Method, device, electronic device and storage medium for predicting the prognosis of meningeal cancer originating from lung adenocarcinoma

Abstract

The present invention discloses a method, device, electronic device and storage medium for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma, belonging to the field of intelligent prediction methods. The method comprises: obtaining genetic information of a cerebrospinal fluid sample from a patient with meningeal carcinoma derived from lung adenocarcinoma; performing feature extraction on the genetic information to obtain genetic characteristics and content data thereof, wherein the genetic characteristics are IL-6 and / or TNF-α; performing classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample has a good prognosis or a poor prognosis. The present invention achieves a more accurate prognosis prediction effect for meningeal carcinoma derived from lung adenocarcinoma, providing a basis for subsequent clinical diagnosis and treatment.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent prediction methods, and in particular relates to a method, device, electronic equipment and storage medium for predicting the prognosis of meningeal carcinoma originating from lung adenocarcinoma. Background Art

[0002] Meningeal carcinomatosis (MC) of lung adenocarcinoma refers to the metastasis of lung adenocarcinoma cells to the pia mater (or spinal) membranes of the brain and spinal cord, presenting as diffuse, multifocal, or localized distribution, with or without central nervous system metastasis, including metastatic tumor nodules within the brain and spinal cord parenchyma. Lung adenocarcinoma cells are invasive and can escape from the primary tumor and enter the bloodstream or lymphatic system. They migrate with the bloodstream or lymph to the meninges, where they establish, grow, and multiply. Metastatic pathways include hematogenous metastasis to the choroidal or pia mater vessels, reaching the subarachnoid space, retrograde spread along perineural lymphatic vessels and sheaths, metastasis to the vein of Batson to the submeningeal space, and centripetal spread along perivascular lymphatic vessels.

[0003] Meningeal carcinomatosis, originating from lung adenocarcinoma, is a serious complication of cancer. Its clinical manifestations include: symptoms of increased intracranial pressure (persistent and progressive headaches; nausea and vomiting are often projectile; and visual impairment such as blurred vision and diplopia), signs of meningeal irritation (manifested as neck stiffness, positive Kernig and Brudzinski signs), symptoms of brain parenchyma involvement (may include impaired consciousness such as drowsiness and coma, and cognitive impairment such as memory loss and disorientation), and symptoms of cranial and spinal nerve damage (may include diplopia, eye movement disorders, facial numbness, hearing loss, dysphagia, hoarseness, etc.; spinal nerve involvement may cause radicular pain and segmental sensory deficits).

[0004] Currently, commonly used methods for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma include: clinical factor assessment, imaging assessment, and cerebrospinal fluid cytology assessment. Among them, the impact of each factor in the clinical factor assessment on prognosis is difficult to accurately quantify, and the subjective judgment of different doctors may vary. The effectiveness of some treatments is also affected by a variety of other factors, such as patient tolerance and compliance to drugs. Tiny meningeal metastases may be difficult to accurately detect with imaging examinations, leading to an underestimation of the severity of the disease. Imaging examinations can only provide morphological information of the tumor and cannot reflect the biological characteristics and functional status of tumor cells. Cerebrospinal fluid cytology examinations may have false negative results, which are affected by factors such as the timing and amount of sampling.

[0005] Although the methods currently commonly used to predict the prognosis of meningeal carcinoma derived from lung adenocarcinoma can achieve certain results, the prediction accuracy of these methods for the prognosis of meningeal carcinoma derived from lung adenocarcinoma is not high and it is difficult to meet the requirements of clinical application. Summary of the Invention

[0006] In view of this, and in response to the problems existing in the above-mentioned prior art, the object of the present invention is to provide a method, device, electronic device and storage medium for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma.

[0007] The present invention adopts the following technical solutions to achieve the above-mentioned invention objectives:

[0008] In a first aspect, the present invention provides a method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α, the method comprising:

[0009] Obtain genetic information from cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma;

[0010] Extracting features from the genetic information to obtain genetic features and content data thereof, wherein the genetic features are IL-6 and / or TNF-α;

[0011] Classification prediction is performed based on the content data of the genetic characteristics to obtain a classification result of whether the sample is a good prognosis sample or a poor prognosis sample.

[0012] Furthermore, the classification result is obtained based on a prediction model, and the method for constructing the prediction model includes:

[0013] Obtaining the content data of the genetic features in the cerebrospinal fluid samples of the training set and the clinical characteristics corresponding to the cerebrospinal fluid samples, wherein the clinical characteristics are good prognosis or poor prognosis, extracting the content data of the genetic features in the training set and inputting them into the machine learning model to construct a prediction model, thereby obtaining a constructed prediction model and threshold;

[0014] If the IL-6 content is higher than a threshold value and / or the TNF-α content is lower than a threshold value, a classification result is obtained that the sample is a sample with a good prognosis;

[0015] If the IL-6 content is lower than a threshold value and / or the TNF-α content is higher than a threshold value, a classification result is obtained that the sample is a poor prognosis sample.

[0016] Furthermore, the machine learning model is a Lasso regression model, a linear regression model, a logistic regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a decision tree model, a support vector machine model, a naive Bayes model and / or a perceptron model.

[0017] Furthermore, the content data of the genetic characteristics are obtained by using any one or more of the following methods: enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid phase chip technology.

[0018] In a second aspect, the present invention provides a system for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α, the system comprising:

[0019] Genetic information acquisition unit: used to obtain genetic information of cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma;

[0020] Genetic feature extraction unit: used for extracting features from the genetic information to obtain genetic features and content data thereof, wherein the genetic features are IL-6 and / or TNF-α;

[0021] A disease prognosis prediction unit is used to perform classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample has a good prognosis or a poor prognosis;

[0022] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes:

[0023] Obtaining the content data of the genetic features in the cerebrospinal fluid samples of the training set and the clinical characteristics corresponding to the cerebrospinal fluid samples, wherein the clinical characteristics are good prognosis or poor prognosis, extracting the content data of the genetic features in the training set and inputting them into the machine learning model to construct a prediction model, thereby obtaining a constructed prediction model and threshold;

[0024] If the IL-6 content is higher than a threshold value and / or the TNF-α content is lower than a threshold value, a classification result is obtained that the sample is a sample with a good prognosis;

[0025] If the IL-6 content is lower than a threshold value and / or the TNF-α content is higher than a threshold value, a classification result is obtained that the sample is a poor prognosis sample.

[0026] Furthermore, the machine learning model is a Lasso regression model, a linear regression model, a logistic regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a decision tree model, a support vector machine model, a naive Bayes model and / or a perceptron model.

[0027] In a third aspect, the present invention provides a device or electronic device for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma, wherein the device or electronic device comprises a memory and a processor;

[0028] The memory is used to store program instructions;

[0029] The processor is used to call program instructions, and when the program instructions are executed, the prognosis prediction method for lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α described in the first aspect of the present invention is implemented.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α as described in the first aspect of the present invention.

[0031] In a fifth aspect, the present invention provides the use of a reagent for detecting IL-6 and / or TNF-α levels in cerebrospinal fluid samples in the preparation of a product for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma.

[0032] Furthermore, the reagents include reagents for detecting the expression levels of IL-6 and / or TNF-α in cerebrospinal fluid samples using one or more of enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid phase chip technology.

[0033] In some embodiments, the reagents include reagents for detecting the expression levels of IL-6 and / or TNF-α mRNA in a cerebrospinal fluid sample, and / or reagents for detecting the expression levels of proteins and / or polypeptides encoded by IL-6 and / or TNF-α in a cerebrospinal fluid sample.

[0034] In some embodiments, the reagent for detecting the expression level of IL-6 and / or TNF-α mRNA in a cerebrospinal fluid sample includes a probe that specifically recognizes IL-6 and / or TNF-α, and / or a primer that specifically amplifies IL-6 and / or TNF-α.

[0035] In some embodiments, the reagent for detecting the expression level of the protein and / or polypeptide encoded by the IL-6 and / or TNF-α in the cerebrospinal fluid sample includes an antibody, antibody fragment and / or affinity protein that specifically binds to the IL-6 and / or TNF-α.

[0036] In a sixth aspect, the present invention provides a product for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma, wherein the product comprises the reagent for detecting the level of IL-6 and / or TNF-α in cerebrospinal fluid samples as described above.

[0037] In some embodiments, the product comprises a detection kit, a detection chip, or a detection test strip.

[0038] In some embodiments, the detection kit further includes instructions or labels, positive controls, negative controls, buffers, adjuvants, or solvents; the instructions or labels describe in detail how to use the detection kit provided by the present invention to detect cerebrospinal fluid samples and how to use the detection kit to predict the prognosis of patients with meningeal carcinoma derived from lung adenocarcinoma.

[0039] In some embodiments, the detection kit may also include a variety of different reagents suitable for practical use (such as for different detection methods), and is not limited to the reagents currently listed in the present invention. As long as the reagent is based on the detection of IL-6 and / or TNF-α in cerebrospinal fluid samples to predict the prognosis of meningeal carcinoma derived from lung adenocarcinoma, it is included in the scope of protection of the present invention.

[0040] In some embodiments, the preparation of the detection chip can adopt conventional preparation methods of biochips known to those skilled in the art, including but not limited to: using a solid phase carrier of a modified glass slide or silicon wafer, the 5' end of the probe contains an amino-modified poly dT string, the oligonucleotide probe is prepared into a solution, and then it is spotted on the modified glass slide or silicon wafer using a spotter, arranged into a predetermined sequence or array, and then fixed by leaving it overnight to obtain the detection chip of the present invention.

[0041] In some embodiments, the primers included in the products of the present invention can be prepared by chemical synthesis, using methods well known to those skilled in the art to appropriately design the primers with reference to known information and to prepare the primers by chemical synthesis. In some embodiments, the antibodies included in the products of the present invention can be antibodies or fragments of any structure, size, immunoglobulin class, origin, etc., as long as they bind to the target protein. The antibodies or fragments included in the products of the present invention can be monoclonal or polyclonal. An antibody fragment refers to a portion of an antibody or a peptide containing a portion of an antibody that retains the antibody's binding activity for the antigen. Antibody fragments can include F(ab')2, Fab', Fab, single-chain Fv (scFv), disulfide-bonded Fv (dsFv) or polymers thereof, dimerized V regions (diabodies), or peptides containing CDRs. Antibodies can be obtained by methods well known to those skilled in the art. For example, a polypeptide that retains all or part of the target protein or a mammalian cell expression vector that incorporates a polynucleotide encoding the polypeptide can be prepared as an antigen. After immunizing an animal with the antigen, immune cells are obtained from the immunized animal and fused with cancer cells to obtain hybridomas. Antibodies are then collected from the hybridoma cultures. Finally, monoclonal antibodies against IL-6 and / or TNF-α can be obtained by subjecting the obtained antibodies to antigen-specific purification using IL-6 and / or TNF-α or a portion thereof as an antigen.

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] (1) The present invention provides a novel method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α. The method comprises: obtaining genetic information from a cerebrospinal fluid sample of a patient with lung adenocarcinoma-derived meningeal carcinoma; extracting features from the genetic information to obtain genetic features and their content data, wherein the genetic features are IL-6 and / or TNF-α; and performing classification prediction based on the content data of the genetic features to obtain a classification result of whether the sample has a good prognosis or a poor prognosis. The method provided by the present invention can be used for early prediction of disease progression, guiding individualized treatment, or monitoring treatment response.

[0044] (2) The present invention creatively discovered for the first time that there is a close correlation between the expression levels of IL-6 and / or TNF-α in cerebrospinal fluid samples of patients with lung adenocarcinoma-derived meningeal carcinoma and the prognosis of patients with lung adenocarcinoma-derived meningeal carcinoma. Using the expression levels of IL-6 and / or TNF-α in cerebrospinal fluid samples as genetic information for the prognosis prediction of lung adenocarcinoma-derived meningeal carcinoma not only has high prediction accuracy but also high prediction efficiency, meeting the requirements of clinical application. The present invention provides a new idea and strategy for the prognosis prediction of lung adenocarcinoma-derived meningeal carcinoma, provides a more reliable basis for clinical treatment, and can be used to help clinicians formulate more accurate treatment plans. It has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 : A schematic flow chart of a method for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma based on IL-6 and / or TNF-α provided in an embodiment of the present invention;

[0046] Figure 2 : Schematic diagram of a prognosis prediction system for lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α provided by an embodiment of the present invention;

[0047] Figure 3 : Schematic diagram of a device or electronic device for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma provided by an embodiment of the present invention;

[0048] Figure 4 : The corresponding results of the comparison of IL-6, IL-1β, and TNF-α levels in CSF and serum of MC patients;

[0049] Figure 5 : The corresponding result graph shows that the levels of IL-6, IL-1β and TNF-α in CSF of MC group were significantly higher than those in CSF of control group;

[0050] Figure 6 : The corresponding result graph shows that there is no statistically significant difference in the levels of serum IL-6, IL-1β and TNF-α between the MC group and the lung cancer group;

[0051] Figure 7 : The corresponding result graphs show that IL-6 in CSF is positively correlated with TNF-α, and IL-1β in serum is positively correlated with TNF-α, where Panel A: TNF-α, Panel B: IL-1β;

[0052] Figure 8 : Elevated TNF-α is associated with worse OS, while elevated IL-6 is associated with better OS. Graph A: TNF-α, Graph B: IL-6. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0054] In some processes described in the specification and claims of the present invention and the accompanying drawings, multiple operations are included in a specific order. However, it should be understood that these operations may not be performed in the order in which they are presented herein or may be performed in parallel. Operation numbers such as S1, S2, etc. are merely used to distinguish different operations and do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be performed sequentially or in parallel.

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] Figure 1 The present invention provides a flowchart of a method for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma based on IL-6 and / or TNF-α. Specifically, the method comprises the following steps:

[0057] S1: Obtain genetic information of cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma;

[0058] In some embodiments, the patient refers to any animal, including humans and non-human animals. The non-human animals include all vertebrates, for example, mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any livestock or pets; and non-mammals, such as chickens, amphibians, reptiles, etc. In a specific embodiment of the present invention, the patient is a human.

[0059] In some embodiments, the IL-6 (interleukin-6) and TNF-α (tumor necrosis factor-α) are both important cytokines that play a key role in the body's immune response, inflammation regulation, and other processes.

[0060] IL-6 is a glycoprotein whose gene is located on human chromosome 7. Mature IL-6 consists of 184 amino acids and contains four α-helical structures. Its spatial structure is crucial for its binding to receptors and exerting biological functions.

[0061] TNF-α is a cytokine whose gene is located on human chromosome 6. The TNF-α protein it encodes consists of 233 amino acids. TNF-α can exist in the form of a trimer, and this trimer structure is the key form for its biological activity.

[0062] S2: extracting features from the genetic information to obtain genetic features and content data thereof, wherein the genetic features are IL-6 and / or TNF-α;

[0063] In some embodiments, the content data of the genetic characteristics are obtained using any one or more of the following methods: enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid chip technology, but are not limited to the methods listed. It is understood in the art that any method that can be used to detect the content data of the genetic characteristics can be used in the present invention.

[0064] In one embodiment, the enzyme-linked immunosorbent assay (ELISA) utilizes the specific binding of antigen and antibody. A known IL-6 or TNF-α antibody is coated on a solid phase carrier. After the cerebrospinal fluid sample is added, the IL-6 or TNF-α in the sample will bind to the coated antibody. Then, an enzyme-labeled secondary antibody is added and binds to the antigen-antibody complex bound to the solid phase carrier. Finally, a substrate is added, and the enzyme catalyzes the color development of the substrate. The absorbance value is measured by a microplate reader, and the concentration of IL-6 or TNF-α in the cerebrospinal fluid sample is calculated according to the standard curve.

[0065] In one embodiment, the Western blot method is to first perform polyacrylamide gel electrophoresis on the proteins in the cerebrospinal fluid sample to separate the proteins according to their molecular weight, then transfer the separated proteins to a solid phase membrane, and then hybridize with specific IL-6 or TNF-α antibodies. Finally, protein bands bound to the antibodies are detected by chemiluminescence or colorimetry, and the expression level of IL-6 or TNF-α in the cerebrospinal fluid sample is semi-quantitatively analyzed based on the intensity of the bands.

[0066] In one embodiment, the real-time fluorescence quantitative PCR (qPCR) uses total RNA in cerebrospinal fluid as a template, reverse transcribes it into cDNA, and then performs PCR amplification using the cDNA as a template. A fluorescent group is added to the PCR reaction system, and the intensity of the fluorescent signal is proportional to the amount of PCR product. By monitoring the changes in the fluorescent signal in real time, a standard curve is used to quantitatively analyze the mRNA expression level of IL-6 or TNF-α, thereby indirectly reflecting its protein expression level.

[0067] In one embodiment, the flow cytometry is to incubate cells in the cerebrospinal fluid sample with fluorescently labeled IL-6 or TNF-α antibodies, the antibodies specifically bind to IL-6 or TNF-α on the cell surface or inside the cells, and then the fluorescence signal is detected by flow cytometry, and the expression level of IL-6 or TNF-α is analyzed based on the fluorescence intensity and cell number.

[0068] In one embodiment, the liquid phase chip technology is to fix specific antibodies against multiple cytokines such as IL-6 and TNF-α on different microspheres to form different detection microspheres. These detection microspheres are mixed with cerebrospinal fluid samples, and the cytokines in the sample are bound to the corresponding antibodies. Then, a fluorescently labeled secondary antibody is added, and the fluorescent signal on the microspheres is detected by a liquid phase chip analyzer, and multiple cytokines are quantitatively analyzed at the same time.

[0069] S3: performing classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample is a good prognosis sample or a poor prognosis sample;

[0070] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes:

[0071] Obtaining the content data of the genetic features in the cerebrospinal fluid samples of the training set and the clinical characteristics corresponding to the cerebrospinal fluid samples, wherein the clinical characteristics are good prognosis or poor prognosis, extracting the content data of the genetic features in the training set and inputting them into the machine learning model to construct a prediction model, thereby obtaining a constructed prediction model and threshold;

[0072] If the IL-6 content is higher than a threshold value and / or the TNF-α content is lower than a threshold value, a classification result is obtained that the sample is a sample with a good prognosis;

[0073] If the IL-6 content is lower than a threshold value and / or the TNF-α content is higher than a threshold value, a classification result is obtained that the sample is a poor prognosis sample.

[0074] In some embodiments, the machine learning model is a Lasso regression model, a linear regression model, a logistic regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a decision tree model, a support vector machine model, a naive Bayes model and / or a perceptron model.

[0075] In one embodiment, the threshold in the machine learning model refers to the critical value used to convert the model output into a classification result. In a binary classification problem, the model typically outputs a real value that represents the probability of belonging to a certain category. For example, a logistic regression model outputs a probability value between 0 and 1, and the default threshold is usually 0.5, that is, a probability greater than or equal to 0.5 is classified as a positive class, and a probability less than 0.5 is classified as a negative class.

[0076] In one embodiment, taking machine learning models such as the Lasso regression model, linear regression model, and logistic regression model as examples, these models are all based on statistical and mathematical principles. By learning and fitting a large amount of training data, they find the relationship between the input features (i.e., the content data of IL-6 and TNF-α) and the output results (good prognosis or poor prognosis). Taking the logistic regression model as an example, it maps the content data of IL-6 and TNF-α to a probability value between 0 and 1 by constructing a logical function, representing the probability that the sample has a good prognosis, and determines whether the sample has a good prognosis or a poor prognosis by setting a threshold. Other models, such as the random forest model, construct multiple decision trees and combine the results of these decision trees to perform classification predictions.

[0077] In one embodiment, the following method can be used to determine the threshold: When constructing a prediction model, an appropriate threshold is found by analyzing the training set data and training the model to achieve the best classification effect on the training set. This threshold is determined based on the degree of match between the model's prediction results and the actual clinical characteristics (good prognosis or poor prognosis). Evaluation metrics such as accuracy, recall, and F1 value are typically used as optimization targets, and the threshold is adjusted to optimize these evaluation metrics.

[0078] In one embodiment, a precision-recall curve can be used to select the optimal threshold. By plotting the precision-recall curve, the impact of different thresholds on model performance can be intuitively seen. The point on the curve that achieves the best balance between precision and recall is the optimal threshold.

[0079] In one embodiment, the optimal threshold may be selected using the F1 score, which is the harmonic mean of precision and recall. The optimal threshold is selected by maximizing the F1 score.

[0080] In one embodiment, an optimal threshold value can be selected using a ROC curve. A receiver operating characteristic (ROC) curve can also be used to select the optimal threshold value, and the performance of the model can be evaluated by calculating the area under the curve (AUC) to determine the optimal threshold value.

[0081] In one embodiment, the effectiveness of the constructed prediction model can also be predicted, that is, another data set containing the genetic feature content data corresponding to patients with lung adenocarcinoma-derived meningeal carcinoma with a good prognosis and patients with lung adenocarcinoma-derived meningeal carcinoma with a poor prognosis is taken as a validation set, and the effectiveness of the constructed prediction model is further verified in the validation set.

[0082] In one embodiment, when the method for constructing the above-mentioned prediction model is determined, the threshold value is included in the prediction model, that is, when the prediction model is determined, the threshold value is also determined. Based on the determined threshold value, the classification result of whether the sample to be tested is a sample with good prognosis or a sample with poor prognosis can be predicted. The specific judgment result based on the prediction model is: if the content of IL-6 is higher than the threshold value and / or the content of TNF-α is lower than the threshold value, the classification result of the sample is a sample with good prognosis; if the content of IL-6 is lower than the threshold value and / or the content of TNF-α is higher than the threshold value, the classification result of the sample is a sample with poor prognosis.

[0083] In some embodiments, the prognostic prediction result includes a good prognosis or a poor prognosis, wherein the good prognosis includes cure, remission or stability, and the poor prognosis includes progression or death.

[0084] In some embodiments, the term "cure" means that the disease can be completely cured through existing treatment methods and the expected course of treatment, with the patient's body returning to its pre-illness state, with no more cancer cells or pathogenic factors in the body, and with symptoms and signs completely resolved, with no recurrence for a considerable period of time. For example, in some early-stage malignant tumors, if complete surgical resection of the lesion is followed by long-term follow-up without finding tumor recurrence, the prognosis for cure can be considered achieved.

[0085] In some embodiments, the remission can be divided into complete remission and partial remission. Complete remission refers to the complete disappearance of the symptoms and signs of the disease, and the return to normal of relevant examination indicators such as imaging examinations and laboratory tests. However, it does not mean that there are no cancer cells or pathogenic factors in the body, but that they cannot be found by current detection methods. Partial remission refers to a significant reduction in the symptoms and signs of the disease, a reduction in tumor volume or an improvement in related abnormal indicators, but not yet a complete return to normal. For example, in tumor treatment, if the tumor volume is reduced by more than a certain percentage after chemotherapy and the symptoms are significantly alleviated, it is considered a partial remission.

[0086] In some embodiments, "stable" means that the disease is relatively static, with no significant changes in symptoms or signs, no significant tumor growth or shrinkage, and all diagnostic indicators remaining essentially at their original levels. This may be due to treatment effectively controlling disease progression or the natural course of the disease remaining relatively stable.

[0087] In some embodiments, progression means that the disease continues to progress, with worsening symptoms and signs, increased tumor size, the appearance of new lesions or metastases, and deterioration of relevant examination indicators. This may be due to ineffectiveness of existing treatment options, which are unable to effectively inhibit disease progression, or the disease itself developing resistance to treatment.

[0088] In some embodiments, death is the worst prognosis, that is, the patient ultimately loses his life due to the severity of the disease, ineffective treatment or other uncontrollable factors.

[0089] In one embodiment, the present invention compared IL-6, IL-1β, and TNF-α levels in CSF and serum. Studies have found that the PI3K / Akt signaling pathway plays an important regulatory role in inflammatory responses, cell activation, apoptosis, and other processes. The PI3K / Akt pathway is generally considered to be an upstream activator of the NF-κB signaling cascade, and inflammatory cytokines can participate in tumor regulation by activating the NF-κB pathway. Therefore, this study analyzed IL-6, IL-1β, and TNF-α levels in the cerebrospinal fluid (CSF) and serum of tumor and non-tumor patients. A total of 132 samples were analyzed, including 52 CSF samples and 30 serum samples from patients with meningeal carcinoma (MC) derived from lung adenocarcinoma, 25 serum samples from patients with stage IV lung cancer, and 25 CSF samples from a control group (non-neoplastic central nervous system disease). To analyze cytokine levels in patients with lung adenocarcinoma and MC, we compared IL-6, IL-1β, and TNF-α between the different groups (as shown in Table 1).

[0090] Table 1 Levels of IL-6, IL-1β, and TNF-α in CSF and serum

[0091]

[0092] The results showed that in the MC group, the levels of IL-6 and TNF-α in CSF were significantly higher than those in serum, and the differences were statistically significant (P<0.001, Figure 4 However, the IL-1β level was slightly higher than that in serum, but the difference was not statistically significant (P=0.07, Figure 4The levels of IL-6, IL-1β, and TNF-α in the CSF of the MC group were significantly higher than those in the CSF of the control group, and the differences were statistically significant (P<0.0001, Figure 5 There was no statistically significant difference in the levels of serum IL-6, IL-1β and TNF-α between the MC group and the lung cancer group (P>0.05, Figure 6 ).

[0093] In one embodiment, the present invention studies the correlation analysis between CSF and serum IL-6, IL-1β, and TNF-α. Specifically, the correlation between different cytokines is further analyzed ( Figure 7 ), we found that IL-6 in CSF was positively correlated with TNF-α (r=0.38, P<0.001, Figure 7 A), but there was no correlation between IL-6 and IL-1β, IL-1β and TNF-α (P>0.05). Serum IL-1β and TNF-α were positively correlated (r=0.0.95,P<0.0001, Figure 7 B), but there was no correlation between IL-6 and IL-1β, or between IL-6 and TNF-α (P>0.05).

[0094] In one embodiment, the present invention studies the relationship between IL-6, IL-1β, and TNF-α levels in CSF and survival status and survival analysis. Specifically, we compared the relationship between cytokine levels in CSF of MC patients and KPS and ECOG PS scores, and found that there was no correlation between IL-6, IL-1β, and TNF-α levels and KPS and ECOGPS scores (P>0.05). This shows that different levels of cytokines have nothing to do with survival status scores. In order to clarify the relationship between IL-6, IL-1β, and TNF-α levels and overall survival (OS) ( Figure 8 ), we found that TNF-α (40.8 months vs 14.4 months, P = 0.018, Figure 8 A), IL-6 (14.4 months vs not reached, P = 0.034, Figure 8 B) was significantly correlated with the survival of MC patients, while IL-1β was not associated with the survival (61.5 months vs 14.4 months, P = 0.058).

[0095] The above results show that TNF-α and IL-1β are significantly correlated with the prognosis of MC patients, with elevated TNF-α associated with poor OS and elevated IL-6 associated with better OS. This means that the present invention, through the collection of real clinical samples and extensive experimental verification, has demonstrated that TNF-α and / or IL-1β in cerebrospinal fluid samples can be used to effectively and accurately predict the prognosis of patients with meningeal carcinoma derived from lung adenocarcinoma. Based on this groundbreaking research result, the present invention has developed a novel method for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma based on the levels of IL-6 and / or TNF-α in cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma, as described above.

[0096] Figure 2 A schematic diagram of a prognosis prediction system for lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α is provided in an embodiment of the present invention. Specifically, the system includes:

[0097] Genetic information acquisition unit: used to obtain genetic information of cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma;

[0098] Genetic feature extraction unit: used for extracting features from the genetic information to obtain genetic features and content data thereof, wherein the genetic features are IL-6 and / or TNF-α;

[0099] A disease prognosis prediction unit is used to perform classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample has a good prognosis or a poor prognosis;

[0100] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes:

[0101] Obtaining the content data of the genetic features in the cerebrospinal fluid samples of the training set and the clinical characteristics corresponding to the cerebrospinal fluid samples, wherein the clinical characteristics are good prognosis or poor prognosis, extracting the content data of the genetic features in the training set and inputting them into the machine learning model to construct a prediction model, thereby obtaining a constructed prediction model and threshold;

[0102] If the IL-6 content is higher than a threshold value and / or the TNF-α content is lower than a threshold value, a classification result is obtained that the sample is a sample with a good prognosis;

[0103] If the IL-6 content is lower than a threshold value and / or the TNF-α content is higher than a threshold value, a classification result is obtained that the sample is a poor prognosis sample.

[0104] In some embodiments, the machine learning model is a Lasso regression model, a linear regression model, a logistic regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a decision tree model, a support vector machine model, a naive Bayes model and / or a perceptron model.

[0105] In some embodiments, the content data of the genetic characteristics are obtained using any one or more of the following methods: enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid phase chip technology.

[0106] Figure 3 A schematic diagram of a device or electronic device for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma provided by an embodiment of the present invention. Specifically, the device or electronic device includes: a memory and a processor;

[0107] The memory is used to store program instructions;

[0108] The processor is used to call program instructions, and when the program instructions are executed, the above-mentioned method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α is implemented.

[0109] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the aforementioned method for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma based on IL-6 and / or TNF-α is implemented.

[0110] The embodiments of the present invention also provide the use of a reagent for detecting IL-6 and / or TNF-α levels in cerebrospinal fluid samples in the preparation of a product for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma.

[0111] In some embodiments, the reagents include reagents for detecting the expression levels of IL-6 and / or TNF-α in cerebrospinal fluid samples using one or more of enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid phase chip technology.

[0112] In one embodiment, the cerebrospinal fluid sample is obtained from a subject in need thereof, preferably a patient with lung adenocarcinoma-derived meningeal carcinoma in need of prognosis prediction. The present invention demonstrates through detailed experiments that TNF-α and IL-6 are significantly correlated with survival in patients with lung adenocarcinoma-derived meningeal carcinoma, with elevated TNF-α being associated with poorer overall survival (OS) and elevated IL-6 being associated with better OS.

[0113] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separate, and the components shown as units 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0117] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0118] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0119] The above is a detailed introduction to a computer device provided by the present invention. For those skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma based on IL-6 and / or TNF-α, characterized in that: The method comprises: Obtain genetic information from cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma; Extracting features from the genetic information to obtain genetic features and content data thereof, wherein the genetic features are IL-6 and / or TNF-α; Performing classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample is a good prognosis sample or a poor prognosis sample; The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: Obtaining the content data of the genetic features in the cerebrospinal fluid samples of the training set and the clinical characteristics corresponding to the cerebrospinal fluid samples, wherein the clinical characteristics are good prognosis or poor prognosis, extracting the content data of the genetic features in the training set and inputting them into the machine learning model to construct a prediction model, thereby obtaining a constructed prediction model and threshold; If the IL-6 content is higher than a threshold value and / or the TNF-α content is lower than a threshold value, a classification result is obtained that the sample is a sample with a good prognosis; If the IL-6 content is lower than a threshold value and / or the TNF-α content is higher than a threshold value, a classification result is obtained that the sample is a poor prognosis sample.

2. The method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α according to claim 1, characterized in that: The machine learning model is a Lasso regression model, a linear regression model, a logistic regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a decision tree model, a support vector machine model, a naive Bayes model and / or a perceptron model.

3. The method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α according to claim 1, characterized in that: The content data of the genetic characteristics are obtained by any one or more of the following methods: enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid phase chip technology.

4. A prognosis prediction system for lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α, characterized in that: The system comprises: Genetic information acquisition unit: used to obtain genetic information of cerebrospinal fluid samples from patients with meningeal carcinoma derived from lung adenocarcinoma; Genetic feature extraction unit: used for extracting features from the genetic information to obtain genetic features and content data thereof, wherein the genetic features are IL-6 and / or TNF-α; A disease prognosis prediction unit is used to perform classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample has a good prognosis or a poor prognosis; The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: Obtaining the content data of the genetic features in the cerebrospinal fluid samples of the training set and the clinical characteristics corresponding to the cerebrospinal fluid samples, wherein the clinical characteristics are good prognosis or poor prognosis, extracting the content data of the genetic features in the training set and inputting them into the machine learning model to construct a prediction model, thereby obtaining a constructed prediction model and threshold; If the IL-6 content is higher than a threshold value and / or the TNF-α content is lower than a threshold value, a classification result is obtained that the sample is a sample with a good prognosis; If the IL-6 content is lower than a threshold value and / or the TNF-α content is higher than a threshold value, a classification result is obtained that the sample is a poor prognosis sample.

5. The IL-6 and / or TNF-α-based system for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma according to claim 4, characterized in that: The machine learning model is a Lasso regression model, a linear regression model, a logistic regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a decision tree model, a support vector machine model, a naive Bayes model and / or a perceptron model.

6. An electronic device for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma, characterized in that: The electronic device includes a memory and a processor; The memory is used to store program instructions; The processor is used to call program instructions, and when the program instructions are executed, the prognosis prediction method for lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α according to any one of claims 1 to 3 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for predicting the prognosis of lung adenocarcinoma-derived meningeal carcinoma based on IL-6 and / or TNF-α according to any one of claims 1 to 3 is implemented.

8. Use of reagents for detecting IL-6 and / or TNF-α levels in cerebrospinal fluid samples in the preparation of a product for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma.

9. The use according to claim 8, characterized in that The reagents include reagents for detecting the expression levels of IL-6 and / or TNF-α in cerebrospinal fluid samples using one or more of enzyme-linked immunosorbent assay, immunoblotting, real-time fluorescence quantitative PCR, flow cytometry, and liquid phase chip technology.

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