An immune-related biomarker, reagent kit, and immune age prediction model based on conventional transcriptome sequencing methods

Abstract

This invention provides a biomarker for assessing human immune status or determining immune age based on conventional transcriptome sequencing methods. The biomarker comprises any two or more genes selected from the following gene families: SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO. This invention also provides the application of the above biomarker in preparing kits for assessing human immune status or determining immune age. Furthermore, this invention provides a method for constructing a model for assessing human immune status or determining immune age, and a predictive model. This invention can be widely applied to the diagnosis and assessment of premature immune aging and immune rejuvenation.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection and relates to a biomarker and a reagent kit. Specifically, it relates to an immune-related biomarker, reagent kit, and immune age prediction model based on conventional transcriptome sequencing methods. Background Technology

[0002] In recent years, with the accelerating aging of the population, increasing social pressure, and the continuous deterioration of the natural environment, age-related degenerative changes have been recognized as a major risk factor for human disease and death. As a complex degenerative process, aging significantly impacts the immune system, leading to alterations in immune function and subtypes, a phenomenon known as immunosenescence. Simultaneously, the inflammatory components of the immune system in older adults remain elevated for extended periods. These changes influence susceptibility to cancer, cardiovascular disease, neurodegenerative diseases, and other illnesses. Traditionally, an individual's chronological age (cAge) is considered their actual age, but the rate of physiological aging of the immune system does not necessarily match chronological age, resulting in differences in biological age (bAge) among individuals.

[0003] Peripheral blood lymphocytes (T cells, B cells, and NK cells), dendritic cells, and monocytes constitute a diverse population of peripheral blood mononuclear cells (PBMCs). These immune cells are widely involved in and regulate immunosenescence-related processes. Simultaneously, the senescence of immune cells accelerates the overall aging process. Although the concept of immunoage emerged in the 1970s, assessing immunoage has remained a challenging problem. Traditional methods typically rely on immunosenescence biomarkers obtained through in vitro tissue and immune cell culture screening, or on the number and proportion of an individual's immune cells to predict their immune status and aging process. While these methods have some value, they also have limitations. Studies have shown that these traditional methods either lack the precision assessment capabilities based on population trials or lack in-depth assessment of immunosenescence changes at the gene and messenger ribonucleic acid (mRNA) transcriptional levels, failing to provide a multi-faceted and multi-dimensional determination of an individual's immune system health and aging degree.

[0004] Therefore, current methods for determining immune age are limited, lacking highly accurate tools to comprehensively and multidimensionally assess the age and health status of the immune system from a deeper perspective of genetic and mRNA changes. A new immunoaging assessment technology is needed, based on gene population expression, to introduce new dimensions and depth into assessing the health and aging of the immune system, achieving a more comprehensive, accurate, and user-friendly assessment of immune system age and health. Summary of the Invention

[0005] To address the aforementioned technical problems in the prior art, this invention provides an immune-related biomarker, kit, and immune age prediction model based on conventional transcriptome sequencing methods. This immune-related biomarker, kit, and immune age prediction model based on conventional transcriptome sequencing methods aims to solve the technical problem that existing biomarker sets cannot determine an individual's immune age and degree of immune aging at the gene and transcriptional levels.

[0006] This invention provides a biomarker for assessing human immune status or determining immune age, which is composed of any two or more genes selected from the following genes: SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO.

[0007] The present invention also provides the application of the above-mentioned biomarkers in the preparation of kits for assessing human immune status or determining immune age.

[0008] This invention also provides a kit for assessing human immune status or determining immune age, containing primers for detecting any two or more genes selected from the following gene groups: SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO. Primers for detecting the SELL gene are shown in SEQ ID NO. 1-2; primers for detecting the TMIGD2 gene are shown in SEQ ID NO. 3-4; primers for detecting the NOSIP gene are shown in SEQ ID NO. 5-6; primers for detecting the DENND2D gene are shown in SEQ ID NO. 7-8; primers for detecting the CCT2 gene are shown in SEQ ID NO. 9-10; primers for detecting the NSMCE1 gene are shown in SEQ ID NO. 11-12; primers for detecting the CCR7 gene are shown in SEQ ID NO. 13-14; and primers for detecting the S100A6 gene are shown in SEQ ID NO. 11-12. Primers for detecting the S100A4 gene are shown in SEQ ID NO. 15-16; primers for detecting the LGALS1 gene are shown in SEQ ID NO. 19-20; and primers for detecting the TSPO gene are shown in SEQ ID NO. 21-22.

[0009] Furthermore, it also includes reagents used for PCR detection of the biomarkers.

[0010] The present invention also provides primers for detecting the above-mentioned biomarkers, the gene sequences of which are shown in SEQ ID NO. 1 to 22.

[0011] The present invention also provides the use of primers or probes for detecting the above-mentioned biomarkers in the preparation of kits for assessing human immune status or determining immune age. The biomarkers are any two or more combinations of the following: SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene.

[0012] This invention also provides a method for constructing an immune age determination model, the specific steps of which are as follows:

[0013] Step 1: Construct a multiple linear regression model as an immune age prediction model. The specific model formula is as follows:

[0014] Immunity age=β0+β1*X1+β2*X2+β3*X3+β4*X4+β5*X5+β6*X6+β7*X7+β8*X8+β9*X9+β10*X10+β11*X11;

[0015] In the formula, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, and X11 represent the expression levels of the SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO genes, respectively.

[0016] In the formula, β0, β1, β2, β3, β4, β5, β6, β7, β8, β9, β10, and β11 are, respectively, the intercept term, the regression coefficient of the SELL gene, the regression coefficient of the TMIGD2 gene, the regression coefficient of the NOSIP gene, the regression coefficient of the DENND2D gene, the regression coefficient of the CCT2 gene, the regression coefficient of the NSMCE1 gene, the regression coefficient of the CCR7 gene, the regression coefficient of the S100A6 gene, the regression coefficient of the S100A4 gene, the regression coefficient of the LGALS1 gene, and the regression coefficient of the TSPO gene;

[0017] Step 2: Select multiple genes from the following genes and define them as target genes: SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene. Define the other genes as non-target genes.

[0018] Peripheral blood samples from healthy individuals of all ages were obtained as training samples, and the expression levels of various target genes in each training sample were detected.

[0019] Step 3: Construct a target gene expression matrix. Each row in the target gene expression matrix represents a target gene, and each column in the target gene expression matrix represents a training sample. Each element in the target gene expression matrix contains the age information of the sample source of the training sample in the column to which the element belongs, as well as the expression level of the target gene in the column to which the element belongs in the training sample.

[0020] Step 4: Set the expression levels of all non-target genes in the immune age prediction model constructed in Step 1 to 0. Then, based on the data in the target gene expression matrix, use the least squares method to fit the immune age prediction model, solve for the intercept term β0 and the regression coefficients of each target gene in the immune age prediction model, and thus obtain the trained immune age prediction model.

[0021] Step 5: Using the trained immune age prediction model, predict the immune age of the target user. The specific prediction method is as follows:

[0022] First, obtain peripheral blood samples from the target user, and then detect the expression levels of various target genes in the peripheral blood samples.

[0023] The expression levels of various target genes in the peripheral blood samples of the target users are then input into the trained immune age prediction model, which is used to predict the immune age of the target users.

[0024] Furthermore, in step 5, after predicting the immune age of the target user using the immune age prediction model, the predicted immune age of the target user is subtracted from the actual age of the target user.

[0025] If the obtained age difference is less than -2.5 years, the target user's immune status is marked as accelerated immune aging;

[0026] If the obtained age difference is greater than 2.5 years, the target user's immune status will be marked as immune aging deceleration;

[0027] If the obtained age difference is greater than or equal to -2.5 years and less than or equal to 2.5 years, the target user's immune status is marked as normal.

[0028] Furthermore, the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene were all selected as target genes.

[0029] The present invention also provides an immune age prediction system, the system comprising:

[0030] The data acquisition module is used to acquire target data; the target data is the expression level of target genes in human peripheral blood; the target genes are any two or more combinations of the following genes: SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene.

[0031] The prediction module inputs the expression levels of various target genes in the peripheral blood samples of the target user into the trained immune age prediction model, and uses the immune age prediction model to predict the immune age of the target user.

[0032] Furthermore, the system also includes:

[0033] The evaluation module uses an immune age prediction model to predict the immune age of the target user, and then subtracts the predicted immune age of the target user from the target user's actual age.

[0034] If the obtained age difference is less than -2.5 years, the target user's immune status is marked as accelerated immune aging;

[0035] If the obtained age difference is greater than 2.5 years, the target user's immune status will be marked as immune aging deceleration;

[0036] If the obtained age difference is greater than or equal to -2.5 years and less than or equal to 2.5 years, the target user's immune status is marked as normal.

[0037] The marker genes in this invention were selected through careful systematic testing and routine transcriptome sequencing of peripheral blood mononuclear cell samples from 29 healthy subjects (0-90 years old) across the entire lifespan. By screening peripheral blood mononuclear cells from these 29 subjects across the entire lifespan using routine transcriptome sequencing, we identified a group of genes whose expression patterns showed a monotonically increasing or decreasing trend with age, exhibiting a strong correlation with age-related changes. Among these, 138 genes were upregulated with age, and 933 genes were downregulated with age. Combined single-cell transcriptome analysis revealed four age-upregulated genes shared by both omics approaches: S100A6, LGALS1, S100A4, and TSPO; and seven age-downregulated genes shared by both omics approaches: SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, and CCR7.

[0038] This invention provides a more accurate assessment of immune system health by effectively quantifying the true aging rate of the human immune system, identifying early weakening of the immune system, and providing a scientific basis for developing personalized interventions to reduce the risk of disease and death in the elderly, thus contributing to the healthy aging process of humankind.

[0039] This invention demonstrates through linear regression model fitting analysis that detecting changes in the expression of two or more genes within a gene cluster can be used to measure and assess the degree of immunosenescence, while also distinguishing between accelerated immunosenescence (premature immunodeficiency) and decelerated immunosenescence (immunoaging). The aforementioned gene cluster is used to prepare a diagnostic kit for assessing the immunosenescence status and determining immune age in a population throughout its entire lifespan. This kit primarily detects changes in gene expression levels within this gene cluster, providing an accurate assessment of an individual's immunosenescence status and offering important guidance for determining individual immune age.

[0040] This invention uses the gene cluster disclosed in this invention to detect one or more of its 11 genes, and constructs a multiple linear regression model based on gene expression levels to calculate an individual's immune age. This model can accurately assess an individual's immune aging status at the transcriptional level, avoiding cumbersome statistical analysis.

[0041] The gene cluster detection results of this invention can not only accurately assess the state of the immune system, but also compare actual age with immune age, thereby determining the rate and extent of immunoaging. Compared with traditional methods, immunoaging assessment provides more accurate and comprehensive information, helping to predict potential immune-related risks for individuals, determine whether an individual is experiencing accelerated or decelerated immunoaging, and provide a scientific basis for developing personalized interventions to reduce the risk of disease and death in the elderly, thus contributing to the healthy aging process of humanity. The kit of this invention provides a rapid, convenient, and efficient detection method for individual immunoaging assessment, while also being cost-effective. Finally, the genes listed in the marker gene table can serve as potential targets for immunoaging intervention, which is of great significance for future research on the treatment of immunoaging.

[0042] Compared with existing technologies, the technological advancements of this invention are significant. The biomarkers, kits, and predictive models based on conventional transcriptome sequencing methods of this invention, through the identification of multiple gene expression patterns, are simple, highly accurate, and low-cost. They can be widely applied to the diagnosis and assessment of premature immune aging and immune degeneration, and have significant application potential, especially for the accurate detection and determination of individual immune health and immune aging status. This provides a new approach to assessing the true aging rate of the human immune system and contributes to the healthy aging management of the elderly population. Attached Figure Description

[0043] Figure 1 The diagram shows the determination of immune age and the assessment of immune aging status.

[0044] Figure 2 This study revealed age-related genes exhibiting monotonic trends in peripheral blood mononuclear cells, identified through routine transcriptome sequencing. Among these, 138 genes were upregulated by age, and 933 genes were downregulated by age.

[0045] Figure 3 The study revealed age-related genes with shared monotonic trends in peripheral blood mononuclear cells, identified by single-cell transcriptome sequencing and conventional transcriptome sequencing; among them, four genes were upregulated by age and seven genes were downregulated by age.

[0046] Figure 4 The study showed a strong correlation between the expression of gene clusters in peripheral blood mononuclear cells of individuals of different ages and age. Three genes were used as examples: a. LGALS1 gene, b. CCR7 gene, and TMIGD2 gene.

[0047] Figure 5 The results show that the immune age calculated based on marker genes in healthy individuals fits well with actual age and has a strong correlation.

[0048] Figure 6The results showed that the immune age of 151 volunteers tested by the kit as a test set fit well with their actual age and had a strong correlation. Detailed Implementation

[0049] Example 1

[0050] I. Collection, storage, and recording of peripheral blood mononuclear cells

[0051] The peripheral blood mononuclear cells collected in this study were obtained from 29 healthy volunteers of all ages recruited for the study, including 4 newborns, 3 infants aged 1 year, 5 adolescents aged 12 years, 4 young adults aged 18 years, 1 adult aged 30 years, 5 middle-aged people aged 50 years, 5 elderly people aged 70 years, and 2 elderly people aged ≥90 years. Inclusion criteria: ① Self-sufficient or basically self-sufficient in daily living (excluding newborns and infants); ② Age-related changes in vital organs did not lead to significant functional abnormalities; ③ Health risk factors were controlled within an age-appropriate range; ④ Good nutritional status; ⑤ Basically normal cognitive function (excluding newborns and infants); ⑥ Optimistic and positive attitude, self-satisfied (excluding newborns and infants); ⑦ Possessing a certain level of health literacy and maintaining a healthy lifestyle (excluding newborns and infants). Exclusion criteria: Patients with acute and infectious clinical symptoms within three weeks prior to sampling, including but not limited to fever, headache, cough, malaise, sore throat, loss of smell, runny nose, abdominal pain, and diarrhea.

[0052] Fasting peripheral venous blood (10 ml) from the subjects was collected into centrifuge tubes. Peripheral blood was separated into different layers using density gradient centrifugation. Ficoll lymphocyte separation medium was added to the centrifuge tubes to achieve a final volume ratio of peripheral blood, PBS, and lymphocyte separation medium of 1:1:1. The sample was centrifuged horizontally at 400 g for 30 minutes. After centrifugation, three layers were observed in the tube: an upper layer of plasma and PBS, a lower layer of erythrocytes and granulocytes, and a middle layer of lymphocyte separation medium. At the interface between the upper and middle layers, there was a white, cloudy layer dominated by peripheral blood mononuclear cells. The peripheral blood mononuclear cells in the white cloudy layer were retained and stored at -80°C.

[0053] II. Synthesis of RNA and cDNA extracted from peripheral blood mononuclear cells

[0054] Total RNA was extracted from the test samples at 4°C using RNA extraction reagent (RNA separation reagent). The concentration and quality of the RNA were measured using a UV spectrophotometer to ensure that the RNA quality met the requirements. Reverse transcription of 3 μg of total RNA was performed using a Promega reverse transcription kit with Oligo-dT primers. PCR was performed using β-actin primers to verify the quality of the cDNA. Primers were designed using Beacon Designer software.

[0055] III. The naming of marker genes in the gene cluster of this invention is sometimes based on the www.ncbi.nlm.nih.gov database, and sometimes on widely accepted names. The marker genes were selected through careful systematic transcriptome sequencing and single-cell transcriptome sequencing of peripheral blood mononuclear cell samples from 29 healthy subjects (0-90 years old) across the entire lifespan. By simultaneously performing routine transcriptome sequencing and single-cell transcriptome sequencing on these 29 peripheral blood mononuclear cell samples, we used gene expression matrices to analyze genes whose expression patterns showed a monotonically increasing or decreasing trend with age. These genes are often highly correlated with age-related changes. Using this as a screening criterion, in routine transcriptome sequencing analysis, we identified a group of genes containing 138 age-upregulated genes and 933 age-downregulated genes. Figure 2 a and Figure 2 b). However, joint analysis with the simultaneously measured single-cell transcriptomics expression matrix revealed that only four genes showed upregulated expression with age in the dual-omics data: S100A6, LGALS1, S100A4, and TSPO genes. Figure 3 a). Similarly, seven genes showed down-regulation with age in the dual-omics data: SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, and CCR7 gene. Figure 3 b). Figure 4 It shows three representative genes ( Figure 4 a.LGALS1 gene, Figure 4 b. CCR7 gene, Figure 4 c. The expression of the TMIGD2 gene in peripheral blood mononuclear cells of individuals of different ages showed a strong correlation with age (correlation coefficient and p-value are shown in the figure).

[0056] The Gene Bank sequence numbers of these eleven genes are as follows: S100A6 gene (GI:1519242588), LGALS1 gene (GI:47678552), S100A4 gene (GI:47496636), TSPO gene (GI:1519242477), SELL gene (GI:1676452999), TMIGD2 gene (GI:2068206705), NOSIP gene (GI:1890321565), DENND2D gene (GI:1890275057), CCT2 gene (GI:48146258), NSMCE1 gene (GI:2462547944), and CCR7 gene (GI:1890278173). The sequence information of these 11 genes can be retrieved through the gene website of the National Center for Biotechnology Information (NCBI) at https: / / www.ncbi.nlm.nih.gov / nuccore.

[0057] Table 1. Eleven gene markers included in the selected gene cluster as markers of immune aging.

[0058]

[0059]

[0060] Meanwhile, for the 11 selected genes (SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO), we determined their CDS region sequences based on their human-encoding mRNAs. Using the Primer-Blast online website, we limited the product length to 80-200 bp and designed specific real-time quantitative PCR (qPCR) primers for each selected gene, with two primers for each gene, for a total of 22 primers. The primer design sequences are shown in Table 2.

[0061] Table 2 Primers for 11 gene markers used as markers of immunosenescence

[0062]

[0063]

[0064]

[0065] III. Determining the expression level of marker genes using qPCR

[0066] For the above 11 genes, we performed qPCR using ChamQ Universal SYBR qPCR Master Mix. The system used for each gene is as follows (Table 3):

[0067] Table 3 qPCR experimental system

[0068] reagent components system 2× premixed liquid 5μL Pre-primer (10 μM) 0.2μL Post-primer (10 μM) 0.2μL Ultrapure water 3.6μL cDNA template 1μL

[0069] Calculate the total volume of the system according to the specific sample quantity and the actual number of accessory wells required, as described above. Add 9 μL of the reaction mixture (2× premix, front primer, back primer, ultrapure water) and 1 μL of cDNA template sequentially to a 96-well or 384-well plate using a "9+1" method. Centrifuge at 2500 rpm for 1 minute to thoroughly mix the reaction solution. The reaction was performed under the following conditions in a Roche 480 (USA) quantitative PCR instrument (Table 4):

[0070] Table 4 qPCR reaction conditions

[0071]

[0072] After the reaction is complete, the melting curve is calculated and plotted using the instrument program. If the melting curve shows a single peak around 80℃ (Tm value), it indicates that the primer sequence has good specificity and can be used for quantitative analysis. The difference (ΔCT) between the CT cycle values ​​of the 11 target genes and the internal reference gene such as GAPDH is used as the gene expression level for subsequent analysis.

[0073] IV. Gene Expression Pattern Analysis and Immune Age Calculation

[0074] Step 1: Construct a multiple linear regression model as an immune age prediction model. The specific model formula is as follows:

[0075] Immunity age=β0+β1*X1+β2*X2+β3*X3+β4*X4+β5*X5+β6*X6+β7*X7+β8*X8+β9*X9+β10*X10+β11*X11;

[0076] The asterisk (*) in the formula represents the multiplication symbol. X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, and X11 represent the expression levels of the SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO genes, respectively.

[0077] In the formula, β0, β1, β2, β3, β4, β5, β6, β7, β8, β9, β10, and β11 are, respectively, the intercept term, the regression coefficient of the SELL gene, the regression coefficient of the TMIGD2 gene, the regression coefficient of the NOSIP gene, the regression coefficient of the DENND2D gene, the regression coefficient of the CCT2 gene, the regression coefficient of the NSMCE1 gene, the regression coefficient of the CCR7 gene, the regression coefficient of the S100A6 gene, the regression coefficient of the S100A4 gene, the regression coefficient of the LGALS1 gene, and the regression coefficient of the TSPO gene;

[0078] Step 2: From the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene, select multiple gene combinations to define as target gene combinations, and define other genes as non-target genes. These 11 gene types result in a total of 2047 target gene combinations.

[0079] Peripheral blood samples from healthy individuals of all ages were obtained as training samples, and the expression levels of various target genes in each training sample were detected.

[0080] Step 3: Construct a target gene expression matrix. Each row in the target gene expression matrix represents a target gene, and each column in the target gene expression matrix represents a training sample. Each element in the target gene expression matrix contains the age information of the sample source of the training sample in the column to which the element belongs, as well as the expression level of the target gene in the column to which the element belongs in the training sample.

[0081] Step 4: Set the expression levels of all non-target genes in the immune age prediction model constructed in Step 1 to 0. Then, based on the data in the target gene expression matrix, use the least squares method to fit the immune age prediction model, solve for the intercept term β0 and the regression coefficients of each target gene in the immune age prediction model, and thus obtain the trained immune age prediction model.

[0082] Step 5: Using the trained immune age prediction model, predict the immune age of the target user. The specific prediction method is as follows:

[0083] First, obtain peripheral blood samples from the target user, and then detect the expression levels of various target genes in the peripheral blood samples.

[0084] The expression levels of various target genes in the peripheral blood samples of the target users are then input into the trained immune age prediction model, and the immune age prediction model is used to predict the immune age of the target users.

[0085] After predicting the immune age of the target user using the immune age prediction model, the predicted immune age of the target user is subtracted from the actual age of the target user.

[0086] If the obtained age difference is less than -2.5 years, the target user's immune status is marked as accelerated immune aging;

[0087] If the obtained age difference is greater than 2.5 years, the target user's immune status will be marked as immune aging deceleration;

[0088] If the obtained age difference is greater than or equal to -2.5 years and less than or equal to 2.5 years, the target user's immune status is marked as normal.

[0089] We selected 2047 target gene combinations from 11 genes—SELL, TMIGD2, NOSIP, DENND2D, CCT2, NSMCE1, CCR7, S100A6, S100A4, LGALS1, and TSPO—for testing. We used the lm() function in R software to fit the immune age prediction model and calculated the R-squared value of the model. 2 Table 5 outputs the R-values ​​and P-values ​​for the 11 gene combinations that ranked the top 20 in terms of fit. 2 Value, P-value, and AIC.

[0090] Table 5 shows the fitting effect of the top 20 gene permutations and combinations on immune age calculation.

[0091]

[0092]

[0093] By ranking the fitting results, it can be seen that when all 11 genes are selected as target genes, the immune age prediction model is the optimal model.

[0094] With all 11 genes selected as target genes, the values ​​of the regression coefficients β0, β1, β2, β3, β4, β5, β6, β7, β8, β9, β10, and β11 in the immune age prediction model obtained using the training samples acquired in this embodiment are as follows:

[0095]

[0096]

[0097] Further statistical analysis showed that the immune age calculated from gene expression matrix data had a significant and strong correlation with the actual age, R2 =0.9037, P = 6.1 × 10 -11 ( Figure 5 ).

[0098] The present invention also provides an immune age prediction system and a computer program for performing the above-described prediction method.

[0099] The system includes:

[0100] The data acquisition module is used to acquire target data; the target data is the expression level of target genes in human peripheral blood; the target genes are any two or more combinations of the following genes: SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene.

[0101] The prediction module inputs the expression levels of various target genes in the peripheral blood samples of the target user into the trained immune age prediction model, and uses the immune age prediction model to predict the immune age of the target user.

[0102] The evaluation module uses an immune age prediction model to predict the immune age of the target user, and then subtracts the predicted immune age of the target user from the target user's actual age.

[0103] If the obtained age difference is less than -2.5 years, the target user's immune status is marked as accelerated immune aging;

[0104] If the obtained age difference is greater than 2.5 years, the target user's immune status will be marked as immune aging deceleration;

[0105] If the obtained age difference is greater than or equal to -2.5 years and less than or equal to 2.5 years, the target user's immune status is marked as normal.

[0106] The difference between chronological age and immune age is also known as the age gap (Δ difference). The positive or negative value of the age gap can clearly distinguish an individual's immune aging status.

[0107] The calculation formula is: Δ difference = Actual age - Immune age. If the Δ difference is less than -2.5 years, it indicates that the individual's immune system is aging faster (premature immune aging), while if the Δ difference is greater than 2.5 years, it indicates that the individual's immune system is aging slower (immune de-aging). If the Δ difference is between -2.5 years and 2.5 years, it indicates that the individual's immune system is aging at a stable rate (immune health). This specific threshold (-2.5 years and 2.5 years) was determined based on experimental data and statistical analysis to clarify the classification of immune aging status.

[0108] Furthermore, we validated this study in 151 volunteers aged 0-90 years, collecting peripheral blood mononuclear cells and performing analysis using a kit. The resulting expression matrix data of 11 genes was input into the aforementioned formula to calculate the immune age. The results showed a significant and strong correlation between the calculated immune age and the actual age, R0. 2 =0.897, P = 1.89 × 10 -75 ( Figure 6 Among them, the test results of 51 individuals showed that the difference between their actual age and immune age was less than -2.5 years, and they were judged to have premature immune aging using the above calculation formula; the test results of 72 individuals showed that the difference between their actual age and immune age was greater than 2.5 years, and they were judged to have reversed immune aging using the above calculation formula; the test results of 28 individuals showed that the difference between their actual age and immune age was between -2.5 years and 2.5 years, and they were judged to be healthy using the above calculation formula (Table 7).

[0109] Table 7. Individual immune senescence status of 151 volunteers as the test set.

[0110] Immune aging status Number of examples Premature Immune Failure 51 Immune Rejuvenation 72 Immune health 28

[0111] Measuring age gaps can help healthcare professionals assess the early signs of weakening in an individual's immune system. This provides a scientific basis for developing personalized interventions to reduce the risk of morbidity and mortality in older adults, contributing to a healthier aging process.

[0112] Example 2

[0113] I. Recommended usage of the reagent kit:

[0114] 1. Reaction System: The reaction was performed in a 96-well PCR plate. Each marker gene required three parallel reactions, and two housekeeping genes were also tested as internal controls. The reaction system for each well was as follows: q-PCR master mix (8 μl), upstream primer (10 pmol / μl, 1 μl), downstream primer (1 pmol / μl, 1 μl), FAM-labeled universal Z probe (10 pmol / μl, 1 μl), PCR-grade H2O (1 μl), and cDNA or housekeeping gene sample (4 μl). The cDNA sample could be extracted from fresh peripheral blood mononuclear cells or resuscitated peripheral blood mononuclear cells, dissolved in PCR-grade H2O, and 4 μl was added to the reaction system.

[0115] 2. Reaction Conditions: The reaction was performed in a Bio-Rad Q-PCR thermocycler under the following conditions: preheating at 94℃ for 12 minutes, followed by 50 cycles. Each cycle consisted of treatment at 94℃ for 15 seconds, 55℃ for 40 seconds, and 72℃ for 1 second. Transcripts were quantified simultaneously with sample amplification using an internal control. Results were presented in two ways: one as the transcript level based on the average total RNA amount, and the other as the relative ratio of the target gene to the GAPDH or CK19 gene amounts.

[0116] 3. Interpretation of immune age assessment results: In this embodiment, the gene expression matrix obtained by the kit will be automatically input into the pre-written multiple linear regression model formula to calculate the immune age. The specific formula and its parameters are shown in Example 1.

[0117] By automatically outputting the immune age value, we can obtain the subject's true immune age and differentiate the aging state of the immune system by comparing the immune age with the actual age. This includes accelerated immune aging (premature immune failure, age difference less than -2.5 years) and decelerated immune aging (reverse immune aging, age difference greater than 2.5 years). This method provides a more accurate way to assess the health and aging status of the immune system, and is expected to provide a scientific basis for developing personalized interventions for the elderly, thus promoting healthy aging in humans.

[0118] This invention provides novel methods and tools for assessing an individual's immune age. Immune age is a key biomarker used to measure the state and health of the immune system and to predict potential immune-related health risks. The gene clusters and reagent kits disclosed in this invention enable convenient and efficient assessment of immune age, which is of great significance in assessing the true state of the human immune system. Furthermore, the immune age assessment provided by this invention offers more accurate and comprehensive information, helping to predict potential immune-related risks, including accelerated and decelerated immune aging, and is expected to provide a scientific basis for developing personalized interventions and improving healthy aging processes.

[0119] The foregoing has fully explained the technical solution and beneficial effects of the present invention. The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that the public can have a deeper understanding of the invention. The embodiments described are all the best technical solutions and are not intended to limit the present invention.

Claims

1. A biomarker for assessing human immune status or determining immune age, characterized in that, The biomarkers are selected from any one of the following groups: (1) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO gene. (2) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene and LGALS1 gene. (3) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene and TSPO gene; (4) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene and S100A4 gene. (5) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, LGALS1 gene and TSPO gene. (6) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene and TSPO gene; (7) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene and LGALS1 gene. (8) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene and S100A6 gene; (9) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO gene. (10) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene and LGALS1 gene. (11) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, LGALS1 gene and TSPO gene. (12) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene and LGALS1 gene; (13) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene and TSPO gene; (14) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene and TSPO gene; (15) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene and S100A4 gene; (16) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene and S100A6 gene; (17) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO gene. (18) SELL gene, NOSIP gene, DENND2D gene, CCT2 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO gene. (19) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, CCR7 gene, S100A6 gene, S100A4 gene and LGALS1 gene. (20) SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, CCR7 gene, S100A6 gene, LGALS1 gene and TSPO gene.

2. The biomarker according to claim 1, characterized in that, The biomarkers consist of the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene.

3. The use of the biomarker of claim 1 in the preparation of a kit for assessing human immune status or determining immune age.

4. A kit for assessing human immune status or determining immune age, characterized in that, Primers containing the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO gene. The primers for detecting the SELL gene are shown in SEQ ID NO. 1-2; the primers for detecting the TMIGD2 gene are shown in SEQ ID NO. 3-4; the primers for detecting the NOSIP gene are shown in SEQ ID NO. 5-6; the primers for detecting the DENND2D gene are shown in SEQ ID NO. 7-8; the primers for detecting the CCT2 gene are shown in SEQ ID NO. 9-10; the primers for detecting the NSMCE1 gene are shown in SEQ ID NO. 11-12; the primers for detecting the CCR7 gene are shown in SEQ ID NO. 13-14; the primers for detecting the S100A6 gene are shown in SEQ ID NO. 15-16; the primers for detecting the S100A4 gene are shown in SEQ ID NO. 17-18; the primers for detecting the LGALS1 gene are shown in SEQ ID NO. 19-20; and the primers for detecting the TSPO gene are shown in SEQ ID NO. 21-22.

5. The kit for assessing human immune status or determining immune age according to claim 4, characterized in that, It also includes reagents used for PCR detection of the biomarkers.

6. A primer for detecting the biomarker of claim 1, characterized in that, Its gene sequence is shown in SEQ ID NO.1~22.

7. The use of the primers for detecting biomarkers according to claim 6 in the preparation of a kit for assessing human immune status or determining immune age, characterized in that, The biomarkers are a combination of the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene.

8. A method for constructing an immune age prediction model, characterized in that, The specific steps are as follows: Step 1: Construct a multiple linear regression model as an immune age prediction model. The specific model formula is as follows: Immunity age=β0 + β1 * X1 + β2 * X2 + β3 * X3 + β4 * X4 + β5 * X5 +β6 * X6 +β7 * X7 +β8 * In the formula, X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, and X11 represent the expression levels of the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene, and TSPO gene, respectively. In the formula, β0, β1, β2, β3, β4, β5, β6, β7, β8, β9, β10, and β11 are, respectively, the intercept term, the regression coefficient of the SELL gene, the regression coefficient of the TMIGD2 gene, the regression coefficient of the NOSIP gene, the regression coefficient of the DENND2D gene, the regression coefficient of the CCT2 gene, the regression coefficient of the NSMCE1 gene, the regression coefficient of the CCR7 gene, the regression coefficient of the S100A6 gene, the regression coefficient of the S100A4 gene, the regression coefficient of the LGALS1 gene, and the regression coefficient of the TSPO gene; Step 2: Select the SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO gene as target genes. Peripheral blood samples from healthy individuals of all ages were obtained as training samples, and the expression levels of various target genes in each training sample were detected. Step 3: Construct a target gene expression matrix. Each row in the target gene expression matrix represents a target gene, and each column in the target gene expression matrix represents a training sample. Each element in the target gene expression matrix contains the age information of the sample source of the training sample in the column to which the element belongs, as well as the expression level of the target gene in the column to which the element belongs in the training sample. Step 4: Set the expression levels of all non-target genes in the immune age prediction model constructed in Step 1 to 0. Then, based on the data in the target gene expression matrix, use the least squares method to fit the immune age prediction model, solve for the intercept term β0 and the regression coefficients of each target gene in the immune age prediction model, and thus obtain the trained immune age prediction model.

9. The method according to claim 8, characterized in that, Once the immune age of the target user is predicted using the immune age prediction model, the predicted immune age of the target user is subtracted from the actual age of the target user. If the obtained age difference is less than -2.5 years, the target user's immune status is marked as accelerated immune aging; If the obtained age difference is greater than 2.5 years, the target user's immune status will be marked as immune aging deceleration; If the obtained age difference is greater than or equal to -2.5 years and less than or equal to 2.5 years, the target user's immune status is marked as normal.

10. An immune age prediction system, characterized in that, The system includes: The data acquisition module is used to acquire target data; the target data is target gene expression level data from human peripheral blood samples; the target genes are a combination of SELL gene, TMIGD2 gene, NOSIP gene, DENND2D gene, CCT2 gene, NSMCE1 gene, CCR7 gene, S100A6 gene, S100A4 gene, LGALS1 gene and TSPO. The prediction module inputs the expression levels of various target genes in the peripheral blood samples of the target user into the immune age prediction model trained by the method described in claim 8, and uses the immune age prediction model to predict the immune age of the target user.

11. The immune age prediction system according to claim 10, characterized in that, The system also includes: The evaluation module uses the immune age prediction model to predict the immune age of the target user, and then subtracts the predicted immune age of the target user from the actual age of the target user. If the obtained age difference is less than -2.5 years, the target user's immune status is marked as accelerated immune aging; If the obtained age difference is greater than 2.5 years, the target user's immune status will be marked as immune aging deceleration; If the obtained age difference is greater than or equal to -2.5 years and less than or equal to 2.5 years, the target user's immune status is marked as normal.

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