Application of LCN2 in nasal secretion as biomarker of non-eosinophilic chronic sinusitis with nasal polyp

Abstract

The invention relates to the field of biological medicine, and particularly provides application of LCN2 as a biomarker of non-eosinophilic chronic disease nasosinusitis with nasal polyp. The LCN2 provided by the invention is derived from nasal secretion, and by detecting a nasal secretion sample which is easy to collect, the wound, pain and potential complication risk caused by traditional nasal polyp tissue biopsy are completely avoided, and the detection compliance and acceptability of a patient are remarkably improved. The collection process of the nasal secretions is simple, and the technical requirements on operators are not high. The LCN2 detection method is relatively mature, is controllable in cost, is convenient to popularize and apply in medical institutions at all levels, and is suitable for screening large-scale crowds. The LCN2 level in nasal secretions has high sensitivity and specificity for distinguishing neCRSwNP from eCRSwNP and healthy people, and the ROC area under the curve (AUC) of the LCN2 level shows good diagnostic value.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of biological medicine, and particularly relates to application of LCN2 in nasal cavity secretion as a biomarker for non-eosinophilic chronic rhinosinusitis with nasal polyps. BACKGROUND

[0002] Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common chronic inflammatory disease of the nasal cavity and paranasal sinuses, which has a complex pathogenesis and a prolonged course, and seriously affects the quality of life of patients. The endotypes of CRSwNP have significant heterogeneity, and can be divided into eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (neCRSwNP) according to the degree of eosinophil infiltration in polyp tissues. Among them, eCRSwNP is mainly characterized by type II inflammation, which is manifested as significant eosinophil infiltration and an increase in the level of type II cytokines (such as interleukin-5 (IL-5)). In contrast, neCRSwNP is more common in Asian countries, and its inflammation type is more diverse, which may involve type I (mainly interferon-gamma) and / or type III (mainly IL-17) immune responses. Neutrophil inflammation is the most basic histopathological feature of neCRSwNP patients, and more research is needed to explore the role of neutrophils in the pathogenesis of neCRSwNP in order to find new drug treatment targets.

[0003] LCN2 (Lipocalin-2), also known as neutrophil gelatinase-associated lipocalin (NGAL), belongs to the lipocalin superfamily and mainly exists in the secondary granules of neutrophils. In addition to neutrophils, keratinocytes and respiratory epithelial cells can also secrete LCN2 under inflammatory stimuli. In various chronic inflammatory diseases, including chronic obstructive pulmonary disease, inflammatory bowel disease, psoriasis and rheumatoid arthritis, LCN2 has been confirmed to have a pathogenic role. Recent research has further revealed that LCN2 can regulate neutrophil inflammatory responses in psoriasis by inducing neutrophil chemotaxis and promoting the production of inflammatory mediators by keratinocytes and neutrophils. However, the role of LCN2 in neutrophil inflammation in CRSwNP still needs to be further elucidated. SUMMARY

[0004] In view of the above-mentioned disadvantages of the prior art, the purpose of the present application is to provide the application of LCN2 derived from nasal cavity secretion as a non-eosinophilic chronic rhinosinusitis with nasal polyps biomarker. Through the detection of LCN2 in the nasal cavity secretion of CRSwNP patients and normal controls without sinusitis in the Otolaryngology Hospital of Fudan University from June 2020 to February 2023, it is found that the LCN2 in the nasal cavity secretion of the neCRSwNP group is significantly higher than that of the eCRSwNP group and the control group. The AUC of LCN2 in the nasal cavity secretion for predicting neCRSwNP is high, that is, the accuracy of LCN2 as a predictor is high. Therefore, the nasal cavity secretion LCN2 can become a new biomarker for predicting neCRSwNP.

[0005] To achieve the above-mentioned purposes and other related purposes, the first aspect of the present application provides the application of LCN2 as a non-eosinophilic chronic rhinosinusitis with nasal polyps biomarker.

[0006] The second aspect of the present application provides the application of LCN2 or its detection product in the preparation of a non-eosinophilic chronic rhinosinusitis with nasal polyps detection product.

[0007] The third aspect of the present application provides a system for predicting non-eosinophilic chronic rhinosinusitis with nasal polyps, which at least comprises the following modules: an LCN2 expression amount data module for detecting the LCN2 expression amount data in the provided sample;

[0008] A data analysis module for calculating the disease probability of the to-be-detected sample based on the LCN2 expression amount in the to-be-detected sample by using a logistic regression model, so as to evaluate the risk of non-eosinophilic chronic rhinosinusitis with nasal polyps.

[0009] The fourth aspect of the present application provides a computer readable storage medium, which stores computer instructions, when the computer instructions are executed by a processor, a method for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps is realized, the method comprises: obtaining LCN2 expression amount data of a to-be-detected sample, calculating the disease probability of the to-be-detected sample based on the LCN2 expression amount data of the to-be-detected sample by using a logistic regression model, and judging the risk of non-eosinophilic chronic rhinosinusitis with nasal polyps.

[0010] The fifth aspect of the present application provides an electronic terminal, which comprises a processor, a memory, an input / output interface, a communication port and a bus; the memory is used for storing computer programs, and the processor is used for executing the computer programs stored in the memory, so that the terminal executes the method for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps in the above-mentioned computer readable storage medium.

[0011] The sixth aspect of the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps in the computer readable storage medium described above.

[0012] The beneficial effects of the present application relative to the prior art include:

[0013] 1. High non-invasiveness and patient friendliness: The present application completely avoids the trauma, pain and potential complication risks brought by traditional nasal polyp tissue biopsy by detecting easily collected nasal secretion samples, significantly improving patient detection compliance and acceptance.

[0014] 2. Easy and fast operation, easy to promote: The collection process of nasal secretion is simple, and the technical requirements of the operator are not high. The LCN2 detection method is also relatively mature, the cost is controllable, and it is easy to promote and apply in medical institutions at all levels, and is suitable for large-scale population screening.

[0015] 3. Good diagnostic and predictive performance: The experimental data of the present application show that the LCN2 level in the nasal secretion has high sensitivity and specificity for distinguishing neCRSwNP and eCRSwNP from healthy people, and the area under the ROC curve (AUC) shows good diagnostic value. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The pathological section is shown as a hematoxylin-eosin staining (HE) staining of the nasal polyp tissue specimen of the CRSwNP patient.

[0017] Figure 2 The expression level detection result graph of LCN2, IL-17 and G-CSF in the neCRSwNP group, the eCRSwNP group and the control group is shown.

[0018] Figure 3 The area under the curve (AUC) detection graph of IL-17, G-CSF and LCN2 predicting neCRSwNP is shown.

[0019] Figure 4 A schematic block diagram of an electronic terminal is shown. DETAILED DESCRIPTION

[0020] The embodiments of the present application are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure in the specification. The present application can also be implemented or applied by other different specific embodiments, and various modifications or changes can be made to the details in the specification based on different views and applications without departing from the spirit of the present application.

[0021] In the present application, by detecting LCN2 in nasal secretions of CRSwNP patients and normal controls without sinusitis in the Affiliated Otorhinolaryngology Hospital of Fudan University from June 2020 to February 2023, it is found that the LCN2 in nasal secretions in the neCRSwNP group is significantly higher than that in the eCRSwNP group and the control group. The AUC of LCN2 in predicting neCRSwNP in nasal secretions is higher, that is, the accuracy of LCN2 as a predictor is higher. Therefore, nasal secretion LCN2 can become a new biomarker for predicting neCRSwNP.

[0022] The present application first provides the use of LCN2 as a biomarker for non-eosinophilic chronic rhinosinusitis with nasal polyps.

[0023] Lipocalin-2 (LCN2) is a 25,000 secreted glycoprotein encoded by a gene located at the 9q34.11 chromosomal locus, which belongs to a family of lipophilic small molecule transporters and can be widely expressed in many tissues and cells in the human body. It plays an important role in inducing hematopoietic cell apoptosis, inflammation regulation, metabolic homeostasis, and the occurrence and development of tumors. The main cell surface binding receptors of Lcn2 include macrophage proteins and 24p3R, which enable it to perform multiple functions such as endocytosis and transport.

[0024] In some embodiments, the aforementioned lipocalin-2 is derived from nasal secretions.

[0025] The present application also provides the use of LCN2 or its detection product in the preparation of a non-eosinophilic chronic rhinosinusitis with nasal polyps detection product.

[0026] It should be noted that the detection product of the present application is mainly for patients diagnosed with chronic rhinosinusitis with nasal polyps, and is used to confirm the risk of non-eosinophilic chronic rhinosinusitis with nasal polyps.

[0027] In some embodiments, the detection product can be a substance for detecting the expression amount of LCN2. In some embodiments, the detection product can be selected from any one or more of an antibody, a membrane strip, a chip, a probe, or a primer pair.

[0028] The term "probe" refers to any molecule that can selectively bind to a target biomolecule (e.g., a nucleic acid sequence that hybridizes to the probe). In some embodiments, the probe can be labeled, for example, with a fluorescent group and a quenching group. In some embodiments, the probe can be a Taqman probe with a fluorescent reporter group added to the 5' end and a fluorescent quencher group added to the 3' end.

[0029] Antibody refers to a protein produced by the body due to the stimulation of an antigen, which has a protective effect. In some embodiments, antibodies can be used as detection reagents to detect the expression of genes.

[0030] As used in this application, the term "primer" refers to a naturally occurring oligonucleotide (e.g., a restriction fragment) or a synthetically produced oligonucleotide that can be used as a starting point for the synthesis of a primer extension product, which, under appropriate conditions (e.g., buffer, salt, temperature, and pH) and in the presence of nucleotides and reagents for nucleic acid polymerization (e.g., DNA-dependent or RNA-dependent polymerases), is complementary to the nucleic acid strand (template or target sequence). Typically, a primer set will consist of at least two primers, an "upstream primer" and a "downstream primer," which together define the amplicon (the sequence to be amplified using the primers).

[0031] As some embodiments of the present invention, the objects of the application may include different populations, such as healthy people, people with eosinophilic chronic sinusitis with nasal polyps, and people with non-eosinophilic chronic sinusitis with nasal polyps. Nasal secretions of the subjects to be tested are collected, and the expression level of LCN2 in the nasal secretions is detected.

[0032] In some embodiments of the present invention, the test sample of the product is taken from the nasal secretions of the object of the application.

[0033] As some embodiments of the present invention, the product includes at least one of a reagent, a kit, a membrane strip, a chip, or a detection system.

[0034] The present invention also provides a system for predicting non-eosinophilic chronic sinusitis with nasal polyps, the system comprising at least the following modules:

[0035] The LCN2 expression data module is used to provide LCN2 expression data in the sample to be tested.

[0036] The data analysis module is used to calculate the disease probability of the test sample based on the LCN2 expression level in the test sample using a logistic regression model, thereby determining the risk of non-eosinophilic chronic sinusitis with nasal polyps.

[0037] Specifically, the probability of disease is a value between 0 and 100, with a higher probability value indicating a higher risk of non-eosinophilic sinusitis with nasal polyps. The probability cutoff is set to 50%. When the probability value exceeds 50%, the model determines it as a positive prediction, meaning a high risk of non-eosinophilic sinusitis with nasal polyps; while when the probability value is less than or equal to 50%, it is determined as a negative prediction, indicating a relatively low risk.

[0038] In a preferred embodiment, the sample to be tested is derived from the nasal secretions of the subject being tested.

[0039] In a preferred embodiment, the logistic regression model is constructed and trained using LCN2 expression data of known samples.

[0040] Preferably, the LCN2 expression data module and the data analysis module are connected by wired or wireless means.

[0041] Preferably, the data analysis module comprises a computer host, a central processing unit, a network server, a display.

[0042] Further, the wireless connection mode can be a wireless local area network, Bluetooth, infrared, etc.; the wired connection mode can be a fixed telephone network, etc. The use of the foregoing connection modes can greatly facilitate the use of the detection system by the user, and at the same time, the increasingly developed information technology and increasingly popular network resources can be used to accurately predict the risk probability value of whether the subject has non-eosinophilic chronic rhinosinusitis with nasal polyps.

[0043] Further, the data analysis module further comprises calculating the probability of non-eosinophilic chronic rhinosinusitis with nasal polyps based on the information provided by the LCN2 expression data module, and judging the risk of non-eosinophilic chronic rhinosinusitis with nasal polyps based on the probability of disease.

[0044] It should be noted that the division of each module of the above device is only a logical division of functions, and all or part of it can be integrated into a physical entity, or physically separated. These modules can all be implemented in the form of software called by a processing element; they can all be implemented in the form of hardware; some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the database acquisition module can be a separate processing element, or it can be integrated into a chip, and in addition, it can be stored in the form of program code in the memory, called and executed by a certain processing element to perform the functions of the above database acquisition module. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be independently implemented. The processing element described herein can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of the hardware in the processor element or the instructions in the form of software.

[0045] For example, the above modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), or one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), or Graphics Processing Units (GPUs), etc. For another example, when a certain module above is implemented in the form of a processing element scheduling code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can invoke code. For another example, the modules can be integrated together to implement in the form of a system-on-a-chip (SOC).

[0046] The application further provides a computer readable storage medium, which stores computer instructions, and when the computer instructions are executed by a processor, a method for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps is implemented, and the method comprises the following steps: obtaining LCN2 expression data of a sample to be detected, and judging a risk of non-eosinophilic chronic rhinosinusitis with nasal polyps based on the LCN2 expression data of the sample to be detected.

[0047] The application further provides an electronic terminal, such as a mobile phone. Figure 4 As shown in the figure, the electronic terminal comprises a processor 11, a memory 12, an input / output interface 13, a communication port 14 and a bus 15; the memory 12 is used for storing computer programs, and the processor 11 is used for executing the computer programs stored in the memory 12, so that the terminal executes the method for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps in the computer readable storage medium.

[0048] The processor 11 can execute computing instructions (program code) and perform the functions of the detection system described in the present application. The computing instructions can include programs, objects, components, data structures, procedures, modules, and functions (functions refer to specific functions described in the present application). For example, the processor 11 can process instructions for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps in the detection system for non-eosinophilic chronic rhinosinusitis with nasal polyps. In some embodiments, the processor 11 can include a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device, and any circuit and processor capable of executing one or more functions, etc., or any combination thereof. Just to illustrate, Figure 4 Only one processor is described in the present application, but it should be noted that the present application can include multiple processors.

[0049] The memory 12 can store data / information obtained from any component of the non-eosinophilic chronic rhinosinusitis with nasal polyps detection system. In some embodiments, the memory 12 can include a mass storage, a removable storage, a volatile read and write memory, and a read only memory (ROM), etc., or any combination thereof. The exemplary mass storage can include a magnetic disk, an optical disk, and a solid state drive, etc. The removable storage can include a flash drive, a floppy disk, an optical disk, a memory card, a U disk, a compact disk, and a mobile hard disk, etc. The volatile read and write memory can include a random access memory (RAM). The RAM can include a dynamic RAM (DRAM), a double rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor (Z-RAM), etc. The ROM can include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (PEROM), an electrically erasable programmable ROM (EEPROM), an optical disk ROM (CD-ROM), and a digital versatile disk ROM, etc.

[0050] The input / output interface 13 can be configured to input or output signals, data or information. In some embodiments, the input / output interface 13 can be configured to enable the interaction between a user (e.g., a person corresponding to the sample to be tested, a user of the non-eosinophilic chronic rhinosinusitis with nasal polyps detection system, etc.) and the processor. In some embodiments, the user can input the feature information of the person corresponding to the sample to be tested through the input / output interface 13. In some embodiments, the input / output interface 13 can include input devices and output devices. Exemplary input devices can include a keyboard, a mouse, a touch screen, a microphone, etc., or any combination thereof. Exemplary output devices can include a display device, a speaker, a printer, a projector, etc., or any combination thereof. Exemplary display devices can include a liquid crystal display (LCD), a light-emitting diode (LED)-based display, a flat panel display, a curved display, a television device, a cathode ray tube (CRT), etc., or any combination thereof.

[0051] The communication port 14 can be connected to a network for data communication. The connection can be a wired connection, a wireless connection, or a combination of both. The wired connection can include a cable, an optical cable, or a telephone line, etc., or any combination thereof. The wireless connection can include Bluetooth, WiFi, WiMax, WLAN, ZigBee, a mobile network (e.g., 3G, 4G, or 5G, etc.), etc., or any combination thereof. In some embodiments, the communication port 14 can be a standardized port, such as RS232, RS485, etc. In some embodiments, the communication port 14 can be a specially designed port.

[0052] The bus 15 can connect the processor 11, the memory 12, and other components. The memory 12 communicates with the processor 11 through the bus 15. The processor 11 reads the data in the memory 12, executes the program, and possibly writes the results back to the memory 12 through the bus 15. The input / output interface 13 exchanges data with the processor 11 and the memory 12 through the bus 15. For example, when the user inputs data on the keyboard, the keyboard sends the data to the input / output interface 13 of the computer, and then transmits them to the processor 11 or the memory 12 through the data bus 15. The communication port 14 exchanges data with the processor 11 and the memory 12 through the bus 15. For example, when the computer receives network data through the Ethernet port, these data are first transmitted to the network interface card (NIC), and then transmitted to the processor 11 or the memory 12 through the network bus.

[0053] The embodiments of the present application also provide a computer program product, which includes a computer program. When the computer program is executed by a processor, the method for detecting non-eosinophilic chronic rhinosinusitis with nasal polyps in the computer readable storage medium is implemented.

[0054] The application can be embodied as a computer program product including a computer readable storage medium and a computer program code encoded on the medium for performing the techniques described above.

[0055] The application can be implemented on any electronic device, such as a handheld computer, a personal digital assistant (PDA), a personal computer, a public information kiosk, a cellular telephone, and the like.

[0056] Some aspects of the application include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the application can be embodied in software, firmware or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by a variety of operating systems.

[0057] The present application also relates to an apparatus for performing the operations herein. This apparatus can be specially constructed for the required purposes, or it can comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program can be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs) or any type of media suitable for storing electronic instructions, and can be coupled to a computer system bus. Furthermore, the computers described herein can include a single processor or can be architectures adopting multi-processor designs for increasing computing capacity.

[0058] Before further description of the specifics of the present application, it is to be understood that the application is not limited in scope by the following particular embodiments described herein; it being further understood that the embodiments use terminology specifically related to the specific embodiments and that terminology of the present application should be taken to have the broadest possible interpretation in accordance with the principles of patent law.

[0059] When numerical ranges are given, it should be understood that every numerical range encompasses any number falling within the range, unless otherwise indicated. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Except in the Examples, or where otherwise explicitly indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The materials, methods, and examples provided herein are illustrative only and, except in the Examples, or where otherwise indicated, are not intended to limit the scope of the application. Other embodiments are within the scope of the application.

[0060] Unless otherwise indicated, the experimental methods, detection methods, preparation methods disclosed in the present application all use conventional techniques in the fields of molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology and related fields.

[0061] Example 1

[0062] 1. Experimental subjects

[0063] The clinical tissue specimens and questionnaire collection used in this experimental study were informed to the patients before surgery, and the patients agreed and signed the informed consent, and were approved by the ethics committee of the Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Fudan University.

[0064] The ethmoidal mucosa or nasal polyp tissues of CRSwNP patients who underwent FESS in our department from June 2020 to February 2023 were collected. At the same time, the uncinate process tissues (UT) of patients who underwent pituitary or skull base surgery in our department and whose clinical symptoms and imaging suggested no sinusitis were selected as normal controls. Combined with patient medical history, clinical symptoms, nasal endoscopy, imaging results, etc., according to the European Position Statement on Rhinosinusitis and Nasal Polyps (EPOS-2020) and the Chinese Guideline for the Diagnosis and Treatment of Chronic Rhinosinusitis (2018), patients with CRSwNP who met the criteria were included. The exclusion criteria mainly included: immunodeficiency, coagulopathy, aspirin intolerance, fungal sinusitis, primary ciliary dysfunction, cystic fibrosis, and patients who did not agree to take specimens during surgery.

[0065] Fresh tissue specimens for detection were collected and placed in 4% paraformaldehyde (PFA) at room temperature for preservation. Subsequently, HE staining was performed.

[0066] 2. Staining and immunohistochemistry

[0067] Paraffin embedding: the collected tissue specimens were fixed in 4% PFA for 24 h, then removed; dehydrated in 75% ethanol for 4 h; dehydrated in 80% ethanol for 1 h; dehydrated in 90% ethanol for 1 h; dehydrated in anhydrous ethanol (I) for 1 h; dehydrated in anhydrous ethanol (II) for 1 h; transparent in ethanol + xylene (1:1) for 2 h; transparent in xylene (I) for 40 min; transparent in xylene (II) for 40 min; 2 h in xylene + paraffin (1:1); 1 h in paraffin (I); 2 h in paraffin (II); the wax block was placed in the embedding machine and cooled and solidified at -20°C; sectioning was performed on a microtome, with each slice being 3-5 μm thick; the slices were spread in a slice spreader; the polylysine pretreated glass slides were picked up; the slices were baked in a 65°C oven for 2 h.

[0068] HE staining: Paraffin sections were placed in an oven for 1 hour; dewaxing was performed using xylene and ethanol; hematoxylin treatment was performed for 10 minutes; residual staining was removed by rinsing; 0.7% hydrochloric acid ethanol was applied for 2 seconds; the sections were rinsed and turned blue for 15 minutes; 95% ethanol (I) was applied for 30 seconds; alcoholic eosin staining was performed for 30 seconds; 95% ethanol (II) was applied for 30 seconds; 95% ethanol (III) was applied for 30 seconds; anhydrous ethanol (I) was applied for 30 seconds; anhydrous ethanol (II) was applied for 30 seconds; carbolic acid xylene was applied for 30 seconds; xylene (I) was applied for 30 seconds; xylene (II) was applied for 30 seconds; the sections were mounted with neutral resin; the sections were observed and photographed under a microscope.

[0069] 3. HE counting and grouping criteria

[0070] HE staining was performed on nasal polyp tissue from CRSwNP patients, and the results were as follows: Figure 1 As shown, based on the previous study's criteria for distinguishing eosinophilic sinusitis with nasal polyps, we counted eosinophils in the tissue under 400X high-power (HPF) microscopy using HE staining results. The specific method was as follows: Two researchers, unaware of the patient's identification number, clinical symptoms, and signs, each selected three areas of high eosinophil density within the stroma beneath the epithelial layer of the nasal polyp to count the eosinophils, and the average value was taken. ≥10 eosinophils under high-power microscopy were defined as eCRSwNP; <10 eosinophils under high-power microscopy were defined as neCRSwNP.

[0071] Subsequently, the NEs were counted separately, and the specific method was as follows: Two researchers who were unaware of the patient's number, clinical symptoms, and signs selected three areas with dense positive cells in the stroma below the epithelial layer of the nasal polyp under HPF and counted them, and took the average value.

[0072] 4. Collection and extraction of nasal secretions

[0073] Place a 0.8 mm diameter circular expandable sponge in the patient's middle nasal meatus and let it stand for 5 minutes. Remove it and place it in a centrifuge tube, then store it at -80°C. Let the centrifuge tube containing the expandable sponge stand at room temperature for 0.5 hours, then transfer it to a 0.8 ml centrifuge column. Centrifuge at 14000 g at 4°C for 20 minutes. Add 100 μL of 1% protease inhibitor mixture (PIC) PBS solution to each tube into the expandable sponge and let it stand for 10 minutes. Centrifuge at 14000 g at 4°C for 20 minutes. Collect the liquid from the bottom of the centrifuge column, which is the nasal secretion, and store it at -80°C.

[0074] 5. Detection of LCN2 expression in nasal secretions

[0075] LCN2 levels were measured by commercial enzyme-linked immunosorbent assay kit. ELISA assay plates and reagents were equilibrated at room temperature for at least 30 minutes; 1x wash buffer was prepared with ultrapure water, and after shaking well, it was ready for use. Preparation of protein standards and test samples: protein standards were gradient diluted with sample diluent, and the concentrations were 5000 pg / mL, 2500 pg / mL, 1250 pg / mL, 625 pg / mL, 313 pg / mL, 156 pg / mL, 78.1 pg / mL respectively; the test sample was diluted 10 times with sample diluent and mixed well. Among them, the sample diluent here refers to the components in the commercial kit, which is generally a PBS solution containing 3% BSA. Standard wells, test samples and blank wells were set up, and the blank wells were added with sample diluent, and the rest of the steps were the same. The test sample and other controls were carefully added to the reaction wells, avoiding contact with the bottom of the well, 100 μL per well, gently shaken and mixed as much as possible to avoid bubbles. After sealing the plate with sealing film, it was reacted at 37°C for 90 minutes; after reaction, the liquid in the enzyme-labeled plate was shaken off, and the prepared biotin anti-human LCN2 antibody working solution was added to each well at 100 μL (TMB empty color developing well was excluded); the enzyme-labeled plate was sealed with sealing film, and reacted at 37°C for 60 minutes; 1x wash buffer was washed for 3 times; 90 uL per well was added in turn, and TMB color developing liquid was added; 37°C, avoid light, react for about 1 minute each time (at least 300 ul of washing liquid per well at this time, the blue color turns to yellow. Use an enzyme-labeled instrument to measure at 450 nm for 3-10 minutes; 100 uL per well was added in turn to terminate the OD value. According to the standard curve, the sample concentration was calculated.

[0076] The results showed that the nasal polyp tissue samples of CRSwNP patients were stained by hematoxylin-eosin staining (HE), and we divided the samples into neCRSwNP and eCRSwNP according to the degree of eosinophil infiltration. Figure 1 The expression of LCN2 in nasal secretions was significantly higher in the neCRSwNP group than in the eCRSwNP group and the control group.

[0077] 6. Detection of G-CSF and IL-17 expression in nasal secretions

[0078] Luminex liquid suspension chip technology was used to detect the concentration of IL-17 and G-CSF protein in nasal secretions. G-CSF and IL-17 were reliable predictors of neCRSwNP. The general steps are as follows: sample preparation: add the corresponding amount of diluted standard diluent to the standard bottle according to the instructions, invert up and down, shake horizontally at low speed for 15 min, and dilute the standard according to different dilution ratios. Sample incubation: shake the Luminex microbeads at 1400 rpm for 30 s, dilute with microbead diluent, shake again at 1400 rpm for 30 s, add 50 μL per well in a 96-well plate. Take 50 μL of prepared standard, sample and blank and add to the corresponding well, shake horizontally at 850 rpm, avoid light, incubate at room temperature for 2 h. Detection antibody incubation: wash the plate 3 times with an automatic plate washer. Add 50 μL of diluted detection antibody to each well, shake horizontally at 850 rpm, avoid light, and incubate at room temperature for 1 h. Color development: wash the plate 3 times with an automatic plate washer; add 50 μL of diluted Streptavidin-PE to each well, shake horizontally, and incubate at room temperature in the dark for 0.5 h; wash 3 times; resuspend with washing solution, shake horizontally, and incubate at room temperature in the dark for 2 min. Read the fluorescence detection value in the calibrated Luminex 200. Data processing: first, analyze the quality control of the standard, then fit the standard curve according to the standard, and obtain the concentration of each sample.

[0079] The results show that we observed that IL-17 and G-CSF were also significantly higher in the neCRSwNP group than in the eCRSwNP group and the control group. Figure 2 ).

[0080] Example 2 - Construction of prediction model and detection of prediction performance

[0081] According to the immunohistochemical results, CRSwNP was defined as neCRSwNP and eCRSwNP, respectively. Further, a prediction model was constructed using a logistic regression algorithm to predict the probability of disease. Finally, ROC analysis was performed on the expression of IL-17, G-CSF, and LCN2 in nasal secretions.

[0082] A prediction model was established using IL-17, G-CSF, and LCN2 in nasal secretions, respectively. Eighty-three nasal secretion samples were collected (of which, 9 were normal control group, 19 were pathologically diagnosed as non-eosinophilic chronic rhinosinusitis with nasal polyps, and 55 were eosinophilic chronic rhinosinusitis with nasal polyps). The expression level of LCN2 protein was detected by ELISA method as training set data, and a prediction model was constructed using a logistic regression algorithm. In addition, the expression levels of IL-17 and G-CSF proteins were detected to construct the model as a control. As shown in Table 1, the AUC of the LCN2 model was 0.89, which was significantly higher than that of the IL-17 model (0.73) and the G-CSF model (0.74). Figure 3As shown, the area under the curve (AUC) of IL-17, G-CSF and LCN2 in predicting neCRSwNP is 0.749, 0.815 and 0.816, respectively. When the expression level of LCN2 protein is input into the prediction model, a probability value between 0 and 100 is obtained, and less than 50% is determined as a person without non-eosinophilic chronic rhinosinusitis with nasal polyps, and more than 50% is determined as a high-risk population with non-eosinophilic chronic rhinosinusitis with nasal polyps. The sensitivity of LCN2 in predicting non-eosinophilic chronic rhinosinusitis with nasal polyps is 0.79, and the specificity is 0.78.

[0083] IL-17, G-CSF and LCN2 in 39 nasal secretion samples (of which 4 are normal control group, 14 are pathological diagnosis of non-eosinophilic chronic rhinosinusitis with nasal polyps, and 21 are eosinophilic chronic rhinosinusitis with nasal polyps) are used as verification data set to verify the model efficiency, as shown in Table 1, we can find that the area under the curve (AUC) of IL-17, G-CSF and LCN2 in predicting neCRSwNP is 0.689, 0.740 and 0.794, respectively. The prediction factor accuracy obtained by the ROC curve of nasal secretion LCN2 set by the present application is high, the sensitivity of LCN2 in predicting non-eosinophilic chronic rhinosinusitis with nasal polyps is 0.79, and the specificity is 0.84. It can be seen from the above results that nasal secretion LCN2 has excellent diagnostic efficiency for non-eosinophilic chronic rhinosinusitis with nasal polyps.

[0084] Table 1 Area under the curve of IL-17, G-CSF and LCN2 in predicting neCRSwNP

[0085]

[0086]

[0087] The prediction model established by using the expression level of LCN2 in nasal secretion is used to predict the risk of non-eosinophilic chronic rhinosinusitis with nasal polyps, when the expression level of LCN2 in nasal secretion is 30000 pg / ml, the probability of predicting neCRSwNP is 74.5%, that is, the patient has a high risk of neCRSwNP.

[0088] In the present application, LCN2 in nasal secretion of CRSwNP patients and normal control without sinusitis is detected, and it is found by analysis that LCN2 in nasal secretion of neCRSwNP group is significantly higher than that of eCRSwNP group and control group. The AUC of LCN2 in nasal secretion in predicting neCRSwNP is high, that is, the accuracy of LCN2 as a prediction factor is high. Therefore, nasal secretion LCN2 can become a new biomarker for predicting neCRSwNP.

[0089] The above examples are intended to be illustrative of the embodiments disclosed herein and are not to be construed as limiting the application. Furthermore, various modifications to the implementations described in this document, as well as alternative implementations not explicitly described, will be apparent to those of ordinary skill in the art. It is intended that the application not be limited to the implementations described herein but will include all implementations and variations thereof that are within the scope of the present application. Although specific embodiments have been illustrated and described herein, it will be appreciated that various modifications and changes can be made to this application by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that the application not be limited to the above-described embodiments, but that all modifications and alterations within the scope of the present application be embraced by the attached claims.

Claims

1. Application of LCN2 as a biomarker for non-eosinophilic chronic sinusitis with nasal polyps.

2. The application as described in claim 1, characterized in that, The LCN2 is derived from nasal secretions.

3. Application of LCN2 or its analytes in the preparation of detection products for non-eosinophilic chronic sinusitis with nasal polyps.

4. The application as described in claim 3, characterized in that, The detectable substance is selected from substances that detect LCN2 expression levels. Preferably, the detectable substance is selected from one or more of antibodies, membrane strips, chips, probes, or primer pairs; and / or, the product is selected from at least one of reagents, kits, membrane strips, chips, or detection systems; and / or, the test sample of the product is nasal secretions.

5. A system for predicting non-eosinophilic chronic sinusitis with nasal polyps, said system comprising at least the following modules: The LCN2 expression data module is used to provide LCN2 expression data in the sample to be tested. The data analysis module is used to calculate the disease probability of the test sample based on the LCN2 expression level in the test sample using a logistic regression model, thereby determining the risk of non-eosinophilic chronic sinusitis with nasal polyps.

6. The system as described in claim 5, characterized in that, The cutoff value for the probability of disease is 50%. When the probability of disease exceeds 50%, the risk of non-eosinophilic chronic sinusitis with nasal polyps is predicted as positive. When the probability of disease is less than or equal to 50%, the risk of non-eosinophilic chronic sinusitis with nasal polyps is predicted as negative. And / or, the sample to be tested is selected from the nasal secretions of the subject being tested.

7. A computer-readable storage medium storing computer instructions that, when executed by a processor, implement a method for detecting non-eosinophilic chronic sinusitis with nasal polyps, the method comprising: LCN2 expression data of the test samples were obtained. Based on the LCN2 expression data of the test samples, the disease probability of the test samples was calculated using a logistic regression model to determine the risk of non-eosinophilic chronic sinusitis with nasal polyps.

8. The computer-readable storage medium as claimed in claim 7, characterized in that, The cutoff value for the probability of disease is 50%. When the probability of disease exceeds 50%, the risk of non-eosinophilic chronic sinusitis with nasal polyps is predicted as positive. When the probability of disease is less than or equal to 50%, the risk of non-eosinophilic chronic sinusitis with nasal polyps is predicted as negative. And / or, the sample to be tested is selected from the nasal secretions of the subject being tested.

9. An electronic terminal, the electronic terminal comprising a processor, a memory, an input / output interface, a communication port, and a bus; the memory for storing a computer program, the processor for executing the computer program stored in the memory, such that the terminal performs the method for detecting non-eosinophilic chronic sinusitis with nasal polyps as described in claim 7 or 8 in a computer-readable storage medium.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method for detecting non-eosinophilic chronic sinusitis with nasal polyps as described in claim 7 or 8 in a computer-readable storage medium.

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