Application of marker detection reagent in preparation of product for predicting prognosis of triple negative breast cancer patient

Abstract

The invention discloses application of a reagent for detecting a marker in preparation of a product for predicting prognosis of a triple negative breast cancer patient, and relates to the technical field of detection markers, and the marker is SPB1 protein or a coding gene thereof. The prognosis result of the SPB1 protein on the triple negative breast cancer patient is evaluated through an ROC curve, the AUC value can reach 0.768, and it is indicated that the SPB1 protein can serve as a prognosis marker of the triple negative breast cancer patient.

Description

Technical Field

[0001] This invention relates to the field of biomarker technology, and more particularly to the application of reagents for detecting biomarkers in the preparation of products for predicting the prognosis of patients with triple-negative breast cancer. Background Technology

[0002] Breast cancer, a common malignant tumor among women worldwide, exhibits significant prognostic heterogeneity that impacts the precision of clinical treatment decisions. Current prognostic assessments primarily rely on histopathological grading, TNM staging, and traditional molecular markers (such as Ki-67, ER, PR, and HER2 status). However, these indicators have particularly limited prognostic value in triple-negative breast cancer (TNBC), the most aggressive subtype lacking effective therapeutic targets, making it difficult to accurately stratify risk and provide individualized treatment guidance for TNBC patients. Therefore, developing novel, specific prognostic biomarkers based on the unique molecular mechanisms of TNBC is an urgent practical need for improving the clinical management of this high-risk subgroup of patients.

[0003] Polyamine metabolic reprogramming, a core mechanism supporting rapid tumor proliferation, has been widely confirmed to be associated with tumor cell cycle progression, apoptosis resistance, and microenvironment remodeling. Studies have found that key regulators of polyamine homeostasis can drive malignant tumor progression by affecting signaling pathways such as MYC and MAPK, and are closely related to chemotherapy resistance. In recent years, advances in metabolomics and molecular biology techniques have made it possible to systematically elucidate tumor-specific metabolic dependencies. For example, the key role of ODC1 in polyamine synthesis has been established as a therapeutic target for various cancers, while the expression level of SAT1 has been shown to be associated with patient survival in some solid tumors. However, the above studies have mostly focused on the classical synthesizing and degrading enzymes of the polyamine pathway. Research on the function and mechanism of upstream or co-regulatory proteins, especially factors with unique binding and transport functions, in specific cancer types (such as TNBC) remains insufficient.

[0004] SPB1 (Spermine Binding Protein 1), a crucial regulator of intracellular polyamine homeostasis, has been primarily studied in previous research for its fundamental functions in regulating cell growth and differentiation by binding polyamines with high affinity and influencing the size of the intracellular free polyamine pool. However, its specific role in the development and progression of malignant tumors, particularly TNBC, has long been neglected. Current research on SPB1 mainly focuses on its basic biochemical characteristics, failing to elucidate whether it is specifically expressed or functionally activated in TNBC. Furthermore, substantial studies systematically validating the association between its protein levels (especially specific functional forms) and the prognosis and treatment response of TNBC patients in clinical cohorts are lacking. This constitutes a significant knowledge gap and technological bottleneck in translating polyamine metabolic regulatory networks into clinical practice for TNBC. Summary of the Invention

[0005] To address the aforementioned technical bottlenecks, this invention reveals that SPB1 protein has a significant negative correlation with the prognosis of triple-negative breast cancer patients and can serve as a prognostic biomarker for triple-negative breast cancer patients.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: In a first aspect, the present invention provides the use of a reagent for detecting a biomarker in the preparation of a product for predicting the prognosis of patients with triple-negative breast cancer, said biomarker being SPB1 protein or its encoding gene, the sequence of which is shown in SEQ ID NO.1.

[0007] Furthermore, in the above applications, the reagents for detecting biomarkers are reagents based on immunohistochemical staining, reagents based on specific antibody detection, reagents based on mass spectrometry analysis, reagents based on label-free proteomics detection, or reagents based on molecular biology detection.

[0008] Furthermore, in the above applications, reagents based on immunohistochemical staining detection methods include buffer solutions or staining reagents; reagents based on specific antibody detection methods include anti-SPB1 antibodies; and reagents based on mass spectrometry analysis detection methods include trypsin, reducing agents, alkylating agents, or desalting columns.

[0009] Furthermore, in the above applications, the product assesses prognosis by detecting the expression level of SPB1 protein or its encoding gene in the sample.

[0010] Furthermore, in the above applications, the expression level of the SPB1 protein or its encoding gene is negatively correlated with the prognosis of patients with triple-negative breast cancer.

[0011] Furthermore, in the above applications, the prognosis includes one or more of overall survival, disease-free survival, and risk of disease progression.

[0012] Furthermore, in the above applications, the sample is a tumor tissue sample or adjacent normal tissue sample from a triple-negative breast cancer patient.

[0013] Furthermore, in the above applications, the detection methods used for the product include one or more of the following: immunohistochemistry, Western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence technology, and real-time quantitative PCR.

[0014] Furthermore, in the above applications, the detection method is immunohistochemistry, and the detection indicators include staining intensity score and staining positivity rate score. The total score is the product of the staining intensity score and the staining positivity rate score.

[0015] Furthermore, in the above applications, the product is a detection reagent or detection kit.

[0016] The beneficial effects of this invention include at least the following: The present invention evaluated the prognostic effect of SPB1 protein on triple-negative breast cancer patients using ROC curve analysis, showing an AUC value of 0.768, indicating that SPB1 protein can serve as a prognostic biomarker for triple-negative breast cancer patients. Attached Figure Description

[0017] Figure 1 To organize the chip manufacturing process.

[0018] Figure 2 is a schematic diagram of the tissue chip array arrangement.

[0019] Figure 3 shows the comparison of SPB1 expression in cancerous and normal tissues using immunohistochemical staining.

[0020] Figure 4 shows the differential expression analysis of SPB1 in cancerous and adjacent tissues.

[0021] Figure 5 shows the correlation analysis between SPB1 and the prognosis of triple-negative breast cancer patients.

[0022] Figure 6 shows the receiver operating characteristic curve of SPB1 in patients with triple-negative breast cancer.

[0023] Figure 7 shows a classic forest plot of binary variables; (A) shows the case of univariate analysis, and (B) shows the case of multivariate analysis.

[0024] Figure 8 shows the differential expression analysis of SPB1 clinical information. (A) Differential expression analysis of SPB1 in patients under 40 years old and over 40 years old; (B) Differential expression analysis of SPB1 in patients with triple-negative breast cancer and non-triple-negative breast cancer; (C) Differential expression analysis of SPB1 in ER-positive and ER-negative patients; (D) Differential expression analysis of SPB1 in stage I and non-stage I patients; (E) Differential expression analysis of SPB1 in HER2-positive and HER2-negative patients; (F) Differential expression analysis of SPB1 in patients with regional lymph node metastasis stage N0 and non-N0; (G) Differential expression analysis of SPB1 in patients with PR-positive and PR-negative; (H) Differential expression analysis of SPB1 in primary tumor extent stage stage I and non-stage I groups. Detailed Implementation

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0027] The following specific embodiments illustrate the solution proposed in this invention: The fabrication process of tissue chip is shown in Figure 1 in the following example.

[0028] Specifically as follows: (1) Obtain HE-stained sections from triple-negative breast cancer patients for pathological diagnosis and mark the extent of the lesion tissue; (2) Design the tissue type and arrangement of the tissue chip array according to the experimental purpose; (3) Select the appropriate tissue case number from the tissue database; (4) Take out the tissue paraffin block and the corresponding HE staining section from the tissue bank according to the tissue case number; (5) Prepare suitable blank receptor wax blocks using a tissue embedding machine (brand: Prescin, model: PBM-A, place of origin: China); (6) Using a tissue array instrument, extract the core of the pathological paraffin block according to the array design and arrange it regularly on the blank recipient paraffin block; (7) The tissue array blocks are heated and fused in a constant temperature oven at 52°C to make the tissue core and the receptor wax block tightly connected; (8) Use a fully automatic tissue slicer (brand: Leica, RM2445, place of origin: Germany) to trim the wax block of the tissue array at a feed speed of 20 micrometers / revolution until 80% of the tissue core is fully exposed; (9) Use a fully automatic tissue slicer to slice the tissue array block at a feed speed of 4 micrometers / revolution, and mount the slices on imported glass slides that have been treated to prevent detachment; (10) The array slices were placed in a 60℃ constant temperature oven and baked for 16 hours; (11) Every 10 tissue microarrays are randomly selected for HE staining. The pathologist performs a follow-up quality inspection on each tissue sample from the randomly selected tissue microarrays, and the results are entered into the tissue microarray database by the information department; (12) The tissue microarray experimental slides were stored in a slide box and kept in the refrigerator at 5°C. (13) Seal the used pathological tissue paraffin blocks with wax and return them and the corresponding HE stained sections to the paraffin block cabinet and section box.

[0029] In the following examples, the immunohistochemical staining method for tissue microarrays is as follows: (1) Reagent preparation (2) Baking the slides: Place the tissue chips in an oven, set the temperature to 63 degrees Celsius, and bake for one hour; (3) Dewaxing: After the film is baked, it is taken out of the oven and placed into a fully automatic dyeing machine (brand: In Leica (ST5020, made in Germany), dewaxing is performed; the dewaxing process is as follows: 1) Two cylinders of xylene, 15 minutes per cylinder (according to the instrument's set time); 2) Two cylinders of anhydrous ethanol, 7 minutes per cylinder (according to the instrument's set time); 3) 90% alcohol, 1 cylinder, 5 minutes (according to the instrument's set time); 4) 80% alcohol, 1 cylinder, 5 minutes (according to the instrument's set time); 5) 70% alcohol, 1 tank, 5 minutes (according to the instrument's set time); (4) Antigen retrieval: Take the slides out of the fully automatic staining machine and rinse them with pure water 3 times, each time for no less than 1 minute; during the rinsing process, put the citric acid retrieval solution on an induction cooker and start heating; after the citric acid retrieval solution boils, put the slides into a pressure cooker, cover the pressure cooker lid, and start timing after the steam is released, 5 minutes; after the time is up, stop heating, open the pressure cooker lid, and let it cool naturally for more than 30 minutes. (5) Blocking: Use commercially available ready-to-use blocking agents (Agilent (made in the USA), Dako EnVision). The peroxidase inhibitor (SM801) in the FLEX+ kit is applied to the slide and left for 10-15 minutes. (6) Add primary antibody: Take out the slide and rinse it 3 times with PBS buffer for 1 minute each time; take out Anti-SPB1 Antibody (MBS7181127) (rabbit polyclonal antibody for detecting SPB1 protein) produced by Shanghai Baili Biotechnology Co., Ltd. from the refrigerator, put it in a centrifuge and centrifuge at 7200 rpm for no less than 30 seconds, add Anti-SPB1 Antibody (MBS7181127) at a dilution of 1:500, incubate at room temperature, and freeze overnight at -4°C; (7) Add secondary antibody: Rinse the slides three times with PBS buffer, one minute each time; for slides that have been kept at 4°C overnight, the slides should be warmed to room temperature for at least 30 minutes before the experiment, and then rinsed with PBS buffer; add secondary antibody (Agilent, made in the USA), the peroxidase marker (goat anti-rabbit secondary antibody) (SM802) from the Dako EnVision FLEX+ kit, i.e., the working solution, and incubate at room temperature for 30 minutes; after the time is up, rinse three times with PBS, each time for at least one minute; (8) DAB staining: Take the substrate buffer out of the refrigerator, add 1 drop of DAB staining agent, mix thoroughly to prepare the DAB substrate working solution (Agilent, USA), the DAB staining solution and substrate buffer in the Dako EnVision FLEX+ kit); add the diluted DAB to the slide, observe the staining intensity, the longest staining time is 5 minutes, and then rinse with tap water for 5 minutes after that time; (9) Hematoxylin counterstaining and mounting: Add Agilent hematoxylin to the slide for 1 minute, then immerse it in 0.25% hydrochloric acid alcohol for at least 2 seconds, rinse with tap water for more than 2 minutes, air dry at room temperature, and then mount the slide. (10) Interpretation method 1) Staining intensity score: 0 (negative), 0.5 (0.5+), 1 (1+), 2 (2+), 3 (3+); 2) Staining positivity rate score: 0%-100%; 3) Total score: the product of "staining intensity score" and "staining positivity rate" (0-300%).

[0030] Example 1 1. Screening of SPB1 as a marker Proteomics sequencing was performed on three pairs of breast cancer tissues and adjacent normal tissues from three patients with triple-negative breast cancer at the Affiliated Hospital of Guangdong Medical University between 2010 and 2013. The results indicated that SPB1 was highly expressed in triple-negative breast cancer, and clinical validation was subsequently conducted.

[0031] 2. Verification of the relationship between SPB1 and clinicopathological features HE-stained slides from 119 patients with triple-negative breast cancer at the Affiliated Hospital of Guangdong Medical University between 2010 and 2013 were used for pathological diagnosis, and the extent of cancerous tissue was marked. Tissue microarrays (159 sites in total) were then prepared using the method described above. The microarrays were subsequently subjected to immunohistochemical staining and analysis. General patient information for the microarray sites is shown in Table 1 below, and the array arrangement is illustrated in the diagram below. Figure 2As shown.

[0032] Figure 3 shows the comparison of SPB1 immunohistochemical staining between cancerous and normal tissues. SPB1 expression in triple-negative breast cancer tissue was higher than that in adjacent breast cancer tissue (as shown under 5x and 20x microscopes).

[0033] Table 1 Schematic diagram of array arrangement Location Organization type Pathological classification Classification Staining strength Positive rate A01 cancer Invasive ductal carcinoma Ⅱ 2.5-3 85% A02 Next to cancer breast tissue / / / A03 cancer Invasive ductal carcinoma Ⅱ 2.5-3 90% A04 Next to cancer breast tissue / / / A05 cancer Invasive ductal carcinoma Ⅱ 3 95% A06 Next to cancer breast tissue / 0.5 10% A07 cancer Invasive ductal carcinoma Ⅱ 3 85% A08 Next to cancer breast tissue / 2.5-3 90% A09 cancer Invasive ductal carcinoma Ⅱ 3 95% A10 Next to cancer breast tissue / 2.5-3 90% A11 cancer Invasive ductal carcinoma / 2-3 95% A12 Next to cancer Breast tissue of breast disease / 0.5-1 35% A13 cancer Invasive ductal carcinoma / 2-2.5 45% A14 Next to cancer mammary duct tissue / 3 85% A15 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 0.5-1 80% A16 Next to cancer Breast disease / 2-3 55% B01 cancer Invasive ductal carcinoma Ⅱ 2.5-3 85% B02 Next to cancer Breast disease (90); ductal epithelial hyperplasia, local carcinogenesis (10) / 2.5-3 80% B03 cancer Invasive ductal carcinoma Ⅱ / / B04 Next to cancer Breast disease / / / B05 cancer Invasive ductal carcinoma Ⅱ 3 95% B06 Next to cancer breast tissue / / / B07 cancer Invasive ductal carcinoma Ⅱ 3 90% B08 Next to cancer breast tissue / / / B09 cancer Invasive ductal carcinoma Ⅱ 2-3 99% B10 Next to cancer Breast disease (95); intraductal epithelial hyperplasia with papillary hyperplasia, and suspected malignant transformation of local epithelium (5) / / / B11 cancer Invasive ductal carcinoma Ⅱ 2.5-3 99% B12 Next to cancer breast tissue / 2-3 80% B13 cancer Invasive ductal carcinoma Ⅱ 3 90% B14 Next to cancer breast tissue / / / B15 cancer Invasive ductal carcinoma Ⅱ 2-2.5 95% B16 Next to cancer breast tissue / 1.5-2 80% C01 cancer Invasive ductal carcinoma Ⅱ 2-3 95% C02 Next to cancer breast tissue / / / C03 cancer Invasive ductal carcinoma Ⅱ 2-3 99% C04 Next to cancer breast tissue / / / C05 cancer Invasive ductal carcinoma Ⅱ 2.5-3 95% C06 Next to cancer breast tissue / 2-3 60% C07 cancer Invasive ductal carcinoma Ⅱ 3 85% C08 Next to cancer breast tissue / 2.5-3 80% C09 cancer Invasive ductal carcinoma Ⅱ 1.5-2 85% C10 Next to cancer breast tissue / 2-3 30% C11 cancer Invasive ductal carcinoma Ⅱ 2-2.5 90% C12 Next to cancer breast tissue / 2.5-3 70% C13 cancer Invasive ductal carcinoma Ⅱ 2-3 85% C14 Next to cancer Breast disease / 1-2 25% C15 cancer Invasive ductal carcinoma Ⅱ 2-3 40% C16 Next to cancer Breast disease, local ductal epithelial hyperplasia / 2.5-3 80% D01 cancer Invasive ductal carcinoma Ⅱ 3 90% D02 Next to cancer Breast disease / 2-3 40% D03 cancer Invasive ductal carcinoma Ⅱ 2-2.5 75% D04 Next to cancer Breast disease / 0.5-1 45% D05 cancer Invasive ductal carcinoma Ⅱ 3 60% D06 Next to cancer fibrocystic breast disease / 0.5-1 80% D07 cancer Invasive ductal carcinoma Ⅱ 3 75% D08 Next to cancer Breast disease / 1-2 15% D09 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2-3 70% D10 cancer Invasive ductal carcinoma Ⅱ 2-2.5 85% D11 cancer Invasive ductal carcinoma Ⅱ 2-3 65% D12 cancer Invasive ductal carcinoma Ⅱ 3 90% D13 cancer Invasive ductal carcinoma Ⅱ 2.5-3 80% D14 cancer Invasive ductal carcinoma Ⅱ 3 95% D15 cancer Invasive ductal carcinoma Ⅱ 3 90% D16 cancer Invasive ductal carcinoma Ⅱ 3 85% E01 cancer Invasive ductal carcinoma Ⅱ 3 99% E02 cancer Invasive ductal carcinoma Ⅱ 3 95% E03 cancer Invasive ductal carcinoma Ⅱ 1-2 60% E04 cancer Invasive ductal carcinoma Ⅱ 2.5-3 70% E05 cancer Invasive ductal carcinoma Ⅱ 2.5-3 75% E06 cancer Invasive ductal carcinoma Ⅱ 2-3 85% E07 cancer Invasive ductal carcinoma Ⅱ 2-3 60% E08 cancer Invasive ductal carcinoma Ⅱ 2.5-3 95% E09 cancer Invasive ductal carcinoma Ⅱ 2.5-3 90% E10 cancer Invasive ductal carcinoma Ⅱ 1.5-2 80% E11 cancer Invasive ductal carcinoma Ⅱ 3 85% E12 cancer Invasive ductal carcinoma Ⅱ 2.5-3 90% E13 cancer Invasive ductal carcinoma Ⅱ 3 99% E14 cancer Invasive ductal carcinoma Ⅱ 3 95% E15 cancer Invasive ductal carcinoma Ⅱ 3 99% E16 cancer Invasive ductal carcinoma Ⅱ 3 95% F01 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2-2.5 40% F02 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 1.5-2 60% F03 cancer Invasive ductal carcinoma Ⅱ 2.5-3 80% F04 cancer Invasive ductal carcinoma Ⅱ 2.5-3 85% F05 cancer Invasive ductal carcinoma Ⅱ 2-2.5 90% F06 cancer Invasive ductal carcinoma Ⅱ 2.5-3 95% F07 cancer Invasive ductal carcinoma Ⅱ 2-3 90% F08 cancer Invasive ductal carcinoma Ⅱ 2-3 95% F09 cancer Invasive ductal carcinoma Ⅱ 0.5-1 70% F10 cancer Invasive ductal carcinoma Ⅱ 2-3 99% F11 cancer Invasive ductal carcinoma Ⅱ 3 95% F12 cancer Invasive ductal carcinoma Ⅱ 3 90% F13 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2.5-3 75% F14 cancer Invasive ductal carcinoma Ⅱ 3 85% F15 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2.5-3 90% F16 cancer Invasive ductal carcinoma Ⅱ 2-2.5 95% G01 cancer Invasive ductal carcinoma Ⅱ 3 95% G02 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2.5-3 85% G03 cancer Invasive ductal carcinoma Ⅱ 3 80% G04 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2.5-3 90% G05 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2-3 95% G06 cancer Invasive ductal carcinoma Ⅱ 2-3 80% G07 cancer Invasive ductal carcinoma Ⅱ 2-3 85% G08 cancer Invasive ductal carcinoma Ⅱ 2.5-3 90% G09 cancer Invasive ductal carcinoma Ⅱ 2-3 85% G10 cancer Invasive ductal carcinoma Ⅱ 2-3 80% G11 cancer Invasive ductal carcinoma Ⅱ 2-2.5 90% G12 cancer Invasive ductal carcinoma Ⅱ 2-3 85% G13 cancer Invasive ductal carcinoma Ⅱ 2.5-3 60% G14 cancer Invasive ductal carcinoma Ⅱ 3 85% G15 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2-3 90% G16 cancer Invasive ductal carcinoma Ⅱ 2.5-3 95% H01 cancer Invasive ductal carcinoma Ⅱ-Ⅲ 3 95% H02 cancer Invasive ductal carcinoma Ⅱ H03 cancer Invasive ductal carcinoma Ⅱ 2-3 85% H04 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 3 95% H05 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 2.5-3 80% H06 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 1-1.5 70% H07 cancer Invasive ductal carcinoma Ⅱ 2.5 85% H08 cancer Invasive ductal carcinoma Ⅱ 1-2 80% H09 cancer Invasive ductal carcinoma Ⅱ 3 95% H10 cancer Invasive ductal carcinoma Ⅱ 2-3 99% H11 cancer Invasive ductal carcinoma Ⅱ 2.5-3 95% H12 cancer Invasive ductal carcinoma Ⅱ 2-2.5 80% H13 cancer Invasive ductal carcinoma Ⅱ 2.5 90% H14 cancer Invasive ductal carcinoma Ⅱ 2-3 85% H15 cancer Invasive ductal carcinoma Ⅱ 2.5 70% H16 cancer Invasive ductal carcinoma Ⅱ 3 85% I01 cancer Invasive ductal carcinoma Ⅱ 2.5 95% I02 cancer Invasive ductal carcinoma Ⅱ 2-3 75% I03 cancer Invasive ductal carcinoma Ⅱ 2-2.5 80% I04 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 3 90% I05 cancer Invasive ductal carcinoma Ⅰ-Ⅱ 1.5-2 85% I06 cancer Invasive ductal carcinoma Ⅱ 2-2.5 80% I07 cancer Invasive ductal carcinoma Ⅱ 2.5-3 90% I08 cancer Invasive ductal carcinoma Ⅱ 3 95% I09 cancer Invasive ductal carcinoma Ⅱ 3 60% I10 cancer Invasive ductal carcinoma Ⅱ 3 95% I11 cancer Invasive ductal carcinoma Ⅱ 2.5-3 65% I12 cancer Invasive ductal carcinoma Ⅱ 3 85% I13 cancer Invasive ductal carcinoma Ⅱ 2.5 90% I14 cancer Invasive ductal carcinoma Ⅱ 2-3 95% I15 cancer Invasive ductal carcinoma Ⅱ 2-3 80% I16 cancer Invasive ductal carcinoma Ⅰ 2.5 90% J01 cancer Invasive ductal carcinoma Ⅱ 2.5-3 95% J02 cancer Invasive ductal carcinoma Ⅱ 2.5 60% J03 cancer Invasive ductal carcinoma Ⅱ 3 85% J04 Next to cancer breast tissue / 2.5-3 90% J05 Next to cancer Breast disease, cystic lobular hyperplasia / 2-3 70% J06 Next to cancer Breast disease / 2.5-3 60% J07 Next to cancer Breast disease / 2-3 75% J08 Next to cancer Breast disease (90); some atypical hyperplasia epithelium in the mammary ducts. / / / J09 Next to cancer breast tissue / 2.5 80% J10 Next to cancer Breast disease (cystic lobular hyperplasia) / 3 90% J11 Next to cancer breast tissue / / / J12 Next to cancer breast tissue / / / J13 Next to cancer Breast disease, cystic lobular hyperplasia / 2.5-3 80% J14 Next to cancer breast tissue / 2-2.5 60% J15 Next to cancer Breast disease / / /

[0034] 3. Differential expression analysis of genes in cancer and adjacent normal tissues The immunohistochemical staining results were analyzed using the Wilcoxon test (SPB1 expression in triple-negative breast cancer tissue and normal tissue was verified and scored by pathologists; relevant data were entered in SPSS software (version: 25.0.0.2), nonparametric tests were selected, two independent samples were selected, and the Wilcoxon test was checked) to analyze the difference in gene expression between cancerous and adjacent tissues (p < 0.05 was considered statistically significant). The results are shown in Figure 4. The results show that SPB1 expression in triple-negative breast cancer tissue was higher than that in adjacent tissue (P = 1.5 × 10⁻⁶). -5 This is statistically significant.

[0035] 4. Correlation analysis between SPB1 expression and clinical indicators The immunohistochemical staining results were analyzed using Fisher's test (in SPSS software, version: [version number missing]). In section 25.0.0.2), grouping was set up, relevant data were entered, data weighting was selected, and then Fisher's test was performed to analyze the correlation between SPB1 expression in triple-negative breast cancer tissue and clinical indicators. A p-value < 0.05 was considered statistically significant. The analysis results are shown in Table 2. The results show that SPB1 expression and clinical indicators are correlated. The molecular subtype of breast cancer is related to SPB1 expression, which is higher in triple-negative breast cancer than in non-triple-negative breast cancer (P=0.089), which is statistically significant.

[0036] Table 2 Correlation analysis of SPB1 and clinical indicators in patients with triple-negative breast cancer

[0037] 5. Correlation analysis between SPB1 and patient prognosis This analysis employed Kaplan-Meier survival analysis and log-rank statistical test for univariate analysis of survival (in SPSS software (version: 25.0.0.2), grouping was set up, and the survival time and status of follow-up patients were entered (0 represents survival, 1 represents death). Then, click "Analysis" - "Survival Analysis" - "Kaplan-Meier," drag "Time" into "Time," "Survival Outcome" into "Status," and "Expression Status" into "Factors," and select Log-rank). A p-value < 0.05 was considered statistically significant. The results after 12 years of follow-up are shown in Figure 5. The results showed that the overall survival of breast cancer patients with low SPB1 expression was higher than that of breast cancer patients with high SPB1 expression (P < 0.0001), which was statistically significant.

[0038] 6. Cox Multivariate Regression Analysis Variables statistically significant in univariate analysis were included in the Cox multivariate survival regression analysis. Specifically, the following steps were performed: In SPSS software (version 25.0.0.2), groupings were set up, and the survival time and status of follow-up patients were entered (0 represents survival, 1 represents death). Then, the process was executed: click "Analysis" - "Survival Analysis" - "COX Regression," dragging the variable "Duration" into "Time"; dragging the variable "Survival Status" into "Status"; defining the variable "Survival Status," clicking "Define Event," selecting "Single Value," and entering "1," signifying that the outcome event (death) has occurred; dragging the independent variables to be included in the Cox regression into "Covariates"; a p-value < 0.05 was considered statistically significant. The analysis results are shown in Table 3. Figure 8 As shown, the results indicated that five variables had a p-value less than 0.05 in the Cox univariate analysis: expression level, TNM stage, estrogen receptor, progesterone receptor, and classification / type. Including these five variables in the Cox multivariate analysis revealed a p-value of 0.00162 for TNM stage, suggesting that TNM stage negatively impacts patient survival and can be considered an independent prognostic factor.

[0039] Table 3. Results of Cox Multivariate Survival Regression Analysis

[0040] 7. Mori-Litogram Analysis Use the forestploter package in R (version 4.2.3) to visualize Cox regression model data by plotting forest plots. The classic binary variable forest plot is shown in Figure 7, using the hazard ratio (HR) as an indicator of the magnitude of the clinical factor effect size. In the forest plot, the effect size point estimate = 1 is used as the null line. Factor A (as a reference) is assumed to be to the left of the null line, and factor B to the right. When the 95% CI of the effect size includes 1 (i.e., the horizontal line segment in the forest plot intersects the null line), it indicates that the difference in the incidence of the outcome event between the two groups is not statistically significant, and it cannot be concluded that factors A and B have different effects on the risk of the outcome event. When the 95% CI of the effect size is greater than 1 (i.e., the horizontal line segment in the forest plot does not intersect the null line and is to the right of the null line), it can be considered that the incidence of the outcome event in factor B group is greater than that in factor A group. Generally, if the outcome event is an adverse event such as morbidity or death, it indicates that factor B can increase the incidence of the outcome event compared to factor A, and is a risk factor. Conversely, when the 95% CI of the effect size is less than 1... When the horizontal line segment in the forest plot does not intersect the invalid line and is to the left of the invalid line, the incidence of the outcome event in factor B group can be considered to be lower than that in factor A group. In general, if the outcome event is an adverse event such as morbidity or death, it suggests that factor B can reduce the incidence of the event compared with factor A, and is a protective factor.

[0041] The results showed that, as shown in Figure 7A, univariate analysis revealed that TNM staging increased SPB1 incidence, threatening patient survival, while estrogen receptor, progesterone receptor, and classification / subtyping decreased SPB1 incidence, improving patient survival. Figure 7 In B, multivariate analysis revealed that TNM staging increases the incidence of SPB1, threatening patient survival.

[0042] 8. ROC curve assessment of the prognosis of SPB1 in patients with triple-negative breast cancer. SPB1's efficacy in prognostic assessment for triple-negative breast cancer patients is as follows: Figure 6 As shown in the figure. The results showed that the AUC value reached 0.768, indicating that SPB1 can serve as a prognostic marker for patients with triple-negative breast cancer.

[0043] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0044] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0045] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. The use of a reagent for detecting biomarkers in the preparation of products for predicting the prognosis of patients with triple-negative breast cancer, characterized in that, The biomarker is the SPB1 protein or its encoding gene.

2. The application according to claim 1, characterized in that, The reagents for detecting biomarkers can be based on immunohistochemical staining, specific antibody detection, mass spectrometry, label-free proteomics, or molecular biology detection methods.

3. The application according to claim 2, characterized in that, Reagents for immunohistochemical staining detection methods include buffer solutions or staining reagents; reagents for specific antibody detection methods include anti-SPB1 antibodies; and reagents for mass spectrometry detection methods include trypsin, reducing agents, alkylating agents, or desalting columns.

4. The application according to claim 1, characterized in that, The product assesses prognosis by detecting the expression level of SPB1 protein or its encoding gene in the sample.

5. The application according to claim 4, characterized in that, The expression level of the SPB1 protein or its encoding gene is negatively correlated with the prognosis of patients with triple-negative breast cancer.

6. The application according to claim 4, characterized in that, The prognosis includes one or more of overall survival, disease-free survival, and risk of disease progression.

7. The application according to claim 1, characterized in that, The samples are tumor tissue samples or adjacent normal tissue samples from patients with triple-negative breast cancer.

8. The application according to claim 1, characterized in that, The detection methods used for the product include one or more of the following: immunohistochemistry, Western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence, and real-time quantitative PCR.

9. The application according to any one of claims 1-8, characterized in that, The product is a testing reagent or testing kit.

Images