Bladder cancer TH17 CD4+ T cell subset, its characteristic genes and applications

Abstract

The present invention discloses TH17 CD4+ T cell subsets in bladder cancer, their characteristic genes and applications, relating to the technical field of biomedicine. A biomarker group for identifying, detecting or assaying TH17 CD4+ T cells according to the present invention, the biomarker group includes at least one of the genes IL17A, FURIN, CTSH, KLRB1, or a protein or protein fragment encoded by the gene. The characteristic genes IL17A, FURIN, CTSH, KLRB1 screened by the present invention are lowly expressed in TH17 CD4+ T cells in bladder cancer tissues and can be used for tumor auxiliary diagnosis, prognosis judgment, and preparation of drugs for immunotherapy; the expression of the characteristic genes of the present invention in TH17 CD4+ T cells in normal bladder tissues is higher than that in bladder cancer tissues, indicating that the characteristic genes of the present invention can promote the immune function of TH17 CD4+ T and can be used for the preparation of drugs for treating bladder cancer.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to TH17 CD4+ T cell subsets in bladder cancer, their characteristic genes and applications. Background Art

[0002] Tumorigenesis and development is a complex and multi-step process, in which the tumor microenvironment (TME) plays a crucial role. The TME refers to the numerous immune cells, stromal cells, extracellular matrix and active mediators present in the tumor nests and stroma. The TME can generally be divided into two major categories: the immune microenvironment dominated by immune cells and the non-immune microenvironment dominated by fibroblasts. Among them, the immune microenvironment is an important part of the TME, mainly composed of tumor infiltrating lymphocytes (TILs) and other immune cells. TILs refer to a heterogeneous lymphocyte population mainly composed of T cells present in the tumor parenchyma and tumor stroma, which participate in the positive and negative regulation of tumor immunity in the tumor microenvironment, mainly including T lymphocytes, B lymphocytes, NK cells, macrophages and myeloid-derived suppressor cells, etc. Early diagnosis and screening of cancer, judgment of curative effect and prognosis, and potential therapeutic targets are all of great significance for strengthening the treatment effect of cancer and improving the survival rate of patients. In recent years, the role of TILs in the tumor microecology has been increasingly emphasized. A large number of studies have shown that the infiltration density, type and tumor location of TILs play a dominant role in the prognosis judgment of various tumors such as colorectal cancer, breast cancer, melanoma, etc. Although the mechanism of action between the immune system and cancer tissues plays an important role in the development and improvement of immunotherapy and the detection of tumorigenesis and development, the types and inhibitory pathways of TILs have not been fully understood. The types of human T cells are numerous and complex. The identification and characterization of each T cell subset can provide new immune targets for cancer immunotherapy and molecular markers for prognosis. Therefore, it is necessary for us to understand and explore the heterogeneity and function of bladder cancer infiltrating T cell subsets more deeply, especially the heterogeneity and function of bladder cancer infiltrating CD4+ T cell subsets, so as to move forward towards the precise treatment of bladder cancer. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and provide TH17 CD4+ T cell subsets in bladder cancer, their characteristic genes and applications.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows: a biomarker group for identifying, detecting or identifying TH17 CD4+ T cells, the biomarker group includes at least one of the genes IL17A, FURIN, CTSH, KLRB1, or a protein or protein fragment encoded by the gene.

[0005] The present invention uses 10X single-cell transcriptome sequencing analysis technology to perform single-cell transcriptome sequencing on T cells derived from normal bladder tissue and bladder cancer tissue of bladder cancer patients, and finds that the TH17 CD4+ T cell subset is specifically distributed in tumor tissues and can be used as a target for bladder cancer treatment. Further analyze the gene expression differences between the TH17 CD4+ T cell subset and other T cell subsets, and screen out the genes IL17A, FURIN, CTSH, KLRB1 that are specifically expressed in TH17 CD4+ T cells.

[0006] The present invention also provides the application of the biomarker group in the preparation of drugs for the auxiliary diagnosis, prognosis judgment or immunotherapy of tumors.

[0007] As a preferred embodiment of the application of the present invention, the tumor is bladder cancer.

[0008] The present invention also provides the application of a reagent for detecting the expression level of the biomarker group in the preparation of a product for the auxiliary diagnosis or prognosis judgment of bladder cancer, the biomarker group includes at least one of the genes IL17A, FURIN, CTSH, KLRB1, or a protein or protein fragment encoded by the gene.

[0009] As a preferred embodiment of the application of the present invention, the reagent includes nucleic acid, ligand, enzyme, substrate, antibody.

[0010] The present invention also provides a kit for the auxiliary diagnosis or prognosis judgment of bladder cancer, the kit includes a binder capable of binding to the biomarker group; the biomarker group includes at least one of the genes IL17A, FURIN, CTSH, KLRB1, or a protein or protein fragment encoded by the gene.

[0011] As a preferred embodiment of the kit of the present invention, the binder includes nucleic acid, ligand, enzyme, substrate, antibody.

[0012] As a preferred embodiment of the kit of the present invention, it includes at least one of the following (a)-(d):

[0013] (a) The binder is a nucleic acid probe or ligand capable of specifically binding to at least one of the genes IL17A, FURIN, CTSH, KLRB1;

[0014] (b) The binder is a primer capable of specifically amplifying at least one of the genes IL17A, FURIN, CTSH, and KLRB1;

[0015] (c) The binder is an antibody capable of specifically binding to at least one of the proteins or protein fragments encoded by the genes IL17A, FURIN, CTSH, and KLRB1;

[0016] (d) The binder can bind to an indicator molecule; the indicator molecule includes at least one of a fluorescent substance, a radioactive substance, or an enzyme.

[0017] The present invention also provides the use of IL17A, FURIN, CTSH, or KLRB1 in the preparation of a drug for treating tumors.

[0018] As a preferred embodiment of the use described in the present invention, the tumor includes bladder cancer.

[0019] Advantages of the present invention: The present invention uses single-cell transcriptome sequencing analysis technology to perform single-cell transcriptome sequencing on T cells derived from normal bladder tissues and bladder cancer tissues of bladder cancer patients, and screens out 4 characteristic genes specifically expressed in TH17 CD4+ T cells. The characteristic genes IL17A, FURIN, CTSH, and KLRB1 screened by the present invention are lowly expressed in TH17 CD4+ T cells of bladder cancer tissues and can be used for the preparation of drugs for tumor auxiliary diagnosis, prognosis judgment, and immunotherapy; the expression of the characteristic genes IL17A, FURIN, CTSH, and KLRB1 of the present invention in TH17 CD4+ T cells of normal bladder tissues is higher than that in bladder cancer tissues, indicating that IL17A, FURIN, CTSH, and KLRB1 can promote the immune function of TH17 CD4+ T and can be used for the preparation of drugs for treating bladder cancer. Description of the Drawings

[0020] Figure 1 It is a t-sne graph and a Umap graph for subset identification of bladder cancer cells by 10X single-cell sequencing in Example 1;

[0021] Figure 2 It is a detailed breakdown graph of T cell subsets in Example 1;

[0022] Figure 3 It is a bar graph of the gene expression levels of genes IL23R, RORC, IL17A, FURIN, CTSH, CXCR3, CCR6, KLRB1, and IL22 in IL17 CD4+ T cells and other T cell subsets in bladder cancer samples in Example 1;

[0023] Figure 4It is the KEGG pathway enrichment bubble chart of differentially expressed genes of T cell subsets in Example 1;

[0024] Figure 5 It is the pathway enrichment classification bubble chart of differentially expressed genes of TH17 CD4+ T cells in Example 2;

[0025] Figure 6 It is the flow cytometry sorting result of TH17 CD4+ cells in Example 3;

[0026] Figure 7 It is the bar chart of the expression levels of IL23R, RORC, IL17A, FURIN, CTSH, CXCR3, CCR6, KLRB1, and IL2 in tumor-infiltrating TH17 CD4+ T cells in bladder cancer tumor tissues and normal bladder tissues detected by fluorescence quantitative PCR in Example 3. Specific embodiments

[0027] The above content of the present invention will be further described in detail below through specific embodiments in the form of examples. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following examples. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0028] Example 1 Single-cell transcriptome sequencing

[0029] 1. Collection of clinical samples

[0030] In this study, bladder cancer tissues and adjacent normal tissues from 7 patients with bladder urothelial carcinoma were selected, which were collected and provided by the First Affiliated Hospital of Guangzhou Medical University. All patients have provided written informed consent. Bladder cancer tissues and adjacent normal tissues resected from patients were collected. It was required that all patients had no history of autoimmune diseases or other cancers, had not received neoadjuvant chemotherapy or other anti-tumor treatments before surgery for bladder cancer, and the tumor diameter was greater than 1 cm. The fresh samples should be transported back to the laboratory smoothly at 4°C immediately after the operation and the cell separation experiment should be carried out within 2 h to ensure the maximum cell activity.

[0031] 2. Isolation of T cells in bladder cancer tissues and adjacent normal tissues

[0032] (1) Preparation of single-cell suspension

[0033] Bladder tumors and adjacent normal tissues were immediately processed after being removed from bladder cancer patients. Each sample was cut into small pieces (<1 mm in diameter), and then incubated with 2 ml of trypsin (Gibco, Cat: R001100), 1 ml of collagenase IV (Biofrox, Cat: 2275MG100), and 100 μl of DNase (Service bio, Cat: 1121MG010) on a shaker at 37 °C for 1 h; 4 ml of DMEM dilution suspension was added, and then the suspension was filtered using a 40-μm cell strainer; after centrifugation at 250 g for 5 min, the supernatant was discarded, and then the cells were washed twice with PBS; the cell pellet was resuspended in 1 mL of ice-cold red blood cell lysis buffer and incubated at 4 °C for 10 min; 10 mL of ice-cold PBS was added to the tube, and then centrifuged at 250 g for 10 min; after decanting the supernatant, the pellet was resuspended in 5 ml of calcium- and magnesium-free PBS containing 0.04% weight / volume of BSA; 10 μl of the suspension was counted using a hemocytometer under an inverted microscope; trypan blue was used to quantify viable cells.

[0034] (2) Droplet-based single-cell sequencing

[0035] Barcoded scRNA-seq libraries were prepared using the Chromium Single Cell 3’ Kit v3, and the single-cell suspension was loaded onto the Chromium Single Cell Controller instrument (10× Genomics) to generate single-cell gel bead-in-emulsion (GEMs); to capture 7000 cells in each library, approximately 10000 cells were added to each channel; after generating GEMs, a reverse transcription reaction was performed to generate barcoded full-length cDNA, and then the emulsion was disrupted using a recovery agent, and then cDNA purification was performed using Dyna Beads MyOne Silane Beads (Thermo Fisher Scientific); secondly, the cDNA was amplified by PCR, and the appropriate number of cycles was selected according to the number of recovered cells; the amplified cDNA was fragmented, end-repaired, A-tailed, ligated to an indexed adapter, and then the library was amplified, and each library was sequenced on the Hi Seq X-Ten platform (Illumina) to generate 150-bp paired-end reads.

[0036] (3) Raw data processing and quality control

[0037] Cell Ranger 2.2.0 is used to process raw data, demultiplex cell barcodes, map reads to the transcriptome, and downsample reads (generating normalized aggregated data across samples as needed). These processes produce a raw unique molecular identifier (UMI) count matrix, which is converted into a Seurat object by the R package Seurat 3.0.0. Cells with UMI values < 1000 or UMI counts from mitochondria exceeding 10% are considered low-quality cells and are removed. To eliminate potential doublets, single cells that detected more than 6000 genes are also filtered out. Finally, after quality control and removing batch effects between batches, a total of 131,993 cells are captured. After Seurat and doublet finder filtering, a total of 105,123 high-quality cells are obtained and applied to downstream analysis. After quality control, the UMI count matrix is logarithmically normalized. Since the samples of 7 patients are processed and sequenced in batches, patient numbers are used to eliminate potential batch effects. In this process, the top 3000 variable genes are used to create potential anchors with the Find Integration Anchors function of Seurat. Subsequently, the data is integrated using the IntegrateData function to generate a new matrix with 3000 features, regressing out the potential batch effects. To reduce the dimensionality of the scRNA-Seq dataset, principal component analysis (PCA) is performed on the integrated data matrix. Using the Elbowplot function of Seurat, the top 30 PCs are used for downstream analysis. The main cell clusters are identified using the Find Clusters function provided by Seurat, with the resolution set to the default value (res = 0.8). Then it is displayed using a two-dimensional tSNE or UMAP plot. Conventional markers described in previous studies are used to classify each cell into a known biological cell type. First, the 105,123 cells are divided into 8 major cell types. According to the clustering results, the t-SNE dimensionality reduction algorithm is used to display the distribution of cells in a two-dimensional space, and the results are shown in Figure 1 .

[0038] Subsequently, based on published literature or cell marker databases, the genes upregulated in the subsets were used to define the cell subsets, and T cells were defined as different cell subsets. By comparing the cell subset clustering in adjacent normal tissues and tumor tissues, we found that the TH17 CD4+ T cell subset was specifically distributed in tumor tissues and could potentially be a target for bladder cancer treatment. We further analyzed the gene expression differences between the TH17 CD4+ T cell subset and other T cell subsets (Table 1), as well as the expression levels of these differentially expressed genes in all cell subsets of the samples (Table 2), and screened out the genes specifically expressed in TH17 CD4+ T cells. The specific method was to compare this subset with all other cell subsets in the sample to obtain a list of differential genes between this subset and other subsets, and use a differential analysis algorithm (by default, the Wilcoxon rank-sum test) to find the genes highly expressed differentially in this subset, providing a basis for further screening of characteristic genes. Further, the characteristic genes were highly expressed only in this subset (ave_logFC value > 0.25), had low expression levels in other subsets (P value < 0.05), and had a certain degree of promotion effect on the immune function of TH17 CD4+ T cells.

[0039] Table 1 Differentially expressed genes in the TH17 CD4+ T cell subset infiltrating bladder cancer tissues.

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] Table 2 Gene expression levels in each subset of T cells infiltrating bladder cancer

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] After screening, the characteristic genes of IL17 CD4+ T cells are IL23R, RORC, IL17A, FURIN, CTSH, CCR6, KLRB1, and IL2.

[0055] Perform gene expression analysis on the expression of genes IL23R, RORC, IL17A, FURIN, CTSH, CCR6, KLRB1, and IL22 in IL17 CD4+ T cells and other T cell subsets in bladder cancer samples obtained clinically, and draw a bar chart. The results are shown in Figure 3 .

[0056] Example 2 Pathway Enrichment Analysis of Differentially Expressed Genes in TH17 CD4+ T Cells

[0057] Gene pathway enrichment analysis is a process of analyzing the metabolic pathways and functions of genes in cells. In the present invention, Metascape is used to perform pathway enrichment analysis on differentially expressed genes. Metascape is a web portal that provides gene annotation and analysis resources. The website address is http: / / metascape.org, which integrates more than forty bioinformatics databases. Commonly used databases such as GO / KEGG terms, canonical pathways, hall mark gene sets, UniProt, and DrugBank are all included. We can conveniently use this website to perform biological pathway function enrichment analysis and protein interaction network structure analysis. By submitting the list of differentially expressed genes of the cluster to be analyzed on the website homepage, selecting the species information as H. sapiens, and then clicking the analysis option, we can obtain the results of visual function enrichment analysis and protein interaction analysis. All data can be downloaded and saved through a Zip file package. Taking the bladder cancer samples obtained clinically as an example, analyze the pathway enrichment of differentially expressed genes in the TH17 CD4+ T cell subset. The results are shown in Figures 4 - 5 . The analysis results show that the differentially expressed genes of TH17 CD4+ T cells are mostly enriched in pathways such as lymphocyte activation and differentiation, acquired immune system, cytokine-dependent signaling pathways, and negative regulation of the immune system.

[0058] Example 3 Enrichment and Determination of Characteristic Genes of TH17 CD4+ Cells

[0059] 1. Sort TH17 CD4+ cells

[0060] Collect 5 cases of fresh bladder cancer tissues. According to the method of Example 1 above, cut and digest the tissues to prepare single-cell suspensions. Wash twice with PBS, resuspend with 2 ml of serum-free DMEM / F12, and count for standby.

[0061] ① Set aside 100 μL of the cell suspension as an unstained control, and add medium to make it 200 μL.

[0062] ② Set aside 100 μL of the cell suspension as a CD3 single-positive control, add 5 μL of CD8 antibody, and add medium to make it 200 μL.

[0063] ③ Set aside 100 μL of the cell suspension as a CD4 single-positive control, add 5 μL of CD4 antibody, and add medium to make it 200 μL.

[0064] ④ Set aside 100 μL of the cell suspension as a CD45RA single-positive control, add 5 μL of CD45RA antibody, and add medium to make it 200 μL.

[0065] ⑤ Set aside 100 μL of the cell suspension as a CD27 single-positive control, add 5 μL of CD27 antibody, and add medium to make it 200 μL.

[0066] ⑥ The remaining 1500 μL is used as the experimental group (at least 6×10^6 cells). Add 20 μL of CD3 antibody, 5 μL of CD4 antibody, 5 μL of CD45R antibody, and 5 μL of CD27 antibody, mix well, and place it on a shaker in the dark at 4 - 8 °C for 30 min for staining.

[0067] ⑦ After the staining is completed, centrifuge at 3000 rpm for 5 min, discard the supernatant. Resuspend with 0.5 - 0.6 mL of PBS, centrifuge at 3000 rpm for 5 min, and discard the supernatant. Resuspend with 500 μL of sorting buffer (at least 5×10^6 cells), and flow cytometry to collect CD4+CD45RA-CD27-TH17 CD4+ T cells. Representative flow cytometry sorting results are as Figure 6 shown.

[0068] 2. qPCR determination of the expression level of characteristic genes

[0069] Use qPCR to analyze the gene expression levels of the cells sorted in step 1, which specifically includes the following steps:

[0070] 2.1 Total RNA extraction Extract RNA from cells by the Trizol method

[0071] (1) Culture cells in a medium dish until the confluence reaches over 90%, aspirate the medium, and wash twice with pre-cooled PBS;

[0072] (2) Discard the PBS, add 1 mL of Trizol and pipette the cells until they are completely detached, transfer them into a 1.5 mL RNase-free EP tube, vortex to mix well, and let it stand for 5 minutes;

[0073] (3) Add 200 μL of chloroform, cover the tube cap, vortex vigorously for 15 seconds, and let it stand for 5 minutes; then place the centrifuge tube in a 4 °C centrifuge and centrifuge at 12,000 rpm for 15 minutes;

[0074] (4) Carefully take out the centrifuge tube, transfer the upper clear liquid to a new 1.5 mL centrifuge tube, add an equal volume of isopropanol, invert to mix well, and let it stand at room temperature for 10 minutes; then place the centrifuge tube in a 4 °C centrifuge and centrifuge at 12,000 rpm for 10 minutes;

[0075] (5) Discard the supernatant, retain the bottom precipitate, add 1 mL of 75% ethanol, invert to wash the precipitate thoroughly; then place the centrifuge tube in a 4 °C centrifuge and centrifuge at 12,000 rpm for 10 minutes;

[0076] (6) Discard the supernatant, use a pipette tip to aspirate the excess liquid on the tube wall, dry it in the fume hood for 5 minutes, add 30 μL of enzyme-free water and pipette to dissolve the RNA precipitate, perform quality control and measure the concentration using Nanodrop2000, and store it in a -80 °C refrigerator.

[0077] 2.2 Reverse transcription of RNA to synthesize cDNA

[0078] (1) Use the Takara kit PrimeScriptTM RT regent Kit with gDNA Eraser to reverse transcribe RNA, which consists of two parts. The first part is the reaction system for removing gDNA, including the components in Table 3. After mixing the reaction system, incubate at room temperature for 5 minutes or at 42 °C for 2 minutes, and place it on ice for the next step.

[0079] Table 3

[0080] Component Volume 5×gDNA Eraser Buffer 2 μL gDNA Eraser 1 μL Total RNA 1 μg <![CDATA[RNase Free dH2O]]> variable Total 23 μL

[0081] (2) Synthesis of cDNA: Use the reaction system in Table 4, mix the reaction system, incubate in a PCR instrument at 37 °C for 15 minutes, 85 °C for 5 seconds, and store at 4 °C for the next experiment. For long-term storage, it can be stored at -80 °C.

[0082] Table 4

[0083] Component Volume Reaction solution of step (1) 10 μL 5×PrimeScript Buffer 4 μL PrimeScript RT Enzyme 1 μg RT Primer Mix 4 μL <![CDATA[RNase Free dH2O]]> 4 μL Total 37 μL

[0084] (3) Fluorescent quantitative PCR to detect the expression level of characteristic genes: Using the cDNA obtained in step (2) as a template, perform fluorescent quantitative PCR to detect the expression level of characteristic genes. The reaction system is shown in Table 5. The primer sequences are shown in Table 6.

[0085] Table 5

[0086] Component Volume 10×PCR buffer for KOD-Plus-Neo 5 μL 2 nM dNTPs 5 μL Primer F (10 nM) 1.5 μg Primer R (10 nM) 1.5 μL cDNA 200 ng KOD-Plus-Neo (1 U / μL) 37 μL Total 20

[0087] Table 6

[0088]

[0089] After mixing the reaction system as described above, use a PCR instrument for amplification. The PCR program is shown in Table 7.

[0090] Table 7

[0091]

[0092] The results are as Figure 7 shown. The expression levels of IL17A, FURIN, CTSH, and KLRB1 in the TH17 CD4+T subset of normal bladder tissue are significantly higher than those in bladder cancer tissue, further indicating that IL17A, FURIN, CTSH, and KLRB1 can promote the immune function of TH17 CD4+T. Therefore, IL17A, FURIN, CTSH, and KLRB1 are novel biomarkers for TH17 CD4+T cells, have good application prospects in identifying tumor-infiltrating TH17 CD4+T cells, and are expected to become new targets for bladder cancer immunotherapy.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. Use of a reagent for detecting the expression level of a biomarker panel in the preparation of a product for identifying, detecting or diagnosing TH17 CD4+ T cells in bladder cancer, characterized in that, The biomarker group includes at least one of the genes IL17A, FURIN, CTSH, KLRB1, or a protein or protein fragment encoded by the gene.

2. The application according to claim 1, characterized in that, The reagent includes nucleic acid, ligand, enzyme, substrate, antibody.

3. The application according to claim 1, characterized in that, The reagent includes a binder that can bind to the biomarker group for identifying, detecting or identifying TH17 CD4+ T cells.

4. The application according to claim 3, wherein The binder includes nucleic acid, ligand, enzyme, substrate, antibody.

5. The application according to claim 3, characterized in that, The binder is at least one of the following (a)-(d): (a) The binder is a nucleic acid probe or ligand that can specifically bind to at least one of the genes IL17A, FURIN, CTSH, KLRB1; (b) The binder is a primer that can specifically amplify at least one of the genes IL17A, FURIN, CTSH, KLRB1; (c) The binder is an antibody that can specifically bind to a protein or protein fragment encoded by at least one of the genes IL17A, FURIN, CTSH, KLRB1; (d) The binder can bind to an indicator molecule; the indicator molecule includes at least one of a fluorescent substance, a radioactive substance, or an enzyme.

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