Lung cancer soft meningeal metastasis marker and application thereof

Abstract

The invention discloses a lung cancer soft meningeal metastasis marker and application thereof. The marker for the soft meningeal metastasis of the lung cancer comprises one or more of Hydroxyphenylic acid, Betaine, 3-Methoxytyrosine, L-Kyninuine, 1-Methoxyguanosine and 2-Hydroxyphenylethanosine, and whether a subject suffers from the soft meningeal metastasis of the lung cancer or is suspected to suffer from the soft meningeal metastasis of the lung cancer or not can be diagnosed by detecting the level of the marker in a sample of the subject. The invention has a good application prospect in diagnosis of lung cancer soft meningeal metastasis.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a biomarker for lung cancer leptomeningeal metastasis and its application. Background Technology

[0002] Leptomeningeal metastasis (LM), also known as meningeal carcinomatosis, is a metastatic cancer caused by tumor cells invading the meningeal space. It can simultaneously affect various parts of the brain and spinal cord, leading to a variety of symptoms and signs. It often manifests as atypical symptoms (nonspecific symptoms) such as headache, dizziness, and altered consciousness. It is the third most common central nervous system complication of cancer after brain metastasis and epidural spinal cord compression, posing a challenge to clinicians in diagnosis and treatment. While the survival rate of cancer patients has been further extended with the continuous improvement of systemic treatment methods for solid tumors, the incidence of leptomeningeal metastasis is increasing due to the lack of treatments that can penetrate the blood-brain barrier.

[0003] Common solid tumors that can lead to leptomeningeal metastasis (LM) include lung cancer, breast cancer, melanoma, gastrointestinal malignancies, and cancers of unknown primary origin. The number of solid tumors previously rarely associated with LM (such as prostate cancer, ovarian cancer, cervical cancer, and endometrial cancer) is also increasing. Diagnosing and monitoring multifocal leptomeningeal metastases from the primary site to the subarachnoid space, meninges, and cerebrospinal fluid of the brain and spinal cord is challenging.

[0004] Currently, cerebrospinal fluid analysis combined with various imaging examinations (CT, MRI, and PET-CT) is considered the gold standard for diagnosing leptomeningeal tumor (LM). However, only a small percentage of LM cases are diagnosed, and LM is usually confirmed during autopsy. In vitro analysis of leptomeningeal cancer cells allows researchers to study the disease at the level of cancer subtypes and biomarkers. The diversity of cancer types leads to variations in the characteristics of leptomeningeal metastasis, resulting in complex treatment options. Clinicians can only rely on existing diagnostic methods for a relatively subjective assessment of the condition. Therefore, exploring new diagnostic methods for LM is a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention provides the following technical solution: The first aspect of the present invention provides the use of a reagent for detecting the level of a biomarker in a sample in the preparation of a product for diagnosing or assisting in the diagnosis of leptomeningeal metastasis, said biomarker including one or more of Hydroxyphenyllactic acid, Betaine, 3-Methoxytyrosine, L-Kynurenine, 1-Methylguanosine, and 2-Hydroxyethanesulfonate.

[0006] Furthermore, the leptomeningeal metastasis is cancerous leptomeningeal metastasis.

[0007] Furthermore, the cancer leptomeningeal metastases include lung cancer leptomeningeal metastases, breast cancer leptomeningeal metastases, gastric cancer leptomeningeal metastases, or melanoma leptomeningeal metastases.

[0008] Furthermore, the leptomeningeal metastasis is a lung cancer leptomeningeal metastasis.

[0009] Furthermore, the products include reagent kits, solid substrates, test strips, high-throughput sequencing platforms, systems, equipment, and devices.

[0010] Furthermore, the sample is a cerebrospinal fluid sample.

[0011] Furthermore, the reagents include those used to detect the levels of biomarkers in samples using targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or chromatography-mass spectrometry.

[0012] A second aspect of the present invention provides a product for diagnosing or assisting in the diagnosis of leptomeningeal metastasis, the product comprising a reagent for detecting the level of the biomarkers described in the first aspect of the present invention in a sample.

[0013] Furthermore, the products include reagent kits, solid substrates, test strips, high-throughput sequencing platforms, systems, equipment, and devices.

[0014] Furthermore, the sample was cerebrospinal fluid.

[0015] Furthermore, the reagents include those used to detect the levels of biomarkers in samples using targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or chromatography-mass spectrometry.

[0016] The third aspect of the present invention provides the application of the biomarkers described in the first aspect of the present invention in constructing a diagnostic model for leptomeningeal metastasis.

[0017] The fourth aspect of the present invention provides a method for constructing a diagnostic model for leptomeningeal metastasis, the method comprising the steps of: obtaining the biomarker level data of the first aspect of the present invention in samples from patients with leptomeningeal metastasis and healthy controls, and constructing a diagnostic model based on the biomarker level data.

[0018] Furthermore, the diagnostic model monitors whether subjects have or are suspected of having leptomeningeal metastases based on the level data of the aforementioned biomarkers in the samples.

[0019] Furthermore, the diagnostic model is constructed using machine learning algorithms.

[0020] Furthermore, the machine learning algorithm includes one or more of the following: linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, or random forest model.

[0021] The fifth aspect of the present invention provides a system for diagnosing or assisting in the diagnosis of pia mater metastasis, the system comprising a processor, an input module, and an output module.

[0022] The processor is used to perform logical operations on the input information using bioinformatics methods; the input module is used to input the level of the biomarker described in the first aspect of the present invention in the subject sample, and a computer-readable medium containing instructions that, when executed by the processor, perform an algorithm at the aforementioned biomarker level; the output module is used to output whether the subject has or is suspected of having pia mater metastasis.

[0023] A sixth aspect of the present invention provides an electronic device for diagnosing or assisting in the diagnosis of leptomeningeal metastases, the electronic device comprising: The acquisition and detection module is configured to detect the acquired sample and obtain the level of the marker described in the first aspect of the present invention in the sample.

[0024] The diagnostic module is constructed using the diagnostic model construction method described in the fifth aspect of the present invention, and then outputs diagnostic results based on the model.

[0025] Advantages and beneficial effects of the present invention: This invention discloses for the first time a biomarker for lung cancer leptomeningeal metastasis and its application. The biomarker includes one or more of the following: Hydroxyphenyllactic acid, Betaine, 3-Methoxytyrosine, L-Kynurenine, 1-Methylguanosine, and 2-Hydroxyethanesulfonate. By detecting the levels of these biomarkers in a subject's sample, it is possible to diagnose whether the subject has or is suspected of having lung cancer leptomeningeal metastasis. This invention has promising applications in the diagnosis of lung cancer leptomeningeal metastasis. Attached Figure Description

[0026] Figure 1ROC curves for clinical samples of diagnosing leptomeningeal metastasis with Hydroxyphenyllactic acid; where A is the lung cancer leptomeningeal metastasis detection set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastasis; and D is gastric cancer or melanoma leptomeningeal metastasis.

[0027] Figure 2 The abundance variation of hydroxyphenyllactic acid in patients with leptomeningeal metastases is shown in Figure 1. A represents the lung cancer leptomeningeal metastasis discovery set; B represents the lung cancer leptomeningeal metastasis validation set; C represents breast cancer leptomeningeal metastases; and D represents gastric cancer or melanoma leptomeningeal metastases.

[0028] Figure 3 ROC curves for clinical samples used to diagnose leptomeningeal metastases with Betaine; where A is the lung cancer leptomeningeal metastasis detection set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases.

[0029] Figure 4 The abundance variation of Betaine in patients with leptomeningeal metastases is shown; where A is the lung cancer leptomeningeal metastasis discovery set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases.

[0030] Figure 5 ROC curves for clinical samples of 3-Methoxytyrosine for diagnosing leptomeningeal metastasis; where A is the lung cancer leptomeningeal metastasis detection set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastasis; and D is gastric cancer or melanoma leptomeningeal metastasis.

[0031] Figure 6 The abundance variation of 3-Methoxytyrosine in patients with leptomeningeal metastases is shown; where A is the lung cancer leptomeningeal metastasis discovery set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases.

[0032] Figure 7 ROC curves for clinical samples using L-Kynurenine to diagnose leptomeningeal metastases; where A is the lung cancer leptomeningeal metastasis detection set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases. Figure 8 The abundance variation of L-Kynurenine in patients with leptomeningeal metastases is shown; where A is the lung cancer leptomeningeal metastasis discovery set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases.

[0033] Figure 9ROC curves for clinical samples of 1-Methylguanosine in diagnosing leptomeningeal metastasis; where A is the lung cancer leptomeningeal metastasis detection set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastasis; and D is gastric cancer or melanoma leptomeningeal metastasis.

[0034] Figure 10 The abundance variation of 1-Methylguanosine in patients with leptomeningeal metastases is shown; where A is the lung cancer leptomeningeal metastasis discovery set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases.

[0035] Figure 11 ROC curves for clinical samples of 2-Hydroxyethanesulfonate in diagnosing leptomeningeal metastasis; where A is the lung cancer leptomeningeal metastasis detection set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastasis; and D is gastric cancer or melanoma leptomeningeal metastasis.

[0036] Figure 12 The abundance variation of 2-Hydroxyethanesulfonate in patients with leptomeningeal metastases is shown; where A is the lung cancer leptomeningeal metastasis discovery set; B is the lung cancer leptomeningeal metastasis validation set; C is breast cancer leptomeningeal metastases; and D is gastric cancer or melanoma leptomeningeal metastases. Detailed Implementation

[0037] As used in this invention, the terms “having,” “comprising,” or “including,” or any grammatical variations thereof, are used in a non-exclusive manner. Thus, these terms can all refer to a situation where no other features exist in the entity described in this context besides the features introduced by these terms, and can also refer to a situation where one or more other features are present.

[0038] Furthermore, as used in this invention, the terms “preferred,” “more preferred,” “most preferred,” “particularly,” “more particularly,” “specifically,” “more specifically,” or similar terms are used in combination with optional features without limiting other possibilities.

[0039] Unless otherwise stated, all figures used in this specification and claims to represent volume, weight, temperature, time, density, parts by weight, technical effect, etc., should in any case be understood to be modified by the terms "about" or "approximately". Therefore, unless indicated to the contrary, the numerical parameters listed in the specification and appended claims are approximate values. Unless otherwise stated, the terminology used herein has its common meaning as understood by one of ordinary skill in the art, and each numerical parameter should be interpreted according to the number of significant figures and conventional rounding methods or in a manner understood by one of those skilled in the art.

[0040] In this invention, the term "diagnosis" or "auxiliary diagnosis" refers to making a medical judgment on an individual's health status.

[0041] In this invention, the term "marker" refers to a measurable indicator of an individual's biological state. Such markers can be any substance within an individual, including but not limited to genes, proteins, metabolites, and microorganisms, as long as they are related to a specific biological state (e.g., disease) of the individual being examined. In this invention, a marker refers to a metabolite, and more specifically, the marker includes one or more of Hydroxyphenyllactic acid, Betaine, 3-Methoxytyrosine, L-Kynurenine, 1-Methylguanosine, and 2-Hydroxyethanesulfonate.

[0042] In some embodiments, the kit includes instructions for use or a label, a positive control, a negative control, a buffer, an adjuvant, or a solvent, and one or more containers for containing the compositions contained in the kit. The compositions may be in liquid form or lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers may be formed from a variety of materials, including glass or plastic. The instructions for use or label details how to use the kit to test samples and how the kit can be used to diagnose or aid in the diagnosis of leptomeningeal metastases.

[0043] In some embodiments, the kit may also include a variety of different reagents suitable for practical use (e.g., for different detection methods), and is not limited to the reagents listed in the present invention. Any reagent that is used to diagnose or assist in the diagnosis of leptomeningeal metastasis based on the detection of biological metabolites provided in the first aspect of the present invention is included within the scope of protection of the present invention.

[0044] In some embodiments, the solid substrate, such as a chip, glass slide, array, etc., has reagents capable of detecting and / or quantifying one or more blood metabolites or other sample-derived metabolites immobilized at predetermined locations on the substrate. As an illustrative example, reagents immobilized at discrete predetermined locations can be provided to the chip for the detection and quantification of metabolite markers in blood samples, in any quantity or combination of concentrations.

[0045] In this invention, nuclear magnetic resonance (NMR) refers to a technique for analyzing the structure of matter by utilizing the absorption characteristics of atomic nuclei to radio frequency radiation in a strong magnetic field. When an atomic nucleus is in a strong magnetic field environment, the magnetic moment generated by its spin motion causes energy level splitting. After applying radio frequency radiation of a specific frequency, the nuclear spin system absorbs energy and undergoes energy level transitions. By detecting the difference in absorption frequency (chemical shift) and the peak splitting mode (coupling constant), information on intramolecular atomic interactions can be obtained.

[0046] In this invention, the chromatography method refers to a method for separating and analyzing the components in a complex mixture. It utilizes the fact that different substances have different partition coefficients in a system composed of a stationary phase and a mobile phase. When the two phases move relative to each other, these substances move together with the mobile phase and undergo repeated partitioning between the two phases, thereby achieving separation of the substances.

[0047] In this invention, the spectroscopic method refers to a system of qualitative and quantitative analysis methods that utilize the emission, absorption, or scattering spectral characteristics generated by energy level transitions in molecules or atoms when matter interacts with radiation energy. Spectroscopic methods can be broadly classified into two types: atomic spectroscopy and molecular spectroscopy. The former produces line spectra (such as atomic emission spectra), while the latter presents band spectra (such as ultraviolet-visible spectrophotometry). Based on Boltzmann's distribution law and energy level transition formulas, spectroscopic methods identify elements through characteristic wavelengths and determine concentrations by measuring intensity changes.

[0048] In this invention, the mass spectrometry method refers to a method that uses electric and magnetic fields to separate and detect moving ions (charged atoms, molecules or molecular fragments, isotopic ions, fragment ions, rearranged ions, multiply charged ions, metastable ions, negative ions, and ions generated by ion-molecule interactions) according to their mass-to-charge ratio. Determining the accurate mass of the ions allows for the determination of their compound composition.

[0049] As used herein, the term "sample" refers to a sample obtained or derived from a biological source of interest as described herein (e.g., tissue or organism or cell culture). In some embodiments, a biological sample is or comprises biological tissue or fluid. In some embodiments, a biological sample may be or comprise bone marrow; blood; blood cells; ascites; tissue or fine-needle biopsy samples; cell-containing body fluids; free-floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; lymph; gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs; rinsing or lavage fluids, such as catheter lavage fluid or bronchoalveolar lavage fluid; aspirates; scrapings; bone marrow samples; tissue biopsy samples; surgical samples; feces, other body fluids, secretions and / or excretions; and / or cells derived from them, etc. In some embodiments, a biological sample is or comprises cells obtained from an individual. In some embodiments, the obtained cells are or comprise cells from the individual from whom the sample was obtained. In some embodiments, the sample is a "primary sample" obtained directly from the source of interest by any appropriate means. For example, in some embodiments, primary biological samples are obtained by methods selected from the group consisting of: biopsy (e.g., fine-needle aspiration or tissue biopsy), surgery, collection of bodily fluids (e.g., blood, lymph, feces, cerebrospinal fluid, etc.). More specifically, the sample described in this invention is cerebrospinal fluid.

[0050] In some embodiments, the subject refers to an individual or animal that suffers from or may suffer from the leptomeningeal metastasis or leptomeningeal metastasis-related disease described in this invention. It can also refer to an individual or animal used for a specific purpose, such as scientific research. Specifically, the individual may be an animal, particularly a mammal, such as a human, pig, dog, cat, cow, sheep, horse, mouse, rat, rabbit, guinea pig, monkey, etc. More specifically, the individual described in this invention is a human.

[0051] In this invention, verification using real clinical samples collected by this invention has revealed that the biomarker described in the first aspect of this invention has good diagnostic efficacy for the diagnosis of leptomeningeal metastasis in lung cancer. The diagnostic efficacy is verified by receiver operating characteristic (ROC) curves, and the determination of the area under the curve (AUC) helps to compare the accuracy of classifiers across the overall data range.

[0052] An ROC curve is a graph of the true positive rate (sensitivity) versus the false positive rate (100% specificity) of an experiment. It is useful for depicting the performance of a specific characteristic when distinguishing between two populations. Typically, characteristic data are selected across the entire population in ascending order based on the values ​​of a single characteristic. Then, for each value of that characteristic, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases with values ​​higher than the characteristic value and dividing by the total number of cases. The false positive rate is determined by counting the number of controls with values ​​higher than the characteristic value and dividing by the total number of controls. While this definition refers to cases where the characteristic is higher in cases compared to controls, it also applies to cases where the characteristic is lower in cases compared to controls (in which case samples with values ​​lower than the characteristic value are counted). ROC curves can be generated with respect to individual characteristics and can also be generated with respect to other individual outputs. For example, combinations of two or more characteristics can be mathematically combined (e.g., addition, subtraction, multiplication, etc.) to provide individual sum values ​​that can be plotted on the ROC curve. Furthermore, any combination of multiple features derived from individual output values ​​can be plotted on a ROC curve.

[0053] In this invention, the term "machine learning" refers to algorithms that give computers the ability to learn without explicit programming, including algorithms that learn from data and make predictions about that data. The machine learning algorithms used in the embodiments disclosed herein may include (but are not limited to) Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, regularized logistic regression, XGBoost, decision tree learning, artificial neural networks (ANN), deep neural networks (DNN), support vector machines, rule-based machine learning, etc. Algorithms such as linear regression or logistic regression can be used as part of the machine learning process.

[0054] In this invention, the term "electronic device" means any suitable computing or processing device, or other device constructed or modified to store data or information. Examples of electronic devices suitable for use with embodiments of this invention include stand-alone computing devices; networks, including local area networks (LANs), wide area networks (WANs), the Internet, corporate intranets, or extranets; electronic devices such as personal digital assistants (PDAs), mobile phones, page management programs, etc.; and local and distributed processing systems.

[0055] It should be understood that the systems, apparatuses, and methods described in this invention can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0056] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0058] Example: Diagnostic efficacy study of lung cancer leptomeningeal metastasis markers I. Experimental Methods 1. Clinical Samples This study included 210 clinical cerebrospinal fluid (CSF) samples. Control group patients needed to meet the following criteria: no signs of leptomeningeal involvement on MRI and three consecutive negative CSF cytology results. While this criterion significantly reduced the risk of misclassification, we acknowledge that the possibility of false negatives cannot be completely ruled out due to the limitations of CSF cytology and MRI sensitivity. All lumbar punctures were performed strictly according to relevant clinical guidelines, primarily targeting patients with significant meningeal irritation signs and a high suspicion of meningeal leptomeningeal tumour (LM). The relatively large sample size reflects a case accumulation spanning many years and does not necessarily indicate an abnormally high clinical suspicion of LM at a single institution or time point.

[0059] The cohort consisted of: 1) a discovery set cohort: a control group of 11 patients and 39 patients with lung cancer leptomeningeal metastases (LC-LM) (tumor cells detected in cerebrospinal fluid). 2) a validation set cohort: a control group of 16 patients and 60 patients with lung cancer leptomeningeal metastases (LC-LM) (tumor cells detected in cerebrospinal fluid). In addition, we included 12 patients with lung cancer brain parenchymal metastases (no tumor cells detected in cerebrospinal fluid), 21 patients with breast cancer LM, 15 patients with LM from other tumors such as melanoma and gastric cancer, and 36 patients with other diseases (including meningeal infections). After completing routine clinical examinations, residual cerebrospinal fluid was collected for metabolomics analysis. All cerebrospinal fluid samples were processed within 2 hours of collection: centrifuged at 2500 rpm at 4°C for 5 minutes; immediately after centrifugation, the supernatant was aliquoted into multiple small volumetric tubes, avoiding repeated freeze-thaw cycles, and stored at -80°C until analysis.

[0060] 2. Analytical Methods Diagnostic model construction We leveraged machine learning techniques to construct a data-driven predictive model, transforming complex, high-dimensional metabolomics data into a robust set of diagnostic indicators that can distinguish between LC-LM. First, we focused on significantly differentially expressed metabolites identified through univariate analysis. Then, we implemented an ensemble feature selection strategy by employing four machine learning algorithms—Lasso Minimal Shrinkage and Selection Operator (LASSO), LightGBM, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Each algorithm ranked metabolite importance based on its discriminative power: LASSO produced regression coefficients, while tree-based algorithms (LightGBM, RF, and XGBoost) calculated feature importance scores based on Gini impurity or SHAP values. Through comprehensive screening using cross-validation, we identified a set of metabolite features repeatedly selected by multiple algorithms, thereby increasing confidence in the selected biomarkers.

[0061] Based on an optimized feature set, we constructed a diagnostic classification model for LC-LM. We systematically evaluated nine commonly used machine learning classifiers, including ensemble methods (CatBoost, LightGBM, RF, XGBoost, Gradient Boosting GBoost) as well as logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), and decision trees (DT). In total, we generated and tested 198 different predictive models, covering a wide range of algorithms and feature combinations. This exhaustive approach allowed us to explore the complete model space, avoiding bias towards a single modeling technique. Model training was primarily performed on the discovery set, with performance evaluated using metrics such as AUC, sensitivity, specificity, and accuracy. Model performance was then validated on an independent validation set. Finally, we optimized the number of features incorporated into the model and evaluated its stability and generalization ability to improve its reliability and clinical applicability.

[0062] II. Experimental Results 1. Hydroxyphenyllactic acid The results showed that the abundance of hydroxyphenyllactic acid was significantly upregulated in both the discovery and validation sets of patients with lung cancer leptomeningeal metastases compared to the control group. Figure 2 A, Figure 2 B). The receiver operating characteristic (ROC) curve of the discovery set for hydroxyphenyllactic acid was calculated to distinguish between LC-LM patients and healthy controls. The results showed that the area under the curve (AUC) for hydroxyphenyllactic acid was 0.8594 (…). Figure 1 A).

[0063] To verify the diagnostic efficacy of hydroxyphenyllactic acid, we plotted ROC curves again using validation set cohort data. The results showed that, in this independent validation cohort, hydroxyphenyllactic acid also exhibited high diagnostic performance, with an AUC of 0.9254. Figure 1 B).

[0064] Furthermore, we also found that hydroxyphenyllactic acid showed a significant upregulation trend in abundance in other sources, including breast cancer leptomeningeal metastases, gastric cancer, or melanoma leptomeningeal metastases. Figure 2 C Figure 2 D), and it also demonstrates good diagnostic efficacy in diagnosing leptomeningeal metastases from other sources, including breast cancer ( Figure 1 C) AUC was 0.7972; gastric cancer and melanoma ( Figure 1D) The AUC is 0.7827.

[0065] 2. Betaine The results showed that the abundance of Betaine was significantly upregulated in both the discovery and validation sets of patients with lung cancer leptomeningeal metastases compared to the control group. Figure 4 A, Figure 4 B). The receiver operating characteristic (ROC) curve of Betaine in the discovery set was used to distinguish between LC-LM patients and healthy controls. The results showed that the area under the curve (AUC) of L-Glutamic acid was 0.929 (…). Figure 3 A).

[0066] To verify the diagnostic efficacy of Betaine, we plotted the ROC curve again using the validation set cohort data. The results show that Betaine also exhibits high diagnostic performance in this independent validation cohort, with an AUC of 0.935 (…). Figure 3 B).

[0067] Furthermore, we also found that Betaine showed a significant upregulation trend in abundance in other sources, including breast cancer leptomeningeal metastases, gastric cancer, or melanoma leptomeningeal metastases. Figure 4 C Figure 4 D), and it also demonstrates good diagnostic efficacy in diagnosing leptomeningeal metastases from other sources, including breast cancer ( Figure 3 C) AUC was 0.8739; gastric cancer and melanoma ( Figure 3 D) The AUC is 0.8062.

[0068] 3, 3-Methoxytyrosine The results showed that the abundance of 3-Methoxytyrosine was significantly upregulated in both the discovery and validation sets of patients with lung cancer leptomeningeal metastases compared to the control group. Figure 6 A, Figure 6 B). The receiver operating characteristic (ROC) curve of 3-Methoxytyrosine in the discovery set was used to distinguish between LC-LM patients and healthy controls. The results showed that the area under the curve (AUC) for 3-Methoxytyrosine was 0.864 (…). Figure 5 A).

[0069] To verify the diagnostic efficacy of 3-Methoxytyrosine, we plotted the ROC curve again using validation set data. The results show that, in this independent validation set, 3-Methoxytyrosine also exhibits high diagnostic performance, with an AUC of 0.909 (…). Figure 5 B).

[0070] Furthermore, we also found a significant upregulation trend in 3-Methoxytyrosine abundance in other sources, including breast cancer leptomeningeal metastases, gastric cancer, or melanoma leptomeningeal metastases. Figure 6 C Figure 6 D), and it also demonstrates good diagnostic efficacy in diagnosing leptomeningeal metastases from other sources, including breast cancer ( Figure 5 C) AUC was 0.8219; gastric cancer and melanoma ( Figure 5 D) The AUC is 0.8938.

[0071] 4. L-Kynurenine The results showed that L-Kynurenine abundance was significantly upregulated in both the discovery and validation sets of patients with lung cancer leptomeningeal metastases compared to the control group. Figure 8 A, Figure 8 B). The receiver operating characteristic (ROC) curve of L-Kynurenine in the discovery set was used to distinguish between LC-LM patients and healthy controls. The results showed that the area under the curve (AUC) of L-Kynurenine was 0.906 (…). Figure 7 A).

[0072] To verify the diagnostic efficacy of L-Kynurenine, we plotted the ROC curve again using validation set data. The results show that L-Kynurenine also exhibits high diagnostic performance in this independent validation set, with an AUC of 0.879 (…). Figure 7 B).

[0073] Furthermore, we also found that L-Kynurenine showed a significant upregulation trend in abundance in other sources, including breast cancer leptomeningeal metastases, gastric cancer, or melanoma leptomeningeal metastases. Figure 8 C Figure 8 D), and it also demonstrates good diagnostic efficacy in diagnosing leptomeningeal metastases from other sources, including breast cancer ( Figure 7 C) AUC was 0.8571; gastric cancer and melanoma ( Figure 7 D) The AUC is 0.7951.

[0074] 5. 1-Methylguanosine The results showed that the abundance of 1-Methylguanosine was significantly upregulated in both the discovery and validation sets of patients with lung cancer leptomeningeal metastases compared to the control group. Figure 10 A, Figure 10B). The receiver operating characteristic (ROC) curve of 1-Methylguanosine in the discovery set was used to distinguish between LC-LM patients and healthy controls. The results showed that the area under the curve (AUC) for 1-Methylguanosine was 0.872 (…). Figure 9 A).

[0075] To verify the diagnostic efficacy of 1-Methylguanosine, we plotted ROC curves again using validation set cohort data. The results showed that 1-Methylguanosine also exhibited high diagnostic performance in this independent validation cohort, with an AUC of 0.916 (…). Figure 9 B).

[0076] Furthermore, we also found that 1-Methylguanosine showed a significant upregulation trend in abundance in other sources, including breast cancer leptomeningeal metastases, gastric cancer, or melanoma leptomeningeal metastases. Figure 10 C Figure 10 D), and it also demonstrates good diagnostic efficacy in diagnosing leptomeningeal metastases from other sources, including breast cancer ( Figure 9 C) AUC was 0.8342; gastric cancer and melanoma ( Figure 9 D) The AUC is 0.8148.

[0077] 6. 2-Hydroxyethanesulfonate The results showed that the abundance of 2-Hydroxyethanesulfonate was significantly downregulated in both the discovery and validation sets of patients with lung cancer leptomeningeal metastases compared to the control group. Figure 12 A, Figure 12 B). The receiver operating characteristic (ROC) curve of 2-Hydroxyethanesulfonate in the discovery set was used to distinguish between LC-LM patients and healthy controls. The results showed that the area under the curve (AUC) of 2-Hydroxyethanesulfonate was 0.867 (…). Figure 11 A).

[0078] To verify the diagnostic efficacy of 2-Hydroxyethanesulfonate, we plotted ROC curves again using validation set cohort data. The results show that, in this independent validation cohort, 2-Hydroxyethanesulfonate also exhibits high diagnostic performance, with an AUC of 0.922 (…). Figure 11 B).

[0079] Furthermore, we also found that 2-Hydroxyethanesulfonate showed a significant downregulation trend in abundance from other sources, including breast cancer leptomeningeal metastases, gastric cancer, or melanoma leptomeningeal metastases. Figure 12 C Figure 12 D), and it also demonstrates good diagnostic efficacy in diagnosing leptomeningeal metastases from other sources, including breast cancer ( Figure 11 C) AUC was 0.8995; gastric cancer and melanoma ( Figure 11 D) The AUC is 0.8877.

[0080] Based on the above experimental results, we believe that Hydroxyphenyllactic acid, Betaine, 3-Methoxytyrosine, L-Kynurenine, 1-Methylguanosine, and 2-Hydroxyethanesulfonate can all serve as reliable biomarkers for diagnosing LM, especially LC-LM.

[0081] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. The application of reagents for detecting marker levels in samples in the preparation of products for diagnosing or assisting in the diagnosis of leptomeningeal metastases, characterized in that, The markers include one or more of the following: Hydroxyphenyllactic acid, Betaine, 3-Methoxytyrosine, L-Kynurenine, 1-Methylguanosine, and 2-Hydroxyethanesulfonate.

2. The application according to claim 1, characterized in that, The leptomeningeal metastasis is cancerous leptomeningeal metastasis. Preferably, the cancer pia mater metastasis includes lung cancer pia mater metastasis, breast cancer pia mater metastasis, gastric cancer pia mater metastasis, or melanoma pia mater metastasis. Preferably, the leptomeningeal metastasis is a lung cancer leptomeningeal metastasis.

3. The application according to claim 1, characterized in that, The products include reagent kits, solid substrates, test strips, high-throughput sequencing platforms, systems, equipment, and devices.

4. The application according to claim 1, characterized in that, The sample was a cerebrospinal fluid sample.

5. A product for diagnosing or assisting in the diagnosis of leptomeningeal metastases, characterized in that, The product includes a reagent for detecting the level of the biomarker of claim 1 in a sample; Preferably, the product includes a reagent kit, a solid substrate, a test strip, a high-throughput sequencing platform, a system, equipment, or a device; Preferably, the sample is cerebrospinal fluid.

6. The application of the biomarker described in claim 1 in constructing a diagnostic model for leptomeningeal metastasis.

7. A method for constructing a diagnostic model for leptomeningeal metastasis, characterized in that, The method includes the following steps: acquiring the biomarker level data of claim 1 in samples from patients with leptomeningeal metastasis and healthy controls, and constructing a diagnostic model based on the biomarker level data; Preferably, the diagnostic model monitors whether the subject has or is suspected of having leptomeningeal metastasis based on the level data of the above-mentioned biomarkers in the sample.

8. The method according to claim 7, characterized in that, The diagnostic model is constructed using machine learning algorithms; Preferably, the machine learning algorithm includes one or more of the following: linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, or random forest model.

9. A system for diagnosing or assisting in the diagnosis of leptomeningeal metastases, characterized in that, The system includes a processor, an input module, and an output module; The processor is used to perform logical operations on the input information using bioinformatics methods; the input module is used to input the level of the biomarker as described in claim 1 in the subject sample, and a computer-readable medium containing instructions that, when executed by the processor, perform an algorithm at the aforementioned biomarker level; the output module is used to output whether the subject has or is suspected of having pia mater metastasis.

10. An electronic device for diagnosing or assisting in the diagnosis of leptomeningeal metastasis, characterized in that, The electronic device includes: The acquisition and detection module is configured to detect the acquired sample to obtain the level of the marker of claim 1 in the sample; The diagnostic module is constructed using the method for constructing the diagnostic model as described in any one of claims 7 or 8, and then outputs diagnostic results based on the model.

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