Plasma biomarker for predicting curative effect of non-small cell lung cancer immunotherapy

Abstract

The invention relates to a plasma biomarker for predicting the curative effect of non-small cell lung cancer immunotherapy and application of the plasma biomarker. The biomarker for predicting the curative effect of the non-small cell lung cancer immunotherapy, disclosed by the invention, comprises phosphoglycerate kinase 1 (PGK1), fibronectin 1 (FN1), chromogranin A (CHGA), elastin (ELN), heterogeneous ribonucleoprotein H3 (HNRNPH3) and an Aly / REF output factor (ALYREF). The plasma biomarkers for predicting the non-small cell lung cancer immunotherapy curative effect can be detected by adopting a mass spectrum method, and the non-small cell lung cancer immunotherapy curative effect of a patient is judged by the markers.

Description

Technical Field

[0001] The present invention relates to plasma biomarkers for predicting the efficacy of immunotherapy for non-small cell lung cancer and applications thereof. Background Art

[0002] Lung cancer has the highest morbidity and mortality rate in my country, with non-small cell lung cancer (NSCLC) accounting for approximately 85%. In recent years, immunotherapy has significantly improved survival in patients with advanced, driver gene-negative NSCLC, but only a subset of patients respond to immunotherapy. Results from the KEYNOTE-024 study showed that the objective response rate (ORR), or response rate, of pembrolizumab monotherapy in patients with NSCLC and high PD-L1 expression (TPS ≥ 50%) was 46.1%, yet more than half of patients still failed to respond to immunotherapy. Without considering the TPS score for PD-L1 expression, the response rate was 19.4%, and PD-L1 testing requires tissue specimens. Therefore, the development of novel biomarkers (or models) for predicting efficacy remains necessary to expand the population benefiting from NSCLC immunotherapy. The use of peripheral blood, a readily accessible, minimally invasive, and dynamically monitored sample, for the development of biomarkers (or models) for predicting efficacy offers significant advantages and clinical applicability.

[0003] Protein Crown Proteomics is an innovative plasma proteome high-depth analysis system ProteographTM developed by Seer, an American company, combined with a mass spectrometer to perform unbiased and in-depth detection of proteins in serum plasma.

[0004] Parallel reaction monitoring (PRM) is a targeted detection method similar to multiple reaction monitoring (MRM) technology. The difference between the two is that PRM uses a high-resolution mass spectrometer (Q-Orbitrap / timsTOF), which can significantly reduce background impurity interference and improve the selectivity and specificity of detection. Summary of the Invention

[0005] The present invention provides plasma biomarkers for predicting the efficacy of immunotherapy for non-small cell lung cancer. The plasma biomarkers for predicting the efficacy of immunotherapy for non-small cell lung cancer of the present invention include phosphoglycerate kinase 1 (PGK1), fibronectin 1 (FN1), chromogranin A (CHGA), elastin (ELN), heterogeneous nuclear ribonucleoprotein H3 (HNRNPH3), and Aly / REF export factor (ALYREF). Specifically, the present invention provides a method for predicting the efficacy of immunotherapy for non-small cell lung cancer, the method comprising the step of detecting the levels of PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF in a subject's plasma.

[0006] In one or more embodiments, the detection method comprises: ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry. Preferably, the detection method is mass spectrometry.

[0007] In one or more embodiments, mass spectrometry is used to detect the signal intensity of PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF in the subject's plasma.

[0008] In one or more embodiments, after detecting the signal intensities of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF, the signal intensities are substituted into the following formula: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

[0009] In one or more embodiments, when the plasma level of the marker is detected using the PRM method, the threshold value is 0.3903. If the score is greater than 0.3903, the patient responds to the immunotherapy for non-small cell lung cancer. If the score is less than or equal to 0.3903, the patient does not respond to the immunotherapy for non-small cell lung cancer.

[0010] The present invention also provides the use of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF as biomarkers or their detection reagents in the preparation of reagents or kits for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0011] In one or more embodiments, the reagent detects the amount of the protein in plasma in the sample.

[0012] In one or more embodiments, the prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of the markers is: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

[0013] In one or more embodiments, when the plasma level of the marker is detected using the PRM method, the threshold value is -0.3903.

[0014] In one or more embodiments, the reagent is a reagent used for detecting proteins using any of the following methods: ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry. Preferably, the detection method is mass spectrometry.

[0015] In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, including mobile phase A and mobile phase B.

[0016] In one or more embodiments, the reagents include formic acid in mobile phase A and formic acid acetonitrile in mobile phase B.

[0017] In one or more embodiments, the concentration of the formic acid aqueous solution in the mobile phase A is 0.1%, and the concentration of the formic acid acetonitrile solution in the mobile phase B is 0.1%.

[0018] In one or more embodiments, the agent is an antibody.

[0019] In one or more embodiments, the reagent is a target polypeptide for quantifying protein using a PRM method.

[0020] In one or more embodiments, the reagents further include reagents for separating proteins from plasma.

[0021] The present invention also provides an isolated protein combination, which is a marker for the efficacy of immunotherapy for non-small cell lung cancer. The protein combination comprises PGK1 or a fragment thereof, FN1 or a fragment thereof, CHGA or a fragment thereof, ELN or a fragment thereof, HNRNPH3 or a fragment thereof, and ALYREF or a fragment thereof.

[0022] In one or more embodiments, the Uniprot number for PGK1 is P00558.

[0023] In one or more embodiments, the Uniprot number of FN1 is P02751.

[0024] In one or more embodiments, CHGA has a Uniprot number of P10645.

[0025] In one or more embodiments, the ELN has a Uniprot number of P15502.

[0026] In one or more embodiments, the Uniprot number of HNRNPH3 is P31942.

[0027] In one or more embodiments, ALYREF has a Uniprot number of Q86V81.

[0028] In one or more embodiments, the amino acid sequence of the fragment of PGK1 is shown in SEQ ID NO:1.

[0029] In one or more embodiments, the amino acid sequence of the FN1 fragment is as shown in SEQ ID NO:2.

[0030] In one or more embodiments, the amino acid sequence of the CHGA fragment is shown in SEQ ID NO:3.

[0031] In one or more embodiments, the amino acid sequence of the fragment of ELN is shown in SEQ ID NO:4.

[0032] In one or more embodiments, the amino acid sequence of the fragment of HNRNPH3 is shown in SEQ ID NO:5.

[0033] In one or more embodiments, the amino acid sequence of the fragment of ALYREF is shown in SEQ ID NO:6.

[0034] The present invention also provides a reagent for detecting the contents of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF in plasma.

[0035] In one or more embodiments, the reagent is a reagent used for detecting proteins using any of the following methods: ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry. Preferably, the detection method is mass spectrometry.

[0036] In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, including mobile phase A and mobile phase B.

[0037] In one or more embodiments, the reagents include formic acid in mobile phase A and formic acid acetonitrile in mobile phase B.

[0038] In one or more embodiments, the concentration of the formic acid aqueous solution in the mobile phase A is 0.1%, and the concentration of the formic acid acetonitrile solution in the mobile phase B is 0.1%.

[0039] In one or more embodiments, the agent is an antibody.

[0040] In one or more embodiments, the reagent is a target polypeptide for quantifying protein using a PRM method.

[0041] In one or more embodiments, the reagents further include reagents for separating proteins from plasma.

[0042] The present invention also provides a kit comprising the reagents described in any embodiment herein.

[0043] In one or more embodiments, the kit further comprises a protein combination as described in any embodiment herein.

[0044] In one or more embodiments, the kit further comprises a reagent for detecting an internal standard.

[0045] Use of the reagent described in any embodiment of the present invention in the preparation of a reagent or kit for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0046] Use of the reagent described in any embodiment herein and the protein combination described in any embodiment herein in preparing a reagent or kit for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0047] In one or more embodiments, the kit is a PRM detection kit.

[0048] In one or more embodiments, the reagents include:

[0049] (1) using mass spectrometry to detect the reagents required for the PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF and the optional internal standard; preferably, the reagents are mobile phases used for mass spectrometry detection, including mobile phase A and mobile phase B; more preferably, the reagents include a formic acid aqueous solution in mobile phase A and a formic acid acetonitrile solution in mobile phase B; and

[0050] (2) Optionally, a reagent for separating plasma proteins from plasma, preferably an alcohol reagent and a reagent used for salting out, such as methanol.

[0051] The present invention further provides a device, characterized in that the device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the following steps are implemented:

[0052] (1) Obtain the content of the following proteins in plasma samples: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF,

[0053] (2) obtaining a score using the content by constructing a model, and

[0054] (3) Predict the efficacy of immunotherapy for non-small cell lung cancer based on the score.

[0055] In one or more embodiments, the prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of the markers is: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

[0056] The present invention also provides a system for predicting the efficacy of immunotherapy for non-small cell lung cancer, characterized by comprising:

[0057] Collection device for obtaining the content of the following proteins in plasma samples: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF,

[0058] A data processing device for obtaining a score by constructing a model using the content,

[0059] A determination device is used to predict the efficacy of immunotherapy for non-small cell lung cancer based on a score.

[0060] In one or more embodiments, the prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of the markers is: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

[0061] The present invention also provides a method for constructing a model for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the steps of:

[0062] (1) Obtain the content of the following proteins in plasma samples: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF, and

[0063] (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 ROC curve diagram of model construction;

[0065] Figure 2 ROC curve plot of model validation. DETAILED DESCRIPTION

[0066] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a preferred technical solution.

[0067] The inventors collected plasma from patients with advanced NSCLC and used non-targeted proteomics for research. They eventually found biomarkers that can predict the efficacy of immunotherapy for non-small cell lung cancer through a machine learning-based algorithm. Specifically, the present invention found that phosphoglycerate kinase 1 (PGK1), fibronectin 1 (FN1), chromogranin A (CHGA), elastin (ELN), heterogeneous nuclear ribonucleoprotein H3 (HNRNPH3) and Aly / REF export factor (ALYREF) can be used as specific biomarkers to predict the efficacy of immunotherapy for non-small cell lung cancer. The efficacy of immunotherapy for non-small cell lung cancer in a subject can be diagnosed by detecting the levels of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF in the subject's plasma.

[0068] Quantitative protein detection is a commonly used method in molecular biology. It uses specific technical methods to accurately determine the content of one or more proteins in a sample. Quantitative protein detection is mainly carried out from the perspectives of spectral analysis, chemical analysis, immunological analysis, chromatographic analysis, etc. Specifically, methods for quantitative protein detection include but are not limited to: ELISA, suspension chip, MSD electrochemiluminescence technology, biuret method, BCA method, Lowry method, Bradford method, and ultraviolet spectroscopy. For example, in the present invention, multiple proteins are first screened, and then the signal intensity of the screened PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF proteins is measured by mass spectrometry, and then the proteomics data is searched in the library.

[0069] Herein, immunotherapy includes immune checkpoint inhibitor therapy. In an exemplary embodiment, immune checkpoint inhibitor therapy is PD-1 or PD-L1 inhibitor therapy. In an exemplary embodiment, immunotherapy herein includes the use of PD-1 / PD-L1 inhibitors and chemotherapy drugs. PD-1 monoclonal antibodies include but are not limited to pembrolizumab, carrelizumab, tislelizumab, sintilimab, toripalizumab, slulizumab, and penampalimumab; PD-L1 monoclonal antibodies include but are not limited to atezolizumab and sugemalimab; chemotherapy drugs include but are not limited to: pemetrexed, paclitaxel, albumin paclitaxel, gemcitabine, one or more of platinum drugs, for example: pemetrexed + platinum, paclitaxel / albumin paclitaxel + platinum, or gemcitabine + platinum.

[0070] As used herein, "efficacy" refers to the effect of a medical intervention (such as immunotherapy) in achieving the intended therapeutic goal. In some embodiments, efficacy includes response to treatment, such as complete remission (CR) and partial remission (PR); correspondingly, inefficacy or insignificant efficacy includes non-response to treatment, such as stable disease (SD) and progressive disease (PD).

[0071] Herein, "amount", "content", "concentration" and the like include absolute amount and relative amount. The "amount", "content" and "concentration" herein may be standardized.

[0072] As used herein, a "marker" can be a nucleic acid molecule or a protein expression product thereof. Methods for detecting protein levels are well known in the art, such as ELISA, suspension microarray, MSD electrochemiluminescence technology, or mass spectrometry. In certain embodiments, the present invention relates to detecting the level of a marker protein in a sample derived from blood.

[0073] Therefore, the present invention relates to reagents for detecting protein expression levels. The reagents used in the above-mentioned methods for detecting protein levels are well known in the art. In one or more embodiments, the reagent is a mobile phase for mass spectrometry detection, such as formic acid in mobile phase A and formic acid acetonitrile in mobile phase B, and the concentrations thereof are conventional in the art. The reagent for detecting protein levels can also be an antibody, such as specific antibodies for PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF.

[0074] In an exemplary embodiment, the present invention uses PRM to detect plasma proteins. The steps and reagents required for conventional PRM methods are known in the art, including mass spectrometry reagents and specific peptides used for protein quantification using PRM methods. Designing specific peptides based on protein sequences is a routine technique in the art.

[0075] Therefore, the present invention also relates to a method for pre-treating a sample. Samples derived from blood include, but are not limited to, whole blood, plasma, and serum. Those skilled in the art are aware of methods for pre-treating a sample to obtain components (e.g., nucleic acids, proteins) containing markers to be measured, such as plasma protein extraction kits, proteograph workstations, etc. Therefore, the reagents herein also include reagents for separating proteins from plasma.

[0076] Herein, the sample is from a mammal, preferably a human. The sample can be from any organ (e.g., lung), tissue (e.g., epithelial tissue), cell (e.g., tumor cell), or body fluid (e.g., blood, plasma, serum, tissue fluid, urine). In an exemplary embodiment, the sample is plasma.

[0077] The present invention also relates to a kit for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the reagents described herein. The kit may also contain the markers described herein as internal standards or positive controls. In addition to the reagents described, the kit may also contain other reagents required for protein detection. Exemplarily, the kit may include one or more of the following: tools and / or reagents for collecting samples from subjects, tools and / or reagents suitable for long-term storage of samples and / or controls, and tools and / or reagents for pre-treating samples and / or controls.

[0078] Mass spectrometry

[0079] The term "mass spectrometer" refers to a mass spectrometer (MS), an instrument used to measure the "mass" of molecules. In proteomics research, the molecular "mass" measured by mass spectrometry generally includes the mass-to-charge ratio (m / z) of the precursor ion and the mass-to-charge ratio of the fragment ions (fragmentations) after the precursor ion is broken down. While the appearance of mass spectrometers varies from manufacturer to manufacturer and from type to type, their internal structure primarily consists of three main components: an ion source, a mass analyzer, and a detector. The function of the ion source is to charge the molecules to be tested and enter the mass spectrometer for scanning. In proteomics research, the main ionization method used is electrospray ionization (ESI); the mass analyzer is the core component of the mass spectrometer, and its function is to distinguish the mass size (m / z) of the molecules to be tested so as to separate or scan them; common mass analyzers include quadrupole (Quadrupole), ion trap (Ion trap), time-of-flight (TOF), and electrostatic field orbital trap (Orbitrap), among which TOF and Orbitrap are high-resolution mass analyzers; the function of the detector is to convert and record the signals of the molecules to be tested to obtain the raw data of the mass spectrometer.

[0080] According to the ion source, mass spectrometers can be divided into electron impact ionization source (EI) mass spectrometers, chemical ionization (CI) mass spectrometers, electrospray ionization (ESI) mass spectrometers, atmospheric pressure chemical ionization (APCI) mass spectrometers, matrix-assisted laser desorption ionization (MALDI) mass spectrometers, etc.; according to the mass analyzer, they can be divided into magnetic mass analyzer mass spectrometers, quadrupole mass analyzer mass spectrometers, ion trap mass analyzer mass spectrometers, time-of-flight mass analyzer (TOF) mass spectrometers, Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers; according to the application field, they can be divided into organic mass spectrometers, inorganic mass spectrometers, and biological mass spectrometers; according to the scanning mode, they can be divided into full scan mode, selected ion monitoring mode (SIM), multiple reaction monitoring mode (MRM), parallel reaction monitoring mode (PRM), etc.

[0081] In the field of mass spectrometry, different data acquisition modes and types are also included, such as DDA (Data-Dependent Acquisition), DIA (Data-Independent Acquisition), SIM (Selected Ion Monitoring Data), MRM (Multi-Reaction Monitoring Data), and PRM (Parallel Reaction Monitoring Data). The appropriate data acquisition mode can be selected to acquire the appropriate data based on factors such as the analysis objective, sample type, and data quality requirements.

[0082] This article uses an LC-MS / MS mass spectrometer, an analytical instrument that combines liquid chromatography (LC) with tandem mass spectrometry (MS / MS). Samples are separated by liquid chromatography, which exploits the differences in the distribution coefficients of different substances between the stationary and mobile phases to separate complex sample mixtures into individual components, which then elute sequentially with different retention times. Each component eluting from the liquid chromatography enters the ion source of the mass spectrometer where it is ionized, producing ions with distinct charges and mass numbers. These ions enter the tandem mass spectrometer system, where the mass analyzer separates them according to their mass-to-charge ratio (m / z), producing a mass spectrum arranged in order of mass-to-charge ratio. By analyzing and processing the mass spectrum, qualitative and quantitative results for the sample can be obtained.

[0083] Typically, mass spectrometry methods can be used to detect the abundance of ion responses for phosphoglycerate kinase 1 (PGK1), fibronectin 1 (FN1), chromogranin A (CHGA), elastin (ELN), heterogeneous nuclear nucleoprotein H3 (HNRNPH3), and Aly / REF export factor (ALYREF) in a subject's plasma. For example, peripheral blood can be obtained from the subject and separated using conventional methods to obtain plasma. Subsequently, proteins can be removed from the plasma. Methods for removing proteins from plasma include, but are not limited to, salting out, precipitation with organic solvents, and pH adjustment. For example, neutral salts such as ammonium acetate can be used to dehydrate proteins and aggregate and precipitate them. Alternatively, polar organic solvents can be used to lower the dielectric constant to cause protein aggregation and precipitation. Commonly used organic solvents include alcoholic organic solvents, such as monohydric alcohols with 1-4 carbon atoms, such as methanol and ethanol, as well as acetone, acetonitrile, and the like. In certain embodiments, sulfosalicylic acid can also be used to precipitate proteins. When using sulfosalicylic acid, the pH must be adjusted below the pI of plasma proteins, imparting a positive charge to the proteins, which then combine with salicylate to form insoluble salts and precipitate. Since plasma proteins are all negatively charged, the pH of the plasma should be adjusted below the lowest isoelectric point of the plasma proteins. For example, the isoelectric point of plasma albumin is between 4.7 and 4.9, so adjusting the pH to below 4.7 can achieve protein precipitation and separation.

[0084] After the proteins in the plasma are precipitated, the supernatant is obtained and can be subjected to mass spectrometry analysis. Detection can be performed using mass spectrometry techniques well known in the art. In an embodiment of the present invention, a TOF mass spectrometer is used for mass spectrometry analysis. Conventional chromatographic columns, such as those provided by mass spectrometry companies, can be used as chromatographic columns. Different mobile phases can be selected depending on the column. For example, in an embodiment of the present invention, an acetonitrile-water-formic acid system is used, where mobile phase A is a 0.1% formic acid solution in water and phase B is a 0.1% formic acid solution in acetonitrile. After the column is equilibrated with 100% phase A, the sample is loaded directly onto the column via an autosampler and then subjected to gradient separation on the column at a flow rate of 300 nL / min and a gradient duration of 60 min. The mobile phase B ratio is: 2% for 0 min, 2-22% for 45 min, 22-37% for 5 min, 37-80% for 5 min, and 80% for 5 min.

[0085] Other mass spectrometry operations can be performed according to the operation manual, thereby obtaining the ion response abundance of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF.

[0086] In the exemplary embodiment of this invention, during the sample preparation stage, DDA data of the sample is first collected to obtain full scan information with a large amount of data, which includes daughter ion information of the selected parent ion, which is helpful for the structural identification and analysis of the compound.

[0087] The term "parallel reaction monitoring" (PRM) refers to a targeted quantitative analysis technique based on high-resolution mass spectrometry. In PRM, the characteristic ions of the target compound, including parent ions and daughter ions, must first be identified. The parent ion is the initial ion formed after the target compound is ionized, and the daughter ions are fragment ions produced by the parent ion through processes such as collision-induced dissociation (CID) in the collision chamber of the mass spectrometer. PRM monitors the transition from parent ions to daughter ions of the target compound within a selected mass window. When the target compound in the sample enters the mass spectrometer, the instrument automatically monitors these specific ion reactions, thereby achieving specific detection of the target compound. This monitoring mode is similar to multiple reaction monitoring (MRM), but PRM uses a high-resolution mass spectrometer (Q-Orbitrap / timsTOF), which can significantly reduce background impurity interference and improve the selectivity and specificity of the detection. Typically, one to three specific peptides are selected for the protein to be detected, and three to six fragment ions are selected for each target peptide for quantification.

[0088] PRM method development involves analyzing the acquired DDA mass spectrometry data and then using SpectroDive software to generate a library to identify the specific peptides and fragment ions to be detected, completing the PRM method development. PRM data is then collected. This data effectively eliminates matrix interference and accurately detects target compounds. It also provides high resolution and mass accuracy, enabling the collection of more precise ion mass information.

[0089] In this study, plasma proteins were first enriched, enzymatically digested, desalted, and concentrated. NanoLC-MS / MS analysis, DDA library search, PRM method development, and quantitative analysis were then performed. PRM method development was accomplished using SpectroDive software: 1) the acquired DDA data were matched to the target peptide sequence and library searched using SpectroDive software to generate a corresponding library; 2) based on the library and target peptides, secondary b / y ions were generated for panel screening peptides and confirmation of quantification. Up to three specific peptides were selected for each protein, and three to six fragment ions were selected for each target peptide for quantification. In an exemplary embodiment, in the PRM detection method, the peptide segment of PGK1 is a fragment of PGK1 comprising the sequence shown in SEQ ID NO: 1, preferably as shown in SEQ ID NO: 1; the peptide segment of FN1 is a fragment of FN1 comprising the sequence shown in SEQ ID NO: 2, preferably as shown in SEQ ID NO: 2; the peptide segment of CHGA is a fragment of CHGA comprising the sequence shown in SEQ ID NO: 3, preferably as shown in SEQ ID NO: 3; the peptide segment of ELN is a fragment of ELN comprising the sequence shown in SEQ ID NO: 4, preferably as shown in SEQ ID NO: 4; the peptide segment of HNRNPH3 is a fragment of HNRNPH3 comprising the sequence shown in SEQ ID NO: 5, preferably as shown in SEQ ID NO: 5; the peptide segment of ALYREF is a fragment of ALYREF comprising the sequence shown in SEQ ID NO: 6, preferably as shown in SEQ ID NO: 6.

[0090] The term "bioinformatics analysis" refers to a discipline that integrates mathematics, computer science, and biology to process, analyze, and interpret biological data. Common analytical techniques in bioinformatics include principal component analysis (PCA), hierarchical clustering analysis (HCA), K-means clustering, survival analysis, gene enrichment analysis, and support vector machines (SVM).

[0091] ROC curve

[0092] The term "ROC curve" refers to the Receiver Operating Characteristic Curve, a tool used to evaluate the performance of classification models and is widely used in bioinformatics and medicine. The ROC curve helps us understand the performance of a classifier at different thresholds by plotting the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR). The True Positive Rate (TPR), also known as sensitivity, represents the proportion of samples correctly identified as positive to all actual positive samples. It is calculated as follows:

[0093] TPR=TP / (TP+FN)

[0094] Among them, TP (True Positive) is the number of samples that are actually positive examples and predicted as positive examples by the model, and FN (False Negative) is the number of samples that are actually positive examples but predicted as negative examples by the model.

[0095] The false positive rate (FPR) indicates the proportion of samples that are incorrectly identified as positive to all actual negative samples. The calculation formula is:

[0096] FPR=FP / (TP+FN)

[0097] Among them, FP (False Positive) is the number of samples that are actually negative examples but predicted as positive examples by the model, and TN (True Negative) is the number of samples that are actually negative examples and predicted as negative examples by the model.

[0098] The TPR vs. FPR graph plotted by varying the classification threshold shows that the closer the curve is to the upper left corner, the better the classifier's performance. Ideally, the ROC curve passes through the (0,1) point, meaning that the model is able to distinguish between positive and negative examples, with FPR = 0 (no false positives) and TPR = 1 (all positive examples are correctly identified). If the ROC curve is a diagonal line from the origin (0,0) to the point (1,1), this indicates that the model's predictions are random and have no actual classification ability. In this case, for any given true positive rate, the false positive rate is the same, similar to random guessing.

[0099] The ROC curve provides an intuitive way to evaluate the performance of classification models (such as classifying genes as disease-associated or proteins as functional in bioinformatics). By observing the curve's position on the coordinate plane, one can quickly determine the quality of the model. For example, in cancer biomarker screening, ROC curves can be used to evaluate models that distinguish cancer patients from healthy individuals based on blood biomarker concentrations or signal intensity. If the ROC curve approaches the upper left corner, it means that a high true positive rate is achieved with a low false positive rate, indicating that the model is effectively identifying cancer patients. In practical applications, it is necessary to determine an appropriate classification threshold to classify samples into positive and negative examples. ROC curves can help researchers understand performance at different thresholds. For example, in microbial identification, a classification model is constructed based on the genetic sequence characteristics of microorganisms. The ROC curve can be used to observe the changes in the true positive rate and false positive rate of the model in identifying microbial species at different sequence similarity thresholds. Based on practical requirements (such as the tolerance for false positives and true positives), an appropriate threshold can be selected from the ROC curve for classification.

[0100] AUC (Area Under the Curve) is the area under the ROC curve. It is a quantitative indicator used to more intuitively measure model performance. The AUC value range is from 0.5 to 1. The closer the AUC is to 1, the stronger the model's discriminatory ability. For example, in gene regulatory network prediction, if a model predicting the interaction between transcription factors and target genes has a high AUC value, it means that it is better at distinguishing between truly interacting gene pairs and non-interacting gene pairs. After calculating a series of true positive rate (TPR) and false positive rate (FPR) data points to plot the ROC curve, the trapezoidal method can be used to approximate the area under the curve. This method is based on the principle of dividing the area under the ROC curve into multiple small trapezoids and then calculating the sum of the areas of these trapezoids. Nowadays, AUC values ​​are usually calculated in conjunction with computers, using built-in functions in statistical software or programming languages ​​(such as R and Python).

[0101] In one or more embodiments, when compared with the control sample, the expression level of the subject sample increases or decreases. The expression of the measured protein is mathematically analyzed to obtain a score. For the sample detected, when the score is greater than the threshold value, the result is determined to be positive, i.e., the non-small cell lung cancer immunotherapy has a good efficacy (responds to non-small cell lung cancer immunotherapy), otherwise it is negative, i.e., the non-small cell lung cancer immunotherapy has a poor efficacy (does not respond to non-small cell lung cancer immunotherapy). The method of conventional mathematical analysis and the process of determining the threshold value are known in the art. An exemplary method is a mathematical model, such as a logistic regression model, a support vector machine, and a random forest model. Those skilled in the art know the conventional method of constructing a logistic regression model. For example, for differentially expressed markers, a logistic regression model, a support vector machine model, or a random forest model are constructed for two groups of samples, and the accuracy, sensitivity, specificity, and area under the predictive characteristic curve (ROC) of the model statistics test results are used to calculate the score of the test set samples.

[0102] An exemplary logistic regression model is as follows (wherein each protein name indicates its plasma content): score=4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF).

[0103] When using the PRM method to detect the plasma content of the marker, the threshold is 0.3903. If the score is greater than 0.3903, the patient responds to immunotherapy for non-small cell lung cancer. If the score is less than or equal to 0.3903, the patient does not respond to immunotherapy for non-small cell lung cancer.

[0104] When other detection methods are used to measure the absolute or relative plasma levels of proteins, the judgment threshold may vary due to the different presentation of the test results. However, the specific statistical relationship reflected by the model and the judgment results for the same patient will not change. When other methods are used to measure concentrations or relative concentrations, the threshold can be easily determined by those skilled in the art. For example, the threshold can be obtained by the following method: a) collecting samples from patients with known non-small cell lung cancer immunotherapy response and non-response groups, and obtaining the relative levels of the combination of the five proteins in these samples; b) using logistic regression statistical analysis to construct a model for discriminating the immunotherapy efficacy of the protein combination. At this point, those skilled in the art will obtain a model with specific parameters and a judgment threshold. The more samples, the greater the sensitivity and specificity of the resulting model, and the more accurate the model's diagnostic results. The patient's responsiveness to non-small cell lung cancer immunotherapy can then be determined by the following steps: c) obtaining the relative levels of the protein combination in the test sample, using the same or different method as in step a); d) calculating the model score for the sample based on the corresponding logistic regression model obtained in step b), and then determining the responsiveness of the non-small cell lung cancer immunotherapy.

[0105] For the logistic regression statistical analysis method, the model and parameters derived from the content measured by the same batch of experimental results are unique. Moreover, based on the statistical relationships and statistical principles obtained in this application, it can be seen that the parameters (including coefficients, sensitivity, specificity, and accuracy) of the model derived from the content measured by different batches of experimental results may change slightly, but the specific statistical relationship reflected by the model will not change, that is, it has the ability to discriminate the efficacy of immunotherapy for non-small cell lung cancer.

[0106] After knowing the gene combination, technicians in this field can obtain a logistic regression model based on any non-small cell lung cancer immunotherapy sample, and the model also has the function of screening the efficacy of non-small cell lung cancer immunotherapy.

[0107] According to statistical principles, for a fixed gene combination, the statistical relationship of the model constructed by technicians in this field based on the above method after testing any samples that respond to and do not respond to non-small cell lung cancer immunotherapy is the same. Even if the parameters may change slightly, the model can still be used to identify the efficacy of immunotherapy for non-small cell lung cancer.

[0108] Here, the inventors discovered that plasma levels of PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF are associated with the efficacy of immunotherapy for non-small cell lung cancer. Further research subsequently led to the construction of a mathematical model for determining the efficacy of immunotherapy for non-small cell lung cancer. In one or more embodiments, the logistic regression prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of these markers is: score = 4.008 - 0.01 × PGK1 - 0.001 × FN1 - 0.012 × CHGA - 0.051 × ELN + 0.079 × HNRNPH3 - 0.161 × ALYREF.

[0109] The content of each protein is the relative concentration detected by PRM. The peptide levels of these six proteins were jointly analyzed using the above formula to calculate the scores of each sample. See Table 4. ROC curve analysis is shown in Figure 1 , the AUC was 0.873, the threshold was 0.3903, the specificity was 77.8%, and the sensitivity was 90.3%.

[0110] The present invention also provides a device, characterized in that the device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the following steps are implemented: (1) obtaining the content of the following proteins in a plasma sample: PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF, (2) obtaining a score using the content by constructing a model, and (3) predicting the efficacy of immunotherapy for non-small cell lung cancer based on the score.

[0111] The present invention also provides a system for predicting the efficacy of immunotherapy for non-small cell lung cancer, which is characterized by comprising: a collection device for obtaining the content of the following proteins in a plasma sample: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF; a data processing device for obtaining a score using the content by constructing a model; and a determination device for predicting the efficacy of immunotherapy for non-small cell lung cancer based on the score.

[0112] The present invention also provides a method for constructing a model for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the steps of: (1) obtaining the content of the following proteins in a plasma sample: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF, and (2) calculating the ROC curve and area under the curve value of the protein combination, and establishing a model using a logistic regression statistical analysis method.

[0113] It should be understood that the efficacy of immunotherapy for non-small cell lung cancer described in the present invention is relative. If the threshold detected in a patient is less than or equal to 0.3903, it indicates that the patient's non-small cell lung cancer immunotherapy efficacy is poor (unresponsive to immunotherapy), and compared with subjects with a threshold greater than 0.3903, the non-small cell lung cancer immunotherapy efficacy is good (responsive to immunotherapy). If the threshold detected in a patient is greater than 0.3903 but the patient exhibits symptoms of poor efficacy, other efficacy testing methods conventional in the art can be combined as needed.

[0114] Furthermore, the calculation formula and threshold values ​​described above were obtained using a 250 μL plasma sample. Therefore, the coefficients and threshold values ​​in the calculation formula will vary when using different plasma sample sizes. However, those skilled in the art can readily determine the corresponding coefficients and threshold values ​​using the methods disclosed in this specification.

[0115] The present invention will be described below by way of specific examples. It should be understood that these examples are merely illustrative and are not intended to limit the scope of the present invention. The methods and reagents used in the examples are, unless otherwise stated, conventional methods and reagents in the art.

[0116] Example

[0117] Example 1: High-depth screening of plasma protein corona proteomics

[0118] 1. Sample Pretreatment

[0119] Prepare 250ul of plasma sample and place it in a sample tube. Start the Proteograph automated workstation, and the machine will automatically operate and complete the pre-treatment process, including: 1) nanoparticle NP-plasma incubation and protein corona formation; 2) magnetic bead purification of the NP protein corona; 3) protein digestion and peptide purification.

[0120] 2. Mass spectrometry detection

[0121] Desalted, lyophilized peptides were reconstituted in 0.1% formic acid in water and analyzed by LC-MS / MS. The system consisted of a timsTOF Pro2 mass spectrometer (Bruker Daltonics) connected to an UltiMate 3000 system (Thermo Fisher Scientific, MA, USA). A total of 200 ng of sample was loaded onto an analytical column (25 cm × 75 μm ID, IonOpticks). The sample was separated using an 80-min gradient at 50°C. A total of 2.5 μL of sample was loaded, and the column flow rate was controlled at 300 nL / min. The gradient started at 4% phase B, increased to 28% over 45 minutes, 44% over 10 minutes, and 90% over 10 minutes, held for 7 minutes, and then equilibrated at 4% for 8 minutes.

[0122] The mass spectrometer was used in diaPASEF mode for DIA data acquisition, scanning from 349–1229 m / z with an isolation window width of 40 Da. During the PASEF MSMS scan, the collision energy increased linearly with ion mobility, from 59 eV (1 / K0 = 1.6 Vs / cm2) to 20 eV (1 / K0 = 0.6 Vs / cm2).

[0123] 3. Library Search and Protein Quantification

[0124] The original data files were searched and analyzed using the software DIA-NN (version 1.8.1).

[0125] Table 1 Database matching parameter settings

[0126]

[0127] 4. Experimental Results

[0128] Baseline plasma specimens from 5 patients with advanced NSCLC who were responders (complete response (CR) and partial response (PR)) and 5 patients who were nonresponders (stable disease (SD) and progressive disease (PD)) to PD-(L)1 monoclonal antibody combined with chemotherapy were tested. Significant differences in the protein levels of PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF were found between the two groups. The screening criteria for differentially expressed proteins were Foldchange ≥ 1.2 or ≤ 0.83, and P-value or P-value-chitest < 0.05.

[0129] Example 2, PRM quantitative proteomics validation

[0130] 1. Sample Preparation

[0131] 1.1 Protein enrichment

[0132] The protein enrichment and enzymatic hydrolysis steps were performed using the EasyPeptide Blood Low Abundance Protein Enrichment and Pretreatment Kit (OSFP0002).

[0133] Before use, take out reagent H and disperse the magnetic beads evenly by pipetting or sonication. Pipette 20 μL of reagent H and transfer it to a new EP tube (Note: 2 ml flat-bottom tube is recommended). Place it on a magnetic stand and let it stand for 2 minutes. Discard the supernatant after magnetic separation.

[0134] Add 50 μL of reagent G, place on a constant temperature mixer, shake and wash the magnetic beads for 5 minutes (1300 rpm, room temperature), place on a magnetic stand and let it stand for 2 minutes, magnetically separate and discard the supernatant;

[0135] Resuspend the magnetic beads with 50 μL of reagent G, add 50 μL of plasma, place on a thermomixer, and incubate at 37°C with shaking (1300 rpm) for 1 h;

[0136] Place on a magnetic stand and let it stand for 2 minutes. Discard the supernatant after magnetic separation. Add 150 μL of reagent G and shake and wash for 5 minutes. Repeat the washing three times. The low-abundance protein is enriched on the magnetic beads.

[0137] 1.2 Proteolysis

[0138] Add 20 μL of reagent A to the washed magnetic beads and mix thoroughly by pipetting;

[0139] Heating at 95°C for 5 min (95°C, 1000 rpm);

[0140] After heating, wait until the sample returns to room temperature, add 7.5 μL of reagent B, mix well, and perform enzymatic hydrolysis at 37°C for 2 h (37°C, 1300 rpm);

[0141] After the enzymatic hydrolysis is completed, add 1.5 μL of reagent C, mix well, and terminate the enzymatic hydrolysis reaction. At this time, a precipitate will appear. Centrifuge at 17000g for 2 minutes and then aspirate the supernatant for subsequent desalting.

[0142] 1.3 Peptide Desalting

[0143] Desalting column activation: 100 μL of methanol was added to the desalting column and centrifuged at 700 g for 1 min;

[0144] Desalting column to remove impurities: 100 μL Condition buffer was added to the desalting column and centrifuged at 700 g for 1 min;

[0145] Desalting column equilibration: 100 μL of wash buffer was added to the desalting column and centrifuged at 700 g for 1 min.

[0146] Loading the sample: Load all the samples onto the desalting column, centrifuge at 700g for 1 minute, and repeat the operation once; (Before loading the sample, make sure the pH is <3. You can use 20% TFA to adjust the pH to <3 before loading the sample)

[0147] Desalting column washing: add 100 μL wash buffer to the desalting column, centrifuge at 700 g for 1 min, and repeat the operation once;

[0148] Sample elution: 30 μL of elution buffer was added to the desalting column, centrifuged at 700 g for 1 min, and eluted twice to a final sample volume of 60 μL.

[0149] Concentrate the samples using a vacuum refrigerated centrifugal concentrator.

[0150] 2. nanoLC-MS / MS detection

[0151] 3 μl of total peptide from each sample was separated using a nano-UPLC system (nanoElute2) and then coupled to a mass spectrometer (timsTOF Pro2) equipped with a nanoliter ion source for data acquisition. Chromatographic separation was performed on a 75 μm ID × 15 cm reversed-phase column (PePSep C18, 1.9 μm, 75 μm × 15 cm, Bruker, Germany). The mobile phase was an acetonitrile-water-formic acid system, with mobile phase A consisting of 0.1% formic acid in water and phase B consisting of 0.1% formic acid in acetonitrile. After the column was equilibrated with 100% phase A, the sample was loaded directly onto the column via an autosampler and separated by a gradient flow at a flow rate of 300 nL / min over 60 minutes. The mobile phase B ratio was: 2% for 0 minutes, 2-22% for 45 minutes, 22-37% for 5 minutes, 37-80% for 5 minutes, and 80% for 5 minutes.

[0152] The mass spectrometer was operated in DDA PaSEF mode for DDA data acquisition, with a scan range of 100–1700 m / z. During the PASEF MS / MS scan, the collision energy increased linearly with ion mobility, from 20 eV (1 / K0 = 0.6 Vs / cm2) to 59 eV (1 / K0 = 1.6 Vs / cm2).

[0153] 3. Library Search and Protein Quantification

[0154] 3.1DDA database search and identification

[0155] The raw data files were searched against the database using SpectroDive (4.1.230421.52329; Biognosys AG 2013) software with the Pulsar search engine, and qualitative analysis was performed after the search.

[0156] Table 2: Database matching parameter settings

[0157]

[0158]

[0159] 3.2 PRM method construction and quantitative analysis

[0160] PRM method development was performed using SpectroDive software: 1) The acquired DDA data was imported into SpectroDive and searched to generate a corresponding library. 2) Based on the library and target peptides, a panel was generated for peptide screening and secondary b / y ions for confirmation of quantification. A maximum of three specific peptides were selected for each protein, and three to six fragment ions were selected for quantification for each target peptide.

[0161] 4. Experimental Results

[0162] 4.1 Model Construction

[0163] Baseline plasma specimens from 21 patients with advanced NSCLC who responded to immunotherapy (responder) and 9 patients who did not respond (nonresponder) were collected for PRM targeted detection of PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF (Table 3), and the model was constructed as follows:

[0164] Score = 4.008 - 0.01 × PGK1 - 0.001 × FN1 - 0.012 × CHGA - 0.051 × ELN + 0.079 × HNRNPH3 - 0.161 × ALYREF

[0165] Table 3 PRM targeted detection results

[0166]

[0167] The peptide levels of these six proteins were jointly analyzed using the above formula to calculate the scores of each sample, see Table 4, and ROC curve analysis is shown in Figure 1 The AUC was 0.873, and the interpretation criterion was a score greater than 0.3903. Nineteen of the 21 patients in the response group were positive, and two of the nine patients in the non-response group were positive, with a specificity of 77.8% and a sensitivity of 90.5%. Table 4: PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF combined for the construction of a predictive model for the efficacy of NSCLC immunotherapy.

[0168] Group Score Group Score Responder 2.6392 Responder 0.7173 Responder 2.2925 Responder 1.3308 Responder 0.4776 Responder 1.5097 Responder -0.8764 Responder 2.8508 Responder -0.4976 Responder 2.5514 Responder 0.9055 Responder 1.2553 Responder 0.9487 Nonresponder 0.3903 Responder 0.5332 Nonresponder 0.7349 Responder 2.1912 Nonresponder 1.2895 Responder 1.7671 Nonresponder -0.1139 Responder 1.2649 Nonresponder -0.6792 Responder 3.0004 Nonresponder -0.8741 Responder 0.9744 Nonresponder -0.8672 Responder 1.4274 Nonresponder 0.3274 Responder 2.3806 Nonresponder -1.3105

[0169] 4.2 Model Validation

[0170] Baseline plasma samples from 18 patients with advanced NSCLC in the immunotherapy responder group (responder) and 12 patients in the nonresponder group (nonresponder) were subjected to PRM targeted detection of PGK1, FN1, CHGA, ELN, HNRNPH3, and ALYREF. The peptide levels of these six proteins were jointly analyzed using the above formula to calculate the scores of each sample (see Table 5). ROC curve analysis is shown in Figure 2 According to the interpretation criteria provided by the present invention (score greater than 0.3903), 16 of the 18 response groups were positive, and 2 of the 12 non-response groups were positive, with a specificity of 83.3% and a sensitivity of 88.9%.

[0171] Table 5 Scores for validation of the prediction model for the efficacy of NSCLC immunotherapy using a combination of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF.

[0172]

[0173]

[0174] sequence of this article

[0175] SEQ ID NO:1 PGK1

[0176] IQLINNMLDK

[0177] SEQ ID NO:2 FN1

[0178] VFAVSHGR

[0179] SEQ ID NO:3 CHGA

[0180] ELQDLALQGAK

[0181] SEQ ID NO:4 ELN

[0182] AGYPTGTGVGPQAAAAAAAK

[0183] SEQ ID NO:5 HNRNPH3

[0184] DGMDNQGGYGSVGR

[0185] SEQ ID NO:6 ALYREF

[0186] MDMSLDDIIK

Claims

1. An isolated protein combination, which is a plasma biomarker for predicting the efficacy of immunotherapy for non-small cell lung cancer, wherein the protein combination comprises PGK1 or a fragment thereof, FN1 or a fragment thereof, CHGA or a fragment thereof, ELN or a fragment thereof, HNRNPH3 or a fragment thereof, and ALYREF or a fragment thereof, Preferably, The amino acid sequence of the PGK1 fragment is shown in SEQ ID NO: 1, and / or The amino acid sequence of the FN1 fragment is shown in SEQ ID NO: 2, and / or The amino acid sequence of the CHGA fragment is shown in SEQ ID NO: 3, and / or The amino acid sequence of the ELN fragment is shown in SEQ ID NO: 4, and / or The amino acid sequence of the fragment of HNRNPH3 is shown in SEQ ID NO: 5, and / or The amino acid sequence of the ALYREF fragment is shown in SEQ ID NO:

6.

2. A reagent for detecting the levels of PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF in blood. Preferably, the reagent is a reagent used for detecting proteins using any of the following methods: ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry, More preferably, the reagent is a reagent used for quantifying protein using the PRM method, More preferably, the reagent further includes a reagent for separating proteins from blood.

3. A kit comprising the reagent according to claim 2, Preferably, the kit further comprises the protein combination according to claim 1, Preferably, the kit further comprises a reagent for detecting an internal standard.

4. Use of phosphoglycerate kinase 1 (PGK1), fibronectin 1 (FN1), chromogranin A (CHGA), elastin (ELN), heterogeneous nuclear ribonucleoprotein H3 (HNRNPH3), and Aly / REF export factor (ALYREF) as biomarkers or their detection reagents in the preparation of reagents or kits for predicting the efficacy of immunotherapy for non-small cell lung cancer; Preferably, the reagent detects the content of the protein or the transcription level of the coding sequence of the protein.

5. The use according to claim 4, characterized in that The prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma content of the marker is: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

6. A device, characterized in that The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented: (1) Obtain the content of the following proteins in plasma samples: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF, (2) obtaining a score using the content by constructing a model, and (3) Predict the efficacy of immunotherapy for non-small cell lung cancer based on the score.

7. The device according to claim 6, characterized in that The prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma content of the marker is: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

8. A system for predicting the efficacy of immunotherapy for non-small cell lung cancer, characterized in that: include: Collection device for obtaining the content of the following proteins in plasma samples: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF, A data processing device for obtaining a score by constructing a model using the content, A determination device is used to predict the efficacy of immunotherapy for non-small cell lung cancer based on a score.

9. The system according to claim 8, wherein The prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma content of the marker is: score = 4.008-0.01×PGK1-0.001×FN1-0.012×CHGA-0.051×ELN+0.079×HNRNPH3-0.161×ALYREF.

10. A method for constructing a model for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the steps of: (1) Obtain the content of the following proteins in plasma samples: PGK1, FN1, CHGA, ELN, HNRNPH3 and ALYREF, and (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis method.

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