Joint detection product for detecting lung cancer and application of joint detection product

Abstract

The invention discloses a joint detection product for detecting lung cancer and application of the joint detection product, and belongs to the technical field of biological medicine. The gene combination marker is composed of at least three of the following genes: KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, and SFTPD. According to the invention, by detecting tissue fluid, cerebrospinal fluid, blood, urine, saliva or excrement and other related samples and combining an algorithm to establish a model, early diagnosis and screening of lung cancer are realized. Under the condition that the specificity is 97%, the diagnosis sensitivity can reach 97%.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a joint detection product for detecting lung cancer and an application thereof. Background Art

[0002] Lung cancer is one of the most common malignant tumors in the world, with the highest incidence and mortality rates among cancers. It is a disease that seriously threatens human health and life. In my country, lung cancer ranks first in the incidence and mortality of malignant tumors in both men and women. In 2022, there were 1.0606 million new cases of lung cancer, accounting for 22.0% of all malignant tumors, and 733,300 deaths, accounting for 28.5% of all malignant tumor deaths. Lung cancer is a malignant tumor originating from lung tissue, which can be divided into two major categories: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC accounts for about 85% of all lung cancers, while SCLC accounts for about 15%. The occurrence of lung cancer is a complex process of multi-factor pathogenicity, multi-gene involvement and multi-stage 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 pattern of incidence and mortality of lung cancer is consistent with the time pattern of smoking, with a latent period of more than 20 years. Early stage lung cancer often has no obvious symptoms. Most patients are already in the advanced stage when they present symptoms and seek medical treatment. The overall 5-year survival rate of patients with advanced lung cancer is about 20%, indicating that early diagnosis is the key to lung cancer prognosis. However, due to the lack of ideal diagnostic methods, the early diagnosis rate of lung cancer is only about 14%. Therefore, how to improve the level of early diagnosis of lung cancer has become a serious and urgent task facing lung cancer prevention and treatment workers.

[0003] With the vigorous development of technologies such as genomics and proteomics, the use of biomarkers for auxiliary diagnosis of diseases has become increasingly common. The expert consensus of the Chinese Medical Association's Guidelines for Clinical Diagnosis and Treatment of 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), gastrin-releasing peptide precursor (ProGRP), and squamous cell carcinoma antigen (SCCA) for 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 assessing the status and prognosis of lung cancer patients, lung cancer is a complex disease entity induced by multiple factors, involved in multiple genes, and developed in multiple stages. The diagnostic value of a single marker for lung cancer, especially early lung cancer, is very limited. Secondly, most of the products for detecting these markers on the market currently rely on imports, the equipment is expensive, and the technical requirements for personnel are high, which makes it difficult to apply them in grassroots hospitals with underdeveloped economy and infrastructure. Therefore, the use of appropriate biomarker combinations and joint detection and analysis algorithms has great application value for the early diagnosis of lung cancer, and provides 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. It exists in the cytoplasm of single-layer and stratified epithelial tumor cells. When tumor cells are necrotic and dissolved, the soluble fragment of CK19 (KRT19) is released into the blood, resulting in an increase in serum levels. In the detection of 4 primary lung cancer markers in patients diagnosed with lung cancer, it was found that although the levels of serum neuron-specific enolase (NSE), carbohydrate cancer antigen 199 (CA199), cytokeratin 21 fragment antigen (KRT19) and carcinoembryonic antigen (CEA) were higher than those in the control healthy group, KRT19 had the highest positive rate, accuracy and specificity. The KRT19 level in the patient group of malignant lung cancer represented by squamous cell lung cancer was significantly higher than that in the benign lung disease group and the healthy group, which can help distinguish lung cancer from general lung diseases and reduce misdiagnosis and wrong diagnosis. Mizuguchi et al. believe that KRT19 is the preferred marker for detecting squamous cell lung cancer. It is worth noting that after radiotherapy and chemotherapy, the KRT19 level of 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 with a unique affinity for the surfactant phospholipid dipalmitoylphosphatidylcholine (DPPC) and plays a key role in the innate immune response in the upper respiratory tract. It plays a crucial role in the innate immune response in the upper respiratory tract, reducing surface tension, inhibiting pathogen biofilm formation, and negatively regulating the proteolytic cleavage of SCNN1G, contributing to airway surface fluid homeostasis. Structurally, BPIFA1 functions as a monomer and interacts with SCNN1B, a subunit 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 transcription factor in the NKX2 gene family that contains a homology domain. It is mainly expressed in lung tissue, thyroid epithelial cells and widely distributed in the ventral forebrain. NKX2-1 is continuously expressed in human embryonic lungs and adult lungs to maintain lung development and function, and its abnormal expression is closely related to some lung diseases, especially lung cancer. In recent years, studies have found that because the NKX2-1 gene can stimulate an early fetal gene expression pattern and lead to tumor growth, it is considered as a proto-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 lung adenocarcinoma patients, but has no obvious correlation with lung squamous cell carcinoma. In clinical studies, Kwei et al. also proposed 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 serve as a potential biomarker 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 that regulates cell proliferation, migration and the entire cell cycle. ROS1 is a member of the RTK superfamily and belongs to class II RTK. It is composed of an extracellular ligand-binding domain consisting of 9 repeated fibrin-like motifs, a short transmembrane region and an intracellular TK, which can directly couple extracellular adhesion mediators to produce intracellular signal transduction based on tyrosine phosphorylation. Studies have shown that the activation of ROS1 kinase leads to the activation of several downstream signals of oncogenic pathways, 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-induced lung cancer in mice showed that the expression of ROS gene increased 3 times compared with normal lung tissue. Its increase in the early and late stages of lung cancer indicates that 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, which 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-bonded dimeric secretory globulin SCGB family 1A member found only in mammals. It is mainly produced by club cells in the distal airway epithelium and is a very abundant protein in the lungs. Alveolar macrophages (AMs) are key mononuclear phagocytes in the defense against respiratory infections. In vitro AM culture experiments have shown that exogenous supplementation of SCGB1A1 protein can significantly reduce the response of AM to microbial stimulation, among which 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 two other members of the small secretory globulin family 3A, which are highly expressed mainly in airway epithelial cells. Studies have shown that CCSP, SCGB3A1, and SCGB3A2 are reduced in the airways of newborns with bronchopulmonary dysplasia and mice after airway injury. Studies have confirmed that after removing cancer cells from all epithelial cells in combined tumor tissues and normal lung tissues, the lung epithelial cells are not subdivided into subpopulations, and the largest proportion is ciliated bronchial epithelial cells, which highly express SCGB1A1 and SCGB3A1. SCGB1A1, SCGB3A1, and SCGB3A2 proteins play a role in a variety of lung diseases, including chronic obstructive pulmonary disease, asthma, acute lung injury, lung cancer, lung infection, COVID-19, and pulmonary fibrosis, and have multiple biological functions such as anti-inflammatory, immunomodulatory, and anti-fibrosis.

[0009] SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD are mainly proteins related to surfactant in the lungs. They are members of the C-type lectin subfamily and play a key role in the immune defense, surfactant homeostasis, and pathogen clearance of the lungs. They are also associated with a variety of lung 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 be used as a biomarker to predict the prognosis and treatment response of NSCLC patients. SFTPA1 disruption can lead to a variety of acute or chronic lung diseases, including lung cancer. SFTPA2 is adjacent to SFTPA1 and is located in the chromosome 10q22-23 region. The two have opposite transcription directions and are separated by a DNA fragment of about 40kb. Similar to SFTPA1, it is mainly expressed in alveolar type II epithelial cells and is involved in the immune defense and surfactant homeostasis of the lungs. SFTPC is a hydrophobic protein that, together with the 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 pulmonary surfactant monolayer at the air-liquid interface. SFTPD is a 43kD hydrophilic surfactant protein that directly regulates the function of macrophages and dendritic cells and T lymphocyte-dependent inflammation. It is regarded as the main molecule of the lung's innate defense system and the first line of defense for the lung against infection. It is also beneficial in controlling lung inflammation.

[0010] KRT19, BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD are related to the process of pathological changes in lung cancer patients and are significantly elevated in the blood. Combining the measurement of KRT19 with at least three of the markers among 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 therapeutic interventions.

[0011] At present, the main biomarkers for lung cancer detection based on hematology include CEA, NSE, proGRP, KRT19, etc., and usually a single indicator is used, which has a certain probability of missed detection and false positive. The combined detection and analysis algorithm of KRT19 and at least three markers among BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD is the first lung cancer detection and joint analysis method in China, which has high sensitivity and specificity in distinguishing lung cancer patients from healthy people. At present, the National Medical Products Administration does not have relevant product registration information for the combined detection of KRT19 with at least three of the proteins BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD, so 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: (1) Detecting the antigen concentration (i.e., the concentration of the corresponding protein) of at least three markers of KRT19 and BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD in the sample; the antigen concentration detection method is: at least one of a radiation method, an immunological method, a fluorescence method, a flow cytometry, a latex turbidimetry method, a biochemical method, an enzyme method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.

[0013] (2) Performing logistic regression or support vector machine (SVM) analysis algorithm on the antigen concentration of the marker in the measured sample to establish a computational model.

[0014] Furthermore, the logistic regression equation is: ; in, is the logistic regression model result of lung cancer markers, is the natural constant obtained by regression, is the coefficient of each marker obtained by regression analysis, For each marker antigen concentration, is an integer greater than or equal to 2; the sample is human tissue fluid, cerebrospinal fluid, blood, urine, saliva or feces.

[0015] The biomarker concentration of each sample tested is substituted into the regression equation to calculate the probability of each sample suffering from lung cancer. The probability cut-off value (cut-off) is determined by 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 lung cancer, which can remind doctors or patients that further examination and diagnosis are needed at this time.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention relates to the field of biomedicine technology, and discloses a joint detection kit for detecting lung cancer and its application, which discloses the joint detection of KRT19 and at least three markers of BPIFA1, GGTLC1, LGSN, NAPSA, NKX2-1, ROS1, SCGB1A1, SCGB3A1, SCGB3A2, SFTA2, SFTPA1, SFTPA2, SFTPC, and SFTPD, and realizes 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 models, early diagnosis and screening of lung cancer are realized, and good monitoring value can be provided for the postoperative evaluation of lung cancer patients. Through the joint detection of KRT19 and the other three markers, plus the analysis algorithm, the accuracy of early diagnosis of lung cancer can be greatly improved, and the specificity and sensitivity can be greatly improved, which is 30-50% higher than the performance of a single marker. Under the condition of 97% specificity, its diagnostic sensitivity can reach 97%.

[0017] (2) After collecting and testing patient samples, the present invention can quickly calculate the patient's disease course through an algorithm, which takes only 10-30 minutes. It can quickly assist and guide doctors to intervene in the patient's condition and improve the patient's cure rate.

[0018] (3) The present invention can achieve quantitative detection of marker concentrations by using a kit and a fully automatic chemiluminescence immunoassay analyzer. Combined with rapid calculation of algorithms, it can be applied to initial screening in outpatient and emergency departments to achieve low-cost lung cancer screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the ROC curve diagram of the combined test of (1)(2)(4)(6) in Example 2, where Panel represents the AUC of the combined test of (1)(2)(4)(6).

[0020] Figure 2This is the ROC curve diagram of the combined test of (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) in Example 2, where Panel represents the AUC of the combined test of (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15).

[0021] Figure 3 This is the ROC curve diagram of the combined test of (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) in Example 3, where Panel represents the AUC of the combined test of (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15). DETAILED DESCRIPTION

[0022] Example 1

[0023] This example provides the selection of lung cancer screening and diagnostic markers.

[0024] 1. Samples and Databases The inventors mainly used NCBI, Uniprot, The Human Protein Atlas and peripheral blood cell high-throughput transcriptome sequencing databases for screening to find markers that can be used for lung cancer diagnosis.

[0025] 2. Data Standardization Lung cancer-related genes were queried in the NCBI, Uniprot, and The Human Protein Atlas databases to clarify the molecular basis information of possible lung cancer-related genes, as well as their distribution, expression, and secretion in human tissues. In the Linux system environment of the workstation, the transcriptome sequencing data were aligned to the human reference genome GRCh37 / hg19 using the alignment software STAR, and the number of reads aligned to each gene was calculated using the quantMode-GeneCounts command. Then, the number of reads aligned to each gene was standardized using the "DESeq2" toolkit in the R language and the "vst" command, thereby obtaining a standardized gene marker expression matrix.

[0026] 3. Calculate the contribution of each gene marker to distinguishing lung cancer from healthy people In R language, the "e1071" software package was used with a recursive feature elimination algorithm to calculate the contribution of each gene to distinguishing lung cancer patients from healthy people. The 12 genes in the gene combination markers described in this patent are the top 12 genes, and their contributions are shown in Table 1. The contribution ranking is determined based on the score given by the feature elimination algorithm, and the lower the score, the higher the ranking.

[0027] Table 1 Contribution of each gene marker

[0028] 4. Difference Analysis The differential expression of KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, and SFTPD in lung cancer and healthy subjects are shown in Table 1 , among which KRT19, SFTPC, SFTPA2, SCGB3A2, NKX2-1, NAPSA, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1, and SFTPD showed statistically significant differences.

[0029] Example 2

[0030] 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, and SFTPD for lung cancer.

[0031] 1. Samples and Data The test samples were healthy people and patients diagnosed with lung cancer (age: the age of the participants should be between 18 and 90 years old; gender: the ratio of men to women was balanced; disease status: diagnosed with lung cancer, and the diagnosis was established by medical means such as pathology and imaging; exclusion criteria: only patients diagnosed with lung cancer were included, and other types of lung diseases (such as benign lung diseases, infectious diseases, etc.) and undiagnosed patients were excluded; other conditions: patients had not received other treatments (such as radiotherapy, chemotherapy, etc.) or had received treatment but had no clinical intervention that significantly affected the research results). Serum, the age (between 18 and 90 years old) and gender of the samples were equivalent, and the healthy controls were healthy people who were confirmed to be free of tumors. The antigen concentrations of the above-mentioned KRT19 and at least 3 of the other 14 biomarkers in the samples were determined using chemiluminescence technology. After reading the concentrations, logistic regression analysis was performed using Medcalc software / R language / Python programming to obtain the correlation coefficient and equation, and the receiver operating curve (ROC) was drawn to obtain the area under the receiver operating curve (AUC), sensitivity and specificity.

[0032] 2. Model Construction 50% of all samples (420 cases, 200 lung cancer patients and 220 healthy controls) were randomly selected as the test set for the establishment of the above 15 markers and different combination diagnostic models.

[0033] This embodiment is aimed at the specific application of logistic regression analysis, and the following examples are given: Example 1: (1) (2) (4) (6) (KRT19+SFTPC+SCGB3A2+NAPSA) Joint test combined regression equation: Logit(P)=-6.311+ 0.0024*i(KRT19)+0.00474*i(SFTPC)+0.00578*i(SCGB3A2)+0.00634*i(NAPSA).

[0034] Example 2: (1)(2)(3)(7)(8) Joint test combined 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)).

[0035] Example 3: (1)(2)(3)(7)(8)(9)(10) Joint test combined 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)).

[0036] Example 4: (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) Joint test combined 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)).

[0037] Example 5: (4)(5)(6)(7)(8) Joint test combined 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)).

[0038] 3. ROC curve analysis The remaining 50% of all samples were used as the test set for ROC curve analysis.

[0039] (1) (2) (4) (6) (KRT19 + SFTPC + SCGB3A2 + NAPSA) combined test AUC = 0.85 (see Figure 1 ), with a specificity of 93%, the sensitivity was 83%, which was higher than that of a single combination without joint testing. See Table 2 for details.

[0040] (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) The combined test has an AUC of 1.00, a specificity of 99%, and a sensitivity of 98% (see Figure 2 ), which is higher than the single combination without joint inspection, see Table 2 for details.

[0041] Note: AUC is the area under the receiver operating curve (ROC). The closer the AUC is to 1.00, the better or more accurate the product's diagnostic performance is (the same below).

[0042] Table 2

[0043] In summary, the gene combination markers based on the present invention can effectively diagnose lung cancer, especially early stage lung cancer.

[0044] Example 3 Verify the performance of the lung cancer combination markers (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) screened in Example 2 in distinguishing between benign lung diseases and lung cancer diagnosis.

[0045] 1. Samples and Data The test samples are serum from patients with benign lung disease (age: between 18 and 90 years old; gender: balanced ratio of men and women; disease status: confirmed as benign lung disease, excluding all tumor cases; other conditions: ensuring that patients with benign lung disease have no history of malignant tumors, and confirmed by relevant diagnostic methods (such as imaging examination, pathological diagnosis, etc.) that they have no tumor) and patients diagnosed with lung cancer (age: between 18 and 90 years old; gender: balanced ratio of men and women; disease status: confirmed as lung cancer, and the diagnosis of lung cancer is confirmed by pathology, imaging and other medical methods; other conditions: only patients diagnosed with lung cancer are included, and other types of lung diseases or undiagnosed patients are not included). The samples are selected with age (between 18-90 years old) and gender of the same age, and patients with benign lung disease are people with benign lung disease who are confirmed to have no tumors. The antigen concentration of the combined lung cancer markers (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) in the samples was determined using chemiluminescence technology. After reading the concentration, logistic regression analysis was performed using Medcalc software / R language / Python programming to obtain the correlation coefficient and equation, and the receiver operating curve (ROC) was plotted to obtain the area under the receiver operating curve (AUC), sensitivity, and specificity.

[0046] 2. Model Construction 50% of all samples (420 cases, 200 cases of lung cancer patients and 220 cases of benign lung diseases) were randomly selected as the test set for the construction of (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) lung cancer combined marker diagnostic model.

[0047] (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) Joint test combined regression equation: 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)).

[0048] 3. ROC curve analysis The remaining 50% of all samples were used as the test set for ROC curve analysis.

[0049] (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) Combined test AUC = 0.933 (see Figure 3), with a specificity of 92% and a sensitivity of 94%, see Table 3 for details.

[0050] Table 3

[0051] In summary, the gene combination markers (1)(2)(3)(7)(8)(9)(10)(11)(12)(13)(14)(15) of the present invention have good diagnostic results in distinguishing benign lung diseases from lung cancer.

[0052] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should 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 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.

2. The gene combination marker according to claim 1, characterized in that: The gene combination marker consists of KRT19, SFTPC, SCGB3A2 and NAPSA.

3. The gene combination marker according to claim 1, characterized in that: The gene combination marker consists of KRT19, SFTPC, SFTPA2, ROS1 and SCGB1A1.

4. The gene combination marker according to claim 1, characterized in that: The gene combination marker consists of KRT19, SFTPC, SFTPA2, ROS1, SCGB1A1, SCGB3A1 and LGSN.

5. The gene combination marker according to claim 1, characterized in that: The gene combination marker consists of KRT19, SFTPC, SFTPA2, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1 and SFTPD.

6. The gene combination marker according to claim 1, characterized in that: The gene combination marker consists of SCGB3A2, NKX2-1, NAPSA, ROS1 and SCGB1A1.

7. A product for lung cancer diagnosis, characterized in that: The product contains a reagent for detecting the concentration of the protein expressed by the gene combination marker described in claim 1.

8. Use of a reagent for detecting the protein expression of the gene combination marker according to any one of claims 1 to 6 in the preparation of a lung cancer diagnosis product.

9. The use according to claim 8, characterized in that: The reagent contains a capture antibody and a detection antibody for detecting the protein expressed by the gene combination marker.

10. Use of a reagent for detecting the expression of KRT19, SFTPC, SFTPA2, ROS1, SCGB1A1, SCGB3A1, LGSN, SFTA2, SFTPA1, GGTLC1, BPIFA1 and SFTPD genes in the preparation of a product for distinguishing benign lung diseases from lung cancer.

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