well, block in a 37°C water bath for 2 h, then remove the blocking solution, wash 3 times with washing solution and pat dry to obtain the microplate coated with tumor-associated antigen GSDMD.

[0089] (9) Primary antibody (plasma) reaction: Dilute the plasma sample to be tested with plasma sample diluent at a volume ratio of 1:100. Then, add the diluted plasma sample to the ELISA plate at a density of 50 μL / well according to the plate layout. The sample volume is 50 μL / well. Add 50 μL of antibody diluent (without any plasma sample) to the blank wells. Then, incubate the 96-well ELISA plate in a 37°C water bath for 1 h. Then, discard the liquid in the reaction wells, wash 5 times with washing buffer (sample volume of 300 μL / well), and pat dry.

[0090] (10) Enzyme-labeled secondary antibody incubation: For the secondary antibody reaction, HRP-labeled recombinant protein A was diluted with antibody diluent at the optimal dilution ratio of 1:5000 (v / v) determined in the pre-experiment. Then, the diluted HRP-labeled recombinant protein A was added to each well of the 96-well microplate at a volume of 50 μl / well. The plate was incubated in a 37°C water bath for 1 h. The liquid in the well was then discarded, and the plate was washed 5 times with washing buffer (300 μl / well) and patted dry.

[0091] (11) Color development and termination of reaction: Mix color development solution A and color development solution B in equal volumes at a ratio of 1:1. Then, quickly add the mixed color development solution to the reaction wells of the 96-well microplate at a volume of 50 μl / well. Keep the plate at room temperature and away from light. Observe the color development process at all times during the color development process. After sufficient color development, add 25 μl of termination solution to each reaction well to terminate the color development reaction. Then, use a microplate reader to read the absorbance (optical density, OD) at wavelengths of 450 nm and 620 nm. The absorbance value at wavelength 620 nm is the background value. The difference between the absorbance at wavelengths of 450 nm and 620 nm is used as the absorbance value for subsequent analysis. Zero the plate with a blank control well.

[0092] 4. Data Processing

[0093] The statistical analysis method of this invention utilizes SPSS 26.0 and GraphPad Prism 8.0 software for statistical analysis and visualization of experimental data. Differences in anti-GSDMD autoantibody expression levels among different groups were analyzed using the Mann-Whitney U test, and differences in positive rates were analyzed using the chi-square test. The SBI value corresponding to the maximum Youden's Index (YI) was used as the cutoff value. Diagnostic value indicators such as AUC, specificity, sensitivity, and 95% confidence interval (95% CI) of anti-GSDMD autoantibodies were calculated using ROC curves. All statistical analyses employed two-tailed tests, and a p-value < 0.05 was considered statistically significant.

[0094] 5. Results Analysis

[0095] (1) Preliminary ELISA experiment to explore the optimal protein coating concentration of GSDMD and the optimal dilution concentration of secondary antibody IgG: A double-antibody sandwich ELISA experiment was used to conduct a preliminary analysis on a small number of samples (plasma from 4 patients with lung adenocarcinoma and 4 patients with benign pulmonary nodules) to explore the optimal protein coating concentration of GSDMD and the optimal dilution concentration of secondary antibody IgG in this experiment. The optimal protein coating concentration of GSDMD and the optimal dilution factor of secondary antibody IgG are shown in Table 5.

[0096] Table 5. Preliminary ELISA experiments to explore the optimal protein coating concentration of GSDMD and the optimal dilution factor of secondary antibody IgG.

[0097]

[0098] (2) Preliminary verification of the diagnostic efficacy of ELISA assay for the differential diagnosis of IgG type autoantibodies against GSDMD.

[0099] The expression levels of anti-GSDMD IgG autoantibodies in plasma samples from lung adenocarcinoma patients and healthy controls are as follows: Figure 1 As shown, the expression level of anti-GSDMD autoantibodies in plasma samples from the lung adenocarcinoma group was significantly higher than that in the healthy control group (P<0.0001).

[0100] The expression levels of anti-GSDMD IgG autoantibodies in plasma samples from lung adenocarcinoma, healthy controls, and benign pulmonary nodules are as follows: Figure 2 As shown, the expression level of anti-GSDMD autoantibodies in plasma samples from the lung adenocarcinoma group was significantly higher than that from the healthy control group (P<0.0001) and the benign lung nodule group (P<0.0001). Figure 2 The receiver operating characteristic (ROC) curve analysis showed that when using anti-GSDMD IgG autoantibodies to differentiate between lung adenocarcinoma and healthy controls, the AUC of the ROC curve was 0.77 (95% CI: 0.73–0.80). Furthermore, at a cutoff value of 0.60, the sensitivity and specificity of using anti-GSDMD IgG autoantibodies to differentiate between lung adenocarcinoma and healthy controls reached 78% and 65%, respectively. Moreover, the area under the curve (AUC) for differentiating between lung adenocarcinoma and benign pulmonary nodules using anti-GSDMD IgG autoantibodies was 0.66 (95% CI: 0.61–0.70), and at a cutoff value of 0.60, the sensitivity reached 78% and the specificity reached 54%. This indicates that anti-GSDMD IgG autoantibodies can effectively differentiate between lung adenocarcinoma and healthy controls, as well as benign pulmonary nodules. This also proves that anti-GSDMD autoantibodies can be used for the auxiliary diagnosis of lung adenocarcinoma.

[0101] Figure 3 To detect IgG autoantibodies against GSDMD in combination with the traditional tumor marker CEA using logistic regression, the AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the healthy control group was 0.80 (95% CI: 0.70-0.85), which was higher than the area under the curve (AUC) of 0.55 (95% CI: 0.48-0.63) for CEA alone distinguishing between the lung adenocarcinoma group and the healthy control group. The AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group was 0.64 (95% CI: 0.56-0.71), also higher than the AUC of 0.60 (95% CI: 0.53-0.68) for CEA alone distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group. A total of 451 cases were included in the lung adenocarcinoma group (LUAD), the healthy control group (NC), and the benign pulmonary nodule group (BPN).

[0102] Figure 4To detect IgG autoantibodies against GSDMD using combined logistic regression and the traditional tumor marker CA199, the AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the healthy control group was 0.73 (95% CI: 0.65-0.81), which was higher than the area under the curve (AUC) of 0.51 (95% CI: 0.42-0.61) for CA199 alone distinguishing between the lung adenocarcinoma group and the healthy control group. The AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group was 0.68 (95% CI: 0.60-0.75), also higher than the AUC of 0.50 (95% CI: 0.42-0.59) for CA199 alone distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group. A total of 238 cases were included in the lung adenocarcinoma group (LUAD), the healthy control group (NC), and the benign pulmonary nodule group (BPN).

[0103] Figure 5 To detect IgG autoantibodies against GSDMD in combination with the traditional tumor marker AFP using logistic regression, the AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the healthy control group was 0.77 (95% CI: 0.71-0.83), which was higher than the area under the curve (AUC) of 0.52 (95% CI: 0.45-0.60) for AFP alone distinguishing between the lung adenocarcinoma group and the healthy control group. The AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group was 0.69 (95% CI: 0.61-0.77), also higher than the AUC of 0.54 (95% CI: 0.45-0.62) for AFP alone distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group. A total of 354 cases were included in the lung adenocarcinoma group (LUAD), the healthy control group (NC), and the benign pulmonary nodule group (BPN).

[0104] Figure 6 To detect IgG autoantibodies against GSDMD in combination with the traditional tumor marker CA125 using L1gistic regression, the AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the healthy control group was 0.80 (95% CI: 0.73-0.88), which was higher than the area under the curve (AUC) of 0.62 (95% CI: 0.52-0.71) for CA125 alone distinguishing between the lung adenocarcinoma group and the healthy control group. Similarly, the AUC of the ROC curve distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group was 0.62 (95% CI: 0.55-0.69), which was also higher than the AUC of 0.54 (95% CI: 0.46-0.61) for CA125 alone distinguishing between the lung adenocarcinoma group and the benign pulmonary nodule group. The study included 280 cases in the lung adenocarcinoma group (LUAD), the healthy control group (NC), and the benign pulmonary nodule group (BPN).

[0105] Table 6. AUC after combining GSDMD autoantibody with other tumor markers

[0106]

[0107] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. The use of a reagent for detecting lung adenocarcinoma markers in the preparation of products for diagnosing lung adenocarcinoma, characterized in that, The lung adenocarcinoma markers include anti-GSDMD autoantibodies; the autoantibodies are IgG type autoantibodies in plasma; the diagnosis of lung adenocarcinoma is to differentiate lung adenocarcinoma from healthy individuals.

2. The use according to claim 1, characterized in that, The product also includes serum tumor markers.

3. The use according to claim 2, characterized in that, The serum tumor markers include traditional tumor markers such as CEA, CA199, AFP, CA125, or any combination thereof.

4. The use according to claim 1, characterized in that, The diagnosis of lung adenocarcinoma includes detecting autoantibodies via ELISA.

5. The use according to claim 4, characterized in that, It also includes detecting the expression levels of traditional tumor markers CEA, CA199, AFP, CA125, or any combination thereof.

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