Application of secretory LAMP1 as non-small cell lung cancer immunotherapy curative effect and prognostic marker and evaluation system

Abstract

The invention discloses application of secretory LAMP1 as a non-small cell lung cancer immunotherapy curative effect and prognostic marker and an evaluation system, and relates to the technical field of biological medicine. According to the invention, sLAMP1 in serum is taken as a core biomarker, so that the curative effect and prognosis of ICB treatment of NSCLC patients can be effectively predicted. Meanwhile, a multi-dimensional prognosis evaluation system integrating sLAMP1 concentration, ECOG score, pathological subtype, gender, smoking state, age and other clinical pathological characteristics is constructed, and accurate prediction of one-year and three-year progression-free lifetime of the patient is realized. The system predicts that the AUC of PFS reaches up to 0.94 within 12 months, is easy to popularize in a standardized manner based on a mature ELISA platform, can assist clinicians in early screening of potential benefit crowds, identification of true and false progresses and timely adjustment of treatment strategies, can remarkably improve the precise whole-course management level of NSCLC immunotherapy, and has important clinical transformation value.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application and evaluation system of secretory LAMP1 as a biomarker for the efficacy and prognosis of immunotherapy for non-small cell lung cancer. Background Technology

[0002] In recent years, immune checkpoint blockade (ICB) therapy, represented by inhibitors of programmed death receptor-1 (PD-1) and its ligand (PD-L1), has fundamentally changed the treatment landscape for driver gene-negative advanced non-small cell lung cancer (NSCLC), significantly prolonging patients' overall survival (OS). However, ICB therapy still faces significant challenges in clinical application. Its overall response rate is low, with only about 20%-30% of unselected NSCLC patients achieving a durable clinical response from monotherapy. Most patients exhibit primary resistance or develop acquired resistance after treatment.

[0003] Currently, the main biomarkers guiding ICB (immunotherapy with peripheral blood) use still rely on immunohistochemical detection of PD-L1 expression levels in tumor tissue. However, tissue biopsies have inherent limitations, including being invasive, difficult to replicate, and unable to reflect tumor spatial heterogeneity. Furthermore, the efficacy of immunotherapy is often accompanied by complex tumor microenvironment remodeling, and static tissue biopsies alone cannot capture the dynamic biological changes during treatment. Against this backdrop, the development of peripheral blood-based "liquid biopsy" biomarkers has become a research hotspot in precision medicine. Blood biomarkers, with their advantages of being non-invasive, convenient, and reproducible, can not only reflect systemic tumor burden but also enable early prediction of treatment efficacy and prognostic assessment through longitudinal monitoring, possessing extremely high clinical translational value.

[0004] Despite some progress in the development of liquid biopsy biomarkers, the existing ICB treatment-related assessment system still faces several unresolved issues: First, the accuracy of existing predictive biomarkers is insufficient and plagued by heterogeneity. PD-L1 expression testing, considered the "gold standard," exhibits significant spatial heterogeneity, as the biopsy sample represents only a small portion of the tumor, often leading to misjudgments of PD-L1 expression status (false negatives). Clinical data indicates that some PD-L1-negative patients can still benefit from ICB treatment, while patients with high expression may experience immune escape. Furthermore, another potential biomarker, tumor mutational burden (TMB) testing, is costly and lacks standardized thresholds, making it difficult to widely implement in routine clinical practice. Second, there is a lack of dynamic monitoring methods that can reflect efficacy in real time. Current efficacy assessments primarily rely on RECIST. 1.1 Standard imaging examinations (CT / MRI) are used, but imaging changes often lag behind molecular biological changes, and efficacy can usually be determined 6-12 weeks after treatment. Furthermore, the "pseudo-progression" phenomenon unique to immunotherapy—that is, a temporary increase in tumor size due to immune cell infiltration—is often misinterpreted as disease progression on imaging, leading patients to prematurely discontinue treatment or receive unnecessary overtreatment. Currently, there is still a lack of highly sensitive serological markers to help differentiate between true and false progression. Thirdly, invasive procedures limit the management of the entire disease course, and tissue biopsies carry potential complication risks. (e.g., pneumothorax, hemorrhage), poor patient compliance, and difficulty in repeated sampling during treatment make it impossible for doctors to grasp the clonal evolution and drug resistance mechanisms of tumors under drug pressure in real time, limiting the realization of dynamic management throughout the entire course of the disease; Fourth, single-dimensional assessment has limitations. Existing prognostic assessments mostly rely on single indicators (e.g., only looking at PD-L1 or only looking at clinical stage), ignoring the complex interactions between host immune status, tumor burden and metabolic status. At present, there is still a lack of a multidimensional scoring system that integrates biomarkers and clinicopathological features to achieve individualized and accurate risk stratification.

[0005] In summary, it is necessary to develop a non-invasive, dynamic, and multi-dimensional ICB treatment efficacy assessment and prognostic prediction system, in order to achieve early prediction, dynamic monitoring, and accurate prognostic assessment of NSCLC immunotherapy efficacy through non-invasive liquid biopsy. Summary of the Invention

[0006] The purpose of this invention is to provide an application and evaluation system for secretory LAMP1 as a biomarker for the efficacy and prognosis of immunotherapy in non-small cell lung cancer, thereby addressing the problems existing in the prior art. The biomarker provided by this invention can effectively predict the efficacy and prognosis of immunotherapy for non-small cell lung cancer, and has high clinical translational value.

[0007] To achieve the above objectives, the present invention provides the following solution: This invention provides a biomarker for predicting the efficacy and prognosis of immunotherapy for non-small cell lung cancer, wherein the biomarker is secretory LAMP1.

[0008] The present invention also provides the application of a reagent for detecting the level of secretory LAMP1 in serum in the preparation of products for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0009] The present invention also provides the use of reagents for detecting the level of secretory LAMP1 in serum in the preparation of products for predicting the prognosis of non-small cell lung cancer after immunotherapy.

[0010] Furthermore, the product is a reagent kit. Furthermore, the reagent is an ELISA detection reagent.

[0011] The present invention also provides a product for predicting the efficacy and / or prognosis of immunotherapy for non-small cell lung cancer, including a reagent for detecting the level of secretory LAMP1 in peripheral blood.

[0012] Furthermore, the product is a reagent kit.

[0013] Furthermore, the reagent is an ELISA detection reagent.

[0014] This invention also provides a multidimensional prognostic assessment system for immunotherapy of non-small cell lung cancer, including an input module, a core computing module, and an output and visualization module; The core computing module is used to calculate the risk score, and the calculation formula is: Risk score = (1.8×X_sLAMP1) + (1.3×X_ECOG) + (0.53×X_Pathology) - (0.26×X_Gender) - (0.19×X_Smoking) - (0.17×X_Age); X_sLAMP1, X_ECOG, X_Pathology, X_Gender, X_Smoking, and X_Age are the values ​​of serum sLAMP1 concentration, ECOG score, pathological subtype, gender, smoking status, and patient age, respectively.

[0015] The present invention also provides a method for constructing a multidimensional prognostic prediction model for immunotherapy of non-small cell lung cancer, including the steps of constructing the multidimensional prognostic prediction model with X_sLAMP1, X_ECOG, X_Pathology, X_Gender, X_Smoking and X_Age as input variables; The risk score of the multidimensional prognostic prediction model is calculated as follows: (1.8 × X_sLAMP1) + (1.3 × X_ECOG) + (0.53 × X_Pathology) - (0.26 × X_Gender) - (0.19 × X_Smoking) - (0.17 × X_Age). X_sLAMP1, X_ECOG, X_Pathology, X_Gender, X_Smoking, and X_Age are the values ​​of serum sLAMP1 concentration, ECOG score, pathological subtype, gender, smoking status, and patient age, respectively.

[0016] The present invention discloses the following technical effects: Compared to existing histopathological tests (such as IHC-PD-L1) and routine serological indicators, the biomarkers and evaluation system proposed in this invention demonstrate the following significant technical advantages and clinical application value in the whole-process management of immunotherapy for non-small cell lung cancer: This invention utilizes a liquid biopsy strategy to detect sLAMP1 in circulating plasma. As a lysosome-associated membrane protein, sLAMP1 is actively secreted by lysosomal exocytosis of tumor cells or released into the bloodstream due to high cellular metabolic turnover. Therefore, plasma sLAMP1 levels can systematically integrate the burden and metabolic activity of tumor lesions throughout the body, effectively compensating for the limitations of tissue biopsy's "point-to-area" approach and significantly reducing the risk of missed diagnoses and misdiagnoses.

[0017] This invention, using a multivariate Cox proportional hazards model, confirms that plasma sLAMP1 level is an independent prognostic factor independent of pathological subtype, smoking history, and age (HR=7.11, P<0.001). This indicates that sLAMP1 reflects a different aspect of tumor biology than the PD-L1 immune escape pathway (possibly related to lysosomal-mediated MHC-I molecule degradation and impaired antigen presentation). Introducing sLAMP1 provides a novel assessment dimension for clinical practice, enabling the screening of potentially benefiting or high-risk individuals missed by traditional biomarkers.

[0018] This invention reveals that sLAMP1 exhibits a rapid pharmacokinetic response. Data shows that in patients sensitive to ICB treatment (responders), plasma sLAMP1 levels show a significant decreasing trend in the early stages of treatment (post-treatment). Therefore, this invention provides a biomarker for monitoring treatment efficacy, which can assist physicians in distinguishing between true and false progression before radiographic changes appear, allowing for timely adjustments to treatment strategies and avoiding the accumulation of toxicity and economic losses caused by ineffective treatment.

[0019] The predictive accuracy (AUC) of a single biomarker is typically between 0.6 and 0.7, which is insufficient to meet the needs of precision medicine. This invention constructs a multidimensional predictive model integrating sLAMP1 with clinicopathological features (such as ECOG, gender, and pathological type), achieving a qualitative leap in predictive efficacy. Validation data show that the area under the curve (AUC) for predicting 12-month progression-free survival (PFS) is as high as 0.94, and the calibration curve shows a high degree of consistency between the predicted probability and the actual incidence. Furthermore, decision curve analysis confirms that, across a wide range of threshold probabilities, intervention strategies based on this invention's model can bring patients significantly better net clinical benefits than "full treatment" or "no treatment" options.

[0020] The technical solution of this invention is based on a mature enzyme-linked immunosorbent assay (ELISA) platform, featuring high throughput, low cost, ease of operation, and easy standardization. The testing cycle takes only a few hours and requires no expensive specialized equipment; it can be implemented using existing laboratory facilities in medical institutions at all levels. This significantly reduces the testing burden on patients and improves the clinical accessibility and potential for widespread application of the technology. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 Scatter plot of sLAMP1 levels in the disease remission (PR) group and the disease progression / stable disease (PD / SD) group; Figure 2 A waterfall plot showing the maximum percentage change in target lesion tumor volume from baseline. Figure 3 The progression-free survival (PFS) curves are stratified based on baseline plasma sLAMP1 levels. Figure 4 Paired box plots of plasma sLAMP1 levels in non-responders and responders before and after immunotherapy. Figure 5 Forest plot of univariate Cox regression analysis for factors affecting PFS-related risk factors; Figure 6 Forest plot of multivariate Cox regression analysis after adjusting for age, sex, smoking status, pathological type and ECOG score; Figure 7Nodal plot for individualized prediction of 1-year and 3-year PFS probability in NSCLC patients; Figure 8 Time-dependent receiver operating characteristic curves for predicting the accuracy of 6-month, 12-month, and 18-month PFS using a multidimensional model based on sLAMP1; Figure 9 The calibration curve is used to evaluate the accuracy of the nomogram in predicting 1-year PFS; the dashed line (Ideal) represents the ideal prediction model, and the red line (Bias-corrected) represents the consistency between the actual observation results and the predicted probability after bias correction. Figure 10 This is a decision curve analysis graph. Detailed Implementation

[0023] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0024] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0025] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0026] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0027] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0028] Lysosome-associated membrane protein 1 (LAMP1) is a highly glycosylated type I transmembrane protein, primarily located on the lysosomal membrane within cells, and is crucial for maintaining the integrity of lysosomal structure and regulating autophagic flux. This invention, based on previous basic research, has found that the level of secreted LAMP1 (sLAMP1) in peripheral blood serum is correlated with the immune response, efficacy, and survival prognosis of NSCLC patients after ICB treatment. Based on this, this invention systematically validates this correlation using clinical samples and develops a multidimensional prognostic assessment system to improve the accuracy of immunotherapy efficacy assessment, thereby facilitating the adjustment of treatment strategies and avoiding the accumulation of toxicity and economic losses caused by ineffective treatment.

[0029] Example 1 1. Clinical Samples Patient enrollment: 73 patients with non-small cell lung cancer (NSCLC) who received immune checkpoint inhibitor (ICB) therapy were strictly screened.

[0030] Inclusion criteria: NSCLC confirmed by histopathology; receiving anti-PD-1 / PD-L1 monotherapy or combination therapy; having matched plasma samples at baseline and post-treatment; having complete clinical follow-up data, including age, sex, pathological type, tumor proportion score (TPS), RECIST 1.1 efficacy assessment, progression-free survival (PFS), TNM stage, and ECOG score.

[0031] Clinical endpoints: The primary endpoint was progression-free survival (PFS), and the secondary endpoint was objective response rate (ORR).

[0032] The baseline characteristics of the study population stratified according to ICB treatment response are shown in Table 1.

[0033] Table 1. Baseline characteristics of the study population stratified according to ICB treatment response

[0034] 2. Plasma sample collection and ELISA quantitative detection Plasma preparation: Collect 5 mL of peripheral venous blood using a vacuum blood collection tube containing EDTA anticoagulant. Immediately after collection, gently invert and mix 8-10 times to prevent coagulation. Centrifuge at 1000-2000g for 10-15 minutes at 4°C. Carefully aspirate the supernatant plasma (avoiding the white film layer), aliquot, and store at -80°C.

[0035] ELISA assay: The level of secretory LAMP1 (sLAMP1) in plasma was detected using the Proteintech Human LAMP1 ELISA Kit.

[0036] 3. Correlation between treatment efficacy and tumor burden analysis Patients were divided into a partial remission (PR) group and a disease progression / stable disease (PD / SD) group according to RECIST 1.1 criteria. The Mann-Whitney U test was used to compare baseline plasma sLAMP1 levels between the two groups. Results showed that plasma sLAMP1 levels in the PD / SD group were significantly higher than those in the PR group. Figure 1 ).

[0037] Calculate the maximum percentage change in total target lesion diameter from baseline for each patient. Using the median sLAMP1 level as the boundary, patients are marked as high expression (high, red) and low expression (low, blue), and a waterfall plot is generated. Results are shown below. Figure 2 . Figure 2 Visually, it can be seen that high expression of sLAMP1 is mainly concentrated in patients with increased tumor volume (>0%).

[0038] 4. Survival Analysis and Threshold Determination Threshold setting: The median sLAMP1 concentration in the cohort was used as the cut-off value. Patients were divided into the LAMP1_median-High group and the LAMP1_median-Low group.

[0039] Survival curves (KM Plot): Survival curves were plotted using the Kaplan-Meier method. Results are shown below. Figure 3 The Log-rank test was used to compare the difference in PFS between the two groups. The hazard ratio (HR) and 95% confidence interval (95% CI) were calculated. Results showed that the high-expression group had a significantly worse prognosis (HR=6.35, P<0.0001).

[0040] 5. Dynamic monitoring and analysis Patients were divided into two groups based on their immunotherapy response: non-responders (PD / SD) and responders (PR / CR). Paired plasma sLAMP1 data were extracted before and after treatment, and paired boxplot analysis of plasma sLAMP1 levels was performed. The Wilcoxon matched-pairssigned rank test was used to analyze the differences before and after treatment.

[0041] like Figure 4As shown, sLAMP1 decreased significantly after treatment in the Responder group (P=0.00034), while it did not decrease significantly in the Non-responder group (P=0.16), confirming that dynamic changes in sLAMP1 can reflect the therapeutic effect.

[0042] 6. Predictive Model Construction and Variable Selection Univariate Cox regression analysis: Smoking status, pathological type, plasma sLAMP1, gender, ECOG score, and age were included in the Cox model. Variables with P-values ​​<0.05 or clinical significance were selected. The results showed that sLAMP1 (HR=6.35) and ECOG (HR=3.64) were significantly correlated. Figure 5 ).

[0043] To overcome the limitations of single biomarkers, this invention constructs a multidimensional prognostic assessment system that integrates biological characteristics and clinicopathological parameters.

[0044] (1) Construction of a multidimensional prognostic assessment system Input module: The system is configured to receive and standardize the following five-dimensional variables: X_sLAMP1: serum sLAMP1 concentration (continuous variable, unit: pg / mL); X_Age: patient age (binary variable: <65;>=65); X_Gender: gender (binary variable: Male vs Female); X_ECOG: ECOG score (binary variable: 0 vs 1); X_Pathology: pathological subtype (binary variable: Non-LUSC vs LUSC); X_Smoking: smoking status (binary variable: 0 vs 1).

[0045] Core computation module: This module is built based on a multivariate Cox proportional hazards regression model.

[0046] The risk score is calculated as follows: Risk Score = (1.8 × X_sLAMP1) + (1.3 × X_ECOG) + (0.53 × X_Pathology) - (0.26 × X_Gender) - (0.19 × X_Smoking) - (0.17 × X_Age); Here, each value represents the regression coefficient of each variable in the multifactor Cox regression model, and X represents the value of each variable.

[0047] Weighting characteristics: In this model, the regression coefficient of sLAMP1 is positive and has the largest weight contribution, indicating that its elevated concentration is a strong risk factor for disease progression (PD) and death.

[0048] Output and Visualization Module: The system maps the calculated total risk score to the probability axis of the nomogram.

[0049] Predictive output: Specifically outputs the quantitative values ​​of the patient's 1-year progression-free survival (PFSProbability) and 3-year progression-free survival (PFSprobability) after receiving ICB treatment.

[0050] Model performance: The algorithm has been validated by the consistency index (C-index) and calibration curve.

[0051] (2) Criteria for Variable Definition and Assignment To ensure consistency in calculations, the system uses the following standardized assignment encoding for input variables: X_LAMP1 (plasma sLAMP1 grouping): Assign a value of 1: High expression group (detection concentration > preset cutoff value); Assign a value of 0: Low expression group (detection concentration <= preset cutoff value).

[0052] Technical characteristics: The regression coefficient of sLAMP1 is 1.8, which is the risk factor with the largest weight and the most statistical significance in the model.

[0053] X_ECOG (Fitness and Performance Status Rating): A score of 1 indicates a low level of physical fitness. A value of 0 is assigned: ECOG score of 0 (good physical fitness).

[0054] Technical characteristic: Regression coefficient = 1.3.

[0055] X_Pathology (Pathology Type): A value of 0 indicates non-squamous carcinoma (adenocarcinoma / other). Assign a value of 1: squamous cell carcinoma.

[0056] Technical characteristics: Regression coefficient = 0.53.

[0057] X_Gender (gender): Assigning a value of 0: Male; Assign a value of 1: Female.

[0058] Technical characteristics: Regression coefficient = -0.26 (a negative value indicates a slight reduction in risk score in this model).

[0059] X_Smoking (Smoking state): A value of 0 indicates a smoker. Assign a value of 1: Non-smoker.

[0060] Technical characteristic: Regression coefficient = -0.19.

[0061] X_Age (age): A value of 0 was assigned to the elderly group (>65 years old). Assign a value of 1: younger age group (<= 65 years old).

[0062] Technical characteristic: Regression coefficient = -0.17.

[0063] (3) Operational logic and output After calculating the total risk score according to the above formula, the calculation module performs the following steps: Risk stratification: The system divides patients into low-risk and high-risk groups based on their total score.

[0064] Probability mapping: Combining the baseline hazard function h0(t), the exponential transformation formula is used to output the progression-free survival probability (PFS probability) of patients at a specific time point (e.g., 12 months). Results are shown in [link to results]. Figure 6 .

[0065] Visualization output: A nomogram is generated to visually demonstrate the dominant role of sLAMP1 in prognostic prediction. Results are shown below. Figure 7 .

[0066] Results: Even after adjusting for factors such as age, sex, and pathology, sLAMP1 remained an independent prognostic predictor (HR=7.11, P<0.001).

[0067] 7. Nodal plot visualization and model validation This section was completed using the R language (version 4.1.0, with the rms, survival, timeROC, and stdca packages loaded).

[0068] Nono plot construction: Based on the multi-factor Cox regression coefficients, construct nono plots to predict the 1-year and 3-year PFS probabilities.

[0069] Scoring rules: sLAMP1 concentration is mapped to a score of 0-100, and the scores of each variable are added together to correspond to the total survival probability.

[0070] Discrimination validation: Time-dependent receiver operating characteristic (ROC) curves were plotted, and the results are shown in [link to results]. Figure 8 .

[0071] The area under the curve (AUC) was calculated: the model predicted AUCs of 0.86, 0.94, and 0.89 for PFS at 6 months, 12 months, and 18 months, respectively, showing extremely high prediction accuracy.

[0072] Calibration validation: Internal validation was performed using the Bootstrap method (1000 resampling cycles). Calibration curves were plotted, with the X-axis representing the Nomogram predicted probability and the Y-axis representing the actual observed probability. The results show that the "Bias-corrected" curve closely matches the "Ideal" diagonal line, indicating good agreement between the model's predictions and the actual values. Figure 9 ).

[0073] Clinical benefit assessment: Decision curve analysis was performed to evaluate the net clinical benefit of the sLAMP1 predictive model compared to "Treat All" or "Treat None" strategies. Figure 10 As shown, the LAMP1 level curve is higher than the Treat All and Treat None curves for the vast majority of threshold probabilities, confirming that the model has significant net clinical application benefits.

[0074] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A biomarker for predicting the efficacy and prognosis of immunotherapy in non-small cell lung cancer, characterized in that, The biomarker is secretory LAMP1.

2. Application of reagents for detecting the level of secretory LAMP1 in serum in the preparation of products for predicting the efficacy of immunotherapy for non-small cell lung cancer.

3. Application of reagents for detecting serum secretory LAMP1 levels in the preparation of products for predicting the prognosis of non-small cell lung cancer after immunotherapy.

4. The application according to claim 2 or 3, characterized in that, The product in question is a reagent kit.

5. The application according to claim 2 or 3, characterized in that, The reagent is an ELISA detection reagent.

6. A product for predicting the efficacy and / or prognosis of immunotherapy for non-small cell lung cancer, characterized in that, This includes reagents for detecting the level of secretory LAMP1 in peripheral blood.

7. The product according to claim 6, characterized in that, The product in question is a reagent kit.

8. The product according to claim 6, characterized in that, The reagent is an ELISA detection reagent.

9. A multidimensional prognostic assessment system for immunotherapy of non-small cell lung cancer, characterized in that, It includes an input module, a core computing module, and an output and visualization module; The core computing module is used to calculate the risk score, and the calculation formula is: Risk score = (1.8×X_sLAMP1) + (1.3×X_ECOG) + (0.53×X_Pathology) - (0.26×X_Gender) - (0.19×X_Smoking) - (0.17×X_Age); X_sLAMP1, X_ECOG, X_Pathology, X_Gender, X_Smoking, and X_Age are the values ​​of serum sLAMP1 concentration, ECOG score, pathological subtype, gender, smoking status, and patient age, respectively.

10. A method for constructing a multidimensional prognostic prediction model for immunotherapy of non-small cell lung cancer, characterized in that, The steps include constructing the multidimensional prognostic prediction model using X_sLAMP1, X_ECOG, X_Pathology, X_Gender, X_Smoking, and X_Age as input variables; The risk score of the multidimensional prognostic prediction model is calculated as follows: (1.8 × X_sLAMP1) + (1.3 × X_ECOG) + (0.53 × X_Pathology) - (0.26 × X_Gender) - (0.19 × X_Smoking) - (0.17 × X_Age). X_sLAMP1, X_ECOG, X_Pathology, X_Gender, X_Smoking, and X_Age are the values ​​of serum sLAMP1 concentration, ECOG score, pathological subtype, gender, smoking status, and patient age, respectively.

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