A joint detection product for detecting lung cancer and its application

Abstract

The present invention discloses a combined detection product for detecting lung cancer and its application, belonging to the technical field of biomedicine. The gene combination marker is composed of at least three of the genes KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, and SFTPD. The present invention realizes the early diagnosis and screening of lung cancer by detecting relevant samples such as tissue fluid, cerebrospinal fluid, blood, urine, saliva, or feces, and establishing a model in combination with an algorithm. Under the condition of 97% specificity, the diagnostic sensitivity of the present invention can reach 97%.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and particularly relates to a combined detection product for detecting lung cancer and its application. Background Art

[0002] Lung cancer is one of the most common malignant tumors globally, with its incidence and mortality rates ranking first among cancers, and it is a disease that seriously threatens human health and life. In China, lung cancer ranks first in both the incidence and mortality of male and female malignant tumors. In 2022, the number of new lung cancer cases was 1.0606 million, accounting for 22.0% of all malignant tumors, and the number of deaths was 0.7333 million, accounting for 28.5% of all malignant tumor deaths. Lung cancer is a malignant tumor originating from lung tissue and can be divided into two major categories: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Among them, NSCLC accounts for about 85% of all lung cancers, while SCLC accounts for about 15%. The occurrence of lung cancer is a complex process involving multiple factors, multiple genes, and multiple stages of development. Studies in high-incidence areas of lung cancer have confirmed that it takes about 30 years from exposure to carcinogenic factors to clinical cancer (squamous cell carcinoma). The incidence and mortality patterns of lung cancer are consistent with the time pattern of smoking, with a latency period of more than 20 years. Most early-stage lung cancers have no obvious symptoms. Clinically, most patients are already in the advanced stage when they present with symptoms. The overall 5-year survival rate of patients with advanced lung cancer is about 20%, indicating that early diagnosis is the key to the prognosis of lung cancer. However, due to the lack of an ideal diagnostic method, the early diagnosis rate of lung cancer is only about 14%. Therefore, how to improve the early diagnosis level of lung cancer has become a serious and urgent task faced by lung cancer prevention and treatment workers.

[0003] With the vigorous development of technologies such as genomics and proteomics, the use of biomarkers for the auxiliary diagnosis of diseases has become increasingly common. The expert consensus of the Chinese Medical Association's Clinical Diagnosis and Treatment Guidelines for Lung Cancer (2024 Edition) recommends that commonly used primary lung cancer markers include carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 fragment antigen (CYFRA21-1), pro-gastrin-releasing peptide (ProGRP), and squamous cell carcinoma antigen (SCCA) for the auxiliary diagnosis, efficacy judgment, and follow-up monitoring of lung cancer. Although the levels of the above markers in the human body have certain reference significance for evaluating the status and prognosis of lung cancer patients, lung cancer is a complex disease entity induced by multiple factors, involving multiple genes, and developing in multiple stages. The diagnostic value of a single marker for lung cancer, especially early-stage lung cancer, is very limited. Secondly, most of the products for detecting these markers on the current market rely mainly on imports, with expensive equipment and high technical requirements for personnel, making it difficult to apply in primary hospitals with underdeveloped economies and infrastructure. Therefore, the use of appropriate biomarker combinations plus combined detection and analysis algorithms has great application value for the early diagnosis of lung cancer, providing a more effective solution for the condition assessment and personalized diagnosis and treatment of potential lung cancer patients. Summary of the Invention

[0004] KRT19 is a soluble fragment of cytokeratin 19 (CK19 or CYFRA21-1) in epithelial cells and exists in the cytoplasm of monolayer and stratified epithelial tumor cells. When tumor cells necrose and dissolve, the soluble fragment of CK19 (KRT19) is released into the blood, resulting in an increase in its serum content. In the detection of 4 primary lung cancer markers in patients diagnosed with lung cancer, it was found that although the levels of 4 indicators, namely serum neuron-specific enolase (NSE), carbohydrate antigen 199 (CA199), cytokeratin 21 fragment antigen (KRT19), and carcinoembryonic antigen (CEA), were all higher than those in the control healthy group, the positive rate, accuracy, and specificity of KRT19 were the highest. The KRT19 level in the patient group with malignant lung cancer represented by squamous cell carcinoma of the lung was significantly higher than that in the benign pulmonary disease group and the healthy group, which can assist in differentiating lung cancer from general pulmonary diseases and reducing misdiagnosis and missed diagnosis. Mizugu.chi et al. believe that KRT19 is the preferred marker for detecting squamous cell carcinoma of the lung. It is worth noting that after radiotherapy and chemotherapy, the KRT19 level in patients with non-small cell lung cancer (NSCLC) decreased significantly, indicating that KRT19 can be used as a reliable marker for radiotherapy and chemotherapy in NSCLC patients. Therefore, KRT19 has important value in the diagnosis, treatment monitoring, and prognosis evaluation of lung cancer.

[0005] The BPIFA1 protein is a lipid-binding protein that has a unique affinity for the surfactant phosphatidylcholine dipalmitoylphosphatidylcholine (DPPC) and plays a key role in the innate immune response of the upper respiratory tract. It plays a crucial role in the innate immune response of the upper respiratory tract, reducing surface tension, inhibiting the formation of pathogenic biofilms, and negatively regulating the proteolytic cleavage of SCNN1G, contributing to airway surface liquid homeostasis. Structurally, BPIFA1 functions as a monomer and interacts with the subunit SCNN1B of the heterotrimeric ENaC, inhibiting its proteolytic activation. This multifaceted role highlights the importance of BPIFA1 in coordinating various aspects of airway defense and homeostasis.

[0006] NKX2-1, also known as thyroid transcription factor-1 (TTF-1), is a homeodomain-containing transcription factor in the NKX2 gene family, mainly expressed in lung tissue, thyroid epithelial cells and widely distributed in the ventral forebrain. NKX2-1 is continuously expressed in human fetal and adult lungs to maintain lung development and function, and its abnormal expression is closely related to some lung diseases, especially its relationship with lung cancer. In recent years, studies have found that NKX2-1 gene can trigger an early fetal gene expression pattern leading to tumor growth, so it is considered as an oncogene in the occurrence of lung cancer. Further studies have shown that in primary non-small cell lung cancer, NKX2-1 is closely related to lung adenocarcinoma, specifically expressed in the lungs and highly expressed in patients with lung adenocarcinoma, while there is no obvious correlation with squamous cell lung cancer. Kwei et al. also proposed in their clinical study that NKX2-1 may be a driving core of lung cancer, a necessary factor for the growth and survival of lung adenocarcinoma, and may also be a risk factor for cancer recurrence. Therefore, NKX2-1 can be used as a biomarker with potential value for lung biological characteristics and pathological behaviors.

[0007] ROS1 protein, encoded by 2347 amino acids, is a transmembrane tyrosine kinase (RTK) of the insulin receptor family, regulating cell proliferation, migration and the entire cell cycle. ROS1 is one of the members of the RTK superfamily, belonging to class II RTK, and is composed of an extracellular ligand-binding domain consisting of 9 repeated fibronectin-like motifs, a short transmembrane region and an intracellular TK, which can directly couple extracellular adhesion mediators to generate tyrosine phosphorylation-based intracellular signal transduction. Studies have shown that the activation of ROS1 kinase leads to the activation of several downstream signaling pathways of carcinogenesis, such as PI3K / AKT / mTOR, STAT3, RAS / MAPK / ERK, VAV3 and PLCγ, and can promote the growth of cancer cells through chromosomal rearrangement. Biochip analysis of carcinogenic factors inducing lung cancer in mice showed that the expression of ROS gene was increased by 3 times compared with normal lung tissue, and its elevation in the early and late stages of lung cancer indicates that the ROS gene plays an important role in the occurrence and development of lung cancer.

[0008] SCGB1A1, SCGB3A1 and SCGB3A2 proteins all belong to the Secretoglobin (SCGB) family. They have specific biological functions and potential clinical value in lung diseases. SCGB1A1 protein, also known as Club Cell Secretory Protein (CCSP), is a small, secreted, disulfide-containing dimer secretory globulin that is a member of the SCGB family 1A and is only found in mammals. It is mainly produced by club cells in the distal airway epithelium and is a protein that is very abundant in the lungs. Alveolar macrophages (AMs) are key mononuclear phagocytes for defending against respiratory tract infections. Experiments on in vitro AM culture have shown that exogenous supplementation of SCGB1A1 protein can significantly reduce the response of AMs to microbial stimulation, and SCGB1A1 effectively inhibits the release of cytokines and chemokines (including IL-1b, IL-6, IL-8, MIP-1a, TNF-α and MCP-1). SCGB3A1 and SCGB3A2 are the other two members of the small secretory globulin family 3A and are mainly highly expressed in airway epithelial cells. Studies have shown that CCSP, SCGB3A1 and SCGB3A2 are all decreased in the airways of neonates with bronchopulmonary dysplasia and mice after airway injury. Research has confirmed that after removing cancer cells from all epithelial cells in the combined tumor tissue and normal lung tissue, the lung epithelial cells are not subdivided into subsets, and the relatively large proportion is ciliated bronchial epithelial cells, which highly express SCGB1A1 and SCGB3A1. SCGB1A1, SCGB3A1 and SCGB3A2 proteins play roles in various lung diseases, including chronic obstructive pulmonary disease, asthma, acute lung injury, lung cancer, pulmonary infection, COVID-19 and pulmonary fibrosis, etc., and have multiple biological functions such as anti-inflammatory, immunomodulatory and anti-fibrotic effects.

[0009] SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD are mainly proteins related to pulmonary surfactant and are members of the C-type lectin subfamily. They play key roles in pulmonary immune defense, surfactant homeostasis, and pathogen clearance, and are associated with various pulmonary diseases, especially pulmonary fibrosis and lung cancer prognosis. Studies have shown that high expression of SFTA2 is associated with a better prognosis in NSCLC patients, and it may serve as a biomarker for predicting the prognosis and treatment response of NSCLC patients. Disruption of SFTPA1 can lead to various acute or chronic pulmonary diseases, including lung cancer. SFTPA2 is adjacent to SFTPA1, located in the chromosomal region 10q22-23. The two have opposite transcriptional directions and are separated by a DNA fragment of approximately 40 kb. Similar to SFTPA1, it is mainly expressed in type II alveolar epithelial cells and is involved in pulmonary immune defense and surfactant homeostasis. SFTPC is a hydrophobic protein that, together with surfactant protein B (SP-B) encoded by SFTPB, enhances the ability of surfactant phospholipids to reduce alveolar surface tension. It is embedded in the phospholipid bilayer and plays an important role in the formation and maintenance of the surfactant monolayer at the air-liquid interface of the lung. SFTPD is a 43-kD hydrophilic surfactant protein that directly regulates the functions of macrophages and dendritic cells as well as T lymphocyte-dependent inflammation. It is regarded as a major molecule of the lung's innate defense system and the first line of defense against lung infections, and also helps to control pulmonary inflammation.

[0010] KRT19, BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD are related to the pathological changes in lung cancer patients and are all significantly elevated in the blood. Measuring at least three of the markers KRT19, BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD can help in the early diagnosis of lung cancer. Regular monitoring of these biomarkers can also track the progression of the disease and the effectiveness of treatment interventions.

[0011] Currently, the main biomarkers for hematological lung cancer detection are CEA, NSE, proGRP, KRT19, etc., and usually a single indicator is adopted, with a certain probability of missed detection and false positives. The combined detection of KRT19 with at least three biomarkers among BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, SFTPD and the analysis algorithm are the first domestic lung cancer detection and combined analysis methods, which have high sensitivity and specificity in distinguishing lung cancer patients from healthy people. At present, the National Medical Products Administration has not registered any relevant products for the combined detection of KRT19 with at least three proteins among BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, SFTPD. Therefore, the development of this immunoassay method is of great significance.

[0012] The present invention provides a method for detecting lung cancer, which comprises the following steps:

[0013] (1) Detecting the antigen concentrations (i.e., the concentrations of the corresponding proteins) of KRT19 and at least three biomarkers among BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, SFTPD in a sample; the antigen concentration detection method is at least one of the following methods: radioassay, immunoassay, fluorescence assay, flow-through fluorescence assay, latex turbidimetry, biochemical assay, enzymatic assay, hybridization assay, gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, nucleic acid mass spectrometry, chromatography, chemiluminescence method, magnetoelectric method or photoelectric conversion method.

[0014] (2) Performing logistic regression or support vector machine (SVM) analysis algorithm on the antigen concentrations of the biomarkers in the measured sample to establish a calculation model.

[0015] Further, the logistic regression equation is:

[0016] ;

[0017] Wherein, is the result of the logistic regression model of the lung cancer biomarker, is the natural constant obtained by regression, is the coefficient of each biomarker obtained by regression analysis, is the antigen concentration of each biomarker, is an integer greater than or equal to 2; the sample is human tissue fluid, cerebrospinal fluid, blood, urine, saliva or feces.

[0018] Substitute the biomarker concentration of each detected sample into the regression equation, calculate the probability of each sample having lung cancer, and determine the cut-off value of the probability through the Youden index of the point closest to the upper left corner of the ROC curve. When Logit(P) is greater than the above cut-off value, there is a risk of suffering from lung cancer, which can remind doctors or patients that further examinations are needed for diagnosis at this time.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] (1) The present invention relates to the field of biomedical technology, and discloses a combined detection kit for detecting lung cancer and its application. It discloses the combined detection of KRT19 and at least three biomarkers among BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, SFTPD, realizing its application in lung cancer. By detecting relevant samples such as tissue fluid, cerebrospinal fluid, blood, urine, saliva or feces, and combining algorithms to establish a model, early diagnosis and screening of lung cancer are realized, and good monitoring value can be provided for the evaluation of lung cancer patients after surgery. Through the combined detection of KRT19 and the other three biomarkers, plus the analysis algorithm, the accuracy of early diagnosis of lung cancer can be greatly improved, the specificity and sensitivity can be greatly improved, and the performance is 30-50% higher than that of a single biomarker. Under the condition of 97% specificity, the diagnostic sensitivity can reach 97%.

[0021] (2) After collecting and sampling patient samples, the present invention can predict the patient's disease course through rapid calculation by the algorithm, and the time spent is only 10-30 minutes, which can quickly assist doctors in intervening in the patient's condition and improve the cure rate of patients.

[0022] (3) The rapid detection of biomarker concentration in the present invention can be realized only by a kit and a fully automatic chemiluminescence immunoassay analyzer for quantitative detection. Combined with rapid calculation by the algorithm, it is applied to the primary screening in outpatient clinics and emergency departments, realizing low-cost lung cancer screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is the ROC curve diagram of the combined detection group of (1) (2) (4) (6) in Example 2, where Panel represents the AUC of the combined detection group of (1) (2) (4) (6).

[0024] Figure 2ROC curve graph of the combined detection group (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) in Example 2, where Panel represents the AUC of the combined detection group (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15).

[0025] Figure 3 ROC curve graph of the combined detection group (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) in Example 3, where Panel represents the AUC of the combined detection group (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15). Detailed implementation manners

[0026] Example 1

[0027] This example provides the selection of lung cancer screening and diagnostic markers.

[0028] 1. Samples and database

[0029] The inventors mainly used NCBI, Uniprot, The Human Protein Atlas, and the high-throughput transcriptome sequencing database of peripheral blood blood cells for screening to find markers that can be used for lung cancer diagnosis.

[0030] 2. Data standardization

[0031] Query lung cancer-related genes in the NCBI, Uniprot, and The Human Protein Atlas databases to clarify the molecular basis information of the possible lung cancer-related genes, as well as their distribution, expression, and secretion in human tissues. Under the Linux system environment of the workstation, use the alignment software STAR to align the transcriptome sequencing data to the human reference genome GRCh37 / hg19, and use the quantMode - GeneCounts command to calculate the number of reads aligned to each gene. Then, use the "DESeq2" toolkit in the R language and the "vst" command to standardize the number of reads aligned to each gene, so as to obtain the standardized gene marker expression matrix.

[0032] 3. Calculate the contribution degree of each gene marker to distinguish lung cancer patients from healthy people

[0033] In R language, using the "e1071" package and the recursive feature elimination algorithm, calculate the contribution of each gene to differentiating lung cancer patients from healthy people. The 12 genes in the gene combination marker described in this patent are the top 12 genes, and their contributions are shown in Table 1. Among them, the contribution ranking is determined according to the scores given by the feature elimination algorithm. The lower the score, the higher the ranking.

[0034] Table 1 Contribution of each gene marker

[0035]

[0036] 4. Differential analysis

[0037] The differential expression of KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, SFTPD in lung cancer and healthy people is shown in Table 1, among which KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, SFTPD have statistical differences.

[0038] Example 2

[0039] Verify the performance of the lung cancer combination marker screened in Example 1 for lung cancer diagnosis. The diagnostic performance of the combination marker composed of KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, SFTPD.

[0040] 1. Samples and data

[0041] The test samples were healthy individuals and patients diagnosed with lung cancer (Age: The age of the participants should be between 18 and 90 years old; Gender: The male-female ratio was balanced; Disease status: Diagnosed with lung cancer, and the diagnosis was established through medical means such as pathology and imaging; Exclusion criteria: Only included patients diagnosed with lung cancer, excluding other types of lung diseases (such as benign lung diseases, infectious diseases, etc.) and undiagnosed patients; Other conditions: The patients had not received other treatments (such as radiotherapy, chemotherapy, etc.) or had received treatment but had no clinical interventions that significantly affected the research results). Serum, with the selected samples having comparable ages (between 18 and 90 years old) and genders, and the healthy controls were healthy individuals determined not to have tumors. The antigen concentrations of at least 3 of the above-mentioned KRT19 and 14 other biomarkers in the samples were measured using chemiluminescence technology. After reading the concentrations, Medcalc software / R language / Python programming was used for logistic regression analysis to obtain the correlation coefficient, equation, and the receiver operating characteristic curve (ROC) was plotted to obtain the area under the curve (AUC), sensitivity, and specificity of the receiver operating characteristic curve.

[0042] 2. Model construction

[0043] Randomly select 50% of all the samples (420 cases, 200 lung cancer patients, and 220 healthy controls) as the test set for the establishment of the above 15 biomarkers and different combined diagnostic models.

[0044] For the specific application of logistic regression analysis in this example, the following are examples:

[0045] Example 1: (1)(2)(4)(6) (Combined detection combination of KRT19 + SFTPC + SCGB3A2 + NAPSA) Regression equation: Logit(P) = -6.311 + 0.0024*i(KRT19) + 0.00474*i(SFTPC) + 0.00578*i(SCGB3A2) + 0.00634*i(NAPSA).

[0046] Example 2: (1)(2)(3)(7)(8) Combined detection combination regression equation: Logit(P) = -40.1 + 0.0075*i((1)) + 0.0025*i((2)) - 0.043*i((3)) + 0.006*i((7)) + 0.031*i((8)).

[0047] Example 3: (1)(2)(3)(7)(8)(9)(10) Combined detection combination regression equation: Logit(P) = 10.2 + 0.045*i((1)) + 0.015*i((2)) - 0.024*i((3)) + 0.02*i((7)) - 0.0021*i((8)) - 0.054*i((9)) + 0.0042*i((10)).

[0048] Example 4: (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) Combined detection group regression equation: Logit(P) = -40.1 + 0.0075 * i((1)) + 0.0025 * i((2)) - 0.043 * i((3)) + 0.006 * i((7)) + 0.031 * i((8)) - 0.44 * i((9)) + 0.0426 * i((10)) + 0.137 * i((11)) - 0.018 * i((12)) + 0.053 * i((13)) + 0.278 * i((14)) + 0.407 * i((15)).

[0049] Example 5: (4)(5)(6)(7)(8) Combined detection group regression equation: Logit(P) = 70.1 + 0.00075 * i((4)) - 0.035 * i((5)) + 0.243 * i((6)) + 0.04 * i((7)) + 0.0031 * i((8)).

[0050] 3. ROC Curve Analysis

[0051] Use 50% of the remaining samples of all samples as the test set for ROC curve analysis.

[0052] (1)(2)(4)(6)(KRT19 + SFTPC + SCGB3A2 + NAPSA) combined detection group AUC = 0.85 (see Figure 1 ), at a specificity of 93%, the sensitivity is 83%, higher than the single non-combined detection group. See Table 2 for details.

[0053] (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) combined detection group AUC = 1.00, at a specificity of 99%, the sensitivity is 98% (see Figure 2 ), higher than the single non-combined detection group. See Table 2 for details.

[0054] Note: AUC is the area under the receiver operating characteristic curve (ROC). The closer the AUC is to 1.00, the better or more accurate the diagnostic performance of the product (the same below).

[0055] Table 2

[0056]

[0057] In summary, the gene combination markers based on the present invention can effectively diagnose lung cancer, especially early lung cancer.

[0058] Example 3

[0059] Verify the performance of the lung cancer combined markers (1), (2), (3), (7), (8), (9), (10), (11), (12), (13), (14), (15) screened in Verification Example 2 in differentiating between benign pulmonary diseases and lung cancer diagnosis.

[0060] 1. Samples and data

[0061] The test samples were sera from patients with benign pulmonary diseases (age: between 18 and 90 years old; gender: balanced male to female ratio; disease status: diagnosed with benign pulmonary diseases, excluding all tumor cases; other conditions: ensure that patients with benign pulmonary diseases have no history of malignant tumors and are confirmed not to have tumors through relevant diagnostic methods (such as imaging examinations, pathological diagnoses, etc.)) and patients diagnosed with lung cancer (age: between 18 and 90 years old; gender: balanced male to female ratio; disease status: diagnosed with lung cancer, and the diagnosis of lung cancer is confirmed through medical means such as pathology and imaging; other conditions: only include patients diagnosed with lung cancer, excluding other types of pulmonary diseases or undiagnosed patients). The selected samples were comparable in age (between 18 - 90 years old) and gender, and the benign pulmonary diseases were a population of benign pulmonary diseases confirmed not to have tumors. The antigen concentrations of the lung cancer combined markers (1), (2), (3), (7), (8), (9), (10), (11), (12), (13), (14), (15) in the samples were measured using chemiluminescence technology. After reading the concentrations, logistic regression analysis was performed using Medcalc software / R language / Python programming to obtain the correlation coefficient, equation, and the receiver operating characteristic curve (ROC) was plotted to obtain the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

[0062] 2. Model construction

[0063] Randomly select 50% of all the samples (420 cases, 200 lung cancer patients and 220 patients with benign pulmonary diseases) as the test set for the construction of the diagnostic model of the lung cancer combined markers (1), (2), (3), (7), (8), (9), (10), (11), (12), (13), (14), (15).

[0064] Combined detection regression equation for (1), (2), (3), (7), (8), (9), (10), (11), (12), (13), (14), (15): Logit(P) = -30.2 + 0.075 * i((1)) + 0.00325 * i((2)) - 0.123 * i((3)) + 0.023 * i((7)) + 0.021 * i((8)) - 0.054 * i((9)) + 0.0522 * i((10)) + 0.0133 * i((11)) - 0.123 * i((12)) + 0.153 * i((13)) + 0.0278 * i((14)) + 0.107 * i((15)).

[0065] 3. ROC Curve Analysis

[0066] Use the remaining 50% of all samples as the test set for ROC curve analysis.

[0067] (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) The AUC of the combined detection is 0.933 (see Figure 3 ). At a specificity of 92%, the sensitivity is 94%. See Table 3 for details.

[0068] Table 3

[0069]

[0070] In summary, the (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) gene combination markers based on the present invention have good diagnostic results in differentiating benign lung diseases and lung cancer.

[0071] The above-described embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A gene combination marker for lung cancer diagnosis, characterized in that, The gene combination biomarker consists of KRT19, SFTPC, SCGB3A2, and NAPSA.

2. A gene combination marker for lung cancer diagnosis, characterized in that, The gene combination biomarker consists of KRT19, SFTPC, SFTPA2, ROS1, and SCGB1A1.

3. A gene combination marker for lung cancer diagnosis, characterized in that, The gene combination biomarker consists of KRT19, SFTPC, SFTPA2, ROS1, SCGB1A1, SCGB3A1, and LGSN.

4. A gene combination biomarker for lung cancer diagnosis, characterized in that, The gene combination biomarker consists of KRT19, SFTPC, SFTPA2, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, and SFTPD.

5. A gene combination biomarker for lung cancer diagnosis, characterized in that, The gene combination biomarker consists of SCGB3A2, NKX2-1, NAPSA, ROS1, and SCGB1A1.

6. Use of a reagent for detecting the protein expression level of the gene combination biomarker according to any one of claims 1-5 in the preparation of a lung cancer diagnostic product.

7. The application according to claim 6, characterized in that, The reagent contains a capture antibody and a detection antibody for detecting the protein expressed by the gene combination biomarker.

8. Use of a reagent for detecting the gene expression levels of KRT19, SFTPC, SFTPA2, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, and SFTPD in the preparation of a product for differentiating between benign pulmonary diseases and lung cancer.

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