An aging biomarker and its application

Abstract

This invention relates to the field of biotechnology, specifically disclosing an aging biomarker capable of marking cellular senescence and its applications. The purpose of this invention is to provide a novel biomarker for assessing cellular senescence and its applications, aiming to address the problem of the lack of highly specific biomarkers for the senescence status of immune cells in tumor tissues in existing technologies, particularly the technical bottleneck of accurately assessing the degree of immune cell senescence in the complex tumor microenvironment.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically to an aging biomarker capable of marking cellular senescence and its applications. Background Technology

[0002] Cellular senescence is a stable state of cell cycle arrest, characterized by the permanent exit of cells from the cell cycle and loss of proliferative capacity. Although cellular senescence often occurs alongside the aging process in physiological processes, such as replicative senescence caused by telomere shortening, numerous studies have shown that cellular senescence is not simply a passive, time-driven process, but a highly controlled biological program closely related to the tissue microenvironment and pathological states. Cellular senescence can be induced independently of the natural aging process under various conditions; common inducing factors include, but are not limited to, oncogene activation, DNA damage responses, cell fusion, oxidative stress, or elevated levels of reactive oxygen species (ROS).

[0003] Although senescent cells lose their ability to replicate, they typically retain significant metabolic activity and acquire specific secretory characteristics, known as the senescence-associated secretory phenotype (SASP). SASP profoundly influences the microenvironment of surrounding tissues by releasing various cytokines, chemokines, growth factors, and proteases. Particularly in tumor-associated tissues, senescent cells (including senescent immune cells) can regulate inflammatory states and mediate immune cell recruitment and functional remodeling through SASP, thereby playing a crucial role in tumorigenesis, progression, and immune escape.

[0004] However, recent studies have revealed that cellular senescence exhibits significant tissue and cell type specificity. Cells from different tissue origins and lineages show significant differences in molecular characteristics, functional consequences, and impacts on disease progression when entering a senescent state. In particular, within the tumor microenvironment, the senescence state of immune cells (such as T cells, natural killer cells, and myeloid cells) is considered one of the important determinants of the strength of anti-tumor immune responses.

[0005] Currently, most aging biomarkers widely used in this field are derived from studies conducted on in vitro cultured cell lines or non-tumor tissues. When applied to complex tumor tissues, these traditional biomarkers often fail to accurately reflect the true aging characteristics of immune cells (especially tumor-infiltrating immune cells) in tumor tissues, and cannot effectively assess their specific functional impact on tumor progression.

[0006] Furthermore, although cellular senescence is accompanied by a series of molecular and phenotypic changes, such as morphological alterations, enhanced activity of specific enzymes, chromatin remodeling, and transcriptional reprogramming, these changes are not entirely consistent across different cell types and tissue backgrounds. Existing senescence detection methods and biomarker systems have the following limitations in practical applications: (1) Insufficient specificity: Some existing biomarkers are easily confused with other cellular stress states, leading to false positive results; (2) Strong tissue and cell dependence: lacks universal markers, making it difficult to apply uniformly in a variety of different tissue or cell types; (3) Limited applicability to complex pathological environments: Especially in the complex tumor microenvironment, existing markers are difficult to accurately distinguish the senescence status of specific cell subpopulations (such as immune cells), and cannot meet the needs for accurate assessment of immune senescence in the tumor microenvironment.

[0007] In summary, there is an urgent need to develop a novel biomarker system that, while preserving the common characteristics of cellular senescence, can more accurately characterize the senescence state of specific tissues and cell subpopulations (especially tumor-associated immune cells) to compensate for the shortcomings of existing technologies. Summary of the Invention

[0008] The purpose of this invention is to provide a new biomarker for assessing cellular senescence and its application, aiming to solve the problem of the lack of highly specific biomarkers for the senescence status of immune cells in tumor tissues in the existing technology, especially the technical bottleneck of the difficulty in accurately assessing the degree of immune cell senescence in the complex tumor microenvironment.

[0009] The present invention provides a basic technical solution: an aging biomarker, wherein the biomarker includes protein peptides of ATOX1, NIPBL, NPC2, SF3B1, SRSF11, THOC2, ZDHHC20 and / or their murine RNA, or fragments of the above protein peptides and / or murine RNA.

[0010] Preferably, the biomarker is a biomarker of cellular senescence in skin tumor tissue.

[0011] Preferably, the biomarker is derived from tumor tissue, hair, skin, and / or any one or more of these.

[0012] The present invention also provides another basic approach, a detection reagent or kit comprising reagents for detecting the expression levels of the aforementioned aging biomarkers.

[0013] The present invention also provides another basic approach, the application of aging biomarkers in the preparation of products for aging assessment, or the application of detection reagents or kits in aging assessment.

[0014] Preferably, aging assessment includes: estimating biological age; or assessing the level of immune cell senescence in the tumor microenvironment; or assessing photoaging-induced senescence of mouse skin immune cells.

[0015] The present invention also provides another basic solution, a method for detecting aging, comprising the following steps: detecting the expression level of the aging biomarker as described in any one of claims 1 to 3 in the sample to be tested.

[0016] Preferably, the expression level of the biomarker in the test sample is compared with the expression level in the reference sample; If the expression level of at least one of the protein peptides of ATOX1, NIPBL, NPC2, SF3B1, SRSF11, THOC2, ZDHHC20 and / or their murine RNA or fragments is altered, it indicates the presence of senescent cells in the sample to be tested.

[0017] Preferably, the sample to be tested is derived from laboratory animals or humans, including one or more of tumor tissue, hair, and skin.

[0018] Preferably, the method includes: inferring biological age by the expression pattern of aging characteristic genes of immune cells in skin tissue, using the formula: predicted age = 73.40 + 12.01 * aging score; or assessing the level of immune cell aging in the tumor microenvironment by analyzing the aging biomarkers; or assessing the photoaging-induced aging of mouse skin immune cells through mouse skin model experiments to verify the applicability of aging biomarkers in different models.

[0019] This invention provides a transcriptome-based biomarker for assessing cellular senescence. The biomarker is based on a pan-senescence transcriptional program widely present in cell lines, and is obtained through specialized screening and optimization using transcriptome data from immune cells in real tumor tissues. Specifically, this invention systematically integrates and analyzes transcriptome data from multiple sources, first extracting common transcriptional signals reflecting core characteristics of cellular senescence, then introducing expression data from immune cells in skin tumor tissues, and performing refined screening of the aforementioned pan-senescence program at the tissue and cell type levels, ultimately obtaining a set of molecular combinations of immune cell senescence characteristics closely related to skin tumor progression.

[0020] Compared with the prior art, the present invention has the following beneficial effects: This specialized biomarker combination can not only stably distinguish between senescent and non-senescent cell states, but also has higher sensitivity and specificity in assessing the senescence status of immune cells in complex tumor tissue environments.

[0021] The biomarker set described in this invention has broad application prospects, specifically including: (1) Biological age estimation: The biological age of an individual is assessed by detecting the expression patterns of genes that characterize aging in immune cells of skin tissue; (2) Assessment of immune aging in the tumor microenvironment: By analyzing the expression levels of the aforementioned aging markers, the degree of aging of immune cells in the tumor microenvironment is accurately assessed; (3) Model validation and applicability assessment: It is used to evaluate the aging of mouse skin immune cells induced by photoaging, and to verify the applicability and robustness of this aging marker under different experimental models through mouse skin model experiments. Attached Figure Description

[0022] Appendix Figure 1 The identification process of the pan-aging transcriptional procedure is shown.

[0023] Appendix Figure 2 The results show the discrimination ability of the immune cell senescence feature scoring system described in this invention on (A) training dataset and (B) all datasets from multiple cell sources.

[0024] Appendix Figure 3 The results show the biological process enrichment of gene sets in senescent cells that exhibit a trend of (A) upregulation and (B) downregulation based on this aging characteristic.

[0025] Appendix Figure 4 The annotation results of immune cell types and the distribution of aging characteristic scores are shown based on single-cell transcriptome data from 32 self-tested skin tumors.

[0026] Appendix Figure 5 The results of screening stable genes based on a repeated downsampling strategy and intersecting them with the model gene set are shown, along with the expression trends of the genes in different age groups of cancer patients.

[0027] Appendix Figure 6 The results show that (A) the average score of the stable aging characteristic genes screened in this invention is significantly correlated with age. (B) By fitting weights through Ridge regression and calculating the weighted sum, the patient's biological age can be predicted using the following formula.

[0028] Appendix Figure 7 The results show the comparison of immune cell expression of stable aging characteristic genes screened in this invention in ultraviolet-treated aging tissues and normal tissues. Detailed Implementation

[0029] The following detailed description illustrates the specific implementation method: Example 1: Construction and Application Method of Age Prediction Model Based on Aging Characteristic Genes This embodiment provides a method for screening aging characteristic genes, constructing an aging scoring model, and predicting biological age based on transcriptome sequencing data. The specific steps are as follows: Part 1: Acquisition and Preprocessing of Transcriptome Sequencing Training Data We collected cell line sequencing data from 78 publicly available studies as a training set, which covered 54 cell lines (involving 34 cell types) and included samples before and after treatment with 15 different senescence-inducing methods, totaling 764 samples.

[0030] 1. Data cleaning: The original FASTQ sequencing files are preprocessed uniformly, and the Trim software is used to remove adapter sequences and low-quality sequences; 2. Sequence alignment: The processed sequences were aligned to the human reference genome hg38 version using STAR software; 3. Expression Quantification: The featureCounts software was used to count the reads of gene regions to obtain the original gene expression profile; 4. Data Standardization: The gene expression profiles were subjected to column standardization and log transformation. The processed data were used as input data for subsequent analyses (see Appendix). Figure 1 A), Figure 1 This paper presents a specialized screening process for identifying pan-aging transcriptional procedures, from cell line aging models to immune cells in tumor tissues.

[0031] II. Identification of Genes Sharing Common Aging Characteristics For each study, rank-sum tests were performed on the aging group and the control group to screen for aging-induced transcriptomic changes.

[0032] 1. The screening criteria are: the absolute value of the change factor is greater than 1, and the corrected significance P value is less than 0.05.

[0033] 2. Consistency screening: Genes that show significant differential expression direction in at least 90% of studies are retained as the candidate aging gene set G.

[0034] 3. Weight Calculation: The sample size N in each study is standardized to 0-1 and used as the weight. The fold change of each gene in the candidate aging gene set G is then weighted to obtain the initial integration weight (see Appendix). Figure 1 B).

[0035] III. Optimal Screening of Aging Genes Based on Elastic Network Regression 1. Matrix Construction and Standardization: The expression matrices of all samples involved in the study were limited to the candidate aging gene set G and the features were standardized; at the same time, relative expression (such as centering or quantile scaling) was used to reduce the impact of outliers. 2. Model Algorithm: The elastic network method under the Logistic Regression framework is adopted; 3. Training and Validation: The samples are randomly divided into training and validation sets in a 7:3 ratio for cross-validation (see appendix). Figure 1 C); 4. Gene screening: Under the optimal hyperparameters, obtain the coefficient vector β, and determine the set of genes with non-zero coefficients as the preferred aging gene set G'.

[0036] IV. Model Robustness Verification and Accuracy Assessment To evaluate the performance of the regression model obtained in step three, the following validation was performed: 1. ROC curve analysis: Using the labels before and after induced aging treatment as the gold standard, a classification ROC curve was constructed based on the true positive rate and false positive rate of cross-validation. 2. AUC Calculation: Calculate the area under the curve (AUC) for both the training set and the entire dataset (see appendix). Figure 2 AB).

[0037] 3. Results: This regression model can distinguish between senescent and non-senescent samples in various cell lines (see appendix). Figure 2 C); and for expression data using mixed tissue samples and single-cell sequencing methods, the model maintains high classification accuracy (see Appendix). Figure 2 DE). Figure 2 The results demonstrate the ability of the immune cell senescence scoring system to distinguish between senescent and non-senescent cells in the training set (A) and the full dataset (B), with AUC values ​​of 0.880 and 0.870 respectively, proving that the scoring system can effectively distinguish between senescent and non-senescent cells.

[0038] V. Functional enrichment analysis to verify classification accuracy To further demonstrate the accuracy of the classification, the following analysis is conducted: 1. High-confidence grouping: Based on the aging probability output by the elastic network model, the samples are divided into a predicted aging group and a predicted non-aging group. Among them, the cells with the aging probability in the top 30% are assigned to the high-confidence aging group, and the cells in the bottom 30% are assigned to the high-confidence non-aging control group.

[0039] 2. Differential Analysis: Rank-sum differential tests were performed on samples from the high-confidence aging group and the non-aging control group to obtain the sets of upregulated and downregulated genes. Functional enrichment analysis was performed using hypergeometric tests and the Gene Ontology (GO) database, and the Benjamini-Hochberg (BH) method was used to correct for significance p-values.

[0040] 3. Enrichment analysis: Results showed that upregulated genes in immune cells of the high-confidence aging group were enriched in biological processes such as immune response and inflammatory response, while downregulated genes were mainly involved in cell cycle function (see appendix). Figure 3 AB), Figure 3 Enrichment analysis of upregulated and downregulated gene sets determined by aging characteristic scores revealed the characteristics of aging immune cells in biological processes such as immune response, inflammatory response, and cell division. These results are highly consistent with known biological characteristics of cellular senescence, demonstrating the biological rationale for the model selection results.

[0041] VI. Assessment of the degree of senescence of immune cells in skin tumor tissue The optimized set of aging genes G' obtained in step three was used to assess the degree of aging of immune cells in skin tumor tissue.

[0042] 1. Sample Acquisition and Sequencing: Forty clinical tumor tissue samples were collected (skin tumor tissue samples from multiple patients over 40 years of age were selected, including middle-aged patients aged 40-50 and elderly patients over 60 years of age). Single cells were extracted from each sample for RNA extraction and single-cell transcriptome sequencing. Transcriptome data for each sample were obtained using single-cell transcriptome technology (such as the 10x Genomics platform).

[0043] 2. Data quality control: Quality control of sequencing data, including the removal of low-quality cells (such as cells with abnormal UMI numbers or excessively high proportion of mitochondrial genes).

[0044] 3. Dimensionality reduction and clustering: The gene expression data is standardized. After log normalization of the expression matrix, the 3000 genes with the largest coefficient of variation are extracted for principal component dimensionality reduction and clustering.

[0045] 4. Cell type annotation: Cell types were annotated using classic marker genes, including T cells expressing CD3D, NK cells expressing NCAM1 and FCGR3A, myeloid cells expressing LYZ, and mast cells expressing TPSB2 and CAP3 (see appendix). Figure 4 AB), Figure 4 This indicates the degree of aging of immune cell types in the skin tumor microenvironment.

[0046] 5. Cellular senescence score calculation: Based on the model weights, the expression profiles at the cellular level are weighted and summed to calculate the senescence score for each cell type (see appendix). Figure 4 C).

[0047] 6. Results: The results showed that cell types in the cell cycle had the lowest senescence scores, consistent with known senescence characteristics.

[0048] 7. Screening for age-related genes related to aging An age prediction model was constructed based on single-cell transcriptome data of skin cancer and the corresponding age information of patients.

[0049] 1. Construction of pseudo-mixed tissue samples: First, 200 immune cells were sampled from each sample, and the expression profiles were summed. This process was repeated 200 times to obtain pseudo-mixed tissue samples with statistical power. 2. Age grouping: Patients were grouped into groups ranging from 40 to 90 years old at 10-year intervals; 3. Correlation Screening: Kendall's correlation test was used to identify genes that showed significant positive or negative correlations with age in 200 replicate experiments (P < 0.01). A final set of 25 aging-related genes was obtained (see appendix). Figure 5 ), Figure 5 The results of screening stable aging genes using a repeated downsampling strategy and intersecting them with the model gene set demonstrate the expression trends of aging biomarkers in different age groups.

[0050] 8. Construction and Formula Derivation of Biological Age Prediction Models Based on the set of aging genes significantly associated with age obtained in step seven, a biological age prediction model is constructed. The specific steps are as follows: 1. Sample-level feature extraction: For each sample, the expression values ​​of each senescence gene are averaged and summarized in its corresponding cell set. The influence of cell imbalance and single-cell noise on the results is reduced by repeated random downsampling, thereby obtaining robust sample-level expression features.

[0051] 2. Ridge Regression Model Construction: Using the true age of the samples as the dependent variable and the sample-level expression matrix of aging genes as the independent variable, a ridge regression model is constructed to learn the weight coefficients of each aging gene. The regularization parameter λ of the ridge regression model is determined through cross-validation (see appendix). Figure 6 A).

[0052] 3. Calculation of comprehensive aging score: After the model is fitted, the comprehensive aging score of each sample is calculated by weighting and summing the regression coefficients of each aging gene.

[0053] 4. Model Validation: The aging score showed a significant positive correlation with both the age of patients who tested their skin condition and with the public dataset (see attached). Figure 6 BC).

[0054] 5. Prediction Formula: A linear regression model is constructed with aging score as the independent variable and chronological age as the dependent variable to fit the quantitative relationship between the two, and the prediction formula for biological age is derived from this (see appendix). Figure 6 D), Figure 6 It demonstrates the prediction of biological age based on skin tissue transcriptome data.

[0055] Predicted age = 73.40 + 12.01 * aging score This formula can be directly used to predict the biological age of unknown samples.

[0056] Example 2: Predictive role of aging characteristic genes in a mouse model of UV-induced skin aging This embodiment establishes a mouse ultraviolet (UV)-induced skin photoaging model and, combined with single-cell transcriptome sequencing technology, verifies the expression consistency and predictive ability of the "aging-related genes" screened in Example 1 in cross-species and physical damage-induced aging models. The specific steps are as follows: Step 1: Construction and administration of a mouse model of UV-induced skin aging 1. Laboratory animals and their care SPF-grade C57BL / 6 mice aged 6 to 8 weeks, half male and half female, were selected, for a total of 30 mice.

[0057] Standard rearing conditions: 12h / 12h light cycle, temperature 23±3°C, relative humidity 30–70%, free access to food and water (see attached). Figure 7 A).

[0058] 2. Experimental group modeling Control group: Not exposed to UV radiation, or received the same treatment but with shade; Photoaging group: exposed to a combination of UVA (320–400 nm) and UVB (280–320 nm) light sources to simulate the ultraviolet components in natural sunlight; Operational details: Select the back skin as the irradiation area; shave / remove hair from the back 24–48 hours before irradiation to ensure even irradiation. Protect non-irradiated areas with light-shielding material to avoid systemic photodamage. Select a combination of UVA (320–400 nm) and UVB (280–320 nm) UV light sources to simulate natural light.

[0059] Model evaluation: Continuous irradiation continued until the skin on the backs of mice in the UV-treated group showed significant changes in appearance, including roughness, increased texture, or increased wrinkles, and these changes were consistently observed in multiple mice. Histological analysis showed that the skin in the UV-treated group differed consistently from the control group in terms of epidermal thickness, tissue structure, and distribution of inflammatory cells, indicating that the photoaging model was successfully established.

[0060] Step 2: Acquisition and data preprocessing of single-cell sequencing samples 1. Preparation of single-cell suspension Sample collection: Skin was cut from the irradiated area on the back and rinsed with PBS to remove blood.

[0061] Tissue dissociation: epidermis / dermis separation or full-layer digestion, using a combination of collagenase / neutral protease / DNase for enzymatic digestion, controlling digestion time and temperature to reduce stress transcription artifacts.

[0062] Mechanical separation: Mechanical gentle shearing / blowing assists in dissociation to obtain a single-cell suspension.

[0063] Filtration or quality control: Remove tissue debris sequentially using 40μm or 70μm cell filters. Perform viable cell counting and viability assessment, requiring a cell viability greater than 80% to meet library construction criteria.

[0064] 2. Single-cell transcriptome sequencing Transcriptome detection was performed using a common droplet-based single-cell transcriptome platform and Illumina sequencing technology based on the principle of next-generation sequencing.

[0065] The target cell count was approximately 20,000 per sample. Double droplet and empty droplet controls were implemented.

[0066] The data was downloaded to obtain FASTQ files, which were then aligned to a reference genome (mouse mm10 or human hg38 version) after base quality control. The gene-cell expression matrix was obtained by counting the reads.

[0067] 3. Data filtering and cell annotation Low-quality cell filtration: Low-quality cells are filtered according to the following criteria: number of detected genes less than 200 or more than 4000, number of UMI less than 1000 or more than 20000, and mitochondrial gene ratio greater than 50%.

[0068] Dimensionality reduction and clustering: After log normalization of the expression matrix, the 3000 genes with the largest coefficient of variation are extracted for PCA dimensionality reduction and clustering.

[0069] Cell types and annotation: Cell types are annotated using classic marker genes, including: T cells: Cd3d / e, Trac; Natural killer cells: Nkg7, Prf1; B cells: Ms4a1, Cd79a; Plasma cells: Jchain, Xbp1; Dendritic cells: Itgax, Clec10a; Monocytes and macrophages: Lst1, C1qa / b / c; Neutrophils: S100a8 / a9; Mast cells: Kit, Tpsb2, etc.

[0070] Step 3: Statistical Analysis Statistical analysis was performed with each mouse as an independent sample unit: 1. Sample expression matrix construction: Sum the UMI counts of the target cell population (which can be all immune cells or a certain subpopulation) within the same sample to form a sample count matrix.

[0071] 2. Data standardization: Divide the sample count matrix by the column sum to convert it to CPM and perform a log2(CPM+1) transformation to obtain uniform expression data.

[0072] 3. Differential expression test: Independent samples t-test was used to compare the differences in gene expression levels between the photoaging group and the control group to assess the changes in the expression of aging-related genes in different treatment groups.

[0073] Step 4: Experimental Results 1. Cross-species and mechanism consistency verification: Genes that are species-conserved and expressed in immune cells were screened in mice, resulting in eight genes: ATOX1, NIPBL, NPC2, SF3B1, SRSF11, THOC2, and ZDHHC20. In mouse skin immune cells under light-induced aging and normal conditions, the expression trends of these eight genes were consistent with those in human tissues, indicating that these aging-related genes are consistent across species and different mechanisms of aging. (See appendix) Figure 7 B).

[0074] 2. Identification of Marker Genes: In summary, this invention combines a pan-aging procedure at the cell line level with single-cell data from tissue-derived immune cells and introduces a repeatability and consistency screening mechanism to obtain a stable, reproducible, and applicable scheme for identifying immune cell aging characteristics in complex tissue environments. This scheme significantly improves the reliability and practicality of applying aging-related biomarkers at the tissue and cell type levels.

[0075] Based on this, this embodiment further identifies a set of marker genes consisting of ATOX1, NIPBL, NPC2, SF3B1, SRSF11, THOC2, and ZDHHC20. This set can effectively indicate the aging state of immune cells, providing a reliable molecular target for the subsequent development of anti-aging drugs or the assessment of skin aging.

[0076] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific structures and characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A biomarker of aging, characterized in that: The biomarkers include protein peptides and / or murine RNA of ATOX1, NIPBL, NPC2, SF3B1, SRSF11, THOC2, and ZDHHC20, or fragments of the aforementioned protein peptides and / or murine RNA.

2. The biomarker of aging of claim 1, wherein: The biomarkers mentioned are biomarkers of cellular senescence in skin tumor tissues.

3. The biomarker of aging of claim 1, wherein: The biomarkers are derived from tumor tissue, hair, skin, and / or any one or more of these sources.

4. An assay reagent or kit characterized in that, It includes reagents for detecting the expression levels of the aging biomarkers according to any one of claims 1 to 3.

5. The use of the aging biomarkers according to any one of claims 1 to 3 in the preparation of products for aging assessment, or the use of the detection reagents or kits according to claim 4 in aging assessment.

6. Use according to claim 5, characterized in that: The aging assessment includes: Inferring biological age; Or assess the level of immune cell senescence in the tumor microenvironment; Or to assess photoaging-induced aging of mouse skin immune cells.

7. A method for detecting aging, characterized in that: Includes the following steps: The expression level of the aging biomarker as described in any one of claims 1 to 3 was detected in the sample to be tested.

8. The method according to claim 7, characterized in that: The expression levels of biomarkers in the test sample were compared with the expression levels in the reference sample; If the expression level of at least one of the protein peptides of ATOX1, NIPBL, NPC2, SF3B1, SRSF11, THOC2, ZDHHC20 and / or their murine RNA or fragments is altered, it indicates the presence of senescent cells in the sample to be tested.

9. The method according to claim 7 or 8, characterized in that: The test samples are derived from laboratory animals or humans, including one or more of tumor tissue, hair, and skin.

10. The method according to claim 9, characterized in that: The method includes: Biological age is inferred by analyzing the expression patterns of aging characteristic genes in skin tissue immune cells. The formula is: Predicted age = 73.40 + 12.01 * aging score; Alternatively, the level of immune cell senescence in the tumor microenvironment can be assessed by analyzing the aforementioned senescence biomarkers; Alternatively, mouse skin model experiments can be used to assess photoaging-induced aging of mouse skin immune cells, in order to verify the applicability of aging biomarkers in different models.

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