Marker SKP1 for predicting curative effect of breast cancer immunotherapy and application of marker SKP1

Abstract

The invention discloses a marker SKP1 for predicting the curative effect of breast cancer immunotherapy and application of the marker SKP1, and belongs to the technical field of tumors. Through bioinformatics analysis of gene expression of triple-negative breast cancer compared with other types of breast cancer, the gene SKP1 which is highly expressed in breast cancer and closely related to prognosis is screened out, the gene SKP1 can be used as a novel breast tumor molecular marker, and the high expression of the gene SKP1 predicts poor prognosis of tumor. The expression quantity of the SKP1 in the breast cancer is higher than that of para-carcinoma cells, and the SKP1 is related to immune cold tumors and is also in negative correlation with a biomarker of BrCa immunotherapy, so that a new target spot is provided for diagnosis, prediction and treatment of the breast cancer; the invention further discloses application of the marker SKP1 for predicting the breast cancer immunotherapy curative effect in preparation of a detection kit for identifying breast cancer cells and predicting breast cancer.

Description

Technical Field

[0001] The present invention belongs to the technical field of tumors, and in particular relates to a marker SKP1 for predicting the efficacy of immunotherapy for breast cancer and an application thereof. Background Art

[0002] Breast cancer is one of the main factors threatening women's health worldwide. In recent years, immunotherapy has received increasing attention as a new method for treating breast cancer. However, due to the differences in the tumor immune microenvironment (TIME) between patients, the efficacy of immunotherapy in breast cancer patients is also different. The tumor microenvironment is a highly structured ecosystem that includes a variety of immune cells, tumor-associated fibroblasts CAFs, endothelial cells, and extracellular matrix ECM. These components may vary depending on the tissue type and co-evolve with the progression of the tumor. Among them, SKP family proteins play an important role in regulating tumor cell immune escape. SKP family proteins are an important part of the ubiquitin-proteasome system UPS and are involved in many cellular processes such as cell cycle regulation, signal transduction, and apoptosis. Therefore, SKP family proteins can become an important target for studying cell biology and developing cancer therapeutics. Existing studies have shown that the characteristics of the tumor immune microenvironment of breast cancer patients may significantly affect their response to immunotherapy. However, the selection of tumor markers for predicting the effect of immunotherapy for breast cancer is still facing great challenges.

[0003] The identification of accurate biomarkers can bring significant benefits to clinical practice. To date, ER and HER2 are the most useful biomarkers in breast cancer, used for prognosis and prediction of hormonal therapy and targeted therapy, respectively. At the same time, PI3KCA or BRCA1 / 2 mutations have also been confirmed to be actionable biomarkers that can drive targeted therapy. Immunotherapy is an emerging treatment for breast cancer. Like most treatments, immunotherapy is not beneficial for all patients. Therefore, targeted and accurate predictive biomarkers are needed to improve the efficiency of immunotherapy. Tumor infiltrating lymphocytes have been evaluated as a predictor of immunotherapy efficacy. PD-L1 is the most widely used immunotherapy response biomarker in clinical practice and is also the FDA-approved biomarker VENTANAPD-L1 SP142, which is used to select metastatic TNBC patients who may benefit from immunotherapy such as atezolizumab.

[0004] However, all biomarkers discovered and applied to the prognosis and treatment of breast cancer have certain limitations. Immunohistological evaluation of ER expression is currently the gold standard biomarker for predicting response to hormone therapy. However, ER is not a dichotomous variable, and the definition of ER positivity has certain limitations, because tumors with low or high ER levels are also classified as ER positive, but the sensitivity to endocrine therapy may be different. The emergence of resistance to endocrine therapy in ER-positive tumors has led people to believe that evaluating insufficient ER expression can effectively predict response to treatment. In the KEYNOTE-522 trial, immunotherapy also improved the pCR rate regardless of PD-L1 status. The difference may be due to the lack of standardized immunohistological methods or antibodies to score PD-L1 levels, which weakens the ability to effectively evaluate PD-L1 status and thus predict immunotherapy response. Another limitation of PD-L1 as a predictive biomarker is that the development of immunotherapy strategies may target different immune checkpoints, resulting in different treatment effects. Therefore, research and exploration of new predictive biomarkers for breast cancer is urgently needed. Summary of the invention

[0005] Technical issues solved:

[0006] The present application aims to address the differences in the efficacy of immunotherapy in breast cancer patients in the prior art. The selection of markers for predicting the efficacy of immunotherapy for breast cancer still faces great challenges. All biomarkers currently discovered and applied to the prognosis and treatment of breast cancer have limitations. Some existing biomarkers are not accurate in breast cancer diagnosis and prognosis prediction, and are not effective. The present application provides a marker SKP1 for predicting the efficacy of immunotherapy for breast cancer and its application, seeks a new combination of markers with better sensitivity and specificity for diagnosing breast cancer, and uses it in the preparation of a detection kit for identifying breast cancer cells and predicting breast cancer. The biomarker SKP1 of the present application can be used as a new molecular marker for the diagnosis and prognosis of breast cancer, and its high expression indicates a poor prognosis of the tumor.

[0007] Technical solution:

[0008] To achieve the above objectives, this application is implemented through the following technical solutions:

[0009] SKP1 is a marker for predicting the efficacy of immunotherapy for breast cancer.

[0010] The present application also discloses the use of the marker SKP1 for predicting the efficacy of breast cancer immunotherapy in the preparation of a detection kit for identifying breast cancer cells and predicting breast cancer.

[0011] Principle explanation: This application screens out SKP1, a gene that is highly expressed in breast cancer and closely related to prognosis, through bioinformatics analysis of gene expression in breast cancer compared with other types of cancer. It can be used as a new molecular marker for breast tumors, and its high expression indicates poor prognosis of tumors. SKP1 belongs to the Skp1 / Cullin-1 / F-box ubiquitin ligase complex, which is responsible for the ubiquitination of cell regulators, leading to their proteolysis. At the same time, SKP1 is a traditional drug target for cancer treatment. Skp1 overexpression promotes tumor growth, and reduced Skp1 activity is also related to genomic instability and tumor transformation. The present invention focuses on its immune relevance and prognostic value. The results of immunohistochemistry and bioinformatics analysis show that SKP1 is expressed less in triple-negative breast cancer tissues than in non-triple-negative breast cancer cell lines and breast cancer adjacent tissues; the expression of SKP1 in BrCa increases with the occurrence and development of tumors; SKP1 is highly enriched in immune-related pathways; and it is suggested that it is a marker for immune-cold tumors; and in breast cancer tissues, high expression of SKP1 is associated with poor tumor prognosis.

[0012] Beneficial effects:

[0013] The present application provides a marker SKP1 for predicting the efficacy of immunotherapy for breast cancer and its application, which has the following beneficial effects compared with the prior art:

[0014] 1. SKP1 of the present invention can significantly promote the proliferation of breast cancer cells;

[0015] 2. The present invention provides a new target for the diagnosis, prediction and treatment of breast cancer;

[0016] 3. This application proposes a highly effective biomarker SKP1 related to breast cancer. A large sample of breast cancer patient RNA-seq data and clinical information were obtained from the TCGA public database. The functional role of SKP1 in breast cancer was predicted through gene enrichment analysis, and its intrinsic association with the tumor immune microenvironment was explored. At the same time, combined with animal experiments such as immunohistochemistry, the relationship between SKP1 and multiple immune checkpoint gene markers was revealed, and SKP1 was associated with immune-cold tumors, indicating that SKP1 can become a biomarker of sensitivity to immunotherapy;

[0017] 4. This application provides a new basis and method for the prognosis assessment kit for early diagnosis and treatment of breast cancer individuals. The present invention innovatively explores the relationship between SKP1 and the tumor immune microenvironment, discovers its correlation with the immune cold tumor microenvironment, verifies that high expression of SKP1 is associated with poor prognosis of breast cancer, and provides a strong basis for the development of early treatment of breast cancer individuals and the evaluation of prognosis results, and provides a reliable reference for the timely adjustment of treatment plans for breast cancer patients. At the same time, TNBC shows the greatest prospect in the application of immunotherapy. In the present invention, SKP1 is used as a new target to assist doctors in providing targeted treatment options for breast cancer patients.

[0018] 5. The present invention detected the correlation between SKP1 and other breast cancer biomarkers, and experimental data analysis found that it was negatively correlated with PD-L1 and CLAT4, confirming that high expression of SKP1 is associated with poor prognosis of breast cancer, and the prediction is more accurate and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This application is a diagram of the expression of SKP1 in breast cancer and adjacent tissues;

[0020] Figure 2 The present application is a representative graph and IRS score graph showing the expression of SKP1 in tumor and adjacent tissues by anti-SKP1 staining;

[0021] Figure 3 This application is a low expression map of SKP1 in triple negative;

[0022] Figure 4 This application is an analysis chart of the prognostic value of SKP1 in BrCa from the perspective of OS;

[0023] Figure 5 This application is an analysis chart of the prognostic value of SKP1 in BrCa from the perspective of PFS;

[0024] Figure 6 This application is an analysis chart of the prognostic value of SKP1 in BrCa from the perspective of DFS;

[0025] Figure 7The present application is a graph of proteins and different subtypes of breast cancer and TMN clinical stages, wherein A is a graph of negative correlation between SKP1 and ER sites of breast cancer; B is a graph of negative correlation between SKP1 and PR sites of breast cancer; C is a graph of negative correlation between SKP1 and ER sites of breast cancer; D is a graph of SKP1 and four molecular subtypes of breast cancer; E is a graph of SKP1 and T stage of breast cancer; Figure F is a graph of SKP1 and N stage of breast cancer; Figure G is a graph of SKP1 and M stage of breast cancer; Figure H is a graph of SKP1 and four clinical stages of breast cancer; Figure I is a graph of SKP1 and the age of breast cancer patients;

[0026] Figure 8 This application is a SKP1-related gene screening and Pearson correlation analysis diagram;

[0027] Fig. 9 This application is a heat map of the top 50 genes positively correlated with SKP1;

[0028] Fig.10 This application is a heat map of the top 50 genes negatively correlated with SKP1;

[0029] Fig.11 This application is a GO pathway enrichment map of genes related to SKP1;

[0030] Fig.12 This application is a KEGG pathway enrichment map of genes related to SKP1;

[0031] Fig.13 This application is a graph related to SKP1 and BrCa immune tumor microenvironment immunomodulators, namely chemokines, immunostimulants, MHC and receptors;

[0032] Fig.14 This application is a correlation diagram of SKP1 and common immune infiltration genes;

[0033] Fig.15 This application is a correlation diagram between SKP1 and B-cell immune cell infiltration level;

[0034] Fig.16 This application is a correlation diagram between SKP1 and CD4-Tcell immune cell infiltration level;

[0035] Fig.17 This application is a graph showing the correlation between SKP1 and CD8-Tcell immune cell infiltration levels;

[0036] Fig.18 This application is a graph showing the correlation between SKP1 and the level of Dendritic immune cell infiltration;

[0037] Fig.19This application is a graph showing the correlation between SKP1 and Macrophage immune cell infiltration levels;

[0038] Fig. 20 This application is a graph showing the correlation between SKP1 and Neutrophil immune cell infiltration levels;

[0039] Fig.21 This application is a graph showing the correlation between SKP1 and the level of IPS-CTLA4-Neg-PD1-Neg immune cell infiltration;

[0040] Fig. 22 This application is a graph showing the correlation between SKP1 and the level of immune cell infiltration of iPS-CTLA4-POS-PD1-NEG;

[0041] Fig.23 This application is a graph showing the correlation between SKP1 and the level of immune cell infiltration of iPS-CTLA4-POS-PD1-POS;

[0042] Fig.24 This application is a correlation diagram of SKP1 and iPS-CTLA4-Neg-PD1-POS immune cell infiltration levels;

[0043] Fig.25 This application is a correlation diagram of SKP1 expression and iPS-CTLA4-POS-PD1-POS immunotherapy biomarkers;

[0044] Fig.26 This application is a correlation diagram of SKP1 expression and iPS-CTLA4-POS-PD1-NEG immunotherapy biomarkers;

[0045] Fig. 27 This application is a correlation diagram of SKP1 expression and iPS-CTLA4-Neg-PD1-Neg immunotherapy biomarkers;

[0046] Fig.28 This application is a correlation diagram of SKP1 expression and total-perMB-log immunotherapy biomarkers;

[0047] Fig.29 This application is a diagram of the changes in common genes in BrCa in different SKP1 groups;

[0048] Fig.30 This application is a correlation diagram between SKP1 and TMB score;

[0049] Fig.31 This application is a graph showing the correlation between SKP1 and T cell scores;

[0050] Fig.32This application is a diagram of the predictive effect of SKP1 in immunotherapy, where A is a PCR validation diagram and B is a ROC curve diagram. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings and specific examples, but it should not be construed as limiting the present invention. Without departing from the spirit and substance of the present invention, the modification or replacement of the method, steps or conditions of the present invention all belong to the scope of the present invention. The experimental methods and reagents without specifying the specific conditions in the examples are all in accordance with the conventional conditions in the art.

[0052] Example 1

[0053] The marker SKP1 for predicting the efficacy of immunotherapy for breast cancer and its application, the forward primer sequence of the marker SKP1 detection primer sequence is such as SEQ ID NO.1: 5'-GACCATGTTGGAAGATTTGGGA-3', and the reverse primer sequence is such as SEQ ID NO.2: 5'-TGCACCACTGAATGACCTTTT-3'.

[0054] The bioinformatics analysis of SKP1 expression in breast cancer was performed as follows: SKP1 primer sequence data source:

[0055] https: / / www.genelibs.com / gen / command / search / gen / detail / HGNC:10899

[0056] Figure 1 The present invention uses a set of standardized RNA-seq data of BrCa samples from the TCGA database, and then inputs the data into the Graphpad Prism8 software. After software analysis, a box plot is automatically generated, and the graphic coordinates and colors are manually adjusted. The mRNA levels of SKP1 in cancer and adjacent tissues are compared. Data analysis and histochemical detection found that the expression level of SKP1 in breast cancer is higher than that in adjacent cancer cells.

[0057] Figure 2The present invention uses immunohistochemistry, and the clinical cohort BrCa (Cat. HBreD050Bc01) tissue chip (TMA) was provided by Outdo BioTech (Shanghai, China). The HBreD050Bc01 cohort contains 40 tumor samples and 10 adjacent tumor samples. Specific clinical pathological characteristics were provided by Outdo BioTech. The use of TMA was ethically approved by the Clinical Research Ethics Committee of Outdo Biotech (Shanghai). The above TMA was stained with immunohistochemistry (IHC). The primary antibodies used in the study are as follows: anti-SKP1 (1:1000 dilution, Cat.10990-2-AP, ProteinTech, Wuhan, China) and anti-PD-L1 (Ready-to-use, Cat.17952-1-AP, GT2280, Gene Technology, Shanghai). Antibody staining was counterstained with DAB and hematoxylin. All stained sections were independently evaluated by two pathologists. For semi-quantitative evaluation of SKP1 and PD-L1 staining, the immune response score (IRS) was used (Cai et al., 2021). Briefly, the percentage of positively stained cells was divided into 0-4: 0 (<5%), 1 (6-25%), 2 (26%-50%), 3 (51%-75%), and 4 (>75%). The staining intensity was divided into 0-3: 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). IRS is equal to the percentage of positive cells multiplied by the staining intensity. The results showed that most cells in BrCa samples were stained with anti-SKP1, while most cells in adjacent tumor samples were not stained.

[0058] Figure 3 The present invention uses a set of standardized RNA-seq data from TNBC samples in the TCGA database, and then inputs the data into the Graphpad Prism8 software. After software analysis, a box plot is automatically generated, and the coordinates and colors of the graph are manually adjusted. The results show that the expression of SKP1 in triple-negative breast cancer is much lower than that in adjacent cancer cells.

[0059] Figure 4-Figure 6 The present invention uses the TCGA data set to download the survival data including the three endpoints of overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS), and analyzes them by GraphpadPrism8 software. The prognostic value of the categorical variable is evaluated by the log-rank test, and finally the survival curve is obtained, and the coordinates and colors of the graph are manually adjusted. After analysis, it is found that only OS has a statistical difference. At the same time, the results show that breast cancer patients with high expression of SKP1mRNA have poor OS, which confirms that high expression of SKP1 in BrCa is associated with poor prognosis;

[0060] Figure 7The present invention combines the open database TCGA and GEO joint cohort to obtain standardized RNA-seq data of BrCa samples, and uses Graphpad Prism8 software to obtain a box plot. The present invention analyzes the relationship between SKP1 expression and clinical stage data, and finds that the expression of SKP1 is high in ER and PR positive, but the expression of HER2 is not statistically significant. In the clinical pathological stage, the expression level of SKP1 increases with the increase of T, N, and M stages, which indicates that the expression of SKP1 increases with the occurrence and development of tumors and is related to poor prognosis;

[0061] like Figure 8-Figure 12 As shown: The related gene screening and enrichment analysis of the present invention utilized Linked Omics (https: / / www.linkedomics.org / login.php), an interactive platform for online management of TCGA data (Vasaikar et al., 2018). The present invention used Linked Omics to screen genes related to SKP1 expression in BrCa. Through gene set enrichment analysis (GSEA), the function of SKP1 in BrCa was predicted from the perspectives of biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the association characterization platform, and all parameters used the default options. The results showed that SKP1 was associated with the immune pathway.

[0062] like Fig.13 As shown: The present invention uses the TCGA database and imports the data into origin, and uses the correlation analysis plug-in Correlation Plot for analysis to obtain a correlation analysis heat map of SKP1 and immunomodulators (including chemokines, MHC, immunostimulatory factors and receptors) in BrCa. The results show that with the increase of SKP1 expression level, the regulation levels of a large number of chemokines, paired receptors, immunomodulators, MHC molecules and immune chemokines decrease.

[0063] like Fig.14 As shown: The present invention uses the data from TCGA and imports the data into origin, and uses the correlation analysis plug-in Correlation Plot to analyze and obtain a heat map. The results show that SKP1 is negatively correlated with some genes in immune infiltration.

[0064] like Figure 15-Figure 23As shown: The IPS value of BrCa patients in the present invention was downloaded from the Cancer Immunogroup Atlas (TCIA) (https: / / tcia.at / home). After downloading, the data was analyzed and found to be positively correlated with some immune biomarkers. At the same time, the present invention used the data in the TCGA dataset and the correlation between SKP1 and TIICs levels calculated by the TIMER algorithm. The present invention further studied the relationship between SKP1 expression level and immune infiltration level. The results showed that the infiltration of B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils and dendritic cells in BrCa was negatively correlated with SKP1 expression, indicating that high expression of SKP1 would prevent immune cells, and immune cells attack tumors, so high expression of SKP1 would promote tumor growth;

[0065] like Figure 24-28 As shown: The IPS values ​​of BrCa patients in the present invention were downloaded from The Cancer Immunobiology Atlas (TCIA) (https: / / tcia.at / home), and it was found to be negatively correlated with some biomarkers;

[0066] like Fig.29 As shown: In addition, the simple nucleotide variation data of TCGA samples processed by the present invention using the "MuTect2" software (Beroukhim et al., 2010) were obtained from the Genomic Data Commons (GDC, https: / / portal.gdc.cancer.gov / ) website. A mutation frequency graph was then drawn, and it was found that the mutation frequencies of TP53 and TTN in the high SKP1 group were significantly reduced, while the mutation frequencies of PIK3CA, CDH1, GATA3 and MAP3K1 were significantly increased, indicating that SKP1 overexpression may lead to increased genetic instability;

[0067] like Fig.30 As shown: The present invention used the R software package "maftools" to visualize the mutation landscape and calculate the tumor mutation burden (TMB) of each sample, and found a negative correlation between SKP1 and TMB levels.

[0068] Fig.31 : The present invention also calculated the T cell inflammation score based on the linear combination of the expression levels and weighted coefficients of 18 genes (Ayers et al., 2017), and also found that SKP1 expression was negatively correlated with the T cell inflammation score.

[0069] like Fig.32As shown: The present invention uses receiver operating characteristic (ROC) analysis to evaluate the specificity and sensitivity of PSME2 in predicting immunotherapy response, and calculates the area under the ROC curve (AUC). The results showed that the expression level of SKP1 in the pCR group was lower, indicating that overexpression of SKP1 mRNA will produce less response to immunotherapy, confirming the role of SKP1 as a potential biomarker; ROC validation indicates high credibility and accuracy.

[0070] (1) Sample collection and data processing

[0071] RNA sequencing data and clinical information of breast cancer patients were obtained from the TCGA database. In addition, RNA sequencing data of breast cancer patients receiving immunotherapy were obtained from the GEO database, including GSE194040 invasive early breast cancer and GSE173839 high-risk HER2-negative stage II / III breast cancer datasets. Combining the open database TCGA and GEO joint cohort, breast cancer subtypes and pathological stages were selected, and Graphpad Prism software was used to find that the expression of SKP1 was high in ER and PR positive, but the expression of HER2 was not statistically significant. In the clinical pathological stage, the expression level of SKP1 increased with the increase of T, N, and M stages, which indicates that the expression of SKP1 increases with the occurrence and development of tumors and is related to poor prognosis.

[0072] In order to confirm the biological characteristics of SKP1 expression in breast cancer, this application screened the SKP1 expression of genes related to it and studied the positively and negatively correlated genes. In order to obtain specific results, this application submitted genes related to SKP1 for BP and KEGG analysis. The steps of BP are: (1) Data preparation: First, copy the required genes, open the DAVID website, paste the genes in the sidebar, and select the gene category (such as Homo sapiens). Click to download Chart, then use the combination key Ctrl+A to select all, and Ctrl+C to copy the selected content to a new text file and save it. (2) Data analysis: Use Excel to sort and save the Category. Create a new Excel file, copy the first row of the header file, and it is recommended to copy the file at least four times into different files when saving and indicate it as kegg / bp / cc / mf for data analysis. (3) Drawing: For GO drawing, select the contents of all rows containing GOTERM_MF_DIRECT and paste them into the above MF file. The file must be saved as a csv file, and the address cannot contain Chinese. Use R language to read data, perform P value screening, and use ggplot2 library for drawing. The steps of KEGG are: (1) Data preparation: Save the differential genes or target genes that need to be enriched for analysis as a list with gene name as txt document or excel. (2) Website analysis: Open the DAVID website (https: / / david.ncifcrf.gov / home.jsp), click start analysis, and upload the data. In the new interface, check the drop-down box in front of GO ONTOLOGY and PATHWAY respectively, and select the KEGG database for analysis. (3) View the results: After the analysis is completed, view the result chart. If you need to optimize the results, you can click the options option to adjust it. This application downloaded patient survival data from the TCGA data set. After analysis, it was found that breast cancer patients with high expression of SKP1mRNA had poor OS, confirming that high expression of SKP1 in BrCa is associated with poor prognosis.

[0073] In BP and KEGG pathway analysis, its family members had high enrichment in immune pathways and immune-related pathways, indicating that it has the potential to serve as an immune-hot tumor marker in the immune microenvironment and SKP1 is associated with the immune pathway.

[0074] (2) Correlation between SKP1 expression and BrCa immunotherapy biomarkers

[0075] In order to confirm the role of SKP1 as a key target gene for immunotherapy, this application conducted a correlation analysis between SKP1 and PD-L1 and CLAT4, confirming that there was a high negative correlation between them. High expression of SKP1 showed a lower mutation load, indicating that its expression level was associated with a poor prognosis. Moreover, the mutation frequencies of TP53 and TTN in the high SKP1 group were significantly reduced, but the mutation frequencies of PIK3CA, CDH1, GATA3 and MAP3K1 were significantly increased, and negatively correlated with TMB levels. This indicates that SKP1 overexpression leads to increased genetic instability, and SKP1 is also negatively correlated with T cell inflammation scores, indicating that it has the role of an immune cold tumor marker. This application uses a group of breast cancer patients from the TCGA dataset to compare the mRNA levels of SKP1 in cancer and adjacent tissues. Data analysis and histochemical detection found that the expression of SKP1 in breast cancer was higher than that in adjacent cancer cells. Most cells in BrCa samples were stained with anti-SKP1, while most cells in adjacent tumor samples were not stained, but the expression in triple-negative breast cancer was much lower than that in adjacent cancer cells. The mutation frequencies of TP53 and TTN were significantly reduced in the high SKP1 group, while those of PIK3CA, CDH1, GATA3, and MAP3K1 were significantly increased. SKP1 was also negatively correlated with T cell inflammation scores, suggesting that it is a marker for immune-cold tumors. PCR validation was performed in the immunotherapy cohort, and the results showed that the expression level of SKP1 was lower in the pCR group, indicating that overexpression of SKP1 mRNA would result in less response to immunotherapy, confirming the role of SKP1 as a potential biomarker.

[0076] The correlation between SKP1 and immune function: With the increase of SKP1 expression level, the regulation levels of a large number of chemokines, paired receptors, immunomodulators, MHC molecules and immune chemokines decreased, and were negatively correlated with B cells, CD4T, CD8T positive cells, etc., indicating that the high expression of SKP1 will block immune cells, and immune cells attack tumors, so the high expression of SKP1 will promote tumor growth.

[0077] Example 2

[0078] Using immunohistochemistry, the tissues were stained by histochemical method to observe the expression of SKP1 in breast cancer and adjacent tissues. The steps are as follows:

[0079] Step 1, dewaxing after tissue drying: Immunohistochemical staining is performed directly on tissue sections. First, the tissue sections are placed at 55°C for dewaxing for 30 minutes. Dewaxing and hydration of tissue sections: the tissue sections are immersed in xylene, taken out after 10 minutes, immersed three times, and then placed in 95%, 90%, 85%, 80%, and 75% anhydrous ethanol in sequence, each treatment lasting 5 minutes, and then rinsed with tap water for 1 minute and double distilled water for 1 minute;

[0080] Step 2, antigen repair: During the sectioning process, tissue processing may cause the antigen to be partially shielded or destroyed. In order to restore the antibody reactivity of the antigen, antigen repair is required. The steps are as follows: Pour the sodium citrate antigen repair solution into the box until the slide can be immersed. The slide is placed in the box, heated to 98°C in a water bath and placed in it for 20 minutes. Then take out the box and let it cool to room temperature for 15-20 minutes. Then wash it three times with a PBS shaker, each time for 5 minutes. After drawing a circle with an immunohistochemistry pen, drop the anti-peroxidase blocker on the slice to block it, and cover the tissue slice. After standing for 15 minutes, wash it three times with a PBS shaker, each time for 5 minutes.

[0081] Step 3, blocking: In order to reduce the background interference of the experiment, it is necessary to block the non-specific binding sites in the tissue sections. The steps are as follows: After washing, use a pipette to aspirate 200ul of sheep serum and drop it on the tissue sections in turn. Block for 20 minutes at room temperature in the dark, and then shake off the excess liquid;

[0082] Step 4, primary antibody incubation: add primary antibody dropwise to cover the tissue sections, and then incubate at 4°C overnight for 12 hours. The antibodies are: anti-SKP1 (1:1000 dilution, Cat.10990-2-AP, ProteinTech, Wuhan, China) and anti-PD-L1 (Ready-to-use, Cat.17952-1-AP, GT2280, Genentech, Shanghai). This antibody binds to the target antigen to form an antigen-antibody complex;

[0083] Step 5: Rewarm to ambient temperature, recover the primary antibody for next use, and then record the concentration and number of uses. Wash on a PBS shaker for 3 minutes, for a total of 3 washes;

[0084] Step 6, secondary antibody incubation: Use enzyme-labeled goat anti-mouse / rabbit IgG polymer (secondary antibody) to drop on the slide, react for 20 minutes at room temperature, rinse with PBS for 5 minutes, and wash 3 times in total. The secondary antibody binds to the primary antibody to form an antigen-antibody-antibody complex;

[0085] Step 7, prepare DAB: add five drops of solution A and 2000ul of solution B in the DAB kit into a container until the solution turns orange;

[0086] Step 8, color observation: Use DAB colorimetric method to make the antigen-antibody-antibody complex generate visible staining signals, and then observe the distribution and expression of the target antigen in the tissue under a microscope. The steps are as follows: immediately observe after adding DAB colorimetric solution until the color of the tissue section changes significantly from colorless to yellow, terminate the reaction, rinse the slide with tap water for 1 minute, then stain with hematoxylin dye for 30 seconds, then rinse off the excess dye with tap water, and finally cover the section with acidic ethanol for 2-10 seconds and rinse with running water for 5 minutes;

[0087] Step 9, dehydration: put the slides into 70%, 80%, 90%, and 95% anhydrous ethanol in order, soak for 3 minutes respectively, and then put them into xylene solution and soak for 20 minutes before taking them out;

[0088] Step 10, sealing: After the slide is dried, drop a drop of neutral resin on the center of the slide, immediately cover it with a cover slip, press lightly and wait for it to dry naturally;

[0089] Step 11, result interpretation: Scanning was performed using the Aperio digital pathology slide scanner, and immunohistochemical staining results were scored using immunohistochemical staining results. For semi-quantitative evaluation of SKP1 and PD-L1 staining, the immunoreactivity score (IRS) (Cai et al, 2021) was used. Briefly, the percentage of positively stained cells was scored as 0-4: 0 (<5%), 1 (6-25%), 2 (26%-50%), 3 (51%-75%), and 4 (>75%). The staining intensity was scored as 0-3: 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). IRS is equal to the percentage of positive cells multiplied by the staining intensity. Experimental verification showed that SKP1 was highly expressed in tumor tissues.

[0090] The examples selected in the above materials are for ease of understanding, not to limit the process. People in the same technical field can easily modify the process or transfer it to other cases without creative changes. If these changes also belong to the same claims or similar technologies of the present invention, then the original intention of the invention also includes these changes.

Claims

1. SKP1, a marker used to predict the efficacy of immunotherapy for breast cancer.

2. Use of the marker SKP1 for predicting the efficacy of breast cancer immunotherapy according to claim 1 in preparing a detection kit for identifying breast cancer cells and predicting breast cancer.

Images