A biomarker combination for breast cancer lung metastasis prediction and application thereof

Abstract

The application discloses a biomarker combination for breast cancer lung metastasis prediction and application thereof, and the biomarker combination for breast cancer lung metastasis prediction comprises MUC16, MAP3K13, NR4A3, FAM111A, ZNF562 and NRP1. The application combines a GEO database and data from a TCGA database, analyzes and screens a biomarker combination for breast cancer lung metastasis prediction, further determines potential gene characteristics closely related to development of lung metastasis of breast cancer patients by analyzing expression levels of 6 biomarker genes and interaction with a tumor environment, and provides a more effective and more personalized tool for prediction and treatment of breast cancer.

Description

Technical Field

[0001] This invention relates to the field of medical informatics technology, specifically to a combination of biomarkers for predicting lung metastasis in breast cancer and their applications. Background Technology

[0002] Breast cancer is a prevalent health problem and the second leading cause of cancer-related deaths in women. Its incidence has been on the rise since the mid-2000s, increasing at a rate of 5.2% annually. Metastasis, the spread of cancer cells from the primary site to distant organs, is a major cause of breast cancer death, accounting for approximately 90% of breast cancer deaths due to metastasis-related complications. Breast cancer metastasis is organ-specific, with bone being the most common site of involvement, accounting for 75% of cases. However, bone metastasis has a good prognosis; the 5-year disease-free survival (DFS) rate for guideline-recommended anti-tumor therapy combined with RANKL inhibitors is 89.2%. On the other hand, lung metastasis is the second leading site of distant metastasis in breast cancer, and once it occurs, the prognosis is very poor, with a 5-year overall survival (OS) rate of only 16.8%. The main reason is that the occurrence of lung metastasis in breast cancer cannot be accurately predicted early in clinical practice, leading to delayed treatment for patients. Currently used clinical indicators, such as ER, PR, HER2, Ki67, and other pathological features, cannot accurately predict lung metastasis.

[0003] Studies have shown that multiple factors, such as VEGF (vascular endothelial growth factor), TGFβ (transforming growth factor β), TNF (tumor necrosis factor), CSF3 (colony-stimulating factor 3), SPARC (cysteine-rich acidic secretory protein), MMPs (matrix metalloproteinases), and COX20 (cytochrome C oxidase assembly factor COX20), play a role in the development and progression of lung metastases in breast cancer. However, the high cost and inconvenient detection methods make the detection of these factors challenging, limiting their clinical use.

[0004] Single-cell RNA sequencing (scRNA-seq) is a novel high-throughput sequencing technology for mRNA at the single-cell level. This method involves amplifying a small amount of mRNA in a single isolated cell and then performing high-throughput sequencing. scRNA-seq overcomes the limitations of tissue samples, enabling the identification of heterogeneity at the single-cell level, thus making it possible to study gene expression patterns in individual cells within a tissue or microenvironment. However, there is currently a lack of research on breast cancer lung metastasis prediction models based on single-cell transcriptomics. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a combination of biomarkers for predicting lung metastasis in breast cancer. This combination of biomarkers can be used to construct a predictive model for lung metastasis in breast cancer, thereby providing more effective diagnostic strategies and predictive tools to promote early detection and treatment.

[0006] The present invention also provides the application of the reagents for detecting the above-mentioned combinations of biomarkers.

[0007] The present invention also provides a product.

[0008] The present invention also provides a device for predicting lung metastasis in breast cancer.

[0009] The present invention also provides a computer-readable storage medium.

[0010] According to a first aspect of the invention, a combination of biomarkers for predicting lung metastasis in breast cancer is proposed, the combination of biomarkers for predicting lung metastasis in breast cancer comprising MUC16, MAP3K13, NR4A3, FAM111A, ZNF562 and NRP1.

[0011] According to a second aspect of the invention, the use of a reagent for detecting the combination of biomarkers for predicting lung metastasis of breast cancer as described in the first aspect of the invention is proposed in the preparation of products for predicting lung metastasis of breast cancer.

[0012] In some embodiments of the present invention, the reagents include reagents for detecting the combination of biomarkers at the protein level or the gene level.

[0013] In some embodiments of the present invention, the product includes at least one of a detection plate, a reagent kit, and a detection chip.

[0014] In some embodiments of the present invention, the product includes products for at least one of real-time quantitative reverse transcription PCR, enzyme-linked immunosorbent assay (ELISA), biochips, in situ hybridization, immunoblotting, microbead immunoassay, and microfluidic immunoassay.

[0015] In some embodiments of the present invention, the reagents for detecting the expression level of the biomarker combination by real-time quantitative reverse transcription PCR include, but are not limited to, primer pairs for amplifying the biomarker combination.

[0016] In some embodiments of the present invention, the product for detecting the expression level of the biomarker combination by in situ hybridization includes a probe that hybridizes with the nucleic acid sequence of the biomarker combination.

[0017] In some embodiments of the present invention, the product for detecting the expression level of the biomarker combination via a biochip includes a probe that hybridizes with the nucleic acid sequence of the biomarker combination.

[0018] According to a third aspect of the invention, a product is provided comprising a reagent for detecting a combination of biomarkers for predicting lung metastasis in breast cancer as described in the first aspect of the invention.

[0019] In some embodiments of the present invention, the reagents include reagents for detecting the combination of biomarkers at the protein level or the gene level.

[0020] In some embodiments of the present invention, the product includes at least one of a detection plate, a reagent kit, and a detection chip.

[0021] In some embodiments of the present invention, the product includes products for at least one of real-time quantitative reverse transcription PCR, enzyme-linked immunosorbent assay (ELISA), biochips, in situ hybridization, immunoblotting, microbead immunoassay, and microfluidic immunoassay.

[0022] In some embodiments of the present invention, the reagents for detecting the expression level of the biomarker combination by real-time quantitative reverse transcription PCR include, but are not limited to, primer pairs for amplifying the biomarker combination.

[0023] In some embodiments of the present invention, the product for detecting the expression level of the biomarker combination by in situ hybridization includes a probe that hybridizes with the nucleic acid sequence of the biomarker combination.

[0024] In some embodiments of the present invention, the product for detecting the expression level of the biomarker combination via a biochip includes a probe that hybridizes with the nucleic acid sequence of the biomarker combination.

[0025] According to a fourth aspect of the present invention, a breast cancer lung metastasis prediction device is provided, the breast cancer lung metastasis prediction device comprising the following modules:

[0026] The data acquisition module is used to provide risk level data of the target biomarker combination of the sample to be tested, wherein the target biomarker combination is the biomarker combination for predicting lung metastasis of breast cancer as described in the first aspect of the present invention.

[0027] The assessment module is used to assess the risk of lung metastasis of breast cancer in an individual corresponding to a test sample by using risk level data of a combination of target biomarkers in the test sample.

[0028] In some embodiments of the present invention, the risk level data is a risk score of the target biomarker combination, which is calculated by substituting the expression level of a single target biomarker in each test sample into the LASSO regression model.

[0029] In some embodiments of the present invention, the risk score is calculated as follows: Risk score = 0.030859 × (expression level of MUC16) - 0.08401 × (expression level of MAP3K13) + 0.028720092 × (expression level of NR4A3) - 0.006563639 × (expression level of FAAM111A) + 0.018508065 × (expression level of ZNF562) + 0.005311887 × (expression level of NRP1).

[0030] In some embodiments of the present invention, the LASSO regression model is trained using a target marker detection dataset of known samples.

[0031] In some embodiments of the present invention, the target marker detection dataset of the known samples includes the GEO dataset GSE202695 and the TCGA database.

[0032] In some embodiments of the present invention, the criteria for judging the status of breast cancer lung metastasis are as follows: when the risk score of the test sample is higher than the cutoff value, it is judged as high risk of breast cancer lung metastasis, which represents a poor prognosis; when the risk score of the test sample is equal to or lower than the cutoff value, it is judged as low risk of breast cancer lung metastasis, which represents a good prognosis.

[0033] In some embodiments of the present invention, the cutoff value is determined by Kaplan-Meier analysis.

[0034] According to a fifth aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing computer instructions that, when executed by a processor, implement a method for predicting the risk of lung metastasis in breast cancer, the method comprising:

[0035] Obtain risk level data of target biomarkers for the test sample, wherein the combination of target biomarkers is the combination of biomarkers for predicting lung metastasis of breast cancer as described in the first aspect of the present invention; assess the risk of lung metastasis of breast cancer for the individual corresponding to the test sample using the risk level data of the combination of target biomarkers for the test sample.

[0036] In some embodiments of the present invention, the risk level data is a risk score of the target biomarker combination, which is calculated by substituting the expression level of a single target biomarker in each test sample into the LASSO regression model.

[0037] In some embodiments of the present invention, the risk score is calculated as follows: Risk score = 0.030859 × (expression level of MUC16) - 0.08401 × (expression level of MAP3K13) + 0.028720092 × (expression level of NR4A3) - 0.006563639 × (expression level of FAAM111A) + 0.018508065 × (expression level of ZNF562) + 0.005311887 × (expression level of NRP1).

[0038] In some embodiments of the present invention, the LASSO regression model is trained using a target marker detection dataset of known samples.

[0039] In some embodiments of the present invention, the target marker detection dataset of the known samples includes the GEO dataset GSE202695 and the TCGA database.

[0040] In some embodiments of the present invention, the criteria for judging the status of breast cancer lung metastasis are as follows: when the risk score of the test sample is higher than the cutoff value, it is judged as high risk of breast cancer lung metastasis, which represents a poor prognosis; when the risk score of the test sample is equal to or lower than the cutoff value, it is judged as low risk of breast cancer lung metastasis, which represents a good prognosis.

[0041] In some embodiments of the present invention, the cutoff value is determined by Kaplan-Meier analysis.

[0042] The present invention has at least the following beneficial effects:

[0043] This invention combines the GEO database and the TCGA database to analyze and screen a combination of biomarkers for predicting lung metastasis in breast cancer. By analyzing the expression levels of six biomarker genes and their interactions with the tumor environment, potential gene features closely related to the development of lung metastasis in breast cancer patients were further identified, providing a more effective and personalized tool for the prediction and treatment of breast cancer. The biomarker combination for predicting lung metastasis in breast cancer provided by this invention can be used to construct breast cancer lung metastasis prediction models and devices, thereby enabling rapid and accurate assessment of patients' lung metastasis risk and prognosis in the early stages of the disease. Attached Figure Description

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0045] Figure 1 This is a diagram showing the results of filtering and quality control of single-cell RNA sequencing data in Example 1 of the present invention;

[0046] Figure 2This is a diagram showing the screening results of variable genes from single-cell RNA sequencing data in Example 1 of the present invention;

[0047] Figure 3 The graph shows the PCA linear dimensionality reduction results of the single-cell RNA sequencing data in Example 1 of this invention.

[0048] Figure 4 This is a diagram showing the result of nonlinear dimensionality reduction using tSNE and UMAP methods in Embodiment 1 of the present invention.

[0049] Figure 5 This is a graph showing the pseudo-time analysis results of single-cell RNA sequencing data in Example 1 of the present invention;

[0050] Figure 6 This is a diagram showing the results of differentially expressed gene analysis in Example 1 of the present invention;

[0051] Figure 7 This is a graph showing the results of univariate analysis screening of genes related to lung metastasis prediction in Example 1 of the present invention; where A is a nomogram of univariate analysis, and B is a univariate Cox regression analysis graph and a LASSO regression analysis graph.

[0052] Figure 8 This is the ROC curve of the breast cancer lung metastasis prediction model verified in Embodiment 1 of the present invention;

[0053] Figure 9 This is a graph showing the Kaplan-Meier analysis results of the breast cancer lung metastasis prediction model in Example 1 of the present invention.

[0054] Figure 10 This is the ROC curve of the single-gene prediction model verified in Example 1 of the present invention;

[0055] Figure 11 This is a graph showing the Kaplan-Meier analysis results for validating the single-gene prediction model in Example 1 of this invention;

[0056] Figure 12 This is a diagram showing the results of the Transwell experiment in Example 2 of this invention to verify cell migration and invasion capabilities; the scale bar is 200 μm.

[0057] Figure 13 This is a graph showing the results of qRT-PCR experiments using clinical samples in Example 2 of the present invention.

[0058] Figure 14 This is the ROC curve of the breast cancer prognosis prediction model validated using an external validation set in Embodiment 2 of the present invention;

[0059] Figure 15 This is a heatmap showing the immune infiltration status in high-risk and low-risk group samples in Example 3 of the present invention;

[0060] Figure 16 This is a bar chart showing the immune infiltration status in the high-risk and low-risk group samples in Example 3 of the present invention;

[0061] Figure 17 This is a multiplex immunofluorescence staining result image of the primary breast cancer group and lung metastasis group samples in Example 3 of the present invention; wherein, the scale bar is 200μm. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be further described clearly and completely below in conjunction with the embodiments of this invention. It should be noted that the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0063] Unless otherwise specified, the reagents, materials and apparatus used in the following examples are those that can be purchased from conventional commercial channels or obtained by known conventional methods.

[0064] Example 1: A combination of biomarkers for predicting lung metastasis in breast cancer and a corresponding model for predicting lung metastasis in breast cancer.

[0065] This embodiment uses existing single-cell sequencing data and patient clinical information and gene expression profile data from the TCGA database to screen for a combination of biomarkers for predicting lung metastasis in breast cancer. Based on this combination of biomarkers, a breast cancer lung metastasis prediction model is constructed. The specific screening method and model construction method are as follows:

[0066] 1. Quality analysis and preliminary screening of single-cell data

[0067] Differentially expressed genes in the primary breast cancer and lung metastasis groups were preliminarily screened using the single-cell RNA sequencing (scRNA-Seq) dataset GSE202695 from the GEO database. Single-cell RNA sequencing was performed on primary breast cancer tissue and lung metastasis tissue collected from NSG mice that had received xenografts (PDX) from breast cancer patients and the MDA-MB-231 cell line, respectively, using GSE202695.

[0068] First, data filtering and quality control were performed using the Seurat package in R software. Cells with fewer than 2000 detected genes (min.features=2000) and cells with fewer than 5 gene coverages (min.cells=5) were removed. A total of 2000 highly variable genes (mutable genes) were identified. Data quality was determined by the number of genes detected per cell (nFeature_RNA) and the number of UMIs per cell (nCount_RNA). Figure 1 To screen for differentially expressed genes between the primary breast cancer group and the lung metastasis group, 8039 non-variable genes were removed from all genes, resulting in 2000 variable genes. The 10 genes with the largest variations were then identified. Figure 2 To reduce data dimensionality and facilitate visualization, PCA (Principal Component Analysis) was used for linear dimensionality reduction, and the JackStrawPlot and Eblow Plot tools from the package were used for data visualization. It was found that in the JackStraw Plot, the importance of the principal components began to decrease after the first 8-9 principal components (PCs); when the number of PCs was between 8 and 9, the standard deviation of the data essentially stopped decreasing. The Eblow Plot also showed the same result; between PCs 8 and 9, the standard deviation of the data did not decrease significantly. Figure 3 Therefore, the first nine principal components were selected for cell classification, as they explained most of the variation in the dataset. Subsequently, nonlinear dimensionality reduction was performed using tSNE and UMAP methods, resulting in a clear separation between the primary breast cancer group and the lung metastasis group. Figure 4 Using Monocle for pseudo-time analysis, a clear developmental trajectory was found between the two groups. Figure 5 Finally, the "Limma" package was used to screen 2000 highly variable genes from the dataset for differentially expressed gene analysis. The cutoff criteria for MRG were an absolute value of log2FC ≥ 1 and a p-value < 0.05. Volcano plots showed that compared to the lung metastasis group, the primary group had 12 upregulated genes and 251 downregulated genes. Figure 6 ).

[0069] 2. Further screening of biomarkers, model establishment, and preliminary efficacy validation

[0070] This embodiment uses the TCGA breast cancer prognostic dataset to perform a univariate analysis on the association between differentially expressed genes and overall survival (OS) based on the 263 differentially expressed genes selected earlier. Patient information from the TCGA data used in the analysis is shown in Table 1.

[0071] Table 1. Patient information from the TCGA data used in the analysis.

[0072]

[0073]

[0074] Based on the screening criteria (p<0.05, HR≤0.930 / HR≥1.043, hazard ratio (95% CI) not exceeding the median), 10 prognostic genes were selected. Figure 7 A). Further, LASSO regression was performed using the glmnet package in R to screen for genes associated with overall survival, and this process was repeated 1000 times. Figure 7 B) A combination of biomarkers for predicting lung metastasis in breast cancer was obtained, including six genes: MUC16, MAP3K13, NR4A3, FAM111A, ZNF562, and NRP1.

[0075] Based on the expression levels of the aforementioned six biomarkers, a risk scoring formula was calculated to construct a breast cancer lung metastasis prediction model. The risk scoring formula is as follows:

[0076] Risk score = 0.030859 × (expression level of MUC16) - 0.08401 × (expression level of MAP3K13) + 0.028720092 × (expression level of NR4A3) - 0.006563639 × (expression level of FAM111A) + 0.018508065 × (expression level of ZNF562) + 0.005311887 × (expression level of NRP1), with coefficients automatically generated by the glmnet package after analysis.

[0077] Kaplan-Meier analysis was performed on the obtained breast cancer prognostic prediction model to calculate the optimal cutoff value for the risk score. In the Kaplan-Meier analysis, the Log-rank test was used to calculate the optimal cutoff value: by systematically testing all possible grouping thresholds, the critical point that maximizes the difference between the survival curves of the two groups (maximizing the Log-rank test statistic (chi-square value χ2)) was found.

[0078] The optimal cutoff value for the risk score was found to be -0.10465. Using -0.10465 as the risk score cutoff value, patients were divided into a high-risk group (n=264) and a low-risk group (n=792) based on their risk scores. ROC curves (AUC=0.9) were generated to demonstrate the predictive performance of this breast cancer lung metastasis prediction model. Figure 8 Kaplan-Meier analysis also showed that the survival probability of the high-risk group was significantly lower than that of the low-risk group. Figure 9 ).

[0079] Furthermore, this embodiment also compared the predictive performance of a single-gene model and a six-gene combined assessment model for breast cancer prognosis prediction, finding that the latter had better predictive performance, as demonstrated by ROC curve and Kaplan-Meier plot analysis. Figure 10 and Figure 11 ).

[0080] Example 2: Further Validation of the Breast Cancer Lung Metastasis Prediction Model

[0081] This embodiment validates the breast cancer lung metastasis prediction model provided in Example 1 using cell models, clinical samples, and external validation sets. The specific experimental methods and results are as follows:

[0082] 1. Validation of siRNA knockdown cell model

[0083] Six biomarkers identified in Example 1 were knocked down using siRNA in the human triple-negative breast cancer cell line MDA-MB-231. Transwell assays were then performed to verify cell migration and invasion abilities. The results are as follows: Figure 12 As shown in Table 2. The sequence information of the siRNAs used is shown in Table 2.

[0084] Transwell assay for cell migration: 8 μm pore size Transwell chambers were placed in 24-well plates. 500 μL of serum-free culture medium was added to the upper chamber, and 500 μL of 5% serum culture medium was added to the lower chamber. 100,000 untreated NC cells or MDA-MB-231 cells treated with different siRNAs were added to the upper chamber. The plates were incubated at 37°C and 5% CO2 for 2 hours, fixed with paraformaldehyde for 10 minutes, stained with crystal violet for 10 minutes, and observed under a microscope.

[0085] The Transwell assay for detecting cell invasion is largely the same as the cell migration assay, except that the upper layer of the chamber is coated with 50 μL of serum-free culture medium diluted 1:10 with matrix gel, and after gel treatment for 4 h, different groups of tumor cells are coated on top.

[0086] Table 2 siRNA sequence information

[0087] siMUC16 GGATGAGGCTTATTCATCA(SEQ ID NO:1) siMAP3K13 GGATATTCGTGAACACTAT(SEQ ID NO:2) siNR4A3 GTCCGTACAGATAGTCTGA(SEQ ID NO:3) siFAM111A CCAGCCAGTTGATGAATTA(SEQ ID NO:4) siZNF562 GCCTTGCTGTGCATCTTGA(SEQ ID NO:5) siNRP1 GCGTTACTGTGGACAGAAA(SEQ ID NO:6)

[0088] Depend on Figure 12 The results showed that knocking down MUC16, NRP1, NR4A3, and ZNF562 significantly reduced the migration and invasion abilities of MDA-MB-231 cells. This indicates that the expression levels of these genes are positively correlated with the malignant biological behavior of breast cancer cells and promote breast cancer progression.

[0089] 2. Clinical sample validation

[0090] This embodiment collected samples from 60 breast cancer patients (30 with breast cancer without lung metastasis and 30 with breast cancer with lung metastasis) from Sun Yat-sen Memorial Hospital affiliated to Sun Yat-sen University. RNA extraction and qRT-PCR experiments were performed, and the results are as follows. Figure 13 As shown in Table 3, the clinicopathological characteristics of the patients are shown in Table 4, and the primer sequences used are shown in Table 5.

[0091] Table 3. Patient clinicopathological characteristics

[0092]

[0093] Table 4 Primer sequence information

[0094]

[0095]

[0096] Depend on Figure 13 It can be seen that the expression levels of the six biomarkers screened in Example 1 were significantly different between the primary breast cancer group and the lung metastatic breast cancer group, indicating that the biomarkers screened in Example 1 are all representative. Furthermore, the above six biomarkers all showed significantly differential expression in TNBC patients, suggesting that this group of biomarkers may have better predictive power in the prognosis prediction of TNBC patients.

[0097] 3. External validation set verification

[0098] To verify the stability of the breast cancer prognostic prediction model provided in Example 1, external validation was performed using the external datasets GSE124380, GSE193369, and GSE209998. The results are as follows: Figure 14 As shown.

[0099] GSE124380: The LM2-4175 cell line was initially screened from MDA-MB-231 but exhibits more aggressive characteristics in terms of invasion, migration, and metastasis. Furthermore, the LM2 cell line specifically metastasizes to lung cancer. To understand the molecular mechanisms of lung metastasis in breast cancer, RNA-seq data from the MDA-MB-231 and LM2-4175 cell lines were analyzed.

[0100] GSE193369: Single-cell RNA sequencing data from two LeGO-barcoded MDA-MB-231 cell subclones from primary tumors and metastatic sites (lung and liver) in NOD-SCID-IL2Rγc- / -(NSG) mice.

[0101] GSE209998: Comparative gene expression profiling analysis of RNA sequencing data from primary breast tumors and matched metastatic tumors from patients.

[0102] Depend on Figure 14 As can be seen, the ROC curves of the three external validation sets demonstrate that the model has good predictive performance.

[0103] Example 3: Relationship between a combination of biomarkers for predicting lung metastasis in breast cancer and the tumor immune microenvironment.

[0104] Based on the breast cancer prognostic risk grouping obtained using TCGA data in Example 1, this example uses CIBERSORT to compare the immune infiltration status of the two groups and visualizes it using GraphPad Prism 8.0 (inputting the gene expression profile matrix and a gene expression feature matrix file (LM22.txt) containing 22 human immune cell types into CIBERSORT, and running the expression profile file), the results are as follows. Figure 15 and Figure 16 As shown.

[0105] Depend on Figure 15 and Figure 16 It is known that in the high-risk group, cells that promote immune killing (such as CD8 cells) + T cells, CD4 + The relatively low number of T cells and resting NK cells suggests that the immune microenvironment may play a key role in breast cancer progression and underscores the importance of exploring potential therapeutic targets to improve patient outcomes.

[0106] This embodiment utilizes paraffin-embedded serial sections of samples from 60 breast cancer patients collected in Example 2 from Sun Yat-sen Memorial Hospital affiliated with Sun Yat-sen University, and detects immune infiltration using immunofluorescence. The results show that the group without lung metastasis exhibits cytotoxic PD-1. - CD8 + T lymphocytes (orange cells with white arrows), CD163 - CD68 + M1 macrophages (cells with blue and white arrows), CD20 + B cells (cells with green and white arrows) and CD3 + CD4 + FOXP3 - T lymphocytes (pink cells with white arrows) and other immune-boosting cells accounted for a significantly higher proportion. Meanwhile, the lung metastasis group showed a higher proportion of cytotoxic PD-1. + CD8 + T lymphocytes (green), CD163 + CD68 + M2 macrophages (orange) and CD3 + CD4+ FOXP3 + There was a significant increase in immunosuppressive cells such as T lymphocytes (yellow). Figure 17 These immunosuppressive cells may promote breast cancer progression by facilitating cancer cell invasion and migration, and by creating a favorable environment for tumor growth.

[0107] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. The use of a reagent for detecting a combination of biomarkers for predicting lung metastasis in breast cancer in the preparation of products for predicting lung metastasis in breast cancer, characterized in that, The biomarker combination for predicting lung metastasis in breast cancer is MUC16, MAP3K13, NR4A3, FAM111A, ZNF562 and NRP1; The reagent is used to detect the expression level of the combination of biomarkers at the gene level.

2. The application according to claim 1, characterized in that, The product is at least one of a reagent kit and a detection chip.

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