Early warning evaluation method based on canceration caused by pulmonary nodule immune microenvironment disorder

Abstract

The invention discloses an early warning evaluation method based on canceration caused by pulmonary nodule immune microenvironment imbalance, which comprises the following steps: S1, acquiring autoantibody detection data of normal, pulmonary nodule and lung cancer people from a database, and establishing a pulmonary nodule canceration data set Panne l containing 159 autoantibodies as risk early warning factors of pulmonary nodule canceration; s2, preparing a lung cancer risk factor Panne l protein chip according to the Panne l data, wherein the lung cancer risk factor Panne l protein chip comprises antigen proteins of 159 lung cancer related autoantibodies; s3, using a Panne l protein chip to detect the serum of the crowd; and S4, according to the detection data, establishing a risk early warning model of pulmonary nodule canceration for the analysis and early warning of the lung cancer occurrence risk of the crowd (mainly aiming at the pulmonary nodule crowd). According to the method, the cancer related autoantibodies are systematically detected through a protein chip method for people with high detection rate of pulmonary nodules in China, and the method is used for early warning of the occurrence risk of pulmonary nodule immune abnormality canceration of people.

Description

Technical Field

[0001] This invention relates to the field of early warning of lung cancer, specifically to an early warning assessment method based on the dysregulation of the immune microenvironment of lung nodules that induces cancer. Background Technology

[0002] China's health big data is alarming, showing a trend of an aging population and younger disease prevalence. Among Chinese people, there are as many as 160 million with dyslipidemia, 270 million with hypertension, 90 million with diabetes, 200 million overweight or obese, 120 million with fatty liver disease, and 4 million new cancer cases annually. Cardiovascular and cerebrovascular diseases and malignant tumors have become the leading killers in China; one person dies from cardiovascular and cerebrovascular diseases every 30 seconds, and one person is diagnosed with cancer every 10 seconds. Health management in China faces enormous challenges. Therefore, it is the contemporary responsibility of health researchers to promote proactive health awareness, advocate healthy lifestyles, scientifically assess health risks, and prevent the occurrence of major diseases.

[0003] Cancer development is a long process, and establishing a three-tiered prevention and control system of early warning, early diagnosis, and early treatment is crucial for cancer prevention and control. Based on the characteristics of the cancer course, cancer prevention and control can be divided into stages such as cancer risk warning, early cancer diagnosis, and cancer treatment and prognosis monitoring. Cancer prevention and control emphasizes early warning, early diagnosis, and early treatment. Early cancer warning requires the detection and assessment of many risk factors for cancer development, providing alerts and warnings about the risk of cancer occurrence. Early cancer diagnosis involves the large-scale clinical testing and verification of some cancer risk factors, which are then applied to the medical diagnosis of individual cancer cases. Early cancer treatment involves timely intervention and treatment once a cancer has been diagnosed, stopping the cancer at its source, controlling its progression, and restoring health. Therefore, early cancer warning is an assessment and warning of cancer development risk, falling under the category of preventive medicine; early cancer diagnosis is a medical diagnosis of cancer development, falling under the category of diagnostic medicine; and early cancer treatment falls under the category of therapeutic medicine. Early warning and early diagnosis complement each other: early warning provides more comprehensive risk alerts and warnings for cancer development, providing a basis for early diagnosis; early diagnosis provides more accurate cancer diagnostic information at a specific cross-section, further supporting early warning. A three-tiered prevention and control system of early warning, early diagnosis, and early treatment for tumors forms a large funnel-shaped system for tumor prevention and control, which helps to better prevent and control tumors.

[0004] Lung cancer is a malignant tumor with high incidence and mortality rates worldwide, but there is a lack of corresponding detection methods for early risk warning of lung cancer. According to the latest epidemiological data, lung cancer remains the leading cause of cancer-related deaths in most countries. Due to the widespread presence of risk factors such as smoking and air pollution, the incidence of lung cancer is on the rise. Although some progress has been made in tobacco control and public health education in recent years, the overall mortality rate of lung cancer remains high, mainly due to insufficient public awareness, inadequate screening, and ineffective control of risk factors. Currently, some progress has been made in the early diagnosis of lung cancer, including imaging technology, the application of biomarkers, and artificial intelligence-assisted diagnosis. However, there are currently no relevant lung cancer risk warning detection products or intervention programs on the market regarding early warning of lung cancer and control of risk factors.

[0005] Currently, the antigen chips used for tumor risk early warning are proteome antigen chip products developed and manufactured by CDI Corporation in the United States. These chips contain proteins from the entire human proteome, making them suitable for detecting individual autoantibody profiles. This product can be used to construct an individual baseline and also for detecting autoantibodies and assessing tumor risk in an individual's serum. However, these proteome chips have several significant limitations for risk assessment in preventative healthcare:

[0006] (1) Low safety factor: This protein chip is a product manufactured and operated by CDI Lab in the United States. Due to the global monopoly of this product, and considering the current great uncertainty in the international situation, there is a risk of supply disruption for this chip.

[0007] (2) High testing costs: The research and development of this product involves the production of all histone proteins, which incurs extremely high research and development costs. This product is a monopolistic product with high import prices, and domestic companies cannot effectively participate in pricing. Therefore, it is not suitable for large-scale screening of major health risks in China.

[0008] (3) Lack of Standardization: The protein production of this proteome chip is a large-scale, low-volume production. The main control factor is the soluble expression of the protein. Other key parameters of the protein raw materials (such as quantity and purity) cannot be controlled, thus making standardized production impossible. The significant uncertainty between batches of this chip product leads to chaotic health risk screening data, which is detrimental to health management and product iteration.

[0009] (4) Lack of Specificity: This proteome chip belongs to the field of omics research and development tools, and plays an important role in the discovery of autoantibody risk factors in tumors. However, it is not specific for the detection and early warning of risk factors for specific cancer types. Therefore, using this product for general health risk detection will generate a lot of background information, resulting in a waste of human, material, and financial resources. This product can be used for risk factor screening, but it cannot be used for general health screening of the population. Summary of the Invention

[0010] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide an early warning assessment method for carcinogenesis caused by dysregulation of the immune microenvironment of pulmonary nodules.

[0011] According to the technical solution provided in the embodiments of this application, a method for early warning assessment of carcinogenesis caused by dysregulation of the immune microenvironment of pulmonary nodules includes the following steps:

[0012] S1. Collect systematic detection data of serum autoantibodies from normal individuals, individuals with pulmonary nodules, and individuals with lung cancer from the database, and construct a Pannel database of lung cancer-related autoantibody risk factors. This database contains data on 159 autoantibody risk factors.

[0013] S2. Prepare a lung cancer autoantibody risk factor Pannel protein chip based on the autoantibody risk factor Pannel database in step S1. The Pannel protein chip contains antigen proteins corresponding to 159 lung cancer autoantibody early warning risk factors and system controls.

[0014] S3. Using the aforementioned Pannel protein chip, perform protein chip experiments on serum collected from the population (including healthy individuals without pulmonary nodules, healthy individuals with pulmonary nodules, and lung cancer patients) to analyze the response signals of the corresponding autoantibodies of the individual population, thereby obtaining the corresponding detection data.

[0015] S4. Based on the detection data, establish a risk warning model for lung cancer, analyze and warn of the risk of lung cancer; conduct continuous annual screening of risk factors for individuals, and assess, warn, and intervene in high-risk factors with continuously increasing signal intensity.

[0016] 2. The early warning assessment method for carcinogenesis based on dysregulation of the immune microenvironment of pulmonary nodules according to claim 1, characterized in that: the 159 autoantibody risk factors combined in step S1 of lung cancer, Pannel, include p53, NY-ESO-1, CAGE, GBU4-5, SOX2, HuD, and MAGE. A4, SARS, ZPR1, FAM131A, GGA3, PRKCZ, HDAC1, GOLPH3, NSG1, CD84, EEA1, CEA, CYFRA211, CA1 25. ETHE1, CTAG1A, C1QTNF1, TEX264, CLDN2, HRAS, CHTF18, LRRC45, RPS13, INTS10, S100A7L2 , CDCA7L, LINGO1, HMGA1, ANKRD36B, FCGR2A, HIF3A, EPB41L3, PLA2G4C, LSP1, SPP1, PVALB, RGS20, SNRPA, CDH12, Annexin1, NIP30, Annexin2, CFAP36, MID1IP1, DCD, MED21, TAF10, ZNF69 6. Dr1, HSP70, KEAP1, GPBP1, MYBPH, PGAM1, HNRNPD, IKZF5, NAT9, PNMA1, IMPDH2, NAP1L5, RAB27A, SGPL1, Ubiquillin1, Ubiquillin2, ABCC3, ACTR3, ANXA1, AP3D1, BIRC5, BMI1, C14orf1 04. C1D, CAGE1, CCL18, CCND1, CCT8, CD25, CHK, CTAG1B, CXCL1, DKK1, ECH1, EFHD2, EIF4G1, Ei f4g3, ENO-1, ERBB2, ESO-1, FOXP3, FXR1, GAGE7, HMGB3, HNRNPA1, HSPA9, IGF2BP1, JAK2, MAGE A1, MAGE A3, MUC1, p62, Paxillin, PCNA, PGP9.5. PIK3CA, PRC1, PRKACA, RPS3, S100A10, SOX3, SOX1, SSX1, Survivin, TIZ, TM4SF1, TOP2A, TSHR, UBQLN1, UROD, ZIC2, ZNF258, Galectin-1, ANXA5, APEX1, AQP4, AZGP1, BRSK2, CA2 , CALR, CCDC88A, CCNY, CEACAM5, CENPB, CFH, CHGA, CSF2, DPYSL5, ELAVL3, ELAVL4, ENO1, GAD1, GAD2, KRT19, LCN1, LGALS3, MYCL, PNMA2, PRDX6, PRX, RCVRN, SYP, TPI1, TRPM1, UACA. .

[0017] 3. The method for early warning assessment of carcinogenesis caused by dysregulation of the immune microenvironment of lung nodules according to claim 1, characterized in that: the preparation of the lung cancer autoantibody risk factor Pannel protein chip in step S2 includes antigen proteins corresponding to 159 lung cancer autoantibody early warning risk factors, system controls (IgG, IgM, BSA, GST tag, His Tag, biotin, buffer, Cy3, Cy5), and corresponding labels: LC ProteinArray and Reneocare.Co.LTD.

[0018] 4. The early warning assessment method for carcinogenesis based on dysregulation of the pulmonary nodule immune microenvironment as described in claim 1, characterized in that: the detection method in step S3 is as follows,

[0019] Each of the aforementioned Pannel protein chips is used to detect one serum sample. The autoantibodies in the serum sample bind to the protein antigens immobilized on the Pannel protein chip. Unbound serum proteins are washed away, and the binding of autoantibodies is detected using a fluorescent secondary antibody against human IgG. Finally, the signal is read by a fluorescence scanner. The intensity of the signal is positively correlated with the amount of human serum autoantibodies.

[0020] 5. The early warning assessment method for carcinogenesis caused by dysregulation of the pulmonary nodule immune microenvironment according to claim 1, characterized in that: the risk assessment and early warning system in step S4 is as follows,

[0021] (1) Risk assessment and early warning variables: Signal risk value and number of risk factors of risk factors. Signal risk value conversion scale of risk factors: Signal-to-noise ratio (SNR) ≤ 2, risk value (RF) is assigned 0; 2 < SNR ≤ 5, RF is assigned 1; 5 < SNR ≤ 10, RF is assigned 2; SNR > 10, RF is assigned 3. Counting rule for the number of risk factors: Risk factors with SNR > 2 are counted; risk factors with SNR < 2 are not counted.

[0022] (2) Initial risk assessment and early warning: When the total number of risk factors is less than 33, the total risk value Σ of risk factors is less than 45, and there are no risk factors with risk values of 2 and 3, it is defined as low risk; when the total number of risk factors is less than 33, and there are a small number of risk factors with risk values of 2 and 3, it is defined as medium risk; when the total number of risk factors > 23, the total risk value Σ of risk factors is greater than 15, and there are a relatively large number of risk factors with risk values of 2 and 3, it is defined as high risk. The reference normal dynamic range of risk factor risk values is designed to be between 0 and 1.

[0023] (3) Continuous risk tracking and early warning: An individual baseline database is formed through individual annual inspections. Each test data is compared and analyzed with the previous test data to establish a fingerprint map of autoantibody risk factors, and据此追踪差异变化且持续升高的风险因子,用于肺癌发生风险预警。

[0024] To sum up: This method aims at the high detection rate of pulmonary nodules and the high incidence of lung cancer in China. Through the high-throughput technology of protein chips, it systematically detects the signal of autoantibody risk factors related to cancer types, and is used for early warning of the risk of immune abnormal carcinogenesis of pulmonary nodules. This product system helps to construct a large model for ultra-early risk warning of lung cancer in Chinese people and serves the development of the big health industry.

[0025] Beneficial effects of this application:

[0026] 1. Convenient sampling: It is convenient to obtain peripheral blood samples, with a small amount used, only 0.1 - 0.5 ml each time.

[0027] 2. High detection throughput: It can detect 159 autoantibody risk factors at one time.

[0028] 3. Short experimental time: The entire project can be completed within 1 - 2 days of testing.

[0029] 4. Standardized detection system: Standardized protein raw materials, standardized protein chip spotting, standardized detection process, standardized data analysis, and standardized test reports.

[0030] 5. Personalized risk factor tracking: Continuously track the individual baseline, monitor and warn the baseline dynamics, and timely warn of the risk of immune abnormal carcinogenesis of pulmonary nodules in the population. Description of the drawings

[0031] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0032] Figure 1 For the expression of lung cancer risk factor proteins and the preparation of protein chip products;

[0033] Figure 2 A schematic diagram illustrating the principle of autoantibodies detected by protein chip assay for risk factors of lung cancer;

[0034] Figure 3 Partial display of signal scanning results from chip detection of risk factors for lung cancer in healthy and lung cancer individuals;

[0035] Figure 4 A schematic diagram of risk assessment parameters for signal conversion detection using protein chips;

[0036] Figure 5 A schematic diagram illustrating the ability of protein chip detection and evaluation parameters to distinguish between healthy individuals and lung cancer patients;

[0037] Figure 6 A schematic diagram of an early warning system for the initial assessment of lung cancer risk based on protein chip detection;

[0038] Figure 7 This is a schematic diagram of a continuous tracking, assessment, and early warning system for lung cancer risk based on protein chip detection. Detailed Implementation

[0039] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0040] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments. Unless otherwise specified, the instruments and strains used in this solution are from the following sources:

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[0042]

[0043] An early warning assessment method for carcinogenesis induced by dysregulation of the immune microenvironment of pulmonary nodules, wherein the pannel combination of lung cancer-related autoantibodies (Table 1) is as follows:

[0044] 1.1 Data Source: Autoantibody data from normal individuals, individuals with pulmonary nodules, and individuals with lung cancer were mined and analyzed from the internet and self-developed databases. After screening, analysis, and summarization, a total of 159 autoantibodies were obtained.p53, NY-ESO-1, CAGE, GBU4-5, SOX2, HuD, MAGE A4, SARS, ZPR1, FAM131A, GGA3, PRKCZ, HDAC1, GOLPH3, NSG1, CD84, EEA1, CEA, CYFRA211, CA125, ETHE1, CTAG1A, C1QTNF1, TEX264, CLDN2, HRAS, CHTF18, LRRC45, RPS13, INTS10, S100A7L2 、CDCA7L、LINGO1、HMGA1、ANKRD36B、FCGR2A、HIF3A、EPB41L3、PLA2G4C、LSP1、SPP1、PVALB、R GS20、SNRPA、CDH12、Annexin1、NIP30、Annexin2、CFAP36、MID1IP1、DCD、MED21、TAF10、ZNF69 6、Dr1、HSP70、KEAP1、GPBP1、MYBPH、PGAM1、HNRNPD、IKZF5、NAT9、PNMA1、IMPDH2、NAP1L5、RA B27A、SGPL1、Ubiquillin1、Ubiquillin2、ABCC3、ACTR3、ANXA1、AP3D1、BIRC5、BMI1、C14orf1 04、C1D、CAGE1、CCL18、CCND1、CCT8、CD25、CHK、CTAG1B、CXCL1、DKK1、ECH1、EFHD2、EIF4G1、Ei f4g3、ENO-1、ERBB2、ESO-1、FOXP3、FXR1、GAGE7、HMGB3、HNRNPA1、HSPA9、IGF2BP1、JAK2、MAGE A1、MAGE A3, MUC1, p62, Paxillin, PCNA, PGP9.5, PIK3CA, PRC1, PRKACA, RPS3, S100A10, SOX3, SOX1, SSX1, Survivin, TIZ, TM4SF1, TOP2A, TSHR, UBQLN1, UROD, ZIC2, ZNF258, Galectin-1, ANXA5, APEX1, AQP4 AZGP1、BRSK2、CA2、CALR、CCDC88A、CCNY、CEACAM5、CENPB、CFH、CHGA、CSF2、DPYSL5、ELAVL3、ELAVL 4、ENO1、GAD1、GAD2、KRT19、LCN1、LGALS3、MYCL、PNMA2、PRDX6、PRX、RCVRN、SYP、TPI1、TRPM1、UACA。For details on the risk factor numbers, corresponding gene names, and functional annotations in tumors and lung cancer, please refer to Table 1.

[0045] Table 1: Summary of Risk Factors Related to Carcinogenesis from Immune Dysregulation of Pulmonary Nodules

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[0050]

[0051]

[0052]

[0053]

[0054] An early warning assessment method for cancer development based on dysregulation of the pulmonary nodule immune microenvironment, wherein the steps of protein expression and chip fabrication are as follows (see...). Figure 1 ):

[0055] 2.1 Construction of yeast expression strains

[0056] A risk factor protein expression vector was constructed, and the expression vector was transfected into yeast to create a protein expression strain. The expression strain was then analyzed to confirm its effective protein expression. The specific procedures are as follows:

[0057] 1) The antigen gene expression cassette is directly constructed into the secretory protein expression vector through gene synthesis.

[0058] 2) The expression plasmid was transfected into Saccharomyces cerevisiae INVSC1 competent cells by PEG / LiAc heat shock, plated on defective culture plates, screened and cultured for 3 days, and single clones were selected and stored for later use. At the same time, single clones were selected and activated by overnight culture.

[0059] 3) The expression strain was induced to express protein using galactose medium, and the supernatant of the medium was detected by Western blotting.

[0060] 2.2 Protein Expression and Purification

[0061] The expression strain was cultured in 50 mL of medium. After galactose-induced expression, the culture supernatant was collected for protein extraction and purification.

[0062] 1) Scale-up expression: The INVSC1 strain expressing the target protein was inoculated into 50 mL of SD / URA3 galactose-induced medium and cultured at 220 rpm and 37°C for 24 h. The bacterial cells and supernatant were then collected by centrifugation at 4000 rpm. 2) Protein extraction and purification: The protein supernatant was filtered through a 0.22 μm filter and then purified by Ni column affinity enrichment. The purified protein was obtained by washing with 5 mM imidazole PBS and eluting with 300 mM imidazole PBS.

[0063] 2.3 Protein Standardization

[0064] The purified protein was subjected to SDS-PAGE gel chromatography to ensure a purity greater than 90%, thus reducing background noise. After quantification using Qubit assay, the purified protein was aliquoted, lyophilized, and stored. The lyophilized protein underwent reconstitution stability and solubility testing to assess its suitability for subsequent protein chip fabrication.

[0065] 2.4 Protein chip fabrication

[0066] 1) Substrate preparation: Glass slides are used as substrates for protein chip fabrication. The chip substrate is placed in a cleaning solution to remove surface grease and impurities. It is then rinsed with deionized water and dried with nitrogen or placed in a dust-free environment to air dry naturally.

[0067] 2) Protein sample preparation: Take standardized protein and reconstitute it with PBS.

[0068] 3) Spotting Layout Design: Use specialized software to design the spotting layout, including the protein arrangement order, number of spotting replicates, and concentration gradient detection. A well-designed layout can improve the efficiency and accuracy of the experiment. Label each protein's name, concentration, and other information on the designed layout for easy data analysis and result tracking.

[0069] 4) Spotting Operation: Select an inkjet printer according to experimental requirements and spot the prepared protein sample according to the designed layout. Pay attention to controlling the consistency of the spotting volume and the shape and size of the spots. Avoid air bubbles or missed spots. Maintain environmental stability during the spotting process and avoid the influence of temperature changes and wind speed on the spotting results.

[0070] 5) Chip Drying and Sealing: Place the fabricated chips in a temperature- and humidity-controlled environment, avoiding direct sunlight to prevent protein damage. Drying time depends on environmental conditions and the chip substrate, typically ranging from several hours to several days. Use BSA blocking solution to seal the chips, eliminating non-specific binding and improving signal specificity. Evenly coat the blocking solution onto the chip surface, allow it to stand for a period of time, and then rinse thoroughly with deionized water.

[0071] 6) Chip preservation: Chip products are stored in a -80℃ freezer, clearly marked with batch number, and repeated freeze-thaw cycles are avoided.

[0072] This protein chip contains antigen proteins corresponding to 159 autoantibody early warning risk factors for lung cancer, as well as system controls (IgG, IgM, BSA, GST tag, His Tag, biotin, buffer, Cy3, Cy5), and corresponding identifiers: LCProteinArray and Reneocare.Co.LTD.

[0073] A method for early warning and assessment of carcinogenesis induced by dysregulation of the immune microenvironment of pulmonary nodules, wherein the method includes the step of using the aforementioned Pannel protein chip to perform experimental detection on collected serum to obtain corresponding detection data (see [link to relevant documentation]). Figure 2-3 )as follows:

[0074] 3.1 Detection Principle

[0075] Each protein chip can detect one serum sample. Specific antibodies (mainly IgG antibodies) bind to proteins immobilized on the chip. Unbound antibodies and other proteins are washed away, and then detected with anti-human IgG fluorescent secondary antibody (cy3 labeled, appearing green). The signal is read by a fluorescence scanner, and the strength of the signal is positively correlated with the affinity and quantity of the antibody.

[0076] 3.2 Sample Collection

[0077] Blood samples were collected from 54 healthy controls (including 30 individuals with pulmonary nodules) and 90 individuals with lung cancer. 0.5 ml of serum was collected using brown procoagulant tubes. After standing for 1 hour, the samples were centrifuged at 3000 rpm for 5 minutes, and the supernatant was collected as serum. Individual serum samples were aliquoted into 100 μl portions and stored at -80°C for later use.

[0078] 3.3 Detection Methods

[0079] 1) Warm-up: Take the chip out of the -80℃ freezer, place it in a 4℃ freezer for 30 minutes, and then warm it to room temperature for 15 minutes;

[0080] 2) Sealing: Fix the chip enclosure, add sealing liquid to 14 blocks, place on a side-swinging shaker, and seal at room temperature for 3 hours;

[0081] 3) Serum sample incubation: Discard the blocking solution, quickly add serum incubation solution (the serum is diluted 50 times with the incubation solution) to the 14 blocks, place on a side-shaking incubator, and incubate overnight at 4°C;

[0082] 4) Cleaning: Discard the incubation solution, quickly add the cleaning solution, then discard the cleaning solution again. Repeat this cycle several times. Remove the enclosure, place the container on a horizontal shaker, and clean the container three times at room temperature, 10 minutes each time.

[0083] 5) Secondary antibody incubation: Place on a side-swinging shaker, with secondary antibody incubation solution (the secondary antibody is diluted 1000 times with the incubation solution).

[0084] Incubate at room temperature for 1 hour (from this step onwards, be sure to avoid light);

[0085] 6) Cleaning: Place on a horizontal shaker and clean with cleaning solution at room temperature 3 times, 10 min each time. After completion, clean with ddH2O 2 times, 10 min each time.

[0086] 7) Drying;

[0087] 8) Scanning: Follow the scanner's operating procedures and user manual.

[0088] 9) Data extraction: Obtain raw data using GenePix Pro v6.0 software.

[0089] 3.4 Quantitative Analysis of Detection Data

[0090] 1) Microarray detection: Pannel protein microarray detection was performed on serum samples from 54 healthy controls and 90 lung cancer patients. The microarray scanning results were then archived for subsequent quantitative analysis of autoantibody data.

[0091] 1) Data Quantization: Raw data was acquired using GenePix Pro v6.0 software. The median value of the foreground signal at 532nm was used to represent the signal value F532 of the risk factor; the median value of the background signal at 532nm was used to represent the background value B532 of the risk factor. The ratio of the foreground value to the background value for each protein, i.e., F / B, was defined as the signal value SNR of that risk factor.

[0092] A method for early warning and assessment of carcinogenesis induced by dysregulation of the pulmonary nodule immune microenvironment, including a lung cancer risk early warning system based on detection data. Figure 4-7 The steps are as follows:

[0093] 4.1 Detection Signal Evaluation Parameters

[0094] Risk assessment and early warning variables include risk factor signal risk values ​​and the number of risk factors. Based on the SNR signal value range, risk values ​​are assigned and used as assessment variables in the protein chip risk assessment system. Figure 4) Risk factor signal risk value conversion scale: When the signal-to-noise ratio (SNR) < 2, the risk value (RF) is assigned 0; when 2 < SNR < 5, RF is assigned 1; when 5 < SNR < 10, RF is assigned 2; when SNR > 10, RF is assigned 3. Additionally, the number of risk factors is defined according to the positive SNR signal value and used as a risk assessment variable. Risk factor quantity counting rule: Risk factors with SNR > 2 are counted; risk factors with SNR < 2 are not counted. The reference normal dynamic range of the risk factor risk value is designed to be between 0 and 1.

[0095] 4.2 Sensitivity and specificity of assessment parameters in differentiating healthy people and lung cancer patients

[0096] Statistical analysis was performed on the numerical quantity of risk factors with SNR > 2, and at the same time statistical analysis was carried out according to the total risk factor value. The correlation between the lung cancer group and the healthy group was compared. The results showed that the number of risk factors with signals in the lung cancer group was significantly greater than that in the healthy population, and the total risk factor value in the lung cancer group was significantly greater than that in the healthy population. Further, the specificity and sensitivity of the number of risk factors and the risk factor value to the risk of lung cancer occurrence were evaluated through the ROC curve. The results showed that the AUC value of the ROC for the number of risk factors was as high as 0.96, and the AUC for the total risk factor value was as high as 0.98 ( Figure 5 ). These indicate that the number of risk factors and the total risk factor value can be used to warn of the risk of lung cancer occurrence.

[0097] 4.3 Early warning assessment system for the risk of lung cancer occurrence

[0098] 1) Assessment and early warning for the first detection of the risk of lung cancer occurrence

[0099] Based on the detection data of healthy people and lung cancer patients, the numerical range of risk factors and the total risk factor value range of healthy people, as well as the number of risk factors and the total risk factor value of lung cancer patients, were statistically analyzed to establish an assessment and early warning system for the first detection of the risk of lung cancer occurrence ( Figure 6 ), as follows:

[0100] Low risk: The total number of risk factors is less than 33, the total risk value Σ of risk factors is less than 45, and there are no risk factors with risk values of 2 and 3, which is defined as low risk;

[0101] Medium risk: The total number of risk factors is less than 33, and there are a small number of risk factors with risk values of 2 and 3, which is defined as medium risk;

[0102] High risk: The total number of risk factors > 23, the total risk value Σ of risk factors is greater than 15, and there are a relatively large number of risk factors with risk values of 2 and 3, which is defined as high risk.

[0103] The initial warning system states that the results of this antigen protein chip test are prospective and preliminary, serving as a warning of disease development and trends, but do not imply a specific diagnosis for the individual. This risk detection protein chip can simultaneously detect multiple lung cancer-related indicators, enabling efficient and accurate disease risk assessment and subsequent sub-health intervention. However, risk detection results cannot replace a doctor's diagnosis. Individuals can submit their results to health management experts for supplementary reference in subsequent examinations.

[0104] 2) Continuous tracking, assessment, and early warning of lung cancer risk

[0105] Due to significant individual differences, a single test cannot comprehensively assess disease risk and overall health. Annual testing is recommended to help establish a reliable database of individual autoantibody risks. Figure 7 Heatmaps plotted using SNR values ​​show the baseline differences in individual antibody levels, facilitating intuitive comparison and tracking.

[0106] The tracking and early warning system is explained as follows: Each test is based on the previous baseline data. By comparing and analyzing the fingerprint profiles of autoantibody risk factors from previous physical examinations, and comparing them with disease protein databases, it is possible to detect subtle changes in the early stages of many diseases in a timely manner. This allows for the detection of small lesions that are already developing or cannot be detected by routine hospital examinations 3-5 years in advance, ultimately achieving the goal of early detection, early diagnosis, and early treatment.

[0107] The above description is merely a preferred embodiment of this application and an explanation of the technical principles and other solutions employed. Furthermore, the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for early warning and assessment of carcinogenesis induced by dysregulation of the pulmonary nodule immune microenvironment, characterized by: including the following steps, S1. Collect systematic detection data of serum autoantibodies of normal people, people with lung nodules, and people with lung cancer from the database, and construct a database of lung cancer-related autoantibody risk factor Pannel, which contains 159 autoantibody risk factor data; S2. Prepare a lung cancer autoantibody risk factor Pannel protein chip according to the autoantibody risk factor Pannel database in step S1. The Pannel protein chip contains antigen proteins corresponding to 159 autoantibody warning risk factors of lung cancer and system controls; S3. Use the Pannel protein chip to perform protein chip experimental detection on the serum collected from the population (including healthy people without lung nodules, healthy people with lung nodules, and lung cancer patients), analyze the response signals of the autoantibodies corresponding to the individuals in the population, and thus obtain corresponding detection data; S4. According to the detection data, establish a risk warning model for the occurrence of lung cancer, and analyze and warn of the risk of lung cancer occurrence; Perform annual inspections of continuous risk factor screening on individuals, and evaluate, warn, and intervene in high-risk factors with continuously increasing signal intensity.

2. The early warning assessment method for carcinogenesis based on dysregulation of the pulmonary nodule immune microenvironment as described in claim 1, characterized in that: The panel in step S1, which combines 159 autoantibody risk factors for lung cancer, includes p53, NY-ESO-1, CAGE, GBU4-5, SOX2, HuD, and MAGE. A4, SARS, ZPR1, FAM131A, GGA3, PRKCZ, HDAC1, GOLPH3, NSG1, CD84, EEA1, CEA, CYFRA211, CA125, ETHE1, CTAG1A, C1QTNF1, TEX264, CLDN2, HRAS, CHTF18, LRRC45, RPS13, IN TS10, S100A7L2, CDCA7L, LINGO1, HMGA1, ANKRD36B, FCGR2A, HIF3A, EPB41L3, PLA2G4C, LSP1, SPP1, PVALB, RGS20, SNRPA, CDH12, Annexin1, NIP30, Annexin2, CFAP36, MID1 IP1, DCD, MED21, TAF10, ZNF696, Dr1, HSP70, KEAP1, GPBP1, MYBPH, PGAM1, HNRNPD, IKZF5, NAT9, PNMA1, IMPDH2, NAP1 L5, RAB27A, SGPL1, Ubiquillin1, Ubiquillin2, ABCC3, ACTR3, ANXA1, AP3D1, BIRC5, BMI1, C14orf104, C1 D, CAGE1, CCL18, CCND1, CCT8, CD25, CHK, CTAG1 B. CXCL1, DKK1, ECH1, EFHD2, EIF4G1, Eif4g3, ENO-1, ERBB2, ESO-1, FOXP3, FXR1, GAGE7, HMGB3, HNRNPA1, HSPA9, IGF2BP1, JAK2, MAGE A1, MAGE A3, MUC1, p62, Paxillin, PCNA, PGP9.5、PIK3CA、PRC1、PRKACA、RPS3、S100A10、SOX3、SOX1、SSX1、Survivin、TIZ、TM4SF1、TOP2A 、TSHR、UBQLN1、UROD、ZIC2、ZNF258、Galectin-1、ANXA5、APEX1、AQP4、AZGP1、BRSK2、CA2、 CALR、CCDC88A、CCNY、CEACAM5、CENPB、CFH、CHGA、CSF2、DPYSL5、ELAVL3、ELAVL4、ENO1、GA D1、GAD2、KRT19、LCN1、LGALS3、MYCL、PNMA2、PRDX6、PRX、RCVRN、SYP、TPI1、TRPM1、UACA。.

3. The early warning assessment method for carcinogenesis based on dysregulation of the immune microenvironment of pulmonary nodules according to claim 1, characterized in that: The preparation of the lung cancer autoantibody risk factor Pannel protein chip in step S2 includes antigen proteins corresponding to 159 autoantibody warning risk factors of lung cancer, system controls (IgG, IgM, BSA, GST tag, His Tag, biotin, buffer, Cy3, Cy5), and corresponding identifiers: LC ProteinArray and Reneocare.Co.LTD.

4. The early warning assessment method for carcinogenesis based on dysregulation of the pulmonary nodule immune microenvironment as described in claim 1, characterized in that: The detection method in step S3 is as follows. Each Pannel protein chip detects 1 serum sample. The autoantibodies in the serum sample bind to the protein antigens immobilized on the Pannel protein chip. Wash away the unbound serum proteins, and then use a fluorescent secondary antibody against human IgG to detect the binding situation of the autoantibodies. Finally, read the signal through a fluorescence scanner. The strength of the signal is positively correlated with the amount of human serum autoantibodies.

5. The early warning assessment method for carcinogenesis based on dysregulation of the immune microenvironment of pulmonary nodules according to claim 1, characterized in that: The risk assessment and warning system in step S4 is as follows. (1) Risk assessment warning variables: risk factor signal risk value and risk factor quantity. Risk factor signal risk value conversion scale: signal-to-noise ratio (SNR) ≤ 2, risk value (RF) is assigned 0; 2 < SNR ≤ 5, RF is assigned 1; 5 < SNR ≤ 10, RF is assigned 2; SNR > 10, RF is assigned 3. Risk factor quantity counting rule: Risk factors with SNR > 2 are counted; risk factors with SNR ≤ 2 are not counted. (2) First risk assessment warning: When the total number of risk factors is less than 33, the total risk value Σ of the risk factors is less than 45, and there are no risk factors with risk values of 2 and 3, it is defined as low risk; When the total number of risk factors is less than 33 and there are a small number of risk factors with risk values of 2 and 3, it is defined as medium risk; Risk factors with more than 23 total risk factors, a total risk value Σ greater than 15, and a significant number of risk factors with risk values ​​of 2 and 3 are classified as high-risk. The normal dynamic range for risk factor risk values ​​is designed to be between 0 and 1. (3) Continuous risk tracking and early warning: Individual annual inspection forms an individual baseline database. Each test data is compared and analyzed with the previous test data to establish a fingerprint map of the risk factors of the autoantibody. Based on this, the risk factors that show differences and continue to rise are tracked and used for early warning of the risk of lung cancer.

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