Biomarker and application and analysis method thereof in breast cancer immunotherapy

Abstract

The invention discloses a biomarker as well as application and an analysis method thereof in breast cancer immunotherapy, the biomarker related to breast cancer immunotherapy can be identified, and the expression level of the marker can be used for breast cancer diagnosis, prognosis prediction and immunotherapy selection. The brittle X-associated protein 1 has up-regulated expression in breast cancer patients.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and more particularly to a biomarker, its application in breast cancer immunotherapy, and an analytical method thereof. Background Technology

[0002] Breast cancer is a highly heterogeneous and complex disease, with different subtypes exhibiting unique genetic, metabolic, and immunophenotypic characteristics. Currently, breast cancer treatment primarily involves comprehensive therapies including surgery, chemotherapy, radiotherapy, endocrine therapy, and targeted therapy. Immune checkpoint inhibitors (ICIs), represented by programmed death 1 (PD-1) and programmed death-ligand 1 (PD-L1) antibodies, have emerged as a promising treatment approach in recent years, showing good efficacy in patients with solid tumors such as lung cancer and melanoma. However, only about 10-20% of breast cancer patients respond to ICI treatment, and most do not achieve long-term clinical benefits from ICI therapy or develop ICI resistance. Therefore, exploring the immunosuppressive molecules that limit ICI therapy in the breast cancer tumor microenvironment and investigating new mechanisms of immune escape in breast cancer will be crucial for understanding the natural resistance of breast cancer to ICIs, identifying new therapeutic targets, and expanding the beneficiary population.

[0003] Fragile X-related gene 1 (FXR1) belongs to the Fragile X gene (FXR) family of RNA-binding proteins. This family has three members: FMRP, FXR1, and FXR2. In recent years, researchers have discovered that FXR family members play important roles in tumorigenesis, development, and immune escape by participating in gene transcription and translation regulation. The expression of RNA-binding protein FXR1 is significantly elevated in various solid tumors, including non-small cell lung cancer, colon cancer, ovarian cancer, urothelial carcinoma of the bladder, and breast cancer, and is associated with poor prognosis. Specifically, FXR1 is highly amplified in ovarian cancer patients. By binding to the ARE element in the c-MYC3'UTR and interacting with the transcription complexes eIF4A1 and eIF4E, it circularizes c-MYC mRNA, upregulates c-Myc translation initiation, and promotes ovarian cancer cell survival and proliferation. In lung cancer, FXR1 forms a complex with PRKCI and ECT2 to regulate the amplification of chromosome 3q26-29, thereby driving the development and progression of lung cancer. In breast cancer, FXR1 is highly expressed in tumor tissue and is associated with rapid proliferation and poor long-term prognosis. However, whether FXR1 mediates the tumor's immune response remains unclear. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the technical problem to be solved by the present invention is to provide a biomarker that can identify biomarkers related to breast cancer immunotherapy, and the expression level of the biomarker can be used for breast cancer diagnosis, prognosis prediction and selection of immunotherapy.

[0005] The technical solution of the present invention is: this biomarker, which is Fragile X-related protein 1, is upregulated in breast cancer patients.

[0006] Furthermore, the expression of this substance is negatively correlated with the abundance of cytotoxic immune cells, which is detrimental to the immune system's recognition and killing of breast cancer cells and promotes immune escape of tumor cells.

[0007] It also provides the application of biomarkers in breast cancer immunotherapy, which serve as immunotherapy markers to predict the proportion of CD8+ T cells or NK cells in tumor tissue and assess the benefit of breast cancer patients from immunotherapy.

[0008] It also provides analytical methods for biomarkers, which include the following steps:

[0009] (1) Analysis of the expression of fragile X-related protein 1 and its relationship with the clinical characteristics and prognosis of breast cancer patients: The expression level of fragile X-related protein 1 in normal tissue, breast cancer tissue and breast cancer metastasis tissue samples was analyzed, and the mRNA expression of fragile X-related protein 1 in breast cancer patients in the Cancer Genome Atlas (TCGA) database and its relationship with the survival rate of breast cancer patients were analyzed.

[0010] (2) Analysis of the relationship between FXR1 and immune signaling, immune cell infiltration and immunotherapy response:

[0011] The data selected was the BRCA_GSE148673 dataset from the GEO database. Dimensionality reduction techniques were applied to visualize and analyze gene expression data. The Nebulosa package was used to estimate weighted kernel density, and cell feature convolution was performed by incorporating inter-cell similarity.

[0012] This allows for the recovery of lost gene signals, thereby enabling the visualization of single-cell data.

[0013] Furthermore, in step (2), the expression of the FXR1 gene in different cell types is analyzed, including: calculating the expression level of the FXR1 gene in each cell type and using the Kruskal-Wallis rank-sum test to assess the significant differences in expression levels between different cell types.

[0014] Furthermore, in step (2), all cell subpopulations are extracted from the BRCA_GSE148673 dataset, and these cell subpopulations are divided into two groups according to the positive or negative status of FXR1 expression; firstly, cells expressing FXR1 are identified, and then the cells are divided into positive and negative groups according to their expression levels; the proportion of each cell subpopulation in these two groups is calculated to observe which cell types mainly contribute to the expression of FXR1.

[0015] Furthermore, in step (2), the cells in the sample are analyzed using Cellchat, the data are subset-filtered using the subsetData function, and the overexpressed genes are identified using the identifyOverExpressedGenes function; the cell communication probability is calculated using the computeCommunProb function, the communication situation is filtered using the filterCommunication function, and the aggregateNet function is used to calculate the summed intercellular communication network situation, and the number and probability strength of intercellular communication are further calculated and visualized.

[0016] This invention enables the identification of biomarkers related to breast cancer immunotherapy, and the expression levels of these biomarkers can be used for breast cancer diagnosis, prognosis prediction, and selection of immunotherapy. Attached Figure Description

[0017] Figure 1 The table shows the mRNA expression of FXR1 in breast cancer patients in the Cancer Genome Atlas (TCGA) database and its relationship with the survival rate of breast cancer patients. (A) Expression levels of FXR1 in normal tissue, breast cancer tissue, and breast cancer metastasis tissue samples; (B) Relationship between FXR1 and the survival of breast cancer patients; (C) Relationship between FXR1 and the survival of basal-like breast cancer patients; (D) Relationship between FXR1 and the survival of Luminal A breast cancer patients; (E) Relationship between FXR1 and the survival of Luminal B breast cancer patients; (F) Relationship between FXR1 and the survival of HER2+ breast cancer patients; (G) Relationship between FXR1 and the survival of breast cancer patients who have not received any drug treatment.

[0018] Figure 2 The diagram shows the relationship between FXR1 expression and various immune cells in the breast cancer microenvironment. (A and B) FXR1 expression in various cells of the breast cancer microenvironment; (C) Comparison of FXR1 gene expression levels in each cell type; (D) Proportional distribution of different cell types in FXR1-positive and FXR1-negative groups; (E) Number of ligand-receptor pairs between different cell populations; (F) Communication strength between different cell populations.

[0019] Figure 3 The data shows the relationship between the FXR1 gene and immune signaling (A), immune signaling activation (B), T cell receptor signaling (C), and CD4+ and CD8+ T cell-related features (D) pathways in the gene enrichment analysis.

[0020] Figure 4 This shows the differences in microenvironmental components between the FXR1 gene high / low expression groups.

[0021] Figure 5 The correlation between the average expression level of FXR1 and the proportion of each cell type in the microenvironment is shown.

[0022] Figure 6 The correlation between FXR1 expression and the abundance of tumor-infiltrating lymphocytes in breast cancer is shown. (A) Correlation between FXR1 expression and the abundance of activated CD8+ T cells; (B) Correlation between FXR1 expression and the abundance of natural killer cells; (C) Correlation between FXR1 expression and the abundance of natural killer T cells.

[0023] Figure 7 The difference in survival curves between the high FXR1 expression group and the low FXR1 expression group in pan-immunotherapy is shown. Detailed Implementation

[0024] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0025] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0026] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the contents of this specification shall prevail. Experimental methods not specifically described in the examples are performed according to conventional methods and conditions, or as selected according to the trade instructions.

[0027] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0028] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0029] Experimental results are expressed as mean ± standard error. After parametric or nonparametric variance tests, p < 0.05 is considered statistically significant, and p < 0.01 is considered extremely statistically significant.

[0030] This biomarker, Fragile X-related protein 1, is upregulated in breast cancer patients.

[0031] Furthermore, the expression of this substance is negatively correlated with the abundance of cytotoxic immune cells, which is detrimental to the immune system's recognition and killing of breast cancer cells and promotes immune escape of tumor cells.

[0032] It also provides the application of biomarkers in breast cancer immunotherapy, which serve as immunotherapy markers to predict the proportion of CD8+ T cells or NK cells in tumor tissue and assess the benefit of breast cancer patients from immunotherapy.

[0033] It also provides analytical methods for biomarkers, which include the following steps:

[0034] (1) Analysis of the expression of fragile X-related protein 1 and its relationship with the clinical characteristics and prognosis of breast cancer patients: The expression level of fragile X-related protein 1 in normal tissue, breast cancer tissue and breast cancer metastasis tissue samples was analyzed, and the mRNA expression of fragile X-related protein 1 in breast cancer patients in the Cancer Genome Atlas (TCGA) database and its relationship with the survival rate of breast cancer patients were analyzed.

[0035] (2) Analysis of the relationship between FXR1 and immune signaling, immune cell infiltration and immunotherapy response:

[0036] The data selected was the BRCA_GSE148673 dataset from the GEO database. Dimensionality reduction techniques were applied to visualize and analyze gene expression data. The Nebulosa package was used to estimate weighted kernel density, and cell feature convolution was performed by incorporating inter-cell similarity.

[0037] This allows for the recovery of lost gene signals, thereby enabling the visualization of single-cell data.

[0038] Furthermore, in step (2), the expression of the FXR1 gene in different cell types is analyzed, including: calculating the expression level of the FXR1 gene in each cell type and using the Kruskal-Wallis rank-sum test to assess the significant differences in expression levels between different cell types.

[0039] Furthermore, in step (2), all cell subpopulations are extracted from the BRCA_GSE148673 dataset, and these cell subpopulations are divided into two groups according to the positive or negative status of FXR1 expression; firstly, cells expressing FXR1 are identified, and then the cells are divided into positive and negative groups according to their expression levels; the proportion of each cell subpopulation in these two groups is calculated to observe which cell types mainly contribute to the expression of FXR1.

[0040] Furthermore, in step (2), the cells in the sample are analyzed using Cellchat, the data are subset-filtered using the subsetData function, and the overexpressed genes are identified using the identifyOverExpressedGenes function; the cell communication probability is calculated using the computeCommunProb function, the communication situation is filtered using the filterCommunication function, and the aggregateNet function is used to calculate the summed intercellular communication network situation, and the number and probability strength of intercellular communication are further calculated and visualized.

[0041] This invention enables the identification of biomarkers related to breast cancer immunotherapy, and the expression levels of these biomarkers can be used for breast cancer diagnosis, prognosis prediction, and selection of immunotherapy.

[0042] The embodiments of the present invention will be described in detail below.

[0043] Experimental Example 1: FXR1 Expression and Its Relationship with Clinical Characteristics and Prognosis of Breast Cancer Patients

[0044] To comprehensively illustrate the expression of FXR1 in normal tissue, breast cancer tissue, and breast cancer metastasis tissue samples, this example uses the Kaplan-Meier Plotter website (https: / / kmplot.com / analysis / ) to analyze the expression levels of FXR1 in these samples online. The results are as follows: Figure 1 As shown in Figure A. Figure 1 As shown in Figure A, compared with normal tissues, FXR1 expression was significantly upregulated in both breast cancer and metastatic tissues (P < 0.05). 正常-肿瘤 =3.61e-46,P 正常-转移 =2.15e-03).

[0045] To elucidate the relationship between FXR1 expression and the prognosis of breast cancer patients, this embodiment also analyzed the mRNA expression of FXR1 in breast cancer patients from the Cancer Genome Atlas (TCGA) database online using the Kaplan-Meier Plotter website and its relationship with the survival rate of breast cancer patients. Upper and lower limits can be independently included or excluded from the range. Results are as follows... Figure 1 B- Figure 1 As shown by the Kaplan-Meier curve of G, breast cancer patients with high expression of FXR1, regardless of the total number of breast cancer patients ( Figure 1 B) Basalt-like triple-negative breast cancer ( Figure 1 C), Luminal A type ( Figure 1 D), Luminal B type ( Figure 1 E) or HER2+ type ( Figure 1 F) Breast cancer patients generally exhibit shorter overall survival. Furthermore, in breast cancer patients who have not received any drug treatment, higher FXR1 expression is associated with shorter survival. Figure 1 G).

[0046] The data above indicate that FXR1 is highly expressed in breast cancer and is positively correlated with poor prognosis, suggesting that FXR1 is a potential biomarker for breast cancer and may play a key role in promoting the malignant progression of breast cancer.

[0047] Experimental Example 2: The Relationship between FXR1 and Immune Signaling, Immune Cell Infiltration, and Immunotherapy Response

[0048] 1. The data selected in this embodiment comes from the BRCA_GSE148673 dataset in the GEO database. Uniform Manifold Approximation and Projection dimensionality reduction technology is applied to visualize and analyze gene expression data. The Nebulosa package is used to estimate weighted kernel density, and by incorporating the similarity between cells, convolution of cellular features is allowed to recover lost gene signals, thereby improving the visualization of single-cell data. The results are as follows: Figure 2 As shown in Figures A and 2B, each point in the graph represents a cell. The color scale on the right side of the graph indicates the gene expression level. The results show that FXR1 is highly expressed in breast cancer cells. Using this dataset, the expression of the FXR1 gene in different cell types was further analyzed. First, the expression level of the FXR1 gene in each cell type was calculated, and then the Kruskal-Wallis rank-sum test was used to assess the significance of differences in expression levels between different cell types. The results are as follows... Figure 2As shown in Figure C, FXR1 is expressed at high levels in malignant tumor cells, endothelial cells, and proliferating T cells, while its expression is relatively low in B cells and epithelial cells.

[0049] To elucidate the proportional distribution of different cell types in the FXR1 expression-positive and negative groups, all cell subpopulations were extracted from the BRCA_GSE148673 dataset and divided into two groups based on their FXR1 expression status. First, cells expressing FXR1 were identified, and then divided into positive and negative groups according to their expression levels. Next, the proportion of each cell subpopulation in these two groups was calculated to observe which cell types primarily contribute to FXR1 expression. The analysis results are as follows: Figure 2 As shown in Figure D, malignant tumor cells constituted a significant proportion (70.4%) in the FXR1-positive group, compared to 48.5% in the negative group. This suggests that malignant tumor cells may be the main contributors to FXR1 expression. Furthermore, epithelial cells also accounted for a higher proportion (5.9%) in the positive group, compared to 12.8% in the negative group. Other cell types, such as fibroblasts, endothelial cells, CD8+ T cells, CD4+ conventional T cells, and B cells, showed smaller differences in proportion between the two groups, indicating that their contribution to FXR1 expression was relatively minor.

[0050] To explore the communication between FXR1-expressing tumor cells and other cell subsets, Cellchat was used to analyze the cells in the sample. The subsetData function was used for subset filtering, and the identifyOverExpressedGenes function was used to identify overexpressed genes. The computeCommunProb function was used to calculate cell communication probabilities, and the filterCommunication function was used to filter the communication data to obtain more reliable and meaningful information about cell communication networks. The aggregateNet function was used to calculate the summed intercellular communication network, and the quantity and probability strength of intercellular communication pairs were further calculated and visualized. The results are as follows: Figure 2 As shown in E and 2F, the number of ligand-receptor pairs varies among different cell populations. Figure 2 E) and communication strength ( Figure 2 F), the thickness of the line indicates the strength of the effect, different colors represent different cell subpopulations, circles with arrows represent ligand cells, and circles pointed to by the arrows represent receptor cells. The results show that there is strong communication between various cell subpopulations and FXR1-positive tumor cells, such as macrophages, epithelial cells, fibroblasts, and endothelial cells.

[0051] The results suggest that FXR1 may have important biological significance in the immune microenvironment of breast cancer.

[0052] 2. Obtain breast cancer-related data TCGA.BRCA_HiSeqV2 (n=1218) from the UCSC Xena platform (https: / / xena.ucsc.edu / ) database of the Cancer Genome Atlas (TCGA). Breast cancer patients were divided into a high FXR1 expression group (90%–100%, n=21) and a low FXR1 expression group (0%–10%, n=121) based on their FXR1 expression levels. Obtain GOBP_IMMUNE_RESPONSE (immune signaling), GOBP_ACTIVATION_OF_IMMUNE_RESPONSE (immune signal activation), and GOBP_T_CELL_RECEPTOR_SIGNALING_PATHWAY (T cell receptor signaling) data from the GSEA platform.

[0053] The GOBP_CD4_POSITIVE_OR_CD8_POSITIVE_ALPHA_BETA_T_CELL_LIN EAGE_COMMITMENT dataset (CD4+, CD8+ T cell related characteristics) (http: / / www.gsea-msigdb.org / gsea / index.jsp) was used for signaling pathway analysis with GSEA v4.1.0 software. An FDR q (false discovery rate qvalue) < 0.25 was considered statistically significant. The GSEA analysis results are as follows... Figure 3 The results showed that high expression of FXR1 was associated with immune signaling (…). Figure 3 A) Activation of immune signals ( Figure 3 B) T cell receptor signaling ( Figure 3 C) and CD4+, CD8+ T cell-related characteristics ( Figure 3 D) The enrichment level of the gene set was negatively correlated. These results suggest that FXR1 may be involved in the immune regulation of breast cancer tumors.

[0054] 3. In the immune system, CD8 +T cells, NK cells, and NKT cells are important immune effector cells, and their activation is crucial for defending against pathogens and tumor cells. Seven algorithms—CIBERSORT, CIBERSORT-ABS, XCELL, EPIC, MCPCOUNTER, QUANTISEQ, and TIMER—were used to assess the differences in microenvironmental components between high and low FXR1 gene expression groups. First, based on gene expression profile data, genes were divided into high and low expression groups according to the median gene expression level. The non-parametric Wilcoxon rank-sum test was used to compare the differences in immune cell content between the two groups as assessed by different software, and these immune cells were visualized using a heatmap. The heatmap was constructed based on immune cell content data, with samples arranged from left to right according to gene expression level from low to high. The intensity of the color in the heatmap represents the cell content, with redder colors indicating higher cell content. Results are as follows: Figure 4 As shown, the levels of CD8+ T cells and activated natural killer (NK) cells were significantly reduced in the FXR1 high expression group, suggesting that FXR1 may be related to the function of these cells.

[0055] 4. Using the photothermal database platform (https: / / grswsci.top / ), the correlation between the average expression level of FXR1 and the proportion of each cell type in the microenvironment was studied. The x-axis represents the average expression level of FXR1 in each single-cell dataset, and the y-axis represents the proportion of each cell type in each single-cell dataset. Each scatter point represents a single-cell dataset and is represented by a different color. R is the Spearman correlation coefficient, and the p-value represents significance; p < 0.05 is considered significant. The results are as follows: Figure 5 As shown, the average expression level of FXR1 is compared with that of CD8. + T cell content showed a significant negative correlation.

[0056] 5. The TISIDB platform (http: / / cis.hku.hk / TISIDB / ) was also used to assess the correlation between FXR1 expression and the abundance of tumor-infiltrating lymphocytes in breast cancer. Results showed that in 1100 breast cancer patients, FXR1 expression was correlated with the abundance of activated CD8+ T cells (…). Figure 6 A) Natural Killer Cells (NK) Figure 6 B) Natural Killer T cells (NKT) Figure 6 C) All showed a negative correlation. These results suggest that high expression of FXR1 may inhibit the activation of immune cells, hindering the immune system's ability to recognize and kill breast cancer cells, and promoting immune escape by tumor cells.

[0057] 6. Using the TIDE database, immunotherapy data were subdivided according to treatment stages, and Z-score standardization was performed on the data for each stage to obtain the relative expression levels of FXR1 between the response and non-response groups at different treatment stages. Kaplan-Meier survival analysis was used to assess the impact of different FXR1 expression levels on overall patient survival. The log-rank test was used to compare the differences in survival curves between the high and low FXR1 expression groups in pan-immunotherapy. The red dashed line represents the high FXR1 expression group (n=19), and the blue dashed line represents the low FXR1 expression group (n=19). The vertical axis represents overall survival, and the horizontal axis represents time (months). Results are as follows: Figure 7 As shown, the survival rate of the high FXR1 expression group was significantly lower than that of the low FXR1 expression group. Specifically, the survival rate of the high FXR1 expression group was lower than that of the low FXR1 expression group at all time points. The p-value of the log-rank test was less than 0.05, indicating that the survival difference between the two groups was statistically significant. This result suggests that FXR1 expression level may be associated with the efficacy of pan-immunotherapy, and high FXR1 expression may be associated with a worse prognosis.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A biomarker, characterized in that: It is Fragile X-related protein 1, which is upregulated in breast cancer patients.

2. The biomarker according to claim 1, characterized in that: The expression of this substance is negatively correlated with the abundance of cytotoxic immune cells, which is detrimental to the immune system's recognition and killing of breast cancer cells and promotes immune escape of tumor cells.

3. The application of the biomarker according to claim 1 in breast cancer immunotherapy, characterized in that: The biomarkers are used as immunotherapy markers to predict the proportion of CD8+ T cells or NK cells in tumor tissue and to assess the benefit of breast cancer patients from immunotherapy.

4. The method for analyzing biomarkers according to claim 1, characterized in that: It includes the following steps: (1) Analysis of the expression of fragile X-related protein 1 and its relationship with the clinical characteristics and prognosis of breast cancer patients: The expression level of fragile X-related protein 1 in normal tissue, breast cancer tissue and breast cancer metastasis tissue samples was analyzed, and the mRNA expression of fragile X-related protein 1 in breast cancer patients in the Cancer Genome Atlas (TCGA) database and its relationship with the survival rate of breast cancer patients were analyzed. (2) Analysis of the relationship between FXR1 and immune signaling, immune cell infiltration and immunotherapy response: The data selected is the BRCA_GSE148673 dataset from the GEO database. Dimensionality reduction techniques are applied to visualize the gene expression data. The Nebulosa package is used to estimate the weighted kernel density. By incorporating the similarity between cells, convolution of cell features is performed to recover lost gene signals, thereby visualizing single-cell data.

5. The method for analyzing biomarkers according to claim 4, characterized in that: In step (2), the expression of the FXR1 gene in different cell types is analyzed, including: The expression level of the FXR1 gene in each cell type was calculated, and the Kruskal-Wallis rank-sum test was used to assess the significance of differences in expression levels between different cell types.

6. The method for analyzing biomarkers according to claim 5, characterized in that: In step (2), all cell subpopulations are extracted from the BRCA_GSE148673 dataset, and these cell subpopulations are divided into two groups according to the positive or negative status of FXR1 expression. First, cells expressing FXR1 were identified, and then the cells were divided into positive and negative groups based on their expression levels. The proportion of each cell subpopulation in these two groups was calculated to observe which cell types mainly contribute to the expression of FXR1.

7. The method for analyzing biomarkers according to claim 6, characterized in that: In step (2), the cells in the sample are analyzed using Cellchat, the subsetData function is used to filter the data, the identifyOverExpressedGenes function is used to identify overexpressed genes, the computeCommunProb function is used to calculate the cell communication probability, the filterCommunication function is used to filter the communication situation, the aggregateNet function is used to calculate the summed intercellular communication network situation, and the number and probability strength of intercellular communication are further calculated and visualized.

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