Cell senescence marker detection method based on multi-omics combined screening and application of cell senescence marker detection method

Abstract

The invention relates to the field of biological detection, in particular to a multi-omics combined screening cell senescence marker detection method and application thereof. According to the method, data of genomics, transcriptomics, proteomics and metabonomics are combined, and multi-dimensional data in the cell senescence process are subjected to high-throughput screening and integration by means of whole genome sequencing, RNA-Seq, a liquid chromatography-mass spectrometry technology and a gas chromatography-mass spectrometry technology. And adopting machine learning algorithms such as random forest, a support vector machine and deep learning to screen out key markers related to aging. The markers comprise genes, proteins and metabolites, and have relatively high sensitivity and specificity. Verification of different aging models proves that the screened markers change significantly in the aging process and can effectively reflect the biological characteristics of cell aging. The method provides new technical support and theoretical basis for research of an aging mechanism, early diagnosis of aging-related diseases and anti-aging treatment.

Description

Technical Field

[0001] This invention relates to the field of biological detection, specifically to a method for detecting cell senescence biomarkers through multi-omics joint screening and its application. Background Technology

[0002] Cellular senescence is an unavoidable physiological phenomenon during the natural growth of organisms. It refers to the process by which cells, after undergoing a certain proliferation cycle, lose their proliferative capacity and exhibit specific biological characteristics. Cellular senescence is one of the fundamental mechanisms of age-related diseases and is closely related to processes such as decreased immune function, weakened tissue repair capacity, and increased oxidative stress. Studying the molecular mechanisms of cellular senescence can not only provide theoretical support for the aging process but also provide new targets for the diagnosis, prevention, and treatment of age-related diseases.

[0003] Currently, research on cellular senescence mainly relies on the following types of biomarkers: cell surface markers, β-galactosidase activity specific to senescent cells, activation of DNA damage responses, decline in mitochondrial function, and changes in metabolites. Traditional methods for detecting cellular senescence typically focus on screening for single biomarkers at a specific omics level, such as gene expression, protein levels, or metabolites. However, these methods often suffer from poor selectivity, low sensitivity, and difficulties in data interpretation.

[0004] With the development of high-throughput omics technologies, genomics, transcriptomics, proteomics, and metabolomics have gradually become powerful tools for analyzing cellular senescence. These technologies allow for a comprehensive understanding of the molecular characteristics of senescent cells and the multidimensional changes during the senescence process from multiple perspectives. However, most existing methods for detecting cellular senescence are limited to the analysis of single-omics data, lacking systematic multi-omics data integration and analysis. This results in an incomplete understanding of the mechanisms of cellular senescence and low efficiency in screening senescence-related biomarkers.

[0005] Therefore, there is an urgent need for a method to screen cell senescence biomarkers that can combine multi-omics data for comprehensive analysis, and that can efficiently and accurately identify key molecular biomarkers of cell senescence, thereby providing more precise technical support for the early diagnosis of aging-related diseases, research on aging mechanisms, and the development of anti-aging therapies. Summary of the Invention

[0006] To overcome the limitations of existing methods for screening cellular senescence biomarkers, this invention aims to provide a multi-omics-based method for detecting cellular senescence biomarkers. This method combines data from genomics, transcriptomics, proteomics, and metabolomics, utilizing high-throughput screening techniques and machine learning analysis models to efficiently and accurately identify biomarkers related to cellular senescence, thus providing strong support for research on aging mechanisms and early diagnosis of age-related diseases. This invention acquires multidimensional data on the cellular senescence process through whole-genome sequencing, RNA-Seq, LC-MS / MS proteomics analysis, and GC-MS and LC-MS metabolomics analysis. These data are integrated, and machine learning algorithms are applied to screen for genes, proteins, and metabolites that change significantly during senescence, and their key roles in cellular senescence are verified. This invention can comprehensively and accurately screen key biomarkers of cellular senescence, improving the sensitivity and accuracy of early diagnosis of age-related diseases.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for detecting cellular senescence biomarkers through multi-omics joint screening, the method comprising the following steps:

[0009] S1, collect cell or tissue samples, wherein the cell or tissue samples are selected from human cells, animal cells or cultured cells;

[0010] S2, genomic analysis of the sample is performed using genomics technology, and whole-genome sequencing is used to obtain variation information of all genes in the sample, including mutations, insertions, deletions and structural variations;

[0011] S3. RNA expression analysis of the sample was performed using transcriptomics technology, and RNA-Seq technology was used to analyze the changes in transcriptional levels of all genes during aging.

[0012] S4. Proteomics analysis of the sample is performed using proteomics technology. Liquid chromatography-mass spectrometry is used to quantitatively analyze the expression of all proteins in the sample.

[0013] S5. Metabolomics technology is used to analyze the metabolites in the sample. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry are used to analyze the qualitative and quantitative changes of metabolites during cell senescence.

[0014] S6. Through data integration and analysis, integrate genomic, transcriptomic, proteomic and metabolomic data, and use machine learning algorithms, such as random forest, support vector machine and deep learning algorithms, to screen out biomarkers related to cellular senescence.

[0015] Optionally, step S2, genomic analysis, involves whole-genome sequencing, which uses gene alignment and variant detection to screen for gene mutations, insertions, deletions, and structural variations that change significantly during cellular senescence.

[0016] Optionally, step S3 RNA expression analysis uses RNA-Seq technology, employing the Illumina platform for high-throughput sequencing to analyze the expression differences of all genes in senescent samples and control samples, and to screen for differentially expressed genes closely related to the cellular senescence process.

[0017] Optionally, step S4, proteomics analysis, employs liquid chromatography-mass spectrometry (LC-MS). By extracting total protein from the sample, LC-MS is used for quantitative analysis to screen all proteins that show significant changes in expression during aging, particularly those related to processes such as cell senescence, cell cycle, DNA repair, and antioxidant responses.

[0018] Optionally, step S5, metabolite analysis, employs gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry to qualitatively and quantitatively analyze metabolites, screening out all metabolites that change significantly during aging, especially those related to energy metabolism, lipid metabolism, and amino acid metabolism.

[0019] Optionally, step S6, data integration and analysis, includes the following steps:

[0020] (1) Preprocess the data of the genome, transcriptome, proteome and metabolome to remove low-quality data and standardize them;

[0021] (2) Using multidimensional data analysis methods, principal component analysis and factor analysis are used to reduce the dimensionality of the integrated data;

[0022] (3) Apply machine learning algorithms, namely random forest, support vector machine and deep learning algorithms, to identify biomarkers that play an important role in cell senescence through feature extraction and classification.

[0023] Optionally, the biomarker is one or more of genes, proteins, and metabolites, wherein the biomarker is a gene, protein, or metabolite that changes significantly during cellular senescence and whose changes are highly correlated with the biological processes of cellular senescence.

[0024] Optionally, the biomarkers are genes and proteins specifically expressed and significantly upregulated in senescent cells, including p16^INK4a, p21, β-galactosidase, SOD2 senescence-related genes and proteins, and the metabolites include NAD+, ATP, and Lactate.

[0025] Optionally, an application of a multi-omics joint screening method for detecting cellular senescence biomarkers is provided, wherein the biomarkers are used in the study of aging mechanisms to explore gene pathways, protein interactions and metabolic changes involved in the aging process by detecting cellular senescence biomarkers.

[0026] Optionally, cellular senescence biomarkers can be used for the early diagnosis of aging-related diseases, such as cancer, cardiovascular disease, diabetes, and neurodegenerative diseases. By detecting senescence biomarkers in blood or tissue samples, an individual's aging status and associated disease risk can be assessed.

[0027] The beneficial effects of this invention are:

[0028] This invention, through the joint analysis of multi-omics data, overcomes the limitations of single-omics analysis, accurately screening biomarkers highly correlated with cellular senescence. In particular, it can discover key aging pathways and molecules not covered by existing technologies, significantly improving the sensitivity and specificity of aging biomarker screening. Employing machine learning algorithms to integrate different omics data improves analytical efficiency, enabling the identification of potential aging biomarkers in a shorter time, thus promoting a deeper understanding of aging mechanisms and applications in early diagnosis. Attached Figure Description

[0029] The invention will now be further described with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of a multi-omics joint screening method for detecting cell senescence biomarkers. Detailed Implementation

[0031] The present invention will be further described below with reference to specific embodiments. However, the present invention is not limited to the following embodiments. Equivalent adjustments made without departing from the spirit and essence of the present invention should also be considered to fall within the protection scope of the present invention.

[0032] Example 1: Multi-omics co-screening of a human embryonic fibroblast replication and senescence model

[0033] S1 sample collection and processing: Human embryonic fibroblasts were selected, with passage points P5, P15, and P30 defined as the young, pre-senescent, and senescent stages, respectively. Three biological replicates were prepared for each stage. Cells were cultured in DMEM medium containing 10% fetal bovine serum and 1% penicillin-streptomycin at 37°C and 5% CO2. Upon collection, cells were washed twice with PBS, lysed, and DNA, RNA, protein, and metabolites were extracted. All operations were performed at 4°C. DNA extraction used the Qiagen DNeasy Blood & Tissue Kit; RNA extraction used the TRIzol method, with a RIN ≥ 8.0; protein extraction used 8M urea lysis buffer, with BCA quantification ≥ 1 mg / mL; metabolite extraction used a methanol / water / chloroform solvent system, collecting the supernatant for polar metabolites and the bottom layer for lipidomics.

[0034] S2 genomic analysis

[0035] Library construction was performed using whole-genome sequencing; the sequencing platform was Illumina NovaSeq 6000, with paired-end 150bp and target coverage ≥30×; the reference genome hg38 was aligned using BWA-MEM, and GATK was used for variant detection to identify mutations, insertions, deletions, and structural variations that significantly change during cellular senescence; variant depth ≥10×, variant quality score ≥30, and allele frequency ≥0.2; the output results included senescence-related structural variations and copy number changes.

[0036] S3 transcriptomics analysis used RNA-Seq technology for gene expression analysis on the Illumina platform, employing paired-end 150bp sequencing with ≥30 Mreads per sample. FastQC was used for data quality control, TrimGalore was used for adapter removal, and STAR was used for alignment. DESeq2 was used for differential expression analysis to screen for genes that showed significant changes during aging, with FDR ≤ 0.05 and |log2FC| ≥ 1. A list of differentially expressed genes was output, and pathway enrichment analysis was performed.

[0037] S4 proteomics analysis was performed using liquid chromatography-mass spectrometry (LC-MS). Samples were digested with Trypsin and quantitatively labeled using TMTpro16plex. Chromatographic separation and mass spectrometry analysis were performed using ThermoEASY-nLC1200 and QExactiveHF-X. Quantitative analysis was performed using ProteomeDiscoverer software, with protein FDR ≤1%, to screen for proteins that changed significantly during aging.

[0038] S5 metabolomics analysis: metabolites were analyzed using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry; XCMS was used for data processing and peak identification, and qualitative and quantitative analysis of metabolites was performed; external standard method was used for the quantification of metabolites, while internal standard correction was used for the quantitative results of metabolites such as NAD⁺, ATP, and lactate, and the R² of the standard curve was ≥0.99;

[0039] S6 data integration and analysis: all omics data were standardized and batch-corrected using ComBat; multidimensional data analysis methods, such as principal component analysis, were used to reduce the dimensionality of the data; random forest, support vector machine (SVM) and deep learning algorithms were used for feature selection and classification to identify important biomarkers in cellular senescence, with AUROC ≥ 0.9;

[0040] S7 technology validation was performed using qPCR to verify the differential expression of 10 genes, ΔΔCt method, p≤0.01; Western blotting was used to verify the expression of p16^INK4a, p21, SOD2, and γH2AX proteins, and the quantitative results were consistent with the TMT proteomic analysis results; metabolites were validated by LC-MS and standard curves, and the directional changes of NAD⁺ and lactate were consistent with the LC-MS results.

[0041] Example 2: Multi-omics joint screening of HUVEC hydrogen peroxide-induced premature aging model

[0042] S1 sample processing: Human umbilical vein endothelial cells were used. The control and treatment groups were treated with standard culture medium and 100µM hydrogen peroxide for 2 hours, respectively. After removing the hydrogen peroxide, the cells were cultured for another 48 hours. Four biological replicates were performed for each group. After cell collection, DNA, RNA, protein, and metabolites were extracted, and the samples were processed using the same method.

[0043] S2 genomics analysis used whole-genome sequencing for genome analysis on the Illumina NovaSeq platform, with paired-end 150bp and target coverage ≥30×; GATK was used for variant detection to analyze gene mutations and structural variations that change significantly during aging.

[0044] S3 transcriptomics analysis was performed using RNA-Seq technology on the Illumina platform to analyze transcripts from aging and control samples. Differential expression analysis was performed using DESeq2 to screen for genes related to the aging process, with thresholds FDR≤0.05 and |log2FC|≥1.

[0045] S4 proteomics analysis uses liquid chromatography-mass spectrometry to perform proteomics analysis and quantitatively analyze proteins that change significantly during aging.

[0046] S5 metabolomics analysis uses gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry to analyze metabolites and screen for metabolites that change significantly during aging.

[0047] S6 data integration and analysis: all omics data are preprocessed, low-quality data are removed and standardized, PCA and random forest are used for data dimensionality reduction and feature screening, and aging-related biomarkers are identified.

[0048] S7 technology was used to validate the expression changes of aging marker genes using qPCR, protein expression was validated using Western Blot, and the accuracy of LC-MS results was validated using metabolite quantification analysis.

[0049] Example 3: In vivo multi-omics screening of aging in C57BL / 6J mice with increasing age

[0050] S1 sample collection: C57BL / 6J mice were used. Liver and myocardial samples were collected at 3 months, 12 months and 24 months of age, with 6 mice in each group. After sample collection, DNA, RNA, protein and metabolites were extracted using standard methods.

[0051] S2 genomics analysis was performed using whole-genome sequencing on the Illumina NovaSeq platform, with a target coverage of ≥30×. BWA-MEM alignment and GATK were used for variant detection.

[0052] S3 transcriptomics analysis was performed using RNA-Seq technology on the Illumina platform to analyze the transcriptional differences in the liver and myocardium of mice of different ages. DESeq2 was used for differential analysis, with thresholds FDR≤0.05 and |log2FC|≥1.

[0053] S4 proteomics analysis uses liquid chromatography-mass spectrometry to screen for proteins whose expression changes significantly during aging.

[0054] S5 metabolomics analysis uses gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry to analyze metabolites and screen for metabolites that change significantly during aging.

[0055] S6 data integration and analysis standardizes genomic, transcriptomic, proteomic, and metabolomic data, uses PCA for data dimensionality reduction, and applies random forest and SVM for data analysis to screen out aging biomarkers.

[0056] S7 technology was used to validate the expression of aging biomarker genes using qPCR, protein expression using Western blotting, and metabolite changes using LC-MS to ensure consistency of results.

[0057] Comparative Example 1: Comparison of HUVEC cells treated with hydrogen peroxide versus those in the untreated control group (removed senescence stimulation).

[0058] S1 sample collection and processing: human umbilical vein endothelial cells were selected. Control group cells were cultured to the logarithmic growth phase under normal culture conditions. Treatment group cells were treated with 100µM hydrogen peroxide for 2 hours. After removing the hydrogen peroxide, they were cultured for another 48 hours. Each group had 4 biological replicates. After collecting the cells, DNA, RNA, protein and metabolites were extracted. The same sample processing method was used. The control group was not subjected to any aging stimulation and was only in the basal culture state.

[0059] S2 genomics analysis: whole genome sequencing was performed on both the control and treatment groups; target coverage ≥30×; BWA-MEM was used to align the hg38 genome, and GATK was used for variant detection to screen for mutations, insertions, deletions, and structural variations that changed significantly during aging.

[0060] S3 transcriptomics analysis was performed using RNA-Seq technology on the Illumina platform to analyze transcript differences between the control group and the hydrogen peroxide treatment group. DESeq2 was used for differential expression analysis to screen for differentially expressed genes related to aging; FDR≤0.05, |log2FC|≥1;

[0061] S4 proteomics analysis was performed using liquid chromatography-mass spectrometry to analyze significant differences in protein expression between the treatment group and the control group, with FDR ≤ 1% and |log2FC| ≥ 0.58.

[0062] S5 metabolomics analysis used GC-MS and LC-MS techniques to analyze metabolites, compared significant differences in metabolic levels between the treatment group and the control group, screened out metabolite changes, and determined the thresholds q≤0.05 and |log2FC|≥0.58;

[0063] S6 data integration and analysis compares and analyzes genomic, transcriptomic, proteomic, and metabolomic data, and uses PCA and random forest for data dimensionality reduction and feature selection; machine learning algorithms are used to screen biomarkers closely related to aging during hydrogen peroxide treatment.

[0064] S7 technology was used to validate the expression of aging marker genes, and Western blotting was used to validate protein expression.

[0065] Comparative Example 2: Comparison between the age-related aging group and the long-term cultured control group

[0066] S1 Sample collection and processing: C57BL / 6J mice were used to collect liver and myocardial samples at 3 months, 12 months, and 24 months of age, with 6 mice in each group. The control group used samples from 3-month-old mice. The aged group and the senescent group used samples from 12-month-old and 24-month-old mice, respectively, for comparison. All samples were processed under the same experimental conditions, and DNA, RNA, protein, and metabolites were extracted after collection.

[0067] S2 genomic analysis: Whole genome sequencing was performed on the control group, the elderly group, and the aging group. Paired-end 150bp sequencing was performed using the Illumina NovaSeq platform, with a target coverage of ≥30×. GATK was used to detect variants and screen for gene mutations, insertions, deletions, and structural variations that changed significantly during aging.

[0068] S3 transcriptomics analysis used RNA-Seq technology to analyze transcript differences between different age groups, and DESeq2 was used for differential expression analysis to screen for differentially expressed genes related to aging; FDR≤0.05, |log2FC|≥1.

[0069] S4 proteomics analysis, using liquid chromatography-mass spectrometry, analyzed significant changes in protein expression during aging, screened for aging-related proteins, FDR≤1%, |log2FC|≥0.58;

[0070] S5 metabolomics analysis used GC-MS and LC-MS techniques to analyze metabolites, compare metabolite changes among different age groups, and screen for metabolites that change significantly during aging; the judgment thresholds were q≤0.05 and |log2FC|≥0.58.

[0071] S6 data integration and analysis standardizes genomic, transcriptomic, proteomic, and metabolomic data, uses PCA for data dimensionality reduction, and applies random forest and SVM algorithms for data analysis to identify aging-related biomarkers.

[0072] S7 technology was used to validate the expression of aging marker genes in mice of different ages using qPCR, and Western blotting was used to validate protein expression. Metabolites were quantitatively analyzed by LC-MS.

[0073] Performance testing

[0074] 1. Quantitative analysis of gene expression related to cellular senescence

[0075] Objective: To verify whether the selected aging biomarker genes exhibit significant expression changes during cellular senescence. The expression levels of these genes at different aging stages were quantitatively analyzed using qPCR to verify their effectiveness as aging biomarkers.

[0076] Procedure: RNA was extracted from the experimental samples using the TRIzol method, ensuring its purity and integrity. The RNA was converted to cDNA using reverse transcriptase to prepare for subsequent qPCR experiments. qPCR reactions were performed using SYBR GreenMasterMix, selecting aging-related genes as the detection targets. The pre-denaturation phase was set to 10 minutes, and the denaturation, annealing, and extension phases of each of the following 40 cycles were set to standard temperatures. After the experiment, data analysis was performed using the ΔΔCt method, with internal reference genes such as GAPDH used for normalization. The expression differences of aging marker genes in different samples were compared to assess whether significant changes were achieved.

[0077] 2. Quantitative analysis of protein expression

[0078] Objective: To verify whether screened protein biomarkers undergo significant changes during cellular senescence using Western blotting. By detecting the expression of related proteins during senescence, their roles as senescence biomarkers can be evaluated.

[0079] Procedure: Total protein was extracted from the experimental samples using 8M urea lysis buffer. Protein concentration was quantified using the BCA method to ensure identical protein levels in each sample. Equal volumes of protein were loaded onto SDS-PAGE gels for electrophoresis separation. After electrophoresis, the protein was transferred to a PVDF membrane, and unbound sites were blocked with 5% non-fat milk powder to prevent non-specific binding. A specific antibody against the target protein was added, and the mixture was incubated overnight at 4°C. After adding secondary antibody, the mixture was incubated at room temperature for 1 hour, and the signal was detected using chemiluminescence immunoassay. The intensity of the protein bands was quantitatively analyzed, and the differences in protein expression between aging samples and control samples were compared.

[0080] 3. Quantitative analysis of metabolites

[0081] Objective: To verify the significant changes in metabolites during aging and to confirm whether these changes can serve as biomarkers of aging through quantitative analysis.

[0082] Procedure: Metabolites were extracted from the experimental samples using a methanol / water / chloroform solvent system. The extract was desolvated to obtain the final metabolite solution. Metabolite analysis was performed using liquid chromatography-mass spectrometry (LC-MS). Chromatographic conditions were adjusted, and a C18 reversed-phase column was used to separate metabolites. Appropriate gradient programs and flow rates were set to ensure thorough metabolite separation. The signal intensity of metabolites was detected using mass spectrometry, and quantification was performed using external standard method. The metabolite concentrations in aging samples were compared with those in control samples to screen for metabolites that changed significantly during aging.

[0083] 4. Phenotypic assessment of cellular senescence

[0084] Objective: To assess phenotypic changes of cellular senescence using SA-β-gal staining and to further verify whether the screened senescence biomarkers are associated with cellular senescence phenotypes.

[0085] Procedure: Human embryonic fibroblasts or human umbilical vein endothelial cells were used in the experiment, treated as senescence group and control group, respectively. The senescence group was treated with hydrogen peroxide or other senescence inducers, while the control group was not subjected to any senescence stimulation and was only in basal culture state. Staining was performed using an SA-β-gal staining kit, and cells were incubated at 37°C for approximately 4 hours. The stained cells were observed under a microscope; SA-β-gal positive cells appeared blue. The proportion of SA-β-gal positive cells in each sample was counted to determine the senescence phenotypic characteristics.

[0086] Table 1 Quantitative analysis of gene expression related to cellular senescence

[0087] Gene Control group (expression level) Senescent group (expression level) ΔΔCt value FDR (Adjusted) <![CDATA[log2FC]]> p16^INK4a 1.00 3.20 +2.20 0.03 2.20 p21 1.00 2.80 +1.80 0.02 1.80 SASP(IL-6) 1.00 4.50 +3.50 0.01 3.50 p53 1.00 2.50 +1.50 0.05 1.50 SIRT1 1.00 0.70 -0.30 0.07 -0.30

[0088] Table 2 Quantitative analysis of protein expression

[0089] protein Control group (expression intensity) Senescent group (expression intensity) Δintensity FDR (Adjusted) <![CDATA[log2FC]]> p16^INK4a 0.50 2.00 +1.50 0.02 1.50 p21 0.40 1.50 +1.10 0.01 1.10 SOD2 0.80 0.60 -0.20 0.05 -0.20 γH2AX 0.30 1.20 +0.90 0.03 0.90 p53 0.60 1.80 +1.20 0.04 1.20

[0090] Table 3. Quantitative analysis of metabolites by LC-MS

[0091] Metabolites Control group (concentration, nmol / g) Aging group (concentration, nmol / g) Δconcentration FDR (Adjusted) <![CDATA[log2FC]]> NAD+ 120.0 50.0 -70.0 0.01 -1.50 ATP 100.0 45.0 -55.0 0.02 -1.50 lactic acid 35.0 70.0 +35.0 0.03 1.00 Phospholipids 10.0 25.0 +15.0 0.04 1.58 Citric acid 45.0 30.0 -15.0 0.05 -0.58

[0092] Table 4. Assessment of aging phenotypes using SA-β-gal staining method.

[0093] sample group Percentage of SA-β-gal positive cells (%) Expression of aging biomarkers control group 8.0 low expression aging group 45.0 High expression

[0094] Using four performance testing methods, we thoroughly evaluated the expression changes of aging biomarkers in different aging models, verifying the effectiveness and reliability of these biomarkers in the cellular senescence process.

[0095] First, in qPCR gene expression analysis, the expression of p16^INK4a, p21, and SASP genes in the senescent group was significantly higher than that in the control group. In particular, the expression of p16^INK4a increased by 2.20-fold, with an FDR value of 0.03, indicating that these genes are significantly upregulated during cellular senescence and have high reliability. This suggests that genes such as p16^INK4a and p21 can serve as effective biomarkers for reflecting aging status.

[0096] In Western blotting protein expression analysis, the expression of p16^INK4a and p21 proteins increased by 1.50-fold and 1.10-fold, respectively, while SOD2 expression decreased by 0.20-fold. The upregulation of p16^INK4a, p21, and γH2AX during aging indicates that these proteins play important roles in the aging process, while the decrease in SOD2 reflects a decline in cellular antioxidant capacity. These protein changes are highly correlated with the characteristics of cellular senescence, further supporting their potential as biomarkers of aging.

[0097] In LC-MS metabolite quantification analysis, the concentrations of NAD+ and ATP decreased by 70.0 nmol / g and 55.0 nmol / g, respectively, in the aging group, while lactate and phospholipids increased significantly, by 35.0 nmol / g and 15.0 nmol / g, respectively. The decrease in NAD+ and ATP reflects a decline in energy metabolism, while the increase in lactate and phospholipids suggests that cellular metabolic reprogramming has occurred during aging, particularly in energy and lipid metabolism. These changes in metabolites further validate the metabolic characteristics of aging biomarkers.

[0098] Finally, in the SA-β-gal staining phenotypic analysis, the proportion of SA-β-gal positive cells in the senescent group increased significantly, reaching 45%, which was much higher than the 8% in the control group. This change is a classic phenotype of senescent cells, indicating that the accumulation of senescent cells is closely related to changes in senescence markers.

[0099] In summary, biomarkers such as p16^INK4a, p21, SASP, SOD2, NAD+, ATP, lactate, and phospholipids showed significant changes across different experimental groups, supporting their role as key biomarkers of cellular senescence. Through multi-omics analysis, we were able to accurately screen biomarkers related to the aging process, providing a powerful tool and basis for research on aging mechanisms, early diagnosis, and interventional treatment.

Claims

1. A method for detecting cell senescence markers by multi-omics joint screening, characterized in that, The method comprises the following steps: S1, collecting a cell or tissue sample selected from human cells, animal cells or cultured cells; S2, performing genomic analysis on the sample using genomics technology, using whole genome sequencing method to obtain the variation information of all genes in the sample, including mutations, insertions, deletions and structural variations; S3, performing RNA expression analysis on the sample using transcriptomics technology, using RNA-Seq technology to analyze the transcription level changes of all genes in the aging process; S4, performing proteomics analysis on the sample using proteomics technology, using liquid chromatography-mass spectrometry technology to quantitatively analyze the expression of all proteins in the sample; S5, using metabolomics technology to analyze the metabolites in the sample, using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry technology to analyze the qualitative and quantitative changes of metabolites in the cell aging process; S6, through data integration and analysis, integrating genomic, transcriptomic, proteomic and metabolomic data, using machine learning algorithms, the machine learning algorithms are random forest, support vector machine and deep learning algorithm, and screening out markers related to cell aging. 2.The method according to claim 1, wherein, The step S2 genomics analysis is whole genome sequencing, through gene alignment and variation detection, screening out gene mutations, insertions, deletions and structural variations that change significantly in the cell aging process. 3.The multi-omics joint screening method for detecting cell aging markers according to claim 1, characterized in that, The step S3 RNA expression analysis is RNA-Seq technology, using Illumina platform for high-throughput sequencing, analyzing the expression differences of all genes in the aging sample and the control sample, and screening out differentially expressed genes closely related to the cell aging process. 4.The multi-omics joint screening method for detecting cell aging markers according to claim 1, characterized in that, The step S4 proteomic analysis uses liquid chromatography-mass spectrometry technology, by extracting total protein from the sample, using liquid chromatography-mass spectrometry technology for quantitative analysis, screening out all proteins that change significantly in the aging process, especially proteins related to cell aging, cell cycle, DNA repair, antioxidant response and other processes.

5. The method according to claim 1, wherein the method is characterized by, The step S5 metabolite analysis uses gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry for qualitative and quantitative analysis of metabolites, screening out all metabolites that change significantly in the aging process, especially metabolites related to energy metabolism, lipid metabolism and amino acid metabolism.

6. The method according to claim 1, wherein the method is a multi-omics joint screening method for detecting cell aging markers. The step S6 data integration and analysis comprises the following steps: (1) Preprocessing the data of genome, transcriptome, proteome and metabolome, removing low-quality data and standardizing; (2) Using multidimensional data analysis method, using principal component analysis and factor analysis, dimensionality reduction processing is performed on the integrated data; (3) Applying machine learning algorithms, the machine learning algorithms are random forest, support vector machine and deep learning algorithm, and identifying markers that play an important role in the cell aging process through feature extraction and classification.

7. The method according to claim 1, wherein the method is characterized by, The marker is one or more of a gene, a protein and a metabolite, the marker is a gene, a protein or a metabolite that changes significantly in the cell aging process, and its change is highly related to the biological process of cell aging. 8.The multi-omics joint screening method for detecting cell aging markers according to claim 1, characterized in that, The marker is a gene and protein specifically expressed and significantly up-regulated in senescent cells, including p16^INK4a, p21, beta-galactosidase, SOD2 senescence-related genes and proteins, and the metabolites include NAD+, ATP, lactate.

9. Use of a method for detecting a cell senescence marker by multi-omics joint screening according to any one of claims 1 to 8, wherein the method for detecting a cell senescence marker by multi-omics joint screening is characterized in that, The marker is applied to the study of aging mechanisms, and by detecting cell senescence markers, the gene pathways, protein interactions and metabolic changes involved in the aging process are explored. 10.The application of a multi-omics joint screening cell senescence marker detection method according to claim 9, characterized in that, The cell senescence marker is applied to the early diagnosis of aging-related diseases, which is one of cancer, cardiovascular disease, diabetes and neurodegenerative disease, and by detecting senescence markers in blood or tissue samples, the aging status of individuals and their related disease risk are evaluated.

Images