Assessment method for T cell senescence and invasive lung adenocarcinoma onset risk

Abstract

The invention provides a T cell senescence and invasive lung adenocarcinoma onset risk assessment method based on the release level of mitochondrial DNA (mtDNA) in T cell culture supernatant, and relates to the field of biomedical detection.The method comprises the operation steps that peripheral blood samples of healthy people and lung adenocarcinoma patients are collected respectively, peripheral blood mononuclear cells (PBMC) are separated out, and the peripheral blood mononuclear cells (PBMCs) are obtained; further purifying into CD8 + T cells, activating and culturing, and detecting the release level of mtDNA in culture supernatant. Based on a detection result, constructing a mathematical formula for evaluating T cell senescence and predicting the onset risk of the invasive lung adenocarcinoma, and verifying the mathematical formula; and finally, evaluating the T cell aging and invasive lung adenocarcinoma onset risk of the to-be-detected patient by using the verified mathematical formula. By detecting the release level of mtDNA in activated T cell culture supernatant, a mathematical formula for evaluating T cell senescence and predicting the onset risk of invasive lung adenocarcinoma is constructed, and a new method is provided for tumor early diagnosis and immune senescence research.

Description

Technical Field

[0001] The present invention relates to the field of biomedical detection, and particularly to a method for evaluating the senescence of T cells and the risk of developing invasive lung adenocarcinoma. Background Art

[0002] With the increase of age, the immune function of the body gradually declines, mainly manifested as immune deficiency and enhanced chronic inflammation, and this phenomenon is called immune senescence. As an important part of the immune system, the senescence of T cells is the core of immune senescence. The senescence of T cells not only weakens the immune response ability of the body, but also is closely related to the occurrence of infectious diseases, autoimmune diseases and tumors. In recent years, studies have shown that the senescence of T cells is closely related to the occurrence and development of various malignant tumors, especially lung adenocarcinoma.

[0003] At present, tumor diagnosis mainly relies on imaging examinations and tissue biopsies, but these methods cannot timely reflect the immune status and disease progression of patients. Therefore, it is of great significance to establish a quantitative system for T cell senescence and predict the occurrence and development of tumors by dynamically monitoring the functional status of T cells. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for evaluating the senescence of T cells and the risk of developing invasive lung adenocarcinoma based on the release level of mitochondrial DNA (mtDNA) in the culture supernatant of activated T cells. Based on the mechanism of T cell senescence caused by lysosomal function defects, it is proposed to detect the release level of mtDNA in the culture supernatant of activated T cells, construct a mathematical formula for evaluating T cell senescence and predicting the risk of developing invasive lung adenocarcinoma, and provide a new method for early tumor diagnosis and immune senescence research.

[0005] To achieve the above purpose, the present invention provides the following solution:

[0006] A method for evaluating the senescence of T cells and the risk of developing invasive lung adenocarcinoma, comprising:

[0007] Collecting peripheral blood samples from healthy people and patients with lung adenocarcinoma respectively;

[0008] Separating peripheral blood mononuclear cells (PBMCs) from the above peripheral blood samples, further separating and purifying CD8 + T cells and activating and culturing them, detecting the released mtDNA in their culture supernatant, and obtaining corresponding detection results;

[0009] Constructing a mathematical formula for evaluating T cell senescence and predicting the risk of developing invasive lung adenocarcinoma according to the detection results, and verifying each mathematical formula;

[0010] Using a verified mathematical formula, evaluate the T-cell senescence and the risk of developing invasive pulmonary adenocarcinoma in the patient to be tested.

[0011] Preferably, the test result is the absolute copy number of mtDNA per microliter of culture supernatant.

[0012] Preferably, the mathematical formula for evaluating T-cell senescence is:

[0013] X = (Y + 6.768) / 0.3774

[0014] Wherein, X is the immune age, reflecting the degree of T-cell senescence, and Y is the absolute copy number of mtDNA.

[0015] Preferably, the mathematical formula for predicting the risk of developing invasive pulmonary adenocarcinoma is:

[0016]

[0017] Wherein, Z is the probability of suffering from invasive adenocarcinoma.

[0018] Preferably, the screening criteria for the healthy population are: no autoimmune diseases, cancer history, kidney diseases, diabetes.

[0019] Preferably, the screening criteria for the pulmonary adenocarcinoma patients are: postoperative pathology confirmed as pre-invasive lesion in situ adenocarcinoma, minimally invasive adenocarcinoma and invasive adenocarcinoma.

[0020] Preferably, the volume of the peripheral blood sample is 3 ml.

[0021] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0022] The present invention provides a method for evaluating the T-cell senescence and the risk of developing invasive pulmonary adenocarcinoma, including: respectively collecting peripheral blood of a healthy population and peripheral blood samples of pulmonary adenocarcinoma patients; separating PBMC from the peripheral blood samples, further separating and purifying CD8 + T cells and activating and culturing them, detecting the mtDNA released in their culture supernatants to obtain corresponding test results; constructing mathematical formulas for evaluating T-cell senescence and predicting the risk of developing invasive pulmonary adenocarcinoma according to the test results, and verifying each mathematical formula; using the verified mathematical formulas to evaluate the T-cell senescence and the risk of developing invasive pulmonary adenocarcinoma in the patient to be tested. Based on the mechanism of T-cell senescence caused by lysosomal function defects, the present invention proposes to construct mathematical formulas for evaluating T-cell senescence and predicting the risk of developing invasive pulmonary adenocarcinoma by detecting the release level of mtDNA in the culture supernatants of activated T cells, providing a new method for early tumor diagnosis and immunosenescence research. Description of the Drawings

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0024] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention;

[0025] Figure 2 It is a schematic flowchart for detecting the absolute copy number of mtDNA released in the culture supernatant of activated T cells provided by the embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of the absolute copy number of mtDNA released by activated T cells per microliter of culture supernatant in healthy donors of different age groups and samples at different pathological processes of lung adenocarcinoma provided by the embodiment of the present invention;

[0027] Figure 4 It is a schematic diagram for constructing a mathematical formula for evaluating T cell senescence provided by the embodiment of the present invention;

[0028] Figure 5 It is a schematic diagram of a mathematical formula for predicting the incidence risk of invasive lung adenocarcinoma in the population aged 45 - 64 provided by the embodiment of the present invention. Detailed implementation manners

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0030] The object of the present invention is to provide an evaluation method for T cell senescence and the incidence risk of invasive lung adenocarcinoma based on the mtDNA release level in the culture supernatant of activated T cells. Based on the mechanism of T cell senescence caused by lysosomal function defects, it is proposed to detect the release level of mtDNA in the culture supernatant of activated T cells, construct a mathematical formula for evaluating T cell senescence and predicting the incidence risk of invasive adenocarcinoma, so as to provide a new method for early tumor diagnosis and immune senescence research.

[0031] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific implementation manners.

[0032] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention, asFigure 1 As shown in the figure, the present invention provides a method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma, including:

[0033] Step 100: Collect peripheral blood samples from healthy individuals and patients with lung adenocarcinoma respectively;

[0034] Step 200: Isolate peripheral blood mononuclear cells from the peripheral blood samples, further isolate and purify CD8 + T cells, activate and culture them, and detect the mtDNA released in their culture supernatants to obtain corresponding test results;

[0035] Step 300: Construct mathematical formulas for evaluating T cell senescence and predicting the risk of invasive lung adenocarcinoma based on the test results, and verify each mathematical formula;

[0036] Step 400: Use the verified mathematical formula to evaluate the T cell senescence and the risk of invasive lung adenocarcinoma in the patient to be tested.

[0037] As Figures 2 to 4 shown, the specific process of this embodiment is as follows:

[0038] Process S1: Selection of samples:

[0039] 1) Healthy volunteers: Screen healthy elderly people (65 - 85 years old), healthy middle-aged people (45 - 64 years old), and healthy young people (20 - 35 years old), and collect 3 mL of peripheral blood; The criteria for health are: no history of autoimmune diseases or cancer, no kidney diseases, diabetes, etc.

[0040] 2) Patients with lung adenocarcinoma: Intend to collect 3 mL of peripheral blood from patients with lung adenocarcinoma undergoing surgery at different pathological processes. The postoperative pathology is confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) before lung adenocarcinoma invasion.

[0041] Process S2: Detect the absolute copy number of mtDNA released in the culture supernatant of activated T cells

[0042] 1) Use density gradient centrifugation to separate PBMC.

[0043] 2) Use EasySep TM Human CD8 + T Cell Isolation Kit (STEMCELL Technologies) for isolation and purification CD8 + T cells, which were then activated and cultured for 72 h, and then the supernatant was collected.

[0044] 3) Using an mtDNA absolute copy number quantification kit (ScienCell), the absolute copy number of mtDNA in the culture supernatant was quantified by real-time fluorescence quantitative PCR, and finally the absolute copy number of mtDNA per microliter of the culture supernatant was used as the numerical value presented in the experimental results.

[0045] Optionally, during the process of separating PBMCs by density gradient centrifugation, an automated cell separation device (such as the Ficoll-Paque automated separation system) can also be introduced in this example to improve the separation efficiency and cell viability. In addition, to reduce contamination and cell damage during sample processing, an antioxidant (such as N-acetylcysteine) can be added during centrifugation to protect the integrity of the cell membrane and ensure that the separated PBMCs have higher viability and purity. This improvement not only improves the repeatability of the experiment but also provides a higher-quality cell source for the subsequent separation of CD8 + T cells.

[0046] Furthermore, during the separation and purification CD8 + T cells, the activity and function of the cells can be further improved by optimizing the activation conditions in this example. For example, a cytokine combination of anti-CD3 / CD28 magnetic beads combined with IL-2 and IL-7 was used for activation and culture, and the pH value and redox state in the culture environment were dynamically monitored to simulate the in vivo microenvironment. In addition, microfluidic chip technology can be introduced in this example to integrate the activation and culture of CD8 + T cells into a miniaturized platform, reducing the culture time and increasing the cell yield. This innovative method can more efficiently obtain functional CD8 + T cells and ensure that the released mtDNA is more representative.

[0047] Even further, when detecting the absolute copy number of mtDNA by real-time fluorescence quantitative PCR in this example, digital PCR (dPCR) technology can be introduced to improve the sensitivity and accuracy of the detection. Digital PCR can achieve absolute quantification through single-molecule separation and amplification, avoiding the errors caused by the standard curve dependence in traditional qPCR. In addition, high-throughput sequencing technology can be combined to analyze the sequence integrity and mutation status of mtDNA, thereby further revealing the molecular characteristics of mtDNA release during T cell senescence. This multi-level detection method not only improves the reliability of the data but also provides richer parameter support for the construction of subsequent mathematical models.

[0048] Exemplarily, in this embodiment, digital PCR (dPCR) technology is used to detect the absolute copy number of mitochondrial DNA (mtDNA) in the activated T cell culture supernatant to improve the detection sensitivity and accuracy. In the specific implementation process, first, the collected T cell culture supernatant samples are pretreated, including removing cell debris and impurities, and a pure supernatant is obtained through centrifugation and filtration steps. Subsequently, the free DNA in the supernatant is extracted, and a high-purity DNA extraction kit is used to ensure the integrity and purity of mitochondrial DNA. The extracted DNA samples are dispensed into dPCR chips or droplets, and each reaction unit contains only a small amount or a single DNA molecule. Through specifically designed primers and probes, combined with fluorescence labeling technology, dPCR can perform single-molecule amplification and detection of mtDNA in each reaction unit, thereby achieving absolute quantification.

[0049] During the detection process, dPCR divides the sample into tens of thousands of independent reaction units, avoiding the dependence on the standard curve in traditional qPCR, and directly calculates the absolute copy number of mtDNA through the number of positive reaction units. This method not only improves the detection sensitivity and can accurately detect low concentrations of mtDNA, but also significantly reduces experimental errors, especially suitable for detecting the trace mtDNA release levels that may exist in samples of the elderly and patients with lung adenocarcinoma. In addition, dPCR technology can simultaneously detect specific mutation sites of mtDNA, providing more comprehensive data support for further studying the molecular characteristics of mtDNA during T cell aging. This high-precision detection method provides reliable basic data for the construction of subsequent mathematical formulas.

[0050] Process S3: Mathematical formula construction and verification steps

[0051] 1) Mathematical formula for T cell aging: Establish a database of the absolute copy number of mtDNA of age and healthy volunteers, analyze the data using linear regression, and construct a mathematical formula for evaluating T cell aging (i.e., immune age): X = (Y + 6.768) / 0.3774 (where Y represents the absolute copy number of mitochondrial DNA and X represents the immune age that can reflect the degree of T cell aging).

[0052] 2) Mathematical formula for the incidence risk of invasive adenocarcinoma: Similarly, perform the above series of operations, establish a database of the absolute copy number of mtDNA and the incidence risk of invasive adenocarcinoma, and use logistic regression analysis method to construct a mathematical formula for predicting the incidence risk of invasive adenocarcinoma: (where Z represents the probability of having invasive adenocarcinoma and Y represents the absolute copy number of mitochondrial DNA). This formula is only applicable to predicting the probability of having invasive lung adenocarcinoma in people aged 45 - 64.

[0053] Exemplarily, in this embodiment, a larger-scale healthy volunteer database is also established, covering individuals of different genders and living habits, so as to improve the universality and accuracy of the formula. Secondly, a multivariate regression model is introduced in the linear regression analysis, combining the relationship between age and the absolute copy number of mtDNA with other potential influencing factors (such as BMI, smoking history, chronic medical history, etc.) to optimize the goodness of fit of the formula. In addition, machine learning algorithms (such as random forest regression) can be used to deeply mine the data to identify hidden variables that may affect T cell senescence, thereby further improving the mathematical model. This innovative method can more comprehensively reflect the complexity of T cell senescence and provide a more accurate tool for the assessment of immune age.

[0054] Furthermore, this embodiment also prepares a high-quality training data set. This data set includes the absolute copy number of mtDNA (Y), age (X) of healthy volunteers, and other potential influencing factors (such as BMI, smoking history, chronic medical history, serum inflammatory factor levels, etc.). In the data preprocessing stage, missing values need to be filled (such as mean filling or interpolation method), continuous variables need to be standardized, and categorical variables need to be one-hot encoded. Subsequently, the data set is divided into a training set and a test set (such as a ratio of 8:2) to ensure the generalization ability of the model. In the model construction stage, the random forest regression algorithm is used. Through multiple random samplings and decision tree constructions, the relative importance of each variable to the mtDNA copy number is evaluated, so as to identify hidden variables that may affect T cell senescence.

[0055] After the model training is completed, the performance of the model is evaluated through cross-validation to ensure its stability and accuracy on different data subsets. Subsequently, according to the feature importance scores of the random forest model, the variables with the greatest impact on the mtDNA copy number are selected, and the SHAP (Shapley Additive Explanations) values are combined to further explain the contribution of these variables to the model prediction. Through this method, the potential impact of hidden variables (such as specific inflammatory factors or living habits) on T cell senescence can be identified and these variables can be incorporated into the construction of the mathematical model, thereby optimizing the goodness of fit and prediction ability of the formula. This deep mining method based on machine learning not only improves the scientific nature of the model, but also provides a new perspective for the study of immune senescence.

[0056] Furthermore, when constructing the mathematical formula for the risk of invasive adenocarcinoma, the applicability of the formula can be further refined through stratified analysis. For example, the population aged 45 - 64 can be grouped according to factors such as gender, smoking history, and family tumor history, and mathematical formulas for sub - populations can be established respectively to improve the accuracy of prediction. In addition, interaction terms (such as the interaction between mtDNA copy number and smoking history) can be introduced in the logistic regression analysis to reveal the synergistic effects between different factors. To enhance the predictive ability of the formula, multivariate analysis can also be conducted by combining other biomarkers (such as serum inflammatory factor levels, T cell subset ratios, etc.) to construct a multi - dimensional prediction model. This improvement not only enhances the predictive performance of the formula but also provides a more scientific basis for personalized tumor risk assessment.

[0057] The beneficial effects of the present invention are as follows:

[0058] Establishing the relationship between the quantitative standard of T cell senescence and malignant tumors using the present invention will help prospectively predict the incidence of tumors in clinical decision - making, carry out tumor preventive treatment in advance, and greatly shift the anti - cancer treatment threshold forward. In addition, this method is simple to operate and can be rapidly promoted on a large scale, with significant social and economic benefits. More importantly, this quantitative standard provides a digital representation of the immunity of the elderly, helps to establish an immunity file for elderly individuals, and promotes the formation of new models for disease prevention before its onset, early diagnosis of diseases, prognosis assessment, personalized medicine, and health management.

[0059] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other.

[0060] Specific examples are used in this article to elaborate on the principles and implementation methods of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for assessing T cell senescence and the risk of invasive lung adenocarcinoma, characterized in that: include: Peripheral blood samples were collected from healthy subjects and patients with lung adenocarcinoma; Peripheral blood mononuclear cells were isolated from the peripheral blood samples, further isolated and purified into CD8+T cells, activated and cultured, and the mtDNA released in the culture supernatant was detected to obtain corresponding test results; Based on the test results, mathematical formulas were constructed to evaluate T cell aging and predict the risk of invasive lung adenocarcinoma, and each mathematical formula was verified; A validated mathematical formula was used to assess the patient's T cell senescence and risk of invasive lung adenocarcinoma.

2. The method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma according to claim 1, characterized in that: The detection result is the absolute copy number of mtDNA per microliter of culture supernatant.

3. The method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma according to claim 2, characterized in that: The mathematical formula for evaluating T cell senescence is: X=(Y+6.768) / 0.3774 Among them, X is the immune age, reflecting the degree of T cell aging, and Y is the absolute copy number of mtDNA.

4. The method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma according to claim 2, characterized in that: The mathematical formula for predicting the risk of invasive lung adenocarcinoma is: Where Z is the probability of developing invasive lung adenocarcinoma.

5. The method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma according to claim 1, characterized in that: The screening criteria for the healthy population are: no autoimmune disease, history of cancer, kidney disease, or diabetes.

6. The method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma according to claim 1, characterized in that: The screening criteria for the patients with lung adenocarcinoma are: postoperative pathological diagnosis of pre-invasive lesions, adenocarcinoma in situ, microinvasive adenocarcinoma, and invasive adenocarcinoma.

7. The method for evaluating T cell senescence and the risk of invasive lung adenocarcinoma according to claim 1, characterized in that: The volume of the peripheral blood sample is 3 ml.

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