Biomarker composition for screening high-risk group of death after lung transplantation and use thereof

Abstract

The present invention relates to a biomarker composition for screening a high-risk group of death after lung transplantation and use thereof. As a result of investigating the correlation between the expression of inflammatory cytokines and inflammation-related proteins as biomarkers before lung transplantation and clinical outcomes, a statistically significant difference was confirmed in abundance of various inflammatory cytokines and inflammation-related proteins between survivors and non-survivors, and a new prediction model using eight cytokines and inflammation-related proteins for survivors was found to exhibit a high AUC value. Therefore, the biomarker of the present invention is expected to be usefully employed as a biomarker for screening a high-risk group of death after lung transplantation.

Description

Biomarker composition for screening high-risk groups for death after lung transplantation and use thereof

[0001] The present invention relates to a biomarker composition for screening a high-risk group for death after lung transplantation and the use thereof.

[0002] The present invention claims priority based on Korean Patent Application No. 10-2025-0014771 filed on February 5, 2025, and all contents disclosed in the specification and drawings of said applications are incorporated by reference into the present application.

[0003] Lung transplantation is an established treatment option for patients with end-stage lung disease. Significant advancements in lung transplantation have been made in recent years since the first successful transplant was performed in the 1980s. However, the prognosis for lung transplantation still needs improvement compared to other solid organ transplants, and an imbalance exists between the supply and demand for lung transplants, as only 20% of potential donor lungs are suitable for transplantation. Consequently, much attention has been focused on selecting ideal candidates for lung transplantation during the pre-transplant period.

[0004] Cytokines are proteins that play a crucial role in the immune system; they are primarily secreted by immune cells and regulate immune responses through intercellular signaling. Cytokines are involved in various physiological processes such as inflammation, infection, and wound healing, and representative examples include interleukin, interferon, and tumor necrosis factor (TNF).

[0005] The primary functions of cytokines and inflammation-related proteins are to facilitate interactions between immune cells, enable them to fight pathogens by responding to infections or inducing inflammation, and play an important role in preventing autoimmune diseases or chronic inflammation by regulating immune responses through binding to receptors on cell membranes and activating intracellular signals.

[0006] Therefore, cytokine imbalance can lead to various diseases. Excessive secretion of cytokines and inflammation-related proteins can trigger an overactive inflammatory response, potentially causing autoimmune diseases; conversely, a deficiency can weaken the immune response, making one vulnerable to infection. For this reason, the regulation of cytokines and inflammation-related proteins is being studied as an important strategy in the treatment of immune diseases, yet little is known about research related to lung transplantation.

[0007] The object of the present invention is to provide a method for providing information to predict a high-risk group for death after lung transplantation, comprising the following steps:

[0008] S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject.

[0009] Another object of the present invention is to provide a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following:

[0010] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0011] Another object of the present invention is to provide a kit for predicting a high-risk group for death after lung transplantation, comprising a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following, and instructions:

[0012] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0013] Another objective of the present invention is to provide a biomarker composition for predicting a high-risk group for death after lung transplantation, comprising one or more active ingredients selected from the group consisting of the following:

[0014] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0015]

[0016] However, the technical problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

[0017] The present invention provides a method for providing information for predicting a high-risk group for death after lung transplantation, comprising the following steps:

[0018] S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject.

[0019] In one embodiment of the present invention, the information providing method is,

[0020] S2) When compared to the level of protein or mRNA of the above substance in biological samples isolated from the control group,

[0021] The step of predicting that the subject will die after lung transplantation when the protein or mRNA level of one or more substances selected from the group consisting of NGF, IL-1A, AXIN1, IL-33, IL-2, IL-10RA, and IL-5 is decreased, or when the protein or mRNA level of CCL-3 is increased may be further included, but is not limited thereto.

[0022] In one embodiment of the present invention, the biological sample may be a biological sample isolated from a subject prior to lung transplantation, but is not limited thereto.

[0023] In one embodiment of the present invention, the biological sample may be any one selected from the group consisting of serum, whole blood, blood, and plasma, but is not limited thereto.

[0024] In one embodiment of the present invention, the death may be within one year after lung transplantation, but is not limited thereto.

[0025] The present invention provides a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following:

[0026] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0027] The present invention provides a kit for predicting a high-risk group for death after lung transplantation, comprising a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following, and instructions:

[0028] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0029] In one embodiment of the present invention, the description may teach the information provision method, but is not limited thereto.

[0030] The present invention provides a biomarker composition for predicting a high-risk group for death after lung transplantation, comprising one or more active ingredients selected from the group consisting of the following:

[0031] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0032]

[0033] In addition, the present invention provides a method for treating a high-risk group for death after lung transplantation, comprising the following steps:

[0034] S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject;

[0035] S2) When compared to the level of protein or mRNA of the above substance in biological samples isolated from the control group,

[0036] A step of predicting that the subject will die after lung transplantation if the protein or mRNA level of any one or more substances selected from the group consisting of NGF, IL-1A, AXIN1, IL-33, IL-2, IL-10RA, and IL-5 is decreased, or the protein or mRNA level of CCL-3 is increased; and

[0037] S3) A step of administering a therapeutic agent in a pharmaceutically effective amount to a high-risk group for death after lung transplantation, i.e., a subject, who is predicted to die after lung transplantation.

[0038] In addition, the present invention provides a use for predicting a high-risk group for death after lung transplantation of a composition comprising, as an active ingredient, a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following:

[0039] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0040] In addition, the present invention provides a use for manufacturing a formulation for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a formulation for measuring the level of one or more proteins or mRNA selected from the group formed above.

[0041] According to the biomarker composition for screening high-risk groups for death after lung transplantation and the use thereof, when investigating the association between the expression of inflammatory cytokines and inflammation-related proteins as biomarkers before lung transplantation and clinical outcomes, statistically significant differences in the abundance of various inflammatory cytokines and inflammation-related proteins were confirmed between survivors and non-survivors, and a new predictive model using eight cytokines and inflammation-related proteins for survivors was found to show high AUC values, the biomarker of the present invention is expected to be usefully utilized as a biomarker for lung transplantation.

[0042] Figure 1 is a figure illustrating the workflow for analyzing serum cytokines and inflammation-related proteins using the Olink Inflammation panel.

[0043] Figure 2 shows a flowchart of the sample selection and matching process.

[0044] Figures 3a to 3c show the results of an analysis of the association between serum cytokine and inflammation-related protein abundance levels before and after lung transplantation and survival rates after lung transplantation.

[0045] Figure 3a shows the distribution of serum cytokine and inflammation-related protein abundance levels in recipients before and after lung transplantation.

[0046] Figure 3b shows the distribution of serum cytokine and inflammation-related protein abundance levels in recipients before lung transplantation.

[0047] Figure 3c shows the distribution of serum cytokine and inflammation-related protein abundance levels in recipients after lung transplantation.

[0048] (Here, the X-axis represents NPX (normalized protein expression) values, and the Y-axis represents -log10 (p-value for the difference between the two groups) along with the corresponding cytokine. Points indicating a statistically significant difference in expression between the two groups lie on the dotted horizontal line. Blue dots indicate low expression, and pink dots indicate high expression compared to the other group. Orange highlights indicate cytokines and inflammation-related proteins showing differences in samples before and after transplantation.)

[0049] Figures 4a to 4c relate to a predictive model for survivors after lung transplantation.

[0050] Figures 4a and 4b show the receiver operation characteristic curves of a model predicting survivors for (a) NGF, (b) IL-1A, (c) AXIN1, (d) IL-33, (e) IL-2, (f) CCL-3, (g) IL-10RA, and (h) IL-5, respectively, and Figure 4c shows (i) the sensitivity and specificity of a new prediction model and the receiver operation characteristic curves of a model predicting survivors for a combination of eight cytokines and inflammation-related proteins.

[0051] Figure 5 analyzes the association between serum cytokine and inflammation-related protein abundance levels in survivors before and after transplantation and graft dysfunction after lung transplantation.

[0052] The upper graph of Fig. 5 shows the distribution of serum cytokine and inflammation-related protein abundance levels before transplantation in survivors with and without functional impairment. Additionally, the lower graph of Fig. 5 shows the distribution of serum cytokine and inflammation-related protein abundance levels after transplantation in survivors with and without functional impairment.

[0053] (Here, the X-axis represents NPX (normalized protein expression) values, and the Y-axis represents -log10 (p-value for association with graft dysfunction) along with the corresponding cytokine. Among survivors, points showing statistically significant differences in expression between rejection and cases without graft dysfunction lie on the dotted horizontal line. Blue dots indicate low expression in survivors with graft dysfunction, while pink dots indicate high expression in survivors with graft dysfunction. Orange highlights indicate cytokines and inflammation-related proteins showing differences between pre- and post-transplant samples.)

[0054] Figures 6a to 6c relate to a model predicting rejection after lung transplantation among survivors.

[0055] Figures 6a and 6b show the recipient operating characteristic curves of a model predicting rejection after lung transplantation in survivors for (a) GDNF, (b) NTF3, (c) IL24, (d) TNFRSF, (e) SCF, (f) IL18R1, (g) CCL25, and (h) MCP3, respectively, and Figure 6c shows (i) CXCL1 and a combination of five cytokines and inflammation-related proteins, along with the sensitivity and specificity of the new prediction model.

[0056] Figures 7a and 7b relate to a model predicting rejection after lung transplantation among survivors.

[0057] Figure 7a shows the receiver operation characteristic curves of a model predicting death within 3 months after lung transplantation for (a) FGF19, (b) CCL19, (c) IL22RA1, and (d) TSLP, respectively, and Figure 7b shows (i) the sensitivity and specificity of a new prediction model for a combination of three cytokines and inflammation-related proteins.

[0058] Despite the continuously increasing demand for lung transplants due to end-stage lung disease, donated lung resources remain significantly limited. Therefore, it is essential to efficiently allocate resources by predicting the prognosis early before transplantation and selecting a patient group suitable for the procedure. To this end, the inventors of this invention conducted research to investigate the association between the expression of cytokines and inflammation-related proteins as biomarkers prior to lung transplantation and clinical outcomes, thereby completing this invention.

[0059] The present invention provides a method for providing information to predict a high-risk group for death after lung transplantation, comprising the following steps:

[0060] S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject.

[0061] Including all claims below, the information providing method in this specification is,

[0062] S2) When compared to the level of protein or mRNA of the above substance in biological samples isolated from the control group,

[0063] The method may further include, but is not limited to, a step of predicting that a subject will die after lung transplantation, i.e., a high-risk group for death after lung transplantation, when the protein or mRNA level of one or more substances selected from the group consisting of NGF, IL-1A, AXIN1, IL-33, IL-2, IL-10RA, and IL-5 is decreased or the protein or mRNA level of CCL-3 is increased.

[0064] Including the full claims below, the present specification analyzed pre- and 7-day post-lung transplant serum pairs from lung transplant recipients in a database collected prospectively from February 2020 to February 2024 and classified them into four groups using propensity score matching: survivors without graft dysfunction, survivors with graft dysfunction, survivors who did not survive within 3 months, and survivors who did not survive for 3 months or more. For the lung transplant recipients, the association between changes in pre- and post-transplant serum proteins, namely cytokines and inflammation-related proteins, and clinical outcomes was explored. As a result, there were statistically significant differences in the abundance of several serum proteins, namely cytokines and inflammation-related proteins, between survivors and non-survivors, and a new predictive model using eight cytokines and inflammation-related proteins for survivors was found to have an area under the curve (AUC) of 0.850 (p-value < 0.001).

[0065] In all claims below, “NGF (Nerve Growth Factor)” is a nerve growth factor that plays an important role in the survival and growth of nerve cells, helps in signal transmission between nerve cells and receptors in the nervous system, and is known to be an essential element for the development of the sensory and autonomic nervous systems.

[0066] In the entirety of the following claims, “IL-1A (Interleukin 1 Alpha)” is a cytokine known to play an important role in inducing an inflammatory response. In addition, it plays an important role in inflammatory diseases and autoimmune diseases by inducing inflammation and activating immune cells in response to infection or tissue damage.

[0067] In the entirety of the following claims, “AXIN1 (Axin 1)” is a protein, not a cytokine, that plays an important role in regulating the Wnt / β-catenin signaling pathway. This protein plays an important role in cell division and growth and also acts as a tumor suppressor, so modifications of AXIN1 may be associated with various types of cancer.

[0068] In the entirety of the following claims, “IL-33 (Interleukin 33)” is a cytokine that promotes inflammatory responses. It is primarily associated with allergic reactions and induces Th2 immune responses. This substance plays an important role in allergic diseases and asthma and can contribute to balancing the immune system by regulating apoptosis.

[0069] In the entirety of the following claims, “IL-2 (Interleukin 2)” is a cytokine that plays an important role in promoting the proliferation and survival of T cells. It is known to enhance immune responses and play an important role in the treatment of autoimmune diseases, as well as to help activate T cells and natural killer cells (NK cells).

[0070] In the present specification, including all claims below, “CCL-3 (Chemokine (CC motif) Ligand 3)” is a chemotactic substance that is a cytokine and induces leukocytes to the site of inflammation. It plays an important role in immune responses, as well as inducing macrophages and T cells, thereby playing an important role in inflammatory diseases and infection responses.

[0071] In the entirety of the following claims, “IL-10RA (Interleukin 10 Receptor Alpha)” is a cytokine receptor that acts as a receptor for the IL-10 cytokine. This receptor contributes to suppressing inflammation and preventing excessive activation of the immune system, and can play a therapeutic role in autoimmune diseases and inflammatory diseases.

[0072] In the entirety of the following claims, “IL-5 (Interleukin 5)” is a cytokine that promotes the development and activation of eosinophils, enhances the role of eosinophils in allergic reactions and asthma, and induces inflammation. In addition, it is known to play an important role in the survival and proliferation of eosinophils.

[0073] Including all claims below, this specification confirmed that the 10 proteins in Fig. 3b are proteins that satisfy p < 0.05, Fc ≥ 1.2, and FC ≤ 1.2 in the Student T-test analysis in the volcano plot analysis. Meanwhile, the model using the 8 proteins in Fig. 4c was constructed using proteins that passed the ROC analysis with an AUC of 0.7 or higher and a P < 0.05 criterion in the ROC analysis. The reason IL-5 did not appear in the volcano is that the p-value of the Student t-test was 0.251, the p-value of the ROC analysis was 0.017, and the p-value of the Mann-Whitney test was 0.028. It was confirmed that the model performance improved, with an AUC of 0.83 when combining 7 proteins excluding IL5, and 0.85 when IL5 was added. IL17C shown in Fig. 3b was excluded from the model configuration because its AUC was 0.663.

[0074] Including the full claims below, the serum levels of the eight types of serum proteins, namely cytokines and inflammation-related proteins, have been analyzed. It has been confirmed that mortality after lung transplantation can be predicted based on the increase or decrease in the level of each substance, and in particular, that the best mortality prediction rate after lung transplantation is obtained when all eight types of proteins are combined. Therefore, in one embodiment of the present invention, the level of the substance may more preferably mean the level of proteins, but is not limited thereto.

[0075] Including all claims below, the biological sample may be a biological sample isolated from a subject prior to lung transplantation, but is not limited thereto.

[0076] Including all claims below, the biological sample may be any one selected from the group consisting of serum, whole blood, blood, and plasma, but is not limited thereto.

[0077] In the present specification, including all claims below, it has been confirmed that post-lung transplant death can be predicted by analyzing the expression levels of eight types of proteins of the present invention from the serum of a lung transplant recipient prior to transplantation. In the present specification, including all claims below, the biological sample may be serum isolated from the subject prior to lung transplantation, but is not limited thereto.

[0078] Including the full claims below, the biological sample described herein may be pretreated before use for detection or diagnosis. For example, this may include homogenization, filtration, distillation, extraction, concentration, inactivation of interfering components, addition of reagents, etc. The sample may be prepared to increase the detection sensitivity of protein markers, for example, the sample obtained from a subject may be pretreated using methods such as anion exchange chromatography, affinity chromatography, size exclusion chromatography, liquid chromatography, sequential extraction, or gel electrophoresis.

[0079] Including all claims below, the present specification classifies lung transplant recipients into four groups, wherein cases of death include survivors who do not survive within three months after lung transplantation and survivors who do not survive for three months or more. Meanwhile, since the present invention defines a survivor as a person who has survived for at least one year after lung transplantation, a survivor who does not survive for three months or more may refer to a survivor who died within a period of less than one year. Accordingly, in one embodiment of the present invention, the death may be death within one year after lung transplantation, but is not limited thereto.

[0080] Accordingly, in the entire claim below, “high-risk group” means a group of patients at high risk of death within a period of less than one year after lung transplantation, and “low-risk group” may be defined as the opposite of “high-risk group.”

[0081] Including the entire claims below, the agent for measuring miRNA levels may be a primer, probe, oligonucleotide, antibody or antigen-binding fragment thereof that specifically binds to mRNA, a ligand, aptamer, peptide, receptor, agonist or antagonist, or a combination thereof, but is not limited thereto, and may mean any agent commonly used in the art.

[0082] Including all claims below, the preparation capable of measuring protein levels may be one or more selected from the group consisting of antibodies, peptides, aptamers, proteins, and compounds that specifically bind to said proteins, but is not limited thereto.

[0083] At this time, in the entire specification including the following claims, the method for measuring protein levels is not subject to any particular limitation as long as it is a protein measurement method known in the art, but can be measured by methods such as protein chip analysis, immunoassay, ligand binding assay, MALDI-TOF (Matrix Assisted LaSer Desorption / Ionization Time of Flight Mass Spectrometry) analysis, SELDI-TOF (Sulface Enhanced LaSer Desorption / Ionization Time of Flight Mass Spectrometry) analysis, radioimmunoassay, radioimmunodiffusion method, Ouchteroni immunodiffusion method, Rocket immunoelectrophoresis, tissue immunostaining, complement fixation assay, two-dimensional electrophoresis analysis, liquid chromatography-mass spectrometry (LC-MS), LC-MS / MS (liquid chromatography-mass spectrometry / mass spectrometry), Western blotting, ELISA (enzyme linked immunosorbent assay), FACS, etc.

[0084] In all claims below, the method for measuring mRNA levels is not limited to any specific mRNA measurement method known in the art, provided it is by PCR, RNase protection assay, northern blotting, southern blotting, in situ hybridization, DNA chip, and / or RNA chip.

[0085] In the entirety of the following claims, “the level is increased” means that something that was not previously detected is detected, or that the amount detected is greater than the normal level. For example, “the level is increased” means that the level of the experimental group is at least 1%, 2%, 3%, 4%, 5%, 10% or higher, e.g., 5%, 10%, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% or higher, and / or 0.5 times, 1.1 times, 1.2 times, 1.4 times, 1.6 times, 1.8 times or higher. Specifically, it may mean an increase of 1 to 1.5 times, 1.5 to 2 times, 2 to 2.5 times, 2.5 to 3 times, 3 to 3.5 times, 3.5 to 4 times, 4 to 4.5 times, 4.5 to 5 times, 5 to 5.5 times, 5.5 to 6 times, 6 to 6.5 times, 6.5 to 7 times, 7 to 7.5 times, 7.5 to 8 times, 8 to 8.5 times, 8.5 to 9 times, 9 to 9.5 times, 9.5 to 10 times, or 10 times or more compared to that of the control group, but is not limited thereto. A person skilled in the art can understand the meaning of the opposite term as having the opposite meaning in accordance with the above definition.

[0086] The term “method for providing information” as used herein, including all claims below, refers to a method for providing information regarding the diagnosis or prognosis prediction of a disease, etc., which involves obtaining information regarding the onset or likelihood (risk) of onset and prognosis of a disease by analyzing biological samples of an individual or by confirming the increase or decrease in the expression level of the biomarker of the present invention. For example, it may include a method for providing information regarding whether there is a possibility of the disease of the present invention developing in an individual, whether the likelihood of said disease developing is relatively high, or whether said disease has already developed, by measuring the level of the biomarker according to the present invention and comparing it with a control group. Furthermore, through a method using the biomarker of the present invention, it is possible to predict the risk of exacerbation due to the onset of the disease of the present invention, that is, whether the prognosis will be poor, and this may also be used as a method for providing information regarding the prevention and treatment of the disease of the present invention. In particular, as used herein, including all claims below, the term “disease” or “disease” may refer to death after lung transplantation.

[0087] In all claims below, the term “measurement” in this specification includes both detecting and confirming the presence (expression) of a target substance and detecting and confirming a change in the level of presence (expression level) of the target substance. The measurement may be performed without limitation, including both qualitative methods (analysis) and quantitative methods. The types of qualitative and quantitative methods for measuring the presence of a substance according to the present invention are well known in the art, and the experimental methods described in this specification are included therein.

[0088] In the entirety of the claims below, the term “analysis” may preferably include “measurement,” wherein the qualitative analysis may mean measuring and confirming the presence of a target substance, and the quantitative analysis may mean measuring and confirming a change in the presence level (expression level) or amount of the target substance. In the present invention, analysis or measurement may be performed without limitation by including both qualitative and quantitative methods, and preferably, quantitative measurement may be performed.

[0089] In the entire specification including the following claims, “diagnosis” may have a broad meaning that includes all of the following: determining the susceptibility of an object to a specific disease or condition; determining whether an object currently has a specific disease or condition; determining the prognosis of an object with a specific disease or condition (e.g., identification of tumor status, determination of tumor stage or responsiveness to treatment; in particular, in the present invention, the subject of diagnosis is death after lung transplantation or, if prognosis prediction is included, the prognosis for lung transplantation; in this case, poor prognosis may be death and may include progression to death after lung transplantation); or therametrics (e.g., monitoring the condition of an object to provide information on therapeutic efficacy).

[0090] In the entirety of the following claims, the term “prognosis prediction” may mean predicting the degree of disease progression in a patient group of the disease of the present invention. It may mean predicting the probability of progression, deterioration, recurrence, maintenance, etc. of the disease of the present invention through the increase or decrease in the level of the biomarker of the present invention. In particular, in the entirety of the following claims, prognosis prediction may include death after lung transplantation.

[0091] The present invention provides a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following:

[0092] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0093] The present invention provides a kit for predicting a high-risk group for death after lung transplantation, comprising a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following, and instructions:

[0094] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0095] Including all claims below, the description may teach the information provision method, but is not limited thereto.

[0096] In the entire specification including the following claims, “kit” means a tool that additionally includes a formulation or a substance for the function, storage, etc., of the kit to enable the use of each kit claimed in the present invention. In addition to the said substance, the kit of the present invention may include other components, compositions, solutions, devices, etc., that are typically required for the storage and processing methods thereof. As a specific example, each component may be applied one or more times without limitation on the number of times, there is no restriction on the order in which each substance is applied, and the application of each substance may proceed simultaneously or sequentially.

[0097] In all claims below, the kit may include a container; instructions; etc. The container may serve to package the material and may also serve to store and secure it. The material of the container may take the form, for example, a bottle, a tub, a sachet, an envelope, a tube, an ampoule, etc., and may be formed partially or wholly from plastic, glass, paper, foil, wax, etc. The container may be equipped with a cap that is initially part of the container or can be attached to the container by mechanical, adhesive, or other means and may be fully or partially detachable, and may also be equipped with a stopper that allows access to the contents by a needle. The kit may include an outer package, and the outer package may include instructions regarding the use of the components.

[0098]

[0099] The present invention provides a biomarker composition for predicting a high-risk group for death after lung transplantation, comprising one or more active ingredients selected from the group consisting of the following:

[0100] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0101] In the entirety of the claims below, the term “biomarker” refers to a marker that can distinguish between normal and pathological states, predict prognoses such as treatment response, recovery from disease, or death, and is objectively measurable. It has been confirmed that the levels of biomarkers in biological samples of individuals with the disease of the present invention differ from the respective increases or decreases in levels of normal individuals. Consequently, it has been proven that the biomarkers of the present invention can be used as biomarkers for diagnosing death or predicting (prognosis) after lung transplantation, which can be interchangeably referred to as the disease in the present invention.

[0102] In addition, the present invention may provide a diagnostic device for predicting or diagnosing the prognosis of a disease of the present invention. Specifically, the present invention has identified eight types of biomarkers capable of predicting death after lung transplantation, and can be referred to as a diagnostic device for predicting the prognosis after lung transplantation or diagnosing death after lung transplantation. The measurement unit of the diagnostic device for predicting the prognosis or diagnosing the present invention may measure the expression level of a protein using a preparation that measures the protein expression level of the biomarker according to the present invention on a biological sample obtained from a subject. By confirming the degree of protein expression using the preparation in the measurement unit, it is possible to diagnose whether the disease of the present invention (death after lung transplantation) is present or whether the prognosis of the disease (after lung transplantation) is poor (death).

[0103] Including all claims below, the diagnostic device of the present invention may further include a detection unit that predicts and outputs the prognosis or progression of the disease of the present invention of a subject from the degree of expression of the protein obtained from the measurement unit.

[0104] Including the entire claim below, the detection unit in this specification can diagnose the disease of the present invention or predict the prognosis of the disease by generating and classifying information regarding the disease of the present invention according to the category of the expression level of the protein obtained from the measurement unit.

[0105]

[0106] In addition, the present invention provides a method for treating a high-risk group for death after lung transplantation, comprising the following steps:

[0107] S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject;

[0108] S2) When compared to the level of protein or mRNA of the above substance in biological samples isolated from the control group,

[0109] A step of predicting that the subject will die after lung transplantation if the protein or mRNA level of any one or more substances selected from the group consisting of NGF, IL-1A, AXIN1, IL-33, IL-2, IL-10RA, and IL-5 is decreased, or the protein or mRNA level of CCL-3 is increased; and

[0110] S3) A step of administering a therapeutic agent in a pharmaceutically effective amount to a high-risk group for death after lung transplantation, i.e., a subject, who is predicted to die after lung transplantation.

[0111] In this specification, including all claims below, the term “therapeutic agent” may be a “therapeutic agent administered to a subject at risk of death after lung transplantation,” and refers to a therapeutic agent administered by the method of the present invention to a subject at risk of death after lung transplantation or classified as a high-risk group to prevent, delay, or reduce the risk of death. The term “death” is a concept encompassing all death events that may occur after lung transplantation, including cases where the timing or cause is acute or subacute, and may include, but is not limited to, cases attributable to rejection, infection, ischemia-reperfusion injury, pulmonary failure, immune abnormalities, hemodynamic instability, or a combination thereof. Furthermore, the term “therapeutic agent” is not limited to a specific mechanism of action, pharmaceutical component, formulation, route of administration, or therapeutic strategy, and should be understood as a concept encompassing all various therapeutic means and compositions as long as they can demonstrate an effect of reducing the risk of death after lung transplantation or improving survival probability.

[0112] In addition, the present invention provides a use for predicting a high-risk group for death after lung transplantation of a composition comprising, as an active ingredient, a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following:

[0113] Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

[0114] In addition, the present invention provides a use for manufacturing a formulation for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a formulation for measuring the level of one or more proteins or mRNA selected from the group formed above.

[0115] Preferred embodiments are presented below to aid in understanding the present invention. However, the following embodiments are provided merely to facilitate a better understanding of the invention, and the scope of the invention is not limited by the following embodiments.

[0116]

[0117] [Example]

[0118]

[0119] Sample collection

[0120] Samples were collected with ethylenediaminetetraacetic acid in the intensive care unit prior to arrival at the operating room and 7 days after transplantation. A total of three blood samples, each 4 mL in volume, were collected during a single blood draw and immediately stored on ice. The samples were centrifuged at 1600 rpm for 10 minutes. After the first centrifugation of the three blood collection tubes, the supernatant was carefully transferred to a 15 mL conical tube using a pipette. The combined plasma was then centrifuged a second time at 3000 rpm for 10 minutes. After the second centrifugation of the plasma, 4 mL of the aspirated supernatant was distributed into four 1.8 mL cryogenic vials, each containing 1 mL. The samples were immediately stored in a -80°C cryogenic freezer to ensure that the time elapsed from blood collection to freezer storage did not exceed 5 hours.

[0121]

[0122] Measurement of cytokines and inflammation-related proteins

[0123] To determine the relative quantification of 92 types of cytokines and inflammation-related proteins, the Olink Target 96 inflammation panel utilizing Olink’s Proximity Extension Assay (PEA) technique was used. The 92 types of proteins are as follows (Table 1).

[0124]

[0125]

[0126]

[0127] Briefly, the PEA technique involves producing antibodies against two epitopes of a target protein and conjugating a single-stranded nucleotide complementary to the Fc terminus of each antibody. When the two types of antibodies bind to the target protein, the nucleotides, now closer together, form a complementary bond to create a double-stranded nucleotide, which is then used to measure the amount of the target protein using a multiplexing technique via quantitative polymerase chain reaction (Quantitative PCR) (Fig. 1).

[0128] Specifically, 1 µl of plasma sample is mixed with reaction buffer, Probe A, and Probe B, and incubated overnight in a 96-well plate. Subsequently, PEA enzyme, solution, and PCR polymerase are mixed, and a primary polymerase chain reaction is performed to proceed with extension. Afterward, the solution is transferred to an integrated microfluidic chip and Olink ® Quantitative polymerase chain reaction is performed using the Signature Q100 instrument. The Ct values ​​for each protein are converted to Normalized Protein Expression (NPX), and statistical analysis is performed using these values.

[0129]

[0130] Statistical analysis

[0131] Data were presented as numbers and proportions for categorical variables, and as the mean ± standard deviation or median (interquartile range [IQR]) for continuous variables with normal or non-normal distributions. The chi-square test or Fisher's exact test was used to compare categorical variables, while Student's t-test or Mann-Whitney U test was used to compare continuous variables with normal or non-normal distributions. Volcano plots were used to visualize differences in cytokine expression between the two groups. The ratio of mean expression levels between the two groups was converted to a logarithmic scale and used as the X-axis value, and the statistical significance of the difference in mean expression levels between groups was converted to a logarithmic scale and used as the Y-axis value. To determine the predictive accuracy for cytokine expression, sensitivity, specificity, and the Area Under the Receiver Operational Curve (AUROC) were calculated for each cytokine. Finally, a model was constructed using variables with an AUROC value greater than 0.7. A two-sided p-value < 0.05 was considered to indicate significance. All analyses were performed using SPSS software ver. It was performed using 26.0 (IBM Corporation, Armonk, NY).

[0132]

[0133] Example 1. Sample Selection and Matching Process Flow

[0134] To derive pre-lung transplant biomarkers, a study cohort and patient selection were first performed (Fig. 2). Specifically, out of 108 adult recipients who received lung transplants at Asan Medical Center regardless of underlying disease from February 2020 to February 2024, 6 patients without pre-transplant samples were excluded, resulting in the final selection of 102 patients. The Institutional Review Board of Asan Medical Center approved the study protocol (Approval No. 2019-0981), and written informed consent was obtained from all patients. All procedures performed in the embodiments of this invention were conducted in accordance with the Guidelines for Clinical Good Practice and the Declaration of Helsinki. The statistical and clinical characteristics of the 102 selected patients are shown in Table 2 below (data are reported as numbers (percentages) or medians (interquartile ranges).

[0135]

[0136]

[0137] Subsequently, matching analysis was performed to minimize the influence of confounding factors. The included patients were classified into four groups using propensity score matching based on clinical outcomes: 1) survivors without graft dysfunction, 2) survivors with graft dysfunction, 3) survivors who did not survive within 3 months, and 4) survivors who did not survive for 3 months or longer.

[0138] The definition of graft dysfunction included primary graft dysfunction, acute rejection, and chronic lung graft dysfunction. Cases where the attending physician suspected acute rejection based on clinical findings and administered immunosuppressive therapy, even though pathological confirmation of acute rejection was not performed, were considered graft dysfunction. Additionally, survivors were defined as individuals who survived for at least one year after lung transplantation. Survival time was calculated as the period from lung transplantation to death.

[0139] In the matching procedure, group propensity scores calculated using a multivariate logistic regression model based on gender, age, and underlying diseases were used. Patients in the survivor group without graft dysfunction and the survivor group with graft dysfunction were matched 1:1 based on their propensity scores using an optimal no-replacement algorithm and a caliper width of 0.1. Next, patients who did not survive within 3 months were matched in a 1:1 ratio with cases of survivors with graft dysfunction. Finally, patients who did not survive for more than 3 months were also matched 1:1 with patients who did not survive within 3 months.

[0140] A pair of samples from the matched cohort, both before and after lung transplantation, were used for analysis. The clinical outcomes of the matched cohort are shown in Table 3 below (data are reported as numbers (percentages)).

[0141]

[0142]

[0143] Example 2. Analysis of the correlation between serum cytokine abundance levels before and after lung transplantation and survival rate after lung transplantation

[0144] For the samples of the recipients finally selected in Example 1, cytokine protein abundance before and after lung transplantation was analyzed to investigate the association with survival rate (or mortality rate) after lung transplantation. Proteins satisfying p<0.05, Fc≥1.2, and FC≤1.2 of the Student T-test in the volcano plot analysis were indicated.

[0145]

[0146] As a result, the distribution of serum cytokine protein abundance levels in deceased and surviving lung transplantees was confirmed as shown in Figure 3a. The distribution of serum protein abundance levels in deceased and surviving lung transplantees before transplantation was as shown in Figure 3b, and the distribution of serum protein abundance levels in deceased and surviving lung transplantees after transplantation was confirmed as shown in Figure 3c.

[0147] According to this, it was confirmed that 10 types of cytokines and inflammation-related proteins, IL1A, IL2, IL33, AXIN1, NGF, SULT1A1, IL10RA, CCL3, IL10RB, and IL17C, in serum prior to lung transplantation were statistically significantly increased or decreased. Specifically, the protein expression levels of IL1A, IL2, IL33, AXIN1, NGF, SULT1A1, and IL10RA decreased, while the protein expression levels of CCL3, IL10RB, and IL17C increased. In this case, SULT1A1 and IL10RB were identified as cytokines that showed differences in both pre- and post-lung transplant samples.

[0148]

[0149] Example 3. Establishment of a Survivor Prediction Model Before and After Lung Transplantation

[0150] In Example 2, proteins in the serum of deceased and surviving individuals after lung transplantation, as well as the protein abundance in the serum of deceased and surviving individuals before or after lung transplantation, were analyzed, and it was confirmed that 10 types of proteins significantly increased or decreased. In Example 3, a survival prediction model before and after lung transplantation was established, and the recipient operating characteristic curve of a model predicting survivors using pre-transplant protein markers, along with the sensitivity and specificity of the new prediction model, were analyzed. At this time, proteins presented in the ROC curve and interactive plot that had an AUC of 0.7 or higher and a significant P-value from the ROC analysis were selected and used in the final model.

[0151]

[0152] As a result, Figures 4a to 4c were confirmed. Specifically, Figures 4a and 4b show the results of analyzing the AUC values, sensitivity, and specificity for each of the eight types of cytokines and inflammation-related proteins: (a) NGF, (b) IL-1A, (c) AXIN1, (d) IL-33, (e) IL-2, (f) CCL-3, (g) IL-10RA, and (h) IL-5. In addition, Figure 4c shows the results of analyzing the AUC values, sensitivity, and specificity when the above eight types are combined.

[0153]

[0154] According to this, when 8 types of proteins were combined, the AUC value was found to be 0.850 (p<0.001), sensitivity was 85, and specificity was 80, confirming that combining 8 types of proteins in the serum prior to lung transplantation can predict mortality after lung transplantation with high accuracy.

[0155]

[0156] Example 4. Analysis of the correlation between serum cytokine abundance levels before and after lung transplantation and the occurrence of graft dysfunction after lung transplantation

[0157] For the samples of the recipients finally selected in Example 1, cytokine protein abundance before and after lung transplantation was analyzed to investigate the association with functional impairment after lung transplantation, that is, the occurrence of rejection reaction.

[0158]

[0159] As a result, the distribution of serum cytokine protein abundance levels in survivors with and without functional impairment after lung transplantation was confirmed as shown in Figure 5.

[0160] According to this, the protein expression levels of NTF3, GDNF, CCL25, TNFRS9, and SCF in the serum prior to lung transplantation were found to decrease, while the protein expression levels of IL24, IL18R1, CCL7, and CSCL1 were found to increase. At this time, CCL25 and CXCL1 were found to show the same expression pattern in the serum before and after transplantation.

[0161]

[0162] Example 5. Establishment of a Predictive Model for Graft Dysfunction After Lung Transplantation in Survivors

[0163] In Example 4, serum protein abundance was analyzed in individuals with graft dysfunction and those without graft dysfunction among lung transplant survivors, and it was confirmed that nine types of proteins were significantly increased or decreased. In Example 5, a model for predicting rejection after lung transplantation among survivors was established, and the recipient operating characteristic curve of a model predicting survivors using pre-transplant protein markers and the sensitivity and specificity of the new prediction model were analyzed.

[0164]

[0165] As a result, Figures 6a to 6c were confirmed. Specifically, Figures 6a and 6b show the results of analyzing the AUC values, sensitivity, and specificity for nine types of cytokines: (a) SCF, (b) IL-18R1, (c) CCL-25, (d) MCP-3, (e) GDNF, (f) NTF-3, (g) IL-24, (h) TNF-RSF9, and (i) CXCL-1. In addition, Figure 6c shows the results of analyzing the AUC values, sensitivity, and specificity when five types of cytokines with the highest AUC values ​​among the nine types of cytokines—GDNF, NTF3, IL24, TNFRSF9, and IL18R1—were combined.

[0166]

[0167] According to this, when five types of substances were combined, the AUC value was confirmed to be 1 (p<0.001), and the specificity and sensitivity were 100%, indicating that the ability to distinguish between the impaired group and the recovery group among lung transplant survivors was significantly superior.

[0168]

[0169] Example 6. Establishment of a model to predict mortality within 3 months after lung transplantation

[0170] In this embodiment, a model predicting death within 3 months after lung transplantation was established by analyzing proteins in the serum, and the receiver operation characteristic curve and the sensitivity and specificity of the new prediction model were analyzed.

[0171]

[0172] As a result, Figures 7a and 7b were confirmed. Specifically, Figure 7a shows the results of analyzing the AUC values, sensitivity, and specificity for four types of cytokines: (a) FGF19, (b) CCL19, (c) IL22RA1, and (d) TSLP. In addition, Figure 7b shows the results of analyzing the AUC values, sensitivity, and specificity when combining the three types of cytokines with the highest AUC values ​​among the four types: IL22RA1, RGF19, and CCL19.

[0173]

[0174] According to this, when three substances were combined, the AUC value was confirmed to be 0.920 (p<0.001), with a specificity of 90% and a sensitivity of 90%, indicating that the ability to predict death within 3 months after lung transplantation was significantly superior.

[0175]

[0176] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

[0177] According to the biomarker composition for screening high-risk groups for death after lung transplantation and the use thereof, when investigating the association between the expression of inflammatory cytokines and inflammation-related proteins as biomarkers before lung transplantation and clinical outcomes, statistically significant differences in the abundance of various inflammatory cytokines and inflammation-related proteins were confirmed between survivors and non-survivors, and a new predictive model using eight cytokines and inflammation-related proteins for survivors was found to show high AUC values, the biomarker of the present invention is expected to be usefully utilized as a biomarker for lung transplantation and has industrial applicability.

Claims

1. A method for providing information to predict a high-risk group for death after lung transplantation, comprising the following steps: S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject.

2. In paragraph 1, the above method of providing information is, S2) When compared to the level of protein or mRNA of the above substance in biological samples isolated from the control group, A step of predicting that the subject will die after lung transplantation if the protein or mRNA level of any one or more substances selected from the group consisting of NGF, IL-1A, AXIN1, IL-33, IL-2, IL-10RA, and IL-5 is decreased, or the protein or mRNA level of CCL-3 is increased. A method of providing information that further includes 3. In Paragraph 1, A method of providing information in which the above biological sample is a biological sample isolated from a subject prior to lung transplantation.

4. In Paragraph 1, A method for providing information, wherein the biological sample is any one selected from the group consisting of serum, whole blood, blood, and plasma.

5. In Paragraph 1, A method of providing information in which the above death is a death within one year after lung transplantation.

6. A composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following: Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

7. A kit for predicting a high-risk group for death after lung transplantation, comprising a composition for predicting a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following, and instructions: Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

8. In Paragraph 7, The above instructions are a kit that teaches the method of providing information according to Article 1 or Article 2.

9. A biomarker composition for predicting a high-risk group for death after lung transplantation, comprising one or more active ingredients selected from the group consisting of the following: Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

10. Treatment method for high-risk groups for death after lung transplantation including the following steps: S1) A step of analyzing the level of one or more proteins or mRNA selected from the group consisting of NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5) in a biological sample isolated from a subject; S2) When compared to the level of protein or mRNA of the above substance in biological samples isolated from the control group, A step of predicting that the subject will die after lung transplantation if the protein or mRNA level of any one or more substances selected from the group consisting of NGF, IL-1A, AXIN1, IL-33, IL-2, IL-10RA, and IL-5 is decreased, or the protein or mRNA level of CCL-3 is increased; and S3) A step of administering a pharmaceutically effective amount of a therapeutic agent to a subject predicted to die after the lung transplant.

11. Use of a composition comprising, as an active ingredient, a preparation for measuring the level of one or more proteins or mRNA selected from the group consisting of the following for predicting high-risk groups for death after lung transplantation: Nerve Growth Factor (NGF), Interleukin 1 Alpha (IL-1A), Axin 1 (AXIN1), Interleukin 33 (IL-33), Interleukin 2 (IL-2), Chemokine (CC motif) Ligand 3 (CCL-3), Interleukin 10 Receptor Alpha (IL-10RA), and Interleukin 5 (IL-5).

12. Use for manufacturing a preparation for a high-risk group for death after lung transplantation, comprising as an active ingredient a preparation that measures the level of one or more proteins or mRNA selected from the group consisting of the following: NGF (Nerve Growth Factor), IL-1A (Interleukin 1 Alpha), AXIN1 (Axin 1), IL-33 (Interleukin 33), IL-2 (Interleukin 2), CCL-3 (Chemokine (CC motif) Ligand 3), IL-10RA (Interleukin 10 Receptor Alpha), and IL-5 (Interleukin 5).

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