Diabetic nephropathy specific senescence renal tubular cell subset and characteristic gene and application thereof

Abstract

The invention relates to a characteristic gene of senescent renal tubular cell subgroups related to diabetic nephropathy and application thereof, and the characteristic gene and functional difference of each subgroup of senescent renal tubular cells in a renal puncture sample of a patient with diabetic nephropathy are analyzed through single cell transcriptome sequencing and bioinformatics technologies. The four gene segments CXCL8, MMP7, ADIRF and SCL12A1 are screened out to serve as markers for sorting the diabetic nephropathy specific senescent renal tubular cell subgroups, and compared with normal people, the senescent renal tubular cells of diabetic nephropathy patients have high expression of CXCL8 and MMP7 and low expression of ADIRF and SCL12A1, so that the diabetic nephropathy specific senescent renal tubular cell subgroups can be separated from the senescent renal tubular cells of the diabetic nephropathy patients, and the diabetic nephropathy specific senescent renal tubular cell subgroups can be separated from the senescent renal tubular cells of the diabetic nephropathy patients. The development of the diabetic nephropathy from the early stage to the progression stage is stronger; by detecting the level of the markers in the senescent renal tubular cells, the development and prognosis of the diabetic nephropathy can be predicted in an early stage, and potential effective prevention and treatment measures can be provided for delaying the DN development through targeted intervention of senescent renal tubular cell subgroups.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and in particular relates to diabetic nephropathy-specific senescent renal tubular cell subpopulations and characteristic genes and applications thereof. Background Art

[0002] Diabetic nephropathy (DN) is one of the most common and severe complications of diabetes and the leading cause of end-stage renal failure. Tubulointerstitial fibrosis plays a crucial role in the progression of DN, is closely associated with renal impairment, and determines renal prognosis. Current research suggests that tubular cell senescence is a key cellular biological event in renal interstitial fibrosis in DN. Senescent tubular cells not only secrete a large number of proinflammatory and chemokine-producing senescence-associated secretory phenotypes (SASPs), but also develop an apoptosis-resistant phenotype that allows senescent cells to evade immune cell clearance. This in turn leads to the continued secretion and release of the SASP, which prolongs and amplifies its damaging effects and promotes renal tissue inflammation and fibrosis.

[0003] However, senescent cells are highly heterogeneous, with differences in the genomic and phenotypic expression patterns of senescent cells across different disease contexts and tissues. Characteristic marker genes for senescent renal tubular cells in diabetic renal tissue remain lacking. Currently, assessment of cellular senescence cannot rely solely on a single marker; a comprehensive assessment of cellular senescence status requires multiple markers, including cell cycle inhibitors, decreased cell proliferation, nuclear membrane erosion, and DNA damage. Cellular senescence is a complex process, with distinct genomic expression patterns and associated biological effects at different stages. The initiation and early stages of cellular senescence are primarily driven by activation of p16 and p21 signaling, manifesting as cell cycle arrest. These processes play important roles in embryonic development, tissue repair, and tumor suppression. They also release several SASPs, which facilitate the recruitment of immune cells and promote their clearance, thereby mitigating age-related damage. In the late stages of cellular senescence, pro-inflammatory signaling is activated, leading to the production of numerous inflammatory cytokines, including the SASP, leading to chronic tissue inflammation and age-related diseases. Traditional studies focusing on senescent cell populations have limitations and have not fully revealed the effects and mechanisms of senescent cells at different stages of diabetic nephropathy. The current development of single-cell technology has, on the one hand, solved the challenge of studying trace renal puncture samples, and on the other hand, better studied the heterogeneity between senescent cells and non-senescent cells at the single-cell level, providing strong technical support for analyzing the characteristics and effector mechanisms of single senescent cells. Summary of the Invention

[0004] In response to the deficiencies in the prior art, the present invention proposes diabetic nephropathy-specific senescent renal tubular cell subpopulations and characteristic genes and their applications.

[0005] The technical solution of the present invention is:

[0006] Application of biomarkers in sorting diabetic nephropathy-specific senescent renal tubular cell subpopulations for non-diagnostic purposes, wherein the biomarkers are one or more of CXCL8, MMP7, ADIRF, and SCL12A1.

[0007] Furthermore, the biomarker is one or more of ADIRF and SCL12A1.

[0008] Furthermore, the senescent renal tubular cell subpopulation includes a PT1 subpopulation, the PT1 subpopulation biomarker is SCL12A1, and the PT1 subpopulation is enriched in oxidative phosphorylation and fatty acid degradation signaling pathways.

[0009] Furthermore, the senescent renal tubular cell subpopulation includes a PT2 subpopulation, the PT1 subpopulation biomarkers are CXCL8 and MMP7, and the PT2 subpopulation is enriched in TNF signaling, Rage signaling, and ECM signaling pathways related to inflammation and fibrosis.

[0010] Furthermore, the senescent renal tubular cell subpopulation includes a PT3 subpopulation, the PT1 subpopulation biomarker is ADIRF, and the PT3 subpopulation is enriched in metabolism and mineral absorption signaling pathways.

[0011] Furthermore, the biomarkers CXCL8 and MMP7 are highly expressed in the senescent renal tubular cell subpopulation, and the biomarkers ADIRF and SCL12A1 are lowly expressed in the senescent renal tubular cell subpopulation.

[0012] The use of reagents for detecting CXCL8, MMP7, ADIRF, and SCL12A1 in the preparation of products for evaluating the progression of diabetic nephropathy.

[0013] Furthermore, the expression of CXCL8 and MMP7 gradually increases with the progression of diabetic nephropathy.

[0014] Furthermore, the expression of ADIRF and SCL12A1 gradually decreases with the progression of diabetic nephropathy.

[0015] Compared with the prior art, the present invention has at least the following advantages:

[0016] The present invention relates to diabetic nephropathy-specific senescent renal tubular cell subpopulations and characteristic genes and applications thereof. By using single-cell transcriptome sequencing and bioinformatics techniques, the characteristic genes and functional differences of various senescent renal tubular cell subpopulations in renal puncture samples of diabetic nephropathy patients are analyzed to reveal the mechanism of action of different senescent renal tubular cell subpopulations in the progression of diabetic nephropathy. Four gene fragments, CXCL8, MMP7, ADIRF, and SCL12A1, are screened as markers for sorting diabetic nephropathy-specific senescent renal tubular cell subpopulations. Compared with normal controls, CXCL8 and MMP7 are highly expressed, while ADIRF and SCL12A1 are lowly expressed, and the expression becomes more intense as diabetic nephropathy progresses from the early stage to the advanced stage. By detecting the levels of markers in senescent renal tubular cells, the present invention can make early predictions of the progression and prognosis of diabetic nephropathy, and can also provide potential effective prevention and treatment measures for targeted intervention of senescent renal tubular cell subpopulations to delay DN progression. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention, the following briefly introduces the drawings required for the specific embodiments or the description of the prior art.

[0018] Figure 1 The results of UMAP analysis of cell clustering in kidney tissue samples in Example 5 of the present invention are as follows;

[0019] Figure 2 Figure 5 is a single-cell atlas of senescent renal tubular cells in renal tissue from DN patients in Example 5 of the present invention; A: UMAP map of senescent genes in renal tissue samples; B: Comparison of senescent gene set scores for each cell population; C: UMAP analysis of proximal renal tubular cell clustering; D: Integrated annotation analysis of the differences in the proportions of each PT cell subpopulation between groups; E: UMAP display of the distribution and number changes of New-PT1 cells in each group; F: Heat map analysis of the differential gene expression levels of each PT cell population; G: Comparison of senescent gene set scores for each PT cell population; H: Comparison of senescent gene set scores for New-PT1 cells in the control and DN groups; I: Pathway analysis of differentially expressed genes in New-PT1 cells;

[0020] Figure 3 The characteristics and functions of senescent renal tubular cell subpopulations in renal tissue of DN patients in Example 5 of the present invention;

[0021] A: UMAP plot of senescent PT cell subpopulation analysis; B: The proportion of each senescent PT cell subpopulation in each group; C: Heat map analysis of the top 3 genes in each senescent PT cell subpopulation; D: Pathway analysis of each PT cell subpopulation;

[0022] Figure 4The characteristic gene expression of the senescent renal tubular cell subpopulation in the renal tissue of DN patients in Example 5 of the present invention; A: relative expression level of the characteristic genes; BE: quantitative PCR detection of the expression level of each characteristic gene in high glucose-induced senescent renal tubular cells. DETAILED DESCRIPTION

[0023] The present invention is described in further detail below. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art can make some non-essential improvements and adjustments to the present invention based on the above application content.

[0024] This document provides general and / or specific descriptions of the materials and experimental methods used in the experiments. Unless otherwise specified, all experimental or testing methods are conventional methods. All reagents and instruments used, unless the manufacturer is specified, are commercially available, conventional products and were prepared or used using conventional methods.

[0025] Sources:

[0026] Primers for CXCL8:

[0027] Forward:5'-CTCCAAACCTTTCCACCCCA-3'(SEQ ID NO.1)

[0028] Reverse:5'-TTCTCAGCCCTCTTCAAAAACT-3'(SEQ ID NO.2)

[0029] Primers for MMP7:

[0030] Forward:5'-CATGATTGGCTTTGCGCGAG-3'(SEQ ID NO.3)

[0031] Reverse:5'-AGACTGCTACCATCCGTCCA-3'(SEQ ID NO.4)

[0032] Primers for ADIRF:

[0033] Forward:5'-CGGTCTGCCCCTCATAAA-3'(SEQ ID NO.5)

[0034] Reverse:5'-CTACGGTGGCTCTGCTCTTG-3'(SEQ ID NO.6)

[0035] Primers for SCL12A1:

[0036] Forward:5'-TGCTTCTCATTCGCCTCTCC-3'(SEQ ID NO.7)

[0037] Reverse:5'-CGAACAAACCCGTTAGTTGCTAT-3'(SEQ ID NO.8)

[0038] Preparation of a single cell suspension sample in Example

[0039] 1.1 Renal biopsy specimens were collected from patients diagnosed with DN by renal biopsy. The patients were divided into early DN (n=3) and progressive DN (n=3) according to the degree of pathological damage and renal function. The control group (control) samples were obtained from adjacent renal tissue that was assessed as normal by renal biopsy.

[0040] 1.2 Preparation of single-cell suspension

[0041] Tissue dissociation: Use ophthalmic forceps to transfer fresh tissue to a culture dish containing HBSS buffer. Wash three times in sequence and transfer to a centrifuge tube. Add Tissue Dissociation Mix and mince the tissue with sterile ophthalmic scissors until it is minced. Finally, tilt the centrifuge tube at a 45° angle and place it in a preheated 37°C constant temperature oscillator for digestion and dissociation. Observe the cell suspension every 15 minutes until the tissue is thoroughly digested. Mix 10 μL of the cell suspension with trypan blue dye in a 1:1 volume ratio and observe tissue dissociation. If the tissue is completely digested and there are no cell clumping or aggregation and no impurities under a microscope, the cell suspension meets the requirements and digestion can be stopped. If there is a lot of tissue residue and the cells are severely clumped, the cell suspension does not meet the requirements and digestion can be continued for an additional 5 minutes. Observe and determine whether the cell suspension meets the requirements.

[0042] Cell filtration: Filter the dissociated cell suspension through a 40 μm filter into a 50 mL low-absorption centrifuge tube. Rinse the tube walls with PBS 1-2 times to ensure that all cells are transferred. Adjust the volume of the filtered suspension to 15-25 mL with PBS. Centrifuge the obtained cell suspension at 350 g for 5 minutes. After centrifugation, carefully remove the supernatant along the tube wall with a Pasteur pipette until the supernatant volume remains. Resuspend the cell pellet in the remaining volume. The supernatant can be temporarily stored in the centrifuge tube. Mix 10 μL of the cell suspension with trypan blue dye in a 1:1 volume ratio for staining and examine the cell state under a microscope.

[0043] Erythrocyte lysis and suspension quality control: Transfer the cell suspension to a 15 mL low-binding centrifuge tube and mix the cell suspension to a 1:2 volume ratio of cell suspension to erythrocyte lysis buffer. After mixing, gently invert the tube to mix thoroughly. Incubate on ice for 5-8 minutes, then centrifuge at 300g for 5 minutes. After centrifugation, remove as much supernatant as possible using a Pasteur pipette or pipette. Resuspend the cell pellet in 1 mL of pre-chilled PBS buffer and examine microscopically to determine complete erythrocyte lysis. If secondary erythrocyte lysis is not required, make the cell suspension to 10 mL with pre-chilled PBS buffer, gently invert the tube to mix thoroughly, and centrifuge at 300g for 5 minutes. Aspirate the supernatant. Resuspend the cell pellet in an appropriate volume of PBS, stain 10 μL of the cell suspension with trypan blue, and calculate cell concentration and viability using a counting chamber. Single-cell suspension quality control passes when cell viability is >85%, total cell count >20,000, and impurities or erythrocytes are less than 20%.

[0044] Example 2 Single Cell Sequencing

[0045] Single cell suspension (2 × 10 5 cells / mL) are loaded onto the microporous chip. Place the microfluidic chip on a clean culture dish, use a 200μL pipette to draw 200μL of anhydrous ethanol and inject it into the chip from the injection port. The time is controlled within 10 seconds. The anhydrous ethanol can be sucked back and forth in the chip with a pipette until no bubbles appear in the chip. The liquid at the outlet is removed in time and the cleaning is repeated twice. Barcoding Beads are then collected from the microporous chip, and the mRNA captured by the Barcoding Beads is reverse transcribed to obtain cDNA, which is then amplified by PCR. After the cDNA amplification is completed, it is purified, and the amplified and purified product is subjected to CDNA concentration and fragment size detection as quality inspection. Use Qubit for CDNA concentration detection, and use Agilent fragment analyzer for fragment size detection. CDNA that passes the quality inspection should meet the following conditions at the same time: the main peak fragment size should be around 900bp-2000bp, 1000bp-5000bp accounts for more than 15%, and fragments below 300bp account for less than 40%. If the quality inspection finds that the cDNA fragments below 300bp account for 10-40%, secondary purification is required. The amplified cDNA fragments are then connected to the sequencing adapters, and the product purification, PCR enrichment, amplified product fragment sorting and library quality inspection are performed after the adapter connection. The protocol for the single-cell RNA library kit was used for construction (New Gene Biotech). The individual libraries were diluted to 4 nM and sequenced on an Illumina novaseq 6000 to obtain sequencing data and perform subsequent data analysis.

[0046] Example 3 Bioinformatics Analysis

[0047] 3.1 Preliminary analysis of raw read data (scRNA-seq)

[0048] Raw reads were processed using celescope 1.15.0 with default parameters to generate gene expression profiles. Barcodes and UMIs were extracted from R1 reads and corrected. Adapter sequences and poly A tails were trimmed from R2 reads, and the trimmed R2 reads were aligned to the GRCh38 transcriptome using STAR (v2.6.1a). Uniquely mapped reads were then assigned to genes using FeatureCounts (v2.0.1). Reads with successfully assigned barcodes to the same cell were grouped together with UMIs and genes to generate a gene expression matrix for further analysis.

[0049] 3.2 Dimensionality reduction and cluster analysis

[0050] Scanpy v1.8.2 was used for quality control, dimensionality reduction, and clustering under Python 3.7. For each sample dataset, we filtered the expression matrix according to the following criteria: 1) cells with gene counts less than 200 or in the top 2% of gene counts were excluded; 2) cells in the top 2% of UMI counts were excluded; 3) cells with mitochondrial content >50% were excluded; and 4) genes expressed in fewer than five cells were excluded. After filtering, 70,477 cells were retained for downstream analysis, with an average of 1,793 genes and 6,182 UMIs per cell. The raw count matrix was normalized by the total count per cell and log-transformed into a normalized data matrix. The top 2,000 variable genes were selected by setting flavor = "seurat." Principal component analysis (PCA) was performed on the scaled variable gene matrix, and the first 20 principal components were used for clustering and dimensionality reduction. Cells were separated into 28 clusters using the Louvain algorithm with a resolution parameter of 1.2. Unified Manifold Approximation and Projection (UMAP) was used to visualize and analyze cell clusters.

[0051] 3.3 Analysis of differentially expressed genes (DEGs)

[0052] To identify differentially expressed genes (DEGs), we used the scanpy.tl.rank_genes_groups function based on the Wilcoxon rank sum test with default parameters and selected genes that were expressed in more than 10% of the cells in any group of compared cells and whose mean logarithm was greater than 0.25 as DEGs. Adjusted p-values were calculated using the Benjamini-Hochberg correction, and a value of 0.05 was used as the criterion for assessing statistical significance.

[0053] 3.4 Pathway enrichment analysis

[0054] GO and KEGG analyses were performed using the clusterProfiler R package v4.0.0. Pathways with p_adj values less than 0.05 were considered significantly enriched, and the selected significant pathways were plotted as bar graphs.

[0055] Example 5 PCR verification of target genes

[0056] In the high glucose (30 mmol / L)-induced primary renal tubular cell senescence model, real-time fluorescence quantitative PCR was used to detect the mRNA levels of CXCL8, MMP7, ADIRF, and SCL12A1.

[0057] Primers for CXCL8:

[0058] Forward:5'-CTCCAAACCTTTCCACCCCA-3'(SEQ ID NO.1)

[0059] Reverse:5'-TTCTCAGCCCTCTTCAAAAACT-3'(SEQ ID NO.2)

[0060] Primers for MMP7:

[0061] Forward:5'-CATGATTGGCTTTGCGCGAG-3'(SEQ ID NO.3)

[0062] Reverse:5'-AGACTGCTACCATCCGTCCA-3'(SEQ ID NO.4)

[0063] Primers for ADIRF:

[0064] Forward:5'-CGGTCTGCCCCTCATAAA-3'(SEQ ID NO.5)

[0065] Reverse:5'-CTACGGTGGCTCTGCTCTTG-3'(SEQ ID NO.6)

[0066] Primers for SCL12A1:

[0067] Forward:5'-TGCTTCTCATTCGCCTCTCC-3'(SEQ ID NO.7)

[0068] Reverse:5'-CGAACAAACCCGTTAGTTGCTAT-3'(SEQ ID NO.8)

[0069] The test results are as follows:

[0070] Constructing a single-cell atlas of renal tissue from DN patients: A total of 76,700 cells were analyzed. Based on the single-cell expression profile characteristics and the marker genes of each kidney cell, unsupervised clustering was used to divide the cells into 14 groups. The cell group results are shown in Figure 1 , mainly including proximal tubule cells (PT), distal tubule cells, mesangial cells, podocytes, endothelial cells, immune cells, etc., among which PT cell group is the most important cell type, accounting for the highest proportion, reaching 50.01%.

[0071] Construction of a single-cell atlas of senescent renal tubular cells in DN patients' renal tissue: Based on 26 senescence marker genes such as p16 and p21, a senescence gene set was constructed, and a single-cell atlas of senescent renal cells in DN patients' renal tissue was drawn for the first time. The Umap map showed that PT cells (proximal tubular cells) had higher expression abundance of senescence genes and higher senescence gene set scores ( Figure 2 AB), indicating that PT cells (proximal tubule cells) in DN are one of the most important types of cell senescence.

[0072] Further subgrouping of PT cells (proximal tubule cells) revealed that in addition to the PT-S1, PT-S2, and PT-S3 segments, there were three new PT cell subgroups (New-PT1, New-PT2, and New-PT3) ( Figure 2 C). The chi-square test was used to analyze the distribution of each PT cell subset in different DN groups. The results showed that New-PT1 cells were mainly distributed in the DN group, and the number of New-PT1 cells gradually increased with the progression of DN ( Figure 2 D). UMAP images further analyzed that the number of New-PT1 cells gradually increased with the progression of DN ( Figure 2 E). The heat map shows the differential gene expression levels among different PT subgroups. The results show that New-PT1 cells highly express CXCL8, MMP7, CDKN1A and other cell senescence and SASP-related genes ( Figure 2 F). The boxplot analysis of the senescence gene set score showed that the senescence score of New-PT1 cells was significantly higher than that of other PT subpopulations ( Figure 2G), and as DN progresses, the New-PT1 cell senescence score gradually increases, that is, the senescence characteristics become more obvious ( Figure 2 H), indicating that New-PT1 cells are senescent renal tubular cells. Pathway analysis of New-PT1 cells revealed that they were mainly enriched in fibrin formation, Rap1 signaling pathway, PI3K / Akt signaling pathway, etc. ( Figure 2 I), suggesting that New-PT1 cells play an important role in regulating the inflammation and fibrosis of DN renal tissue.

[0073] Single cell atlas of New-PT1 senescent renal tubular cells in renal tissue of DN patients: Given the large heterogeneity of phenotype and function of senescent cells at different stages of the disease, we further performed subpopulation analysis on New-PT1 cells. Unsupervised clustering revealed that senescent renal tubular cells New-PT1 can be divided into three subpopulations ( Figure 3 A): Recorded as senescent PT1, senescent PT2, and senescent PT3, the number of senescent PT2 subpopulation cells gradually increases with the progression of DN ( Figure 3 B). The expression levels of CXCL8 and MMP7 in senescent PT2 subpopulation cells were significantly higher than those in the other two subpopulations; the expression level of ADIRF in PT3 subpopulation cells was significantly higher than those in the other two subpopulations; the expression level of SCL12A1 in PT1 subpopulation cells was higher ( Figure 3 C). Analysis of differentially expressed genes in different subpopulations of senescent PT cells revealed that PT2 subpopulation cells (Cxcl8 + 、MMP7 + ) are mainly enriched in TNF signaling, Rage signaling pathway, ECM and other signaling pathways related to inflammation and fibrosis; while PT3 subpopulation cells (ADIRF + ) are mainly enriched in amino acid metabolism, mineral absorption and other metabolism-related signaling pathways; PT1 subpopulation cells (SCL12A1 + ) are mainly enriched in signaling pathways such as oxidative phosphorylation and fatty acid degradation ( Figure 3 D) The above results indicate that different senescent PT subpopulations have different gene expressions and biological functions.

[0074] In vitro verification of the expression of characteristic genes in senescent renal tubular cell subpopulations: First, using single-cell transcriptomics data, we analyzed the relative expression levels of characteristic genes in three senescent renal tubular cell subpopulations. The results showed that the expression levels of CXCL8 and MMP7 gradually increased with the progression of DN, while the expression levels of ADIRF and SCL12A1 gradually decreased with the progression of DN ( Figure 4A). Further, by constructing a high-glucose-induced renal tubular cell senescence model, quantitative PCR analysis revealed that the mRNA levels of CXCL8 and MMP7 in senescent renal tubular cells were significantly increased compared with normal controls, while the expression levels of ADIRF and SCL12A1 were decreased compared with normal controls, which was consistent with the single-cell transcriptomics data ( Figure 4 BE). The above indicates that targeted intervention of CXCL8, MMP7, ADIRF, and SCL12A1, markers of senescent renal tubular cell subpopulations, is expected to become a potential effective preventive measure to delay the progression of DN.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. Application of a biomarker in the separation of diabetic nephropathy-specific senescent renal tubular cell subpopulations for non-diagnostic purposes, characterized in that: The biomarkers are one or more of CXCL8, MMP7, ADIRF, and SCL12A1.

2. The use according to claim 1, characterized in that The biomarkers are one or more of ADIRF and SCL12A1.

3. The use according to claim 2, characterized in that The senescent renal tubular cell subpopulation includes a PT1 subpopulation, the PT1 subpopulation biomarker is SCL12A1, and the PT1 subpopulation is enriched in oxidative phosphorylation and fatty acid degradation signaling pathways.

4. The use according to claim 1, characterized in that The senescent renal tubular cell subpopulation includes the PT2 subpopulation, the PT1 subpopulation biomarkers are CXCL8 and MMP7, and the PT2 subpopulation is enriched in TNF signaling, Rage signaling, and ECM signaling pathways related to inflammation and fibrosis.

5. The use according to claim 2, characterized in that The senescent renal tubular cell subpopulation includes a PT3 subpopulation, the PT1 subpopulation biomarker is ADIRF, and the PT3 subpopulation is enriched in metabolism and mineral absorption signaling pathways.

6. The use according to any one of claims 3 to 5, characterized in that: The biomarkers CXCL8 and MMP7 are highly expressed in senescent renal tubular cell subpopulations, and the biomarkers ADIRF and SCL12A1 are lowly expressed in senescent renal tubular cell subpopulations.

7. Application of reagents for detecting CXCL8, MMP7, ADIRF, and SCL12A1 in the preparation of products for assessing the progression of diabetic nephropathy.

8. The use according to claim 7, characterized in that The expression of CXCL8 and MMP7 gradually increases with the progression of diabetic nephropathy.

9. The use according to claim 7, characterized in that The expression of ADIRF and SCL12A1 gradually decreases as diabetic nephropathy progresses.

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